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Summer internship in machine learning

aklami — Wed, 17 Jan 2018 07:57:37 +0000

Are you interested in research and machine learning? We are looking for 1-2 summer interns to join the Multi-source Probabilistic inference group (http://www.hiit.fi/cosco/mupi) to work at the intersection of machine learning methods development and data science applications. We work on a range of interesting applications and your interests are taken into account when deciding what you would be working on. Examples of current domains include ultrasound physics, mobile hyperspectral imaging (http://www.specim.fi/iq/), game design and economy.

We appreciate good programming skills and strong mathematical background, including linear algebra, probability and differential calculus. We expect that you have completed some courses in machine learning and/or artificial intelligence, at least at the level of Introduction to Machine Learning. Candidates with background in computational physics, applied mathematics, statistics and other computational fields are also encouraged to apply.

Interest and existing knowledge of Bayesian methods, probabilistic programming and deep learning is considered a strong advantage. We work mainly with Python, using common machine learning libraries such as TensorFlow and PyTorch. We’ll show you the ropes if you aren’t already familiar with these, but prior knowledge is useful.

Applications are sent through the Department of Computer Science summer intern call by February 4th, 2018.

Past ICL events

troos — Mon, 27 Mar 2017 12:50:21 +0000

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Past Events (Information, Complexity and Learning)

The paper "Sparse Logistic Regression with Logical Features" by Yuan Zou and Teemu Roos was accepted in Proc. 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2016)
"Robust Sequential Prediction in Linear Regression with Student’s t-distribution" by J. Määttä and T. Roos was accepted to the 14th International Symposium on Artificial Intelligence and Mathematics (ISAIM 2016).
"Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data" by Eggeling, Roos, Myllymäki, and Grosse accepted to BMC Bioinformatics.
The paper "On Model Selection, Bayesian Networks, and the Fisher Information Integral" by Yuan Zou and Teemu Roos was accepted in Proc. 2nd Workshop on Advanced Methodologies for Bayesian Networks (AMBN-2015).
ICL group well represented at COIN highlight event: 2nd prize in video contest to Teemu Pitkänen and Ville Hyvönen (watch), other videos by Yuan Zou (watch) and Janne Leppä-aho (watch), and a poster by Jussi Määttä.
Janne Leppä-aho to visit the Statistics Dept at UC Berkeley in October-December 2015.
The paper "Subset Selection in Linear Regression using Sequentially Normalized Least Squares: Asymptotic Theory" by Jussi Määttä, Daniel F. Schmidt, and Teemu Roos was accepted to Scandinavian Journal of Statistics.
Nicos Pavlidis and David Hofmeyer from Lancaster University and Spiros Georgakopoulos from the University of Thessaly visit our group in June 2015.
Teemu Roos gives a talk in the Statistics and Operations Research Center (STOR-i) seminar at Lancaster University on May 21, 2015. Event info.
Quan (Eric) Nguyen visits the Computational Biology Laboratory at University of East Anglia, Norwich, UK on April 20–30, 2015.
Teemu Roos was an instructor at a COST Training School on "Tools and Techniques for Dealing with Complicated Textual Traditions" in Rome, 23–25 March, 2015.
Teemu Roos will present talks at the 2015 Science Forum (Tieteen päivät) on Jan 10, 2015 – Session "Sattuman matematiikka" ("Mathematics of Chance"); E-Philology Lecture Series at Sorbonne University on Jan 12, 2015; and at the 2015 Information Theory and Applications workshop (ITA-2015) in San Diego on Feb 5, 2015.
Janne Leppä-aho was one of two applicants, out of more than 130, who were awarded a four-year PhD scholarship by the DoCS graduate school. Congratulations to Janne!
Teemu Roos gives a talk at a Royal Society meeting on "Evolutionary Analysis beyond the Gene" in Chicheley, UK, on Dec 17-18, 2014.
Ms Xiao (Grace) Yang from Prof Andrew Barron's group at Yale University visits us from October 20–November 23, 2014.
The paper “Random Projection Based Clustering for Population Genomics” by Sotiris Tasoulis, Lu Cheng, Niko Välimäki, Nicholas Croucher, Simon Harris, William Hanage, Teemu Roos, and Jukka Corander was accepted to IEEE Big Data 2014.
October 2014: Pedram Daee starts as a PhD student with a joint affiliation with our group and that of Prof Sami Kaski.
The paper "A Fixed-Point Image Denoising Algorithm with Automatic Window Selection" by Määttä, Siltanen, and Roos was accepted to EUVIP 2014.
Our Mobisys'14 work covered on the CS department web page and a blog by Scientific American.
Dr Nicos Pavlidis from Lancaster University visits us on May 26–June 13, 2014.
ICL summer 2014 internships have been announced. Congrats and welcome to Janne Leppä-aho, Yang Zhao, and Santeri Räisänen!
Teemu Roos will be a co-chair of the 7th WITMSE workshop on July 5-7 in Hawaii, USA.
11th Conference of the European Society for Textual Scholarship (ESTS) will be held in Helsinki on Oct 30–Nov 1, 2014. Paper submission deadline May 14: CFP (pdf)
Our MobiSys'14 paper is among the 25 accepted among 185 submissions – that's 13.5%!
The paper "Bayesian properties of normalized maximum likelihood and its fast computation" by Barron, Roos, and Watanabe was accepted to ISIT-2014.
Our research on stemmatology reported in the Yliopisto magazine (University of Helsinki science magazine): "Tästä alkoi järjestys"
Jan 29, 2014: Teemu Roos gives a talk on "Complexity in Statistical Inference" in the Mathematics and Statistics Colloquium (Univ Helsinki).
The paper "Robust learning of inhomogeneous parsimonious Markov models" by Eggeling, Roos, Myllymäki, and Grosse was accepted to AISTATS-2014.
Prof Andrew Barron from Yale will visit us in September 2013.
Prof Kazuho Watanabe from the Nara Institute of Science and Technology, Japan, will visit us in September 2013. Welcome Kazuho!
Dr Sotiris Tasoulis from University of Central Greece will start as a post-doc in September 2013.
Teemu Roos and Petri Myllymäki will be co-chairs of WITMSE-2013 in Tokyo, Japan, August 26–29, 2013.
Dr Jamie Tehrani from Durham University, England, visits us and gives a talk on the use of phylogenetic ideas in anthropology on June 14, 2013.
Mr Jussi Määttä starts as a PhD student in June 2013.
Associated with our recent CHI-2013 paper, we are lauching a web server for computing the information capacity from your own motion capture data: infocapacity.hiit.fi. Please ask for a beta testing account.
PhD student Ralf Eggeling from the University of Halle-Wittenberg will visit us on April 29--June 30, 2013.
A paper by Oulasvirta, Roos, Modig, and Leppänen ("Information capacity of full-body movements") gets a Best Paper Honarable Mention Award at CHI-2013.

Publications (–2014)

J. Määttä, S. Siltanen, and T. Roos, (2014). A Fixed-Point Image Denoising Algorithm with Automatic Window Selection, in Proc. 5th European Workshop on Visual Information Processing (EUVIP 2014).

S. Tasoulis, L. Cheng, N. Välimäki, N. Croucher, S. Harris, W. Hanage, T. Roos, and J. Corander, (2014). Random projection based clustering for population genomics, in IEEE International Conference on Big Data (IEEE BigData-2014).

A. Barron, T. Roos, and K. Watanabe, (2014). Bayesian properties of normalized maximum likelihood and its fast computation, in IEEE International Symposium on Information Theory (ISIT-2014).

M. Sherman, G. Clark, Y. Yang, S. Sugrim, A. Modig, J. Lindqvist, A. Oulasvirta, and T. Roos, (2014). User-generated free-form gestures for authentication: security and memorability, in MobiSys-2014.

T. Andrews, S. Linkola, T. Roos, and J. van Zundert, (2014). "Connecting the 'webs': Building interoperability into online services for stemmatology," abstract in DHBenelux 2014, 12–13 June 2014, The Hague, Netherlands.

R. Eggeling, T. Roos, P. Myllymäki, and I. Grosse, (2014). Robust learning of inhomogeneous PMMs, in Proc. 17th Conf on Artificial Intelligence and Statistics (AISTATS-2014).

K. Watanabe, T. Roos, and P. Myllymäki, (2013). Achievability of asymptotic minimax regret in online and batch prediction, in Proc. 5th Asian Conference on Machine Learning (ACML-2013), JMLR W&CP 29, pp. 181–196.

A. Oulasvirta, T. Roos, A. Modig, and L. Leppänen, (2013). Information capacity of full-body movements, in Proc. 2013 ACM SIGCHI Conference on Human Factors in Computing Systems (CHI-2013), ACM. Best paper honorable mention award.

T. Roos and Y. Zou, (2013). Keep it simple stupid—On the effect of lower-order terms in BIC-like criteria, invited paper in Proc. 2013 Information Theory and Applications Workshop, (ITA-2013).

History

afrabah1 — Mon, 12 Dec 2016 13:46:13 +0000

Jan. 9

Unsupervised Machine Learning for Matrix Decomposition

Abstract: Unsupervised learning is a classical approach in pattern recognition and data analysis. Its importance is growing today, due to the increasing data volumes and the difficulty of obtaining statistically sufficient amounts of labelled training data. Typical analysis techniques using unsupervised learning are principal component analysis, independent component analysis, and cluster analysis. They can all be presented as decompositions of the data matrix containing the unlabeled samples. Starting from the classical results, the author reviews some advances in the field up to the present day.

Speaker: Erkki Oja

Affiliation: Professor Emeritus, Aalto University

Place of Seminar: Aalto University

Slides

Jan. 16

Probabilistic Programming: Bayesian Modeling Made Easy

Abstract: Probabilistic models are principled tools for understanding data, but difficulty of inference limits the complexity of models we can actually use. Often we need to develop specific inference algorithms for new models (which might take months), and need to restrict ourselves to tractable model families that might not match our beliefs about the data. Probabilistic programming promises to fix this, by separating the model description from the inference: With probabilistic programming languages we can specify complex models using a high-level programming language, letting a black-box inference engine take care of the tricky details. This talk covers the basic idea of probabilistic programming and discusses how well its promises hold now and in the future.

Speaker: Arto Klami

Affiliation: Academy Research Fellow, University of Helsinki

Place of Seminar: University of Helsinki

Slides

Jan. 23

Metabolite Identification Through Machine Learning

Abstract: Identification of small molecules from biological samples remains a major bottleneck in understanding the inner working of biological cells and their environment. Machine learning on data from large public databases of tandem mass spectrometric data has transformed this field in recent years, witnessing an increase of identification rates by 150%. In this presentation, I will outline the key machine learning methods behind this development: kernel-based learning of molecular fingerprints, multiple kernel learning, structured prediction as well as some recent advances.

Speaker: Juho Rousu

Affiliation: Associate Professor, Aalto University

Place of Seminar: Aalto University

Slides

Jan. 30

Likelihood-free Inference and Predictions for Computational Epidemiology

Abstract: Simulator-based models often allow inference and predictions under more realistic assumptions than those employed in standard statistical models. For example, the observation model for an underlying stochastic process can be more freely chosen to reflect the characteristics of the data gathering procedure. A major obstacle for such models is the intractability of the likelihood, which has to a large extent hampered their practical applicability. I will discuss recent advances in likelihood-free inference that greatly accelerate the model fitting process by exploiting a combination of machine learning techniques. Applications to several novel models in infectious disease epidemiology are used to illustrate the potential offered by this approach.

Speaker: Jukka Corander

Affiliation: Professor, University of Helsinki and University of Oslo

Place of Seminar: University of Helsinki

Slides

Feb. 6

Towards Perfect Density Estimation

Abstract: We start by addressing a most simple problem, estimation of a one dimensional density function, and argue that despite of the apparent simplicity of the problem, it is surprisingly difficult to solve it in a holistic manner that is both computationally feasible and theoretically justifiable without strong distributional or other assumptions. We demonstrate how the information-theoretic MDL framework can be used for reaching this goal (almost) perfectly, and show how this simple setup gives interesting perspectives on the fundamental concepts in probabilistic modelling and statistical inference. We also discuss ideas for extending the framework to more complex models with additional practical applications.

Speaker: Petri Myllymäki

Affiliation: Professor, University of Helsinki

Place of Seminar: Aalto University

Slides

Feb. 13

Variable Selection From Summary Statistics

Abstract: With increasing capabilities to measure a massive number of variables, efficient variable selection methods are needed to improve our understanding of the underlying data generating processes. This is evident, for example, in human genomics, where genomic regions showing association to a disease may contain thousands of highly correlated variants, while we expect that only a small number of them are truly involved in the disease process. I outline recent ideas that have made variable selection practical in human genomics and demonstrate them through our experiences with the FINEMAP algorithm (Benner et al. 2016, Bioinformatics).

(1) Compressing data to light-weight summaries to avoid logistics and privacy concerns related to complete data sharing and to minimize the computational overhead.

(2) Efficient implementation of sparsity assumptions.

(3) Efficient stochastic search algorithms.

(4) Use of public reference databases to complement the available summary statistics.

Speaker: Matti Pirinen

Affiliation: Academy Research Fellow, Institute for Molecular Medicine Finland, University of Helsinki

Place of Seminar: University of Helsinki

Slides

Feb. 20

Compressed Sensing for Semi-Supervised Learning From Big Data Over Networks

Abstract: In this talk I will present some of our most recent work on the application of compressed sensing to semi-supervised learning from massive network-structured datasets, i.e., big data over networks. We expect the user of compressed sensing ideas to be game-changing for machine learning from big data in a similar manner as it was for digital signal processing. In particular, I will present a sparse label propagation algorithm which efficiently learn from large amounts of network-structured unlabeled data by leveraging the information provided by a few initially labelled training data points. This algorithm is inspired by compressed sensing recovery methods and allows for a simple sufficient condition on the network structure which guarantees accurate learning.

Speaker: Alexander Jung

Affiliation: Assistant Professor, Aalto University

Place of Seminar: Aalto University

Slides

Feb. 27

Inverse Modeling in Behavioral Sciences and HCI

Abstract: Can one make deep inferences about a person based only on observations of how she acts? I discuss methodology for inverse modeling in behavioral sciences, where the goal is to estimate a cognitive model from limited behavioral data. Given substantial diversity in people's intentions, strategies and abilities, this is a difficult problem and previously unaddressed. I discuss advances achieved with an approach that combines (1) computational rationality, to predict how a person adapts to a task when her capabilities are known, and (2) Approximate Bayesian Computation (ABC) to estimate those capabilities. The benefit is that model parameters are conditioned on both prior knowledge and observations, which improves model validity and helps identify causes for observations. Inverse modeling methods can advance theory-formation by bringing complex behavior within reach of modeling. This talk is based on on-going collaborations with Antti Kangasraasio, Samuel Kaski, Jukka Corander, Andrew Howes, Kumaripaba Athukorala, Jussi Jokinen, Sayan Sarcar, and Xiangshi Ren.

Speaker: Antti Oulasvirta

Affiliation: Associate Professor, Aalto University

Place of Seminar: University of Helsinki

Slides

March. 6

Differentially Private Bayesian Learning

Abstract: Many applications of machine learning for example in health care would benefit from methods that can guarantee data subject privacy. Differential privacy has recently emerged as a leading framework for private data analysis. Differenctial privacy guarantees privacy by requiring that the results of an algorithm should not change much even if one data point is changed, thus providing plausible deniability for the data subjects.

In this talk I will present methods for efficient differentially private Bayesian learning. In addition to asymptotic efficiency, we will focus on how to make the methods efficient for moderately-sized data sets. The methods are based on perturbation of sufficient statistics for exponential family models and perturbation of gradients for variational inference. Unlike previous state-of-the-art, our methods can predict drug sensitivity of cancer cell lines using differentially private linear regression with better accuracy than using a very small non-private data set.

Speaker: Antti Honkela

Affiliation: Assistant Professor, University of Helsinki

Place of Seminar: Aalto University

Slides

March. 13

Small Data AUC Estimation of Machine Learning Methods: Pitfalls and Remedies

Abstract: Asking whether two populations can be distinguished from each other is one of the most fundamental questions in data analysis and area under ROC curve (AUC) is one of the simplest and most practical tools for answering it. Also known as the Wilcoxon-Mann-Whitney U statistic, it can be associated with a p-value indicating how likely one would obtain as good AUC value if the two populations would not be stochastically different. Estimating AUC of a predictive model and its statistical significance has a huge practical importance in fields like medicine, where one often has access to only small amounts of labeled data but large number of features. Leave-pair-out cross-validation (LPOCV) is an almost unbiased AUC estimator of machine learning methods that has also been empirically shown to be the most reliable of the cross-validation (CV) based estimators. We further study the properties of LPOCV and show some serious pitfalls one can encounter when estimating AUC with CV and how to avoid them. In particular, we show how one can produce very promising results with high AUC values even if there is no signal in the data. Finally, we show how to counter these risks with new Wilcoxon–Mann–Whitney U type of permutation tests adjusted for LPOCV, thus upgrading one of the classical statistical tools for CV estimates.

Speaker: Tapio Pahikkala

Affiliation: Assistant Professor, University of Turku

Place of Seminar: University of Helsinki

Slides

March. 20

Future AI: Autonomous machine learning and beyond

Abstract: Many researchers have identified autonomous machine learning (unsupervised, semi-supervised and reinforcement learning) as an important cornerstone of advanced artificial intelligence. The Curious AI Company is developing such autonomous learning systems. We already have state-of-the-art results in several semi-supervised classification tasks but we are also working on bringing autonomy to learning segmentation and hierarchical control, both of them tasks that take a lot of human work when developing for instance self-driving cars. However, we believe there's an even more important blocker on the way to advanced AI: the fundamental inability of currently used parallel distributed neural coding to properly represent objects and their interactions. We are working on deep learning networks whose neuro-symbolic representations will hopefully allow neural networks to understand the world not only in terms of a collection of features but in terms of objects and their interactions, too. This is necessary for many tasks such as communication, reasoning and complex decision making.

Speaker: Harri Valpola

Affiliation: CEO of the Curious AI Company

Place of Seminar: Aalto University

Slides

March. 27

Learning to Rank: Applications to Bioinformatics

Abstract: Learning To Rank (LTR) has been developed in information retrieval for ranking documents regarding the relevance to a given query. Typically LTR builds a ranking model from given relevant (or irrelevant) query-document pairs. Generally, in some respect, LTR can be thought as an attempt to solve a multilabel classification problem, where queries are labels. A lot of settings in bioinformatics can be turned into multilabel classification problems having relatively similar properties. One typical example is biomedical document annotation. Currently PubMed, a database of 26 million biomedical citations, has around 30,000 keywords, called MeSH (Medical Subject Headings) terms, i.e. labels in multilabel classification, where the number of articles per MeSH term is extremely diverse, ranging from only 20 to more than eight million. This large, biased dataset already goes beyond the general sense of settings expected by regular multilabel classifiers. In this talk, I will start with introduction and a brief review of LTR. I then raise three bioinformatics multilabel classification problems that share real data-derived, practical properties, which hamper the application of regular multilabel classifiers. Finally I will show that LTR nicely addresses such large-scale, challenging bioinformatics multilabel classification problems.

A large portion of this talk appeared in ISMB in 2015 and 2016.

Speaker: Hiroshi Mamitsuka

Affiliation: Professor, Kyoto University

Place of Seminar: University of Helsinki

Slides

April. 03

Multilayer Networks

Abstract: Network science has been very successful in investigations of a wide variety of applications from biology and the social sciences to physics, technology, and more. In many situations, it is already insightful to use a simple (and typically naive) representation as a simple, binary graph in which nodes are entities and unweighted edges encapsulate the interactions between those entities. This allows one to use the powerful methods and concepts for example from graph theory, and numerous advances have been made in this way. However, as network science has matured and (especially) as ever more complicated data has become available, it has become increasingly important to develop tools to analyse more complicated structures. For example, many systems that were typically initially studied as simple graphs are now often represented as time-dependent networks, networks with multiple types of connections, or interdependent networks. This has allowed deeper and more realistic analyses of complex networked systems, but it has simultaneously introduced mathematical constructions, jargon, and methodology that are specific to research in each type of system. Recently, the concept of “multilayer networks” was developed in order to unify the aforementioned disparate language (and disparate notation) and to bring together the different generalised network concepts that included layered graphical structures. In this talk, I will introduce multilayer networks and discuss how to study their structure. Generalisations of the clustering coefficient for multiplex networks and graph isomorphism for general multilayer networks are used as illustrative examples.

Speaker: Mikko Kivelä

Affiliation: Postdoctoral Researcher, Aalto University

Place of Seminar: Aalto University

April. 10

Learning With Spectral Kernels

Abstract: Machine learning algorithms learn models that automatically infer data representations and generalise into new data. Gaussian processes are Bayesian kernel-based models with a key advantage of being able to efficiently learn kernel functions from data. All kernel functions can be decomposed into sinusoidal components, which provide a highly expressive basis for learning arbitrary representations. In this talk I will discuss how we can exploit spectral kernel learning for large-scale multi-task learning. We also generalise spectral learning into learning non-stationary kernels with input-specific behavior

Speaker: Markus Heinonen

Affiliation: Department of Computer Science, Aalto University

Place of Seminar: University of Helsinki

Slides

April. 24

Nintendo Wii Fit-Based Balance Testing to Detect Sleep Deprivation: Approximate Bayesian Computation Approach

Abstract: Sleep deprivation deteriorates health and causes accidents. Measuring a person’s postural steadiness may be used to determine his/hers state of alertness. Posturographic measurements are easy to conduct: a person’s body sway is measured during upright stance on a balance board for 60 s. The Nintendo Wii Fit balance board is a portable and affordable alternative to expensive clinical force plates. Body sway may be modeled with a single-link inverted pendulum (Asai et al. 2009). The model parameters, such as time delay and noise intensity in the nervous system, are physiologically relevant. The pendulum is kept upright with controllers, that include stiffness and damping gain parameters. Level of control determines how often the active controller is ON. The model cannot be solved analytically in closed form. Therefore, inferring model parameters and their confidence limits is nontrivial. We used sequential Monte Carlo approximate Bayesian computation (SMC-ABC) algorithm to infer the model parameters. The inferred parameters may allow determining a person’s state of alertness.

Speaker: Aino Tietäväinen

Affiliation: Department of Physics, University of Helsinki

Place of Seminar: Aalto University

Slides

May. 08

Empirical Parameterization of Exploratory Search Systems Based on Bandit Algorithms

Abstract: Exploratory searches are where a user has insufficient knowledge to define exact search criteria or does not otherwise know what they are looking for. Reinforcement learning techniques have demonstrated great potential for supporting exploratory search in information retrieval systems as they allow the system to trade-off exploration (presenting the user with alternatives topics) and exploitation (moving toward more specific topics). Users of such systems, however, often feel that the system is not responsive to user needs. This problem is not an inherent feature of such systems, but is caused by the exploration rate parameter being inappropriately tuned for a given system, dataset or user. In this talk, we discuss two approaches how to optimise exploratory search systems based on bandit algorithms. First, we show that the tradeoff between exploration and exploitation can be modelled as a direct relationship between the exploration rate parameter from the reinforcement learning algorithm and the number of relevant documents returned to the user over the course of a search session. We define the optimal exploration/exploitation trade-off as where this relationship is maximised and show this point to be broadly concordant with user satisfaction and performance. Our second approach aims to dynamically adapt exploration and exploitation in a manner commensurate with the user's individual requirements for each search session. We present a novel study design together with a regression model for predicting the optimal exploration rate based on simple metrics from the first iteration, such as clicks and reading time. We perform model selection based on the data collected from a user study and show that predictions are consistent with user feedback.

Speaker: Dorota Glowacka

Affiliation: Department of Computer Science, University of Helsinki

Place of Seminar: University of Helsinki

Slides

May. 15

Machine Learning for Image-Based Localization

Abstract: Image-based localization refers to a problem where the camera position and orientation for a given query image is computed with respect to a known visual 3D map of the scene. This problem is relevant for applications such as robot self-localization, pedestrian navigation, and augmented reality. Another related problem is the relative pose estimation between two camera views which is required for computing image-based 3D models from a collection of 2D images. Traditionally both of these problems have been approached by using hand-crafted local image features and descriptors, such as the widely used SIFT keypoint detector. However, recently several deep learning based localization approaches have been proposed. They omit local feature matching and directly try to regress the camera pose. In this presentation, we will describe an overview of the problem area and explore some recent deep learning based approaches. We will also present some of our own recent results in this area.

Speaker: Juho Kannala

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Aalto University

Slides

May. 22

On Priors and Bayesian Variable Selection in Large p, Small n Regression

Abstract: The Bayesian approach is well known for using priors to improve inference, but equally important part is the integration over the uncertainties. I first present recent development in hierarchical shrinkage priors for presenting sparsity assumptions in covariate effects. I then present a projection predictive variable selection approach, which is a Bayesian decision theoretical approach for variable selection which can preserve the essential information and uncertainties related to all variables in the study. I also present recent excellent experimental results and easy to use software.

Speaker: Aki Vehtari

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: University of Helsinki

Slides

May. 29

Graphics Meets Vision Meets Machine Learning

Abstract: Realistic three-dimensional modeling and animation are key bottlenecks in the production film, games, VR, and other applications of computer graphics. In this talk, I will describe our recent research that makes use of machine learning techniques for solving hard inference problems for generating 3D content: capture and reproduction of photorealistic surface appearance, facial performance capture, and turning audio into facial animation. These works both push the state of the art forward in research – two of the three projects have been published at ACM SIGGRAPH – and are surprisingly ready for production use already now.

Speaker: Jaakko Lehtinen

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Aalto University

Slides

Jun. 5

Statistical Ecology with Gaussian Processes

Abstract: Ecology studies the distribution and abundance of species, and their interactions with other species and the environment. Key questions in ecology include what are the environmental factors and interspecies dependencies that drive species distributions, how these processes together affect species community structures and how environmental changes, such as climate change, affect species distribution and species communities. These questions are essentially about variable selection and causal and predictive inference. Hence, statistics has a central role in answering them. The species distribution models (SDMs) used for these analyses are traditionally based on generalized linear and additive models. In this talk I will present how Gaussian processes (GPs) can be used in SDMs and what benefits and challenges this provides. I will present recent results on GP based species distribution modeling in the Baltic Sea and Great Barrier Reef, Australia. I will discuss the potential future development and current challenges related to computation and model building.

Speaker: Jarno Vanhatalo

Affiliation: Professor of Statistics, University of Helsinki

Place of Seminar: University of Helsinki

Slides

Jun. 12

Learning Data Representation by Large-Scale Neighbor Embedding

Abstract: Machine learning, the state-of-the-art data science, has been increasingly influencing our life. Encoding data in a suitable vector space is the fundamental starting point for machine learning. A good vector coding should respect the relations among the data items. However, conventional methods that preserve pairwise or higher order relationship are very slow and consequently they can handle only small-scale data sets. We have been developing a family of unsupervised methods called large-scale Neighbor Embedding (NE) which substantially accelerate the vector coding. Our method can thus learn low-dimensional vector representation for mega-scale data according to their neighborhoods in the original space. With our efficient algorithms and a wealth of neighborhood information, Neighbor Embedding significantly outperforms small-scale NE and many other existing approaches for learning data representation. Besides generic feature extraction, our work also delivers two important tools as special cases of Neighbor Embedding for data visualization and cluster analysis, which scales up these applications by an order of magnitude and enables the current-sized visualization and clustering for interactive use. Because neighborhood information is naturally and massively available in many areas, our method has wide applications as a critical component in scientific research, next-generation DNA sequence analysis, natural language processing, educational cloud, financial data analysis, market studies, etc.

Speaker: Zhirong Yang

Affiliation: Department of Computer Science, Aalto University

Place of Seminar: Aalto University

Slides

Video_1 , Video_2

Sep. 11

Latent Stochastic Models for Comparing Tumor Samples of Unknown Purity

Abstract: A challenge in analyzing data from tumor samples is that the biopsies contain an mixture of various cells, including cancer cells, immune cells and stromal cells. This hinders the discovery of clinically relevant information and can lead to systematically biased results. A few recent analysis techniques control for such factors, but only accommodate specific types of data, or require controls which cannot be obtained from each patient. I will present our developments on statistical methods for controlling for the latent and varying fraction of tumor cells in next-generation methylation and RNA sequencing data, which aim to enable unbiased and more accurate comparison of patient-derived samples.

Speaker: Antti Häkkinen

Affiliation: Postdoctoral fellow, Genome-Scale Biology Program, Faculty of Medicine, University of Helsinki

Place of Seminar: Aalto University

Slides

Sep. 18

Fast Nearest Neighbor Search in High Dimensions by Multiple Random Projection Trees

Abstract: Efficient index structures for fast approximate nearest neighbor queries are required in many applications such as recommendation systems. In high-dimensional spaces, many conventional methods suffer from excessive usage of memory and slow response times. We propose a method where multiple random projection trees are combined. We demonstrate by extensive experiments on a wide variety of data sets that the method is faster than existing partitioning tree or hashing based approaches, making it the fastest available technique on high accuracy levels.

Speaker: Teemu Roos

Affiliation: Associate Professor, Department of Computer Science, University of Helsinki

Place of Seminar: University of Helsinki

Slides

Hyvönen et al., "Fast Nearest Neighbor Search through Sparse Random Projections and Voting", IEEE Big Data Conference 2016: [link]

Sep. 25

Computational creativity and machine learning.

Abstract: Computational creativity has been defined as the art, science, philosophy and engineering of computational systems which, by taking on particular responsibilities, exhibit creative behaviours. In this talk I first try to elaborate on what creative responsibilities could be and why they are interesting. I then outline ways in which machine learning can be used to take on some of these responsibilities, helping computational systems become more creative.

Speaker: Hannu Toivonen

Affiliation: Professor of Computer Science, University of Helsinki

Place of Seminar: Aalto University

Slides

Oct. 2

Machine learning for Materials Research.

Abstract: In materials research, we have learnt to predict the evolution of microstructure starting with the atomic level processes. We know about defects -- point and extended, -- and we know that these can be crucial for the final structural (and related mechanical and electrical) properties. Often simple macroscopic differential equations, which are used for the purpose, fail to predict simple changes in materials. Many questions remain unanswered. Why a ductile material suddenly becomes brittle? Why a strong concrete bridge suddenly cracks and eventually collapses after serving for tens of years? Why the wall of high quality steels in fission reactors suddenly crack? Or, why the clean smooth surface roughens under applied electric fields? All these questions can be answered, if one peeks in to atom's behavior imagining it jumping inside the material. But how the atoms "choose" where to jump amongst the numerous possibilities in complex metals? Tedious parameterization can help to deal with the problem, but machine learning can provide a better and more elegant solution to this problem.

In my presentation, I will explain the problem at hand and show a few examples of former and current application of Neural Network for calculating the barriers for atomic jumps with the analysis of how well the applied NN worked.

Speaker: Flyura Djurabekova

Affiliation: Department of Physics, University of Helsinki

Place of Seminar: University of Helsinki

Slides

Oct. 9

Does my algorithm work?

Abstract: It is easy to propose a new algorithm for solving a Machine Learning problem. It is much harder to convince other people that the proposed algorithm actually works. The "gold standard" of tight theoretical guarantees is often out of reach. So what do we do? Typically, an algorithm is validated on a couple of test problems and its output is compared with that of algorithms that are known to work. This is not a great strategy.

In this talk, I will outline a general strategy for assessing whether an algorithm for approximate Bayesian computing works on a given problem. This method does not require evaluation of the true posterior and also indicates ways in which the computed posterior systematically deviates from the true posterior.

Speaker: Daniel Simpson

Affiliation: Professor of Stastical Sciences, University of Toronto

Place of Seminar: Aalto University

Oct. 16

Probabilistic preference learning with the Mallows rank model

Abstract: Ranking and comparing items is crucial for collecting information about preferences in many areas, from marketing to politics. The Mallows rank model is among the most successful approaches to analyze rank data, but its computational complexity has limited its use to a form based on Kendall distance. Here, new computationally tractable methods for Bayesian inference in Mallows models are developed that work with any right-invariant distance. The method performs inference on the consensus ranking of the items, also when based on partial rankings, such as top-k items or pairwise comparisons. When assessors are many or heterogeneous, a mixture model is proposed for clustering them in homogeneous subgroups, with cluster-specific consensus rankings. Approximate stochastic algorithms are introduced that allow a fully probabilistic analysis, leading to coherent quantification of uncertainties. The method can be used, for example, for making probabilistic predictions on the class membership of assessors based on their ranking of just some items, and for predicting missing individual preferences, as needed in recommendation systems.

Speaker: Elja Arjas

Affiliation: Professor (emeritus) of Mathematics and Statistics, University of Helsinki

Place of Seminar: University of Helsinki

Slides

Oct. 23

Computational Challenges in Analyzing And Moderating Online Social Discussions

Abstract: Online social media are a major venue of public discourse today, hosting the opinions of hundreds of millions of individuals. Social media are often credited for providing a technological means to break information barriers and promote diversity and democracy. In practice, however, the opposite effect is often observed: users tend to favor content that agrees with their existing world-view, get less exposure to conflicting viewpoints, and eventually create "echo chambers" and increased polarization. Arguably, without any kind of moderation, current social-media platforms gravitate towards a state in which net-citizens are constantly reinforcing their existing opinions. In this talk we present a ongoing line of work on analyzing and moderating online social discussions. We first consider the questions of detecting controversy using network structure and content, tracking the evolution of polarized discussions, and understanding their properties over time. We then address the problem of designing algorithms to break filter bubbles and reduce polarization. We discuss a number of different strategies such as user and content recommendation, as well as viral approaches.

Speaker: Aristides Gionis

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Aalto University

Slides

Oct. 30

Learning Markov Equivalence Classes of Directed Acyclic Graphs: an Objective Bayes Approach

Abstract: A Markov equivalence class contains all the Directed Acyclic Graphs (DAGs) encoding the same conditional independencies, and is represented by a Completed Partially Directed DAG (CPDAG), also named Essential Graph (EG). We approach the problem of model selection among noncausal sparse Gaussian DAGs by directly scoring EGs, using an objective Bayes method. Specifically, we construct objective priors for model selection based on the Fractional Bayes Factor, leading to a closed form expression for the marginal likelihood of an EG. Next we propose an MCMC strategy to explore the space of EGs, possibly accounting for sparsity constraints, and illustrate the performance of our method on simulation studies, as well as on a real dataset. Our method is fully Bayesian and thus provides a coherent quantification of inferential uncertainty, requires minimal prior specification, and shows to be competitive in learning the structure of the data-generating EG when compared to alternative state-of-the-art algorithms.

Speaker: Guido Consonni

Affiliation: Professor of Statistics, Universita Cattolica del Sacro Cuore

Place of Seminar: University of Helsinki

Slides

Nov. 6

Efficient and accurate approximate Bayesian computation

Abstract: Approximate Bayesian computation (ABC) is a method for calculating a posterior distribution when the likelihood is intractable, but simulating the model is feasible. It has numerous important applications, for example in computational biology, material physics, user interface design, etc. However, many ABC algorithms require a large number of simulations, which can be costly. To reduce the cost, Bayesian optimisation (BO) and surrogate models such as Gaussian processes have been proposed. Bayesian optimisation enables deciding intelligently where to simulate the model next, but standard BO approaches are designed for optimisation and not for ABC. Here we address this gap in the existing methods. We model the uncertainty in the ABC posterior density which is due to a limited number of simulations available, and define a loss function that measures this uncertainty. We then propose to select the next model simulation to minimise the expected loss. Experiments show the proposed method is often more accurate than the existing alternatives.

Speaker: Pekka Marttinen

Affiliation: Academy Research Fellow, Department of Computer Science, Aalto University

Place of Seminar: Aalto University

Slides

Nov. 13

Learning of Ultra High-Dimensional Potts Models for Bacterial Population Genomics

Abstract: The potential for genome-wide modeling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has earlier been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 10000-100000 polymorphisms, representing the amount of core genomic variation observed in analyses of many bacterial species. Here we introduce a novel inference method (SuperDCA) which employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 100000 polymorphisms. Using two large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make additional significant biological findings about this major human pathogen. We also show that our method can uncover signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA thus holds considerable potential in building understanding about numerous organisms at a systems biological level.

Speaker: Jukka Corander

Affiliation: Professor of Statistics, University of Helsinki and University of Oslo

Place of Seminar: University of Helsinki

Nov. 20

Towards Intelligent Exergames

Abstract: Exergames - video games that require physical activity - hold promise of solving the societal hard problem of motivating people to move. At the same time, artificial intelligence and machine learning are transforming how video games are designed, produced, and tested. Work combining both computational intelligence and exergames is sparse, however. In my talk, I delineate the challenges, opportunities, and my group's research towards intelligent exergames, building on our previous research on both exergame design (e.g., Augmented Climbing Wall, Kick Ass Kung-Fu) and intelligent control of embodied simulated agents. Video and examples: http://perttu.info

Speaker: Perttu Hämäläinen

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Aalto University

Nov. 27

Correlation-Compressed Direct Coupling Analysis

Abstract: Direct Coupling Analysis (DCA) is a powerful tool to find pair-wise dependencies in large biological data sets. It amounts to inferring coefficients in a probabilistic model in an exponential family, and then using the largest such inferred coefficients as predictors for the dependencies of interest. The main computational bottle-neck is the inference. As described recently by Jukka Corander in this seminar series DCA has be done on bacterial whole-genome data, at the price of significant compute time, and investment in code optimization.

We have looked at if DCA can be speeded up by first filtering the data on correlations, an approach we call Correlation-Compressed Direct Coupling Analysis (CC-DCA). The computational bottle-neck then moves from DCA to the more standard task of finding a subset of most strongly correlated vectors in large data sets. I will describe results obtained so far, and outline what it would take to do CC-DCA on whole-genome data in human and other higher organisms.

This is joint work with Chen-Yi Gao and Hai-Jun Zhou, available as arXiv:1710.04819.

Speaker: Erik Aurell

Affiliation: Professor of Biological Physics, KTH-Royal Institute of Technology

Place of Seminar: University of Helsinki

Slides

Dec. 18

Eco-friendly Bayes: Reusing Computationally Costly Posteriors in Post-hoc Analyses

Abstract: Bayesian inference has many attractive features, but a major challenge is its potentially very high computational cost. While sampling from the prior distribution is often straightforward, the most expensive part is typically conditioning on the data. In many problems, a single data set data may not be informative enough to enable reliable inference for a given quantity of interest. This can be difficult to assess in advance and may require a considerable amount of computation to discover, resulting in a weakly informative posterior distribution "gone to waste". On the other hand, borrowing strength across multiple related data sets using a hierarchical model may for very costly models be computationally infeasible.

As an alternative approach to traditional hierarchical models, we develop in this work a framework which reuses and combines posterior distributions computed on individual data sets to achieve post-hoc borrowing of strength, without the need to re-do expensive computations on the data. As a by-product, we also obtain a notion of meta-analysis for posterior distributions. By adopting the view that posterior distributions are beliefs which reflect the uncertainty about the value of some quantity, we formulate our approach as Bayesian inference with uncertain observations. We further show that this formulation is closely related to belief propagation. Finally, we illustrate the framework with post-hoc analyses of likelihood-free Bayesian inferences.

Speaker: Paul Blomstedt

Affiliation: Department of Computer Science, Aalto University

Place of Seminar: Aalto University

Jan. 15

Scalable Algorithms for Extreme Multi-Class and Multi-Label Classificiation in Big Data

Abstract: In the era of big data, large-scale classification involving tens of thousand target categories is not uncommon. Also referred to as Extreme Classification, it has also been recently shown that the machine learning challenges arising in ranking, recommendation systems and web-advertising can be effectively addressed by reducing it to extreme multi-label classification framework. In this talk, I will discuss my two recent works, and present TerseSVM and DiSMEC algorithms for extreme multi-class and multi-label classification. The precision@k and nDCG@k results using DiSMEC improve by upto 20% on benchmark datasets over state-of-the-art methods, which are used by Microsoft in production system of Bing Search. The training process for these algorithms makes use of openMP based distributed architectures, and is able to leverage thousands of cores for computation.

Speaker: Rohit Babbar

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Aalto University

Jan. 22

Confident Bayesian Learning of Graphical Models

Abstract: Confident Bayesian learning amounts to computing summaries of a
posterior distribution either exactly or with probabilistic accuracy guarantees. I will review the state of the art in confident Bayesian structure learning in graphical models, focusing on the class of Bayesian networks and its subclass of chordal Markov networks.

Speaker: Mikko Koivisto

Affiliation: Professor of Computer Science, University of Helsinki

Place of Seminar: University of Helsinki

Slides

Jan. 29

Special Seminar

This week we will have multiple machine learning talks at Aalto. We cancel the normal Monday session so that we can meet in them according to the following plan:

Jan 29, Glowacka Dorota

Talk at 13:00 at TUAS 1171-72
Title: Machine Learning Meets The User: Empirical Parametrization of Exploratory Search Systems

Jan 30, Heinonen Markus

Talk at 15:00 at TUAS 1021
Title: Towards Next-Generation Representational Learning

Feb 1, Marttinen Pekka

Talk at 16:00 in Seminar Room T3 (CS building)
Title: Machine Learning for Health

Feb 2, Solin Arno

Talk at 13:00 at TUAS 1171-72
Title: Machine Learning for Sensor Fusion in Positioning and Navigation

NOTE: The TUAS building address is Maarintie 8 (next to the CS building)

Feb. 5

Studying mutational processes in cancer

Abstract: Somatic mutations in cancer have accumulated during its evolution and are caused by different exposures to carcinogens and therapeutic agents, as well as, intrinsic errors that occur during DNA replication. Analysing a set of cancer samples jointly allows to explain their somatic mutations as a linear combination of (to be learned) mutational signatures. In this presentation I will discuss the problem of learning mutational signatures from cancer data using probabilistic modelling and nonnegative matrix factorisation. I further describe our ongoing work using mutational signatures in the context of drug response prediction and extensions of the basic model to explicitly include DNA repair processes.

Speaker: Ville Mustonen

Affiliation: Professor of Mathematics and Natural Science, University of Helsinki

Place of Seminar: University of Helsinki

Feb. 12

Here Be Dragons: High-Dimensional Spaces and Statistical Computation

Abstract: With consistently growing data sets and increasingly complex models, the frontiers of applied statistics is found in high-dimensional spaces. Unfortunately most of the intuitions that we take for granted in our low-dimensional, routine experiences don’t persist to these high-dimensional spaces which makes the development of scalable computational methodologies and algorithms all the more challenging. In this talk I will discuss the counter-intuitive behavior of high-dimensional spaces and the consequences for statistical computation.

Speaker: Michael Betancourt

His research focuses on the development of robust statistical workflows, computational tools, and pedagogical resources that bridge statistical theory and practice and enable scientists to make the most out of their data. The pursuit of general but scalable statistical computation has lead him to the intersection of differential geometry and probability theory where exploiting the inherent geometry of high-dimensional problems naturally leads to algorithms such Hamiltonian Monte Carlo and its generalizations. He is developing both the theoretical foundations and the practical implementations of these algorithms, the latter specifically in the software ecosystem Stan.

Affiliation: Columbia University

Place of Seminar: Aalto University

Slides

Feb. 19

Learning and Stochastic Control in Gaussian Process Driven Physical Systems

Abstract: Traditional machine learning is often overemphasising problems, where we wish to automatically learn everything from the problem at hand solely using a set of training data. However, in many physical systems we already know much about the physics, typically in form of partial differential equations. For efficient learning in this kind of systems, it is beneficial to use gray-box models where only the unknown parts are modeled with data-trained machine learning models. This talk is concerned with learning and stochastic control in physical systems which contain unknown input or force signals that we wish to learn from data. These unknown signals are modeled using Gaussian processes (GP) from machine learning. The resulting latent force models (LFMs) can be seen as hybrid models that contain a first-principles physical model part and a non-parametric GP model part. We present and discuss methods for learning and stochastic control in this kind of models.

Speaker: Simo Särkkä

Simo Särkkä is an Associate Professor and Academy Research Fellow with Aalto University, Technical Advisor of IndoorAtlas Ltd., and an Adjunct Professor with Tampere University of Technology and Lappeenranta University of Technology. His research interests are in multi-sensor data processing systems with applications in artificial intelligence, machine learning, inverse problems, location sensing, health technology, and brain imaging. He has authored or coauthored around 100 peer-reviewed scientific articles and his book "Bayesian Filtering and Smoothing" along with its Chinese translation were published via the Cambridge University Press in 2013 and 2015, respectively. He is a Senior Member of IEEE and serving as an Associate Editor of IEEE Signal Processing Letters.

Affiliation: Professor of Electrical Engineering and Automation, Aalto University

Place of Seminar: University of Helsinki

Feb. 26

Bayesian Deep Learning for Image Data

Abstract: Deep learning is the paradigm that lies at the heart of state-of-the-art machine learning approaches. Despite their groundbreaking success on a wide range of applications, deep neural nets suffer from: i) being severely prone to overfitting, ii) requiring intensive handcrafting in topology design, iii) being agnostic to model uncertainty, iv) and demanding large volumes of labeled data. The Bayesian approach provides principled solutions to all of these problems. Bayesian deep learning converts the loss minimization problem of conventional neural nets into a posterior inference problem by assigning prior distributions on synaptic weights. This talk will provide a recap of recent advances in Bayesian neural net inference and detail my contributions to the solution of this problem. I will demonstrate how Bayesian neural nets can achieve groundbreaking performance in weakly-supervised learning, active learning, few-shot learning, and transfer learning setups when applied to medical image analysis and core computer vision tasks. I will conclude by a summary of my ongoing research in reinforcement active learning, video-based imitation learning, and reconciliation of Bayesian Program Learning with Generative Adversarial Nets.

Speaker: Melih Kandemir

Dr. Kandemir studied computer science in Hacettepe University and Bilkent University between 2001 and 2008. Later on, he pursued his doctoral studies in Aalto University (former Helsinki University of Technology) on the development of machine learning models for mental state inference until 2013. He worked as a postdoctoral researcher in Heidelberg University, Heidelberg Collaboratory for Image Processing (HCI) between 2013 and 2016. As of 2017, he is an assistant professor at Özyeğin University, Computer Science Department. Throughout his career, he took part in various research projects in funded collaboration with multinational corporations including Nokia, Robert Bosch GmbH, and Carl Zeiss AG. Bayesian deep learning, few-shot learning, active learning, reinforcement learning, and application of these approaches to computer vision are among his research interests.

Affiliation: Professor of Computer Science, Özyeğin University

Place of Seminar: Aalto University

Slides

March 5

Artificial Intelligence for Mobility Studies in Urban And Natural Areas

Abstract: Understanding how people use and move in space is important for planning, both in urban and natural areas. Recent research has shown that location-based social media data may reveal spatial and temporal patterns of the use of space, and reveal areas where human activities might be detrimental. We have shown that social media data corresponds to real-life spatial and temporal patterns of visitors in national parks and is able to bring light to use of space in cities, by providing meaningful information about the activities and preferences of people. The overwhelming magnitudes of social media data require special filtering and cleaning and tested analyses approaches. We are now using geospatial analysis methods together with machine learning to understand where, when, how and by whom areas are being used and how people and goods move about and why. Automated text and image content analysis is needed to leverage the full potential of social media data in spatial planning. Also new applications are yet to be discovered.

Speaker: Tuuli Toivonen

Affiliation: Professor of Geoinformatics, University of Helsinki

Place of Seminar: University of Helsinki

Slides

March 12

Finding Outlier Correlations

Abstract: Finding strongly correlated pairs of observables is one of the basic tasks in data analysis and machine learning. Assuming we have N observables, there are N(N-1)/2 pairs of distinct observables, which gives rise to quadratic scalability in N if our approach is to explicitly compute all pairwise correlations.

In this talk, we look at algorithm designs that achieve subquadratic scalability in N to find pairs of observables that are strongly correlated compared with the majority of the pairs. Our plan is to start with an exposition of G. Valiant's breakthrough design [FOCS'12,JACM'15] and then look at subsequent improved designs, including some of our own work.

Based on joint work with M. Karppa, J. Kohonen, and P. Ó Catháin, cf. https://arxiv.org/abs/1510.03895 (ACM TALG, to appear) and https://arxiv.org/abs/1606.05608 (ESA'16).

Speaker: Petteri Kaski

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Aalto University

March 19

Fun With Relative Distance Comparisons

Abstract: The distance between two data points is a fundamental ingredient of many data analysis methods. In this talk I review some of my work on “human computation” algorithms that use relative distance comparisons only. I.e., statements of the form “of items a, b, and c, item c is an outlier”, or “item a is closer to item b than to item c”. Such statements are easier to elicit from human annotators than absolute judgements of distance. I consider the problems of centroid computation (Heikinheimo & Ukkonen, HCOMP 2013), density estimation (Ukkonen et al, HCOMP 2015), embeddings (Amid & Ukkonen, ICML 2015), clustering (Ukkonen, ICDM 2017), as well as give a few sneak previews of ongoing work.

Speaker: Antti Ukkonen

Antti Ukkonen is an Academy research fellow at University of Helsinki. He obtained his doctoral degree at Aalto university in 2008, and has since held positions at Yahoo! Research, Helsinki Institute for Information Technology HIIT, and Finnish Institute of Occupational Health. Currently he is the PI in the “Data Science for the Masses” project funded by Academy of Finland. His research interests include algorithmic aspects of (distributed) human computation and machine learning, as well as applied data science.

Affiliation: Professor of Computer Science, University of Helsinki

Place of Seminar: University of Helsinki

March 26

Minisymposium on Agile Probabilistic AI

Organizers: Aki Vehtari and Arto Klami

Talks

Probabilistic Programming and Stan

Abstract: Probabilistic programming (PP) makes it easy to write new probabilistic models and PP frameworks then allow automated inference for those models. I describe the generic idea of PP, give some examples of software frameworks designed for different PP purposes, and focus more on recent development in Stan.

Speaker: Aki Vehtari

Slides

Automated Variational Inference

Abstract: Automated inference for generic models which can be programmed with probabilistic programming languages is challenging. I describe methods based on modern variational inference which are used to speed-up inference for big data.

Speaker: Arto Klami

Slides

Flash Talks

ELFI - Engine for Likelihood Free Inference

Abstract: ELFI - Engine for Likelihood Free Inference provides a probabilistic programming framework for combining probabilistic models with stochastic simulators and performs automated inference using efficient likelihood free inference algorithms.

Speaker: Henri Vuollekoski

Place of Seminar: Aalto University

April 9

Minisymposium on Simulator-Based Inference

Finnish Center for Artificial Intelligence (FCAI) is proud to present a series of longer machine learning coffee sessions on themes of the research programmes of FCAI. These minisymposiums will last for almost 2 hours and consist of some longer talks and a set of 5-min flash talks. The purpose is both for the researchers already working on the theme to get to know what the others are working on, and for those interested in the theme to get to know who to talk to. Suggestions of flash talk topics are welcome - contact the organizers of the specific symposium.

Organizers: Samuel Kaski and Jaakko Lehtinen

Talks

Inferring Cognitive Simulators From Data

Abstract: I will discuss the intriguing problem of inferring parameters of cognitive simulator models from user interaction data. The parameters can include the goals, interests and capabilities of the user, which become disentangled in the inference.

Speaker: Samuel Kaski

Negative Frequency-Dependent Selection Dictates the Fate of Bacteria

Abstract: In recent work we discovered that negative frequency-dependent selection (NFDS) acting on accessory genes appears as the dominating evolutionary force of populations of Streptococcus pneumoniae, which is a major human pathogen. This discovery was greatly facilitated by the recent advances in ABC inference brought by Bayesian optimization which has accelerated model fitting by several orders of magnitude. I will discuss the biological basis of the NFDS principle and show emerging evidence that it is commonly dictating the fate of bacteria also in ubiquitous organisms such as Escherichia coli.

Speaker: Jukka Corander

Why Learn Something You Know?

Abstract: Much of science can be seen as a search of mathematical models that predict observable quantities based on other observable quantities: future positions of stars based on their past positions, incidence of cancer based on biological health measurements, etc. Unlike handcrafted models derived from first principles employed in e.g. physics, many modern machine learning and AI techniques approach the same issue from a different perspective: fixing a highly powerful but general (not problem-specific) model, and setting its parameters based on data. Results are dramatic but often uninterpretable – we do not know precisely how the model comes to its conclusions. In this overview talk, I’ll present thoughts on combining the two approaches, along with recent exciting examples.

Speaker: Jaakko Lehtinen

Flash Talks

Simulator-Based Inference in Robotics

Speaker: Ville Kyrki

Optimizing Technologies with Bayesian Inference

Speaker: Milica Todorovic

ABC and Model Selection

Speaker: Henri Pesonen

Place of Seminar: University of Helsinki

NOTE: The seminar will be in Chemicum A129, Kumpula

April 16

Machine Learning using Unreliable Components: From Matrix Operations to Neural Networks and Stochastic Gradient Descent

Abstract: Reliable computation at scale is one key challenge in large-scale machine learning today.Unreliability in computation can manifest itself in many forms, e.g. (i) "straggling" of a few slow processing nodes which can delay your entire computation, e.g., in synchronous gradient descent; (ii) processor failures; (iii) "soft-errors," which are undetected errors where nodes can produce garbage outputs. My focus is on the problem of training using unreliable nodes.

First, I will introduce the problem of training model parallel neural networks in the presence of soft-errors. This problem was in fact the motivation of von Neumann's 1956 study, which started the field of computing using unreliable components. We propose "CodeNet", a unified, error-correction coding-based strategy that is weaved into the linear algebraic operations of neural network training to provide resilience to errors in every operation during training. I will also survey some of the notable results in the emerging area of "coded computing," including my own work on matrix-vector and matrix-matrix products, that outperform classical results in fault-tolerant computing by arbitrarily large factors in expected time. Next, I will discuss the error-runtime trade-offs of various data parallel approaches in training machine learning models in presence of stragglers, in particular, synchronous and asynchronous variants of SGD. Finally, I will discuss some open problems in this exciting and interdisciplinary area.

Parts of this work is accepted at AISTATS 2018 and ISIT 2018.

Speaker: Sanghamitra Dutta

Affiliation: PhD Candidate, Carnegie Mellon University

Place of Seminar: Aalto University

April 23

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

Abstract: For the study of molecules and materials, conventional theoretical and experimental spectroscopies are well established in the natural sciences, but they are slow and expensive. Our objective is to launch a new era of artificial intelligence (AI) enhanced spectroscopy that learns from the plethora of already available experimental and theoretical spectroscopy data. Once trained, the AI can make predictions of spectra instantly and at no further cost. In this new paradigm, AI spectroscopy would complement conventional theoretical and experimental spectroscopy to greatly accelerate the spectroscopic analysis of materials, make predictions for novel and hitherto uncharacterized materials, and discover entirely new materials.

In this presentation, I will introduce the two approaches we have used to learn spectroscopic properties: kernel ridge regression (KRR) and deep neural networks (NN). The models are trained and validated on data generated by accurate state-of-the art quantum chemistry computations for diverse subsets of the GBD-13 and GBD-17 molecular datasets [1,2]. The molecules are represented by a simple, easily attainable numerical description based on nuclear charges and cartesian coordinates [3,4]. The complexity of the molecular descriptor [4] turns out to be crucial for the learning success, as I will demonstrate for KRR. I will then show, how we can learn spectra (i.e. continuous target quantities) with NNs. We design and test three different NN architectures: multilayer perceptron (MLP) [5], convolutional neural network (CNN) and deep tensor neural network (DTNN) [6]. Already the MLP is able to learn spectra, but the learning quality improves significantly for the CNN and reaches its best performance for the DTNN. Both CNN and DTNN capture even small nuances in the spectral shape.

* This work was performed in collaboration with A. Stuke, K. Ghosh, L. Himanen, M. Todorovic, and A. Vehtari

[1] L. C. Blum et al., J. Am. Chem. Soc. 131, 8732 (2009)
[2] R. Ramakrishnan et al., Scientific Data 1, 140022 (2014)
[3] M. Rupp et al., Phys. Rev. Lett. 108, 058301 (2012)
[4] H. Huo and M. Rupp, arXiv:1704.06439
[5] G. Montavon et al., New J. Phys. 15, 095003 (2013)
[6] K. T. Schutt et al., Nat. Comm. 8, 13890 (2017)

Speaker: Patrick Rinke

Affiliation: Professor of Physics, Aalto University

Place of Seminar: University of Helsinki

May 7

Fairness-aware machine intelligence: foundations and challenges

Abstract: Algorithmic decision making is pervasive, the prices we pay, the news or movies we see, the jobs or credits we get are advised by algorithms. Not so long ago the public used to think that decision making by computers is inherently objective, but realization that models learned from data are not more objective than the data on which they have been trained is becoming common. Fairness-aware machine learning has emerged as a discipline about ten years ago with the main goal to correct algorithmically for potential biases towards sensitive groups of people. The talk will discuss the main challenges, existing solutions and current trends in this research area.

Speaker: Indre Zliobaite

Affiliation: University of Helsinki

Place of Seminar: University of Helsinki

Machine Learning Coffee Seminars

afrabah1 — Sat, 03 Dec 2016 09:55:24 +0000

About Us

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held weekly on Mondays at 9 am - 10 am. The location alternates between Aalto University and the University of Helsinki. At Aalto University, talks will be held in Konemiehentie 2, seminar room T6 and at the University of Helsinki in Kumpula, seminar room Exactum D122 (Gustaf Hällströmin katu 2b), unless otherwise noted. Talks will begin at 9:15 am and coffee will be served from 9:00 am.

Subscribe to our mailing list where seminar topics are announced beforehand.

Upcoming Talks (2018)

Give a talk
If you are interested in giving a talk, feel free to send your suggested topic and abstract to mlseminar@hiit.fi

^{Spring 2018}

May 14

Minisymposium on Interactive AI

Organizers: Giulio Jacucci and Antti Oulasvirta

Talks

Computational Rationality: Convergence of Machine Learning, Neuroscience, Cognitive Science, Robotics, and HCI

Speaker: Antti Oulasvirta

Interactive Intent Modelling and Knowledge Elicitation

Speaker: Samuel Kaski

Interactive Robot Learning

Speaker: Ville Kyrki

Flash Talks

Emotional Rationality

Speaker: Jussi Jokinen

Place of Seminar: Aalto University

May 21

Abstract:

Speaker: Harri Lähdesmäki

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: University of Helsinki

May 28

Minisymposium on Privacy-Preserving and Secure AI

Organizers: N. Asokan and Antti Honkela

Talks

Speaker: N. Asokan

Speaker: Antti Honkela

Flash Talks

Place of Seminar: Aalto University

History of Previous Talks

Organizers
Samuel Kaski	Professor of Computer Science, Aalto University
Teemu Roos	Associate Professor of Computer Science, University of Helsinki
Homayun Afrabandpey	PhD Student, Aalto University

Break in LDAP logins using secondary LDAP server at 2016-11-15 15:57 - 2016-11-16 10:20

niemimak — Tue, 15 Nov 2016 13:57:00 +0000

Schedule:

2016-11-15 15:57 to 2016-11-16 10:20

Duration:

18h 23min

Affected services:

Web services that use LDAP authentication including wiki.hiit.fi, www.hiit.fi, www.hict.fi, www.reknow.fi, wiki.hiit.fi.

Description:

Secondary LDAP server lose.it.hiit.fi stopped responding at 2016-11-15 16:00 and caused logins to fail on services that use only that server for LDAP authentication.

Break in faramir.it.hiit.fi at 2016-10-13 16:34 - 16:38

niemimak — Fri, 21 Oct 2016 13:34:44 +0000

Schedule:

2016-10-21 16:34 to 16:38

Duration:

4min

Affected services:

www.hiit.fi, www.reknow.fi, www.hict.fi

Description:

faramir.it.hiit.fi was rebooted due to updates.

Break in shell.hiit.fi at 2016-10-20 11:11 - 11:13

niemimak — Thu, 20 Oct 2016 08:11:00 +0000

Schedule:

2016-10-20 11:11 to 11:13

Duration:

2min

Affected services:

Shell.hiit.fi

Description:

Shell.hiit.fi was booted due to kernel update.

Break in faramir.it.hiit.fi at 2016-10-13 16:23 - 16:25

niemimak — Thu, 13 Oct 2016 13:23:00 +0000

Schedule:

2016-10-13 16:23 to 16:25

Duration:

2min

Affected services:

www.hiit.fi, www.reknow.fi, www.hict.fi

Description:

Web server faramir.it.hiit.fi was booted due to updates.

Break in Drupal sites on faramit.it.hiit.fi

niemimak — Wed, 10 Aug 2016 13:09:57 +0000

Schedule:

2016-08-10 16:10 to 16:40

Duration:

30min

Affected services:

www.hiit.fi, www.hict.fi, www.reknow.fi, figs.hiit.fi, fics.hiit.fi

Description:

Authorization configurations will be updated on Drupal sites running on faramir.it.hiit.fi. During the update logged in user may not be available to create, delete or edit any content.

Research assistant (computational social science)

mnelimar — Thu, 07 Jul 2016 09:23:48 +0000

Helsinki Institute for Information Technology HIIT and Department of Computer Science, Aalto University is looking for

a part-time research assistant

to support our research on computational social science. The job includes tasks related to data collection and computational data analysis, together with our researchers and collaborators. Based on your interest, the tasks can also include original research on this topic

The focus of this research is the digital manifestation of political activities. We need to collect data from online media spaces (such as Facebook, Twitter, Suomi24 etc.) and analyse such data using statistics and machine learning tools and existing APIs. The research can focus on topics like electoral campaigning and racisms.

The research assistant is also welcomed to participate in other activities in Digital Content Communities, such as our emerging research on MyData and political study of socio-technical systems.

The required skills for the position are

Good knowledge of Python-language and libraries, such as requests and BeautifulSoup
Good understanding of quantitative research, in particular, statistical testing

Furthermore, following skills are considered an advantage in this position

Basic understanding of social science (sociology, communication research, political science, etc.
Good knowledge of qualitative research, in particular, grounded theory and interview analysis
Basic understanding of supervised and unsupervised machine learning
Understanding of other programming environments, such as R or Javascript

We are happy to provide support for a Bachelor or Master thesis in the context of this work!

Length of contract: 4 months with possible extension based on job performance and progress on studies, starting from September

Work time: about 18 hours per week, flexible timing. We expect to see you in Otaniemi weekly.

Salary: Between 900 and 1100 euros, based on the number of credits. (per Aalto University policy)

In your application

We aim to conduct this application round anonymously. Kindly leave out all identifiable information from your answers.

Summarise your interest towards the position (300 words) and your expectations and hopes towards us and the job (500 words). Also, describe your future plans (200 words).(word lengths only indicator, we are not gonna count them)
Provide a summary of the number credits and grade point average per subject you’re studying. If you have finished your Bachelor thesis, mark the grade also.
Describe tasks that you’ve done using programming tools, e.g. "Data collection from Twitter (Python) " or "Running topic models of the Twitter data (R, Python)"
Describe your familiarity with social science research
Describe your previous work experience if it is relevant, mention institutions and outcomes (no need to provide references at this point of time), e.g., "I have worked at the University of Helsinki, Department of Computer Science as a teaching assistant for introduction for programming courses." or "I worked at Faculty of Social Science in a research project examining voting behaviors. We are working towards a published paper on these findings."

Send your application via E-lomake . Apply by August 5th (18.00). Interviews take place the next week.

More information, contact Matti Nelimarkka (matti.nelimarkka@hiit.fi) via email.

Break in toe.hiit.fi at 2016-06-08 16:40 - 20:07

nuorento — Wed, 08 Jun 2016 15:04:00 +0000

Schedule:

2016-06-08 16:40 to 20:07

Duration:

3h 27min

Affected services:

toe.hiit.fi

Description:

Due to failed systemd configuration after reboot virtual server couldn't find it's root volume group and thus root volume.

We are currently investigating reason for this.

Update at 2016-06-08 20:07 by Markus Nuorento: toe.hiit.fi is operational again. At the same break server was upgraded to Debian GNU Linux (Jessie) 8.5.

Shutdown of HIIT's OS X update service

niemimak — Mon, 30 May 2016 22:00:00 +0000

Schedule:

2016-05-31 23:59

Duration:

indefinite

Affected services:

Managed Software Center or Managed Software Update (Munki)

Description:

HIIT IT has provided updates to OS X clients via munki-packages.hiit.fi since August 2012, but this service has become redundant as both mother universities offer similar service. The shutdown of munki-packages.hiit.fi affects all HIIT's Macs, which haven't already been reinstalled to use services provided by Aalto University or University of Helsinki. Everyone is encouraged to have their Macs reinstalled as soon as possible, but not later than May 31st 2016.

Digivaalit 2015 - medianäkyvyys

mnelimar — Wed, 25 May 2016 12:06:20 +0000

Mediassa...

HS Tiede (3.5.2016): Yhteiskuntatiede ottaa haltuun isot aineistot
Journalism Research News (23.4.2016): First Twitter-data analyses of the Finnish election
Karjalainen (9.4.2016), pääkirjoitus: Some-hallinta ei takaa vaalimenestystä
YLE (7.4.2016): Tutkimus paljasti yllättäviä nimiä vaalikeskustelun kärjessä
Helsingin Sanomat (7.4.2016): Tutkimus kartoitti sosiaalisen median ”supervaikuttajia” eduskuntavaalien alla – opposition ehdokkaat pärjäsivät
MTV3 (7.4.2016): Tutkimus: Vain muutama ehdokas pystyi vaikuttamaan median agendaan eduskuntavaaleissa
Verkkouutiset (7.4.2016): Supervaikuttajat pystyivät vaikuttamaan viime eduskuntavaalien media-agendaan
Turun Sanomat (7.4.2016): Tutkimus: Vain kourallinen ehdokkaista saa medianäkyvyyttä
Karjalainen (7.4.2016): Nyt löytyi viitteitä median punavihreästä kuplasta? Supervaikuttajien nimet. Sama uutinen julkaistu myös Etelä-Suomen Sanomissa, Savon Sanomissa ja Keskisuomalaisessa.
Vihreä Lanka (7.4.2016): Somen supervaikuttajat julki – nämä vihreät pääsivät listalle
Iltalehti (7.4.2016): Tutkimus: verkon supervaikuttajat vaikuttivat vaalien media-agendaan
Oulun uutiset (7.4.2016): Aktiivisesti viestivät supervaikuttajat hallitsivat vaalijulkisuutta
Salon Seudun Sanomat (7.4.2016): Vilkkaasti viestivät oppositioehdokkaat vaikuttivat vaalien agendaan
Turun Sanomat (7.4.2016): Verkon supervaikuttajat vaikuttivat vaalien media-agendaan
Elisa.net (7.4.2016): Vilkkaasti viestivät oppositioehdokkaat vaikuttivat vaalien agendaan
KP24.fi (7.4.2016): Vilkkaasti viestivät oppositioehdokkaat vaikuttivat vaalien agendaan. Sama uutinen myös SL24.fi.
Aalto-yliopisto (7.4.2016): Aktiivisesti viestivät supervaikuttajat hallitsivat vaalijulkisuutta
Helsingin yliopisto (6.4.2016): Aktiivisesti viestivät supervaikuttajat hallitsivat vaalijulkisuutta
Helsingin Sanomain Säätiö (6.4.2016): Sosiaalisella medialla suuri merkitys vuoden 2015 eduskuntavaaleissa
Mikrobitti 4/2016, Harto Pönkän kolumni: Minne menet, Twitter? [screenshot]
Helsingin Sanomat Tiede (8.1.2016): Tulossa 2016: hypernopeaa putkipostia ja raskasta seuraa Higgsille. Tiedetoimitus ennustaa kahdeksan uutista alkavalle vuodelle.
Politiikkaradio: Perinteiset vaalit ”hashtag” -mausteella
Turun Sanomat (20.4.2015): HY: Eduskuntavaaleista tuli hashtag-vaalit
Karjalainen (20.4.2015): Tutkijat syynäsivät 175 000 twiittiä: Näiden puolueiden ehdokkaat äänekkäimpiä
Markkinointi ja Mainonta (20.4.2015): Vaalien voittaja on hashtag
MTV3 (20.4.2015): Tutkimus: Hashtagit yleistyivät vaaleissa – ehdokkaat laiskoja tviittaamaan
Vantaan Sanomat (20.4.2015): Näin sauhusivat #Eduskuntavaalit-viestit – katso kollaasi 200 000 twiitin massasta
YLE Helsinki (20.4.2015): Etelän kokoomuslaiset ja vihreät vaalitwiittasivat innokkaimmin – enimmäkseen keskenään
Aamulehti (20.4.2015): #talous, #työ ja #turpo ? eduskuntavaaleista tuli hashtag-vaalit
Suomenmaa (20.4.2015): Keskustalaiset pöhisivät somessa sotesta ja turvallisuuspolitiikasta
YLE Kotimaa (20.4.2015): Etelä-Suomen vaaliehdokkaat olivat äänekkäimpiä sosiaalisessa mediassa
Verkkouutiset (20.4.2015): Eduskuntavaaleista tuli hashtag-vaalit
Länsiväylä: Näin sauhusivat #Eduskuntavaalit-viestit – katso kollaasi 200 000 twiitin massasta
YleX (20.4.2015): Eduskuntavaalitwiittailua tutkittiin - nämä eivät olleet mitkään Twitter-vaalit, sanovat tutkijat
YleX (16.4.2015): Poliitikko ei pärjää ilman sosiaalista mediaa / Tallenne areenassa
Helsingin yliopiston sivuilla: Helsingin yliopisto: Eduskuntavaaleista tuli hashtag-vaalit / Riksdagsvalet blev ett hashtagg val / Hashtags in parliamentary elections
YLE:n Ajatasassa useampia tutkijoita valtiotieteellisen tiedekunnan eduskuntavaalitilaisuudesta: YLE Ajantasa 9.2.2015
YLE Kotimaa (23.3.2015): Ylen vaaligallerian videoita on katsottu yli 2 miljoonaa kertaa
YLEn iltauutiset 9.2.2015
Helsingin yliopisto: Tutkijat selvittävät #vaalit2015-digijulkisuuden

Blogeissa...

CSC:n blogi / Seija Sirkiä (6.5.2016): Digivaalit 2015 - Sivuosan sankari ei paistattele mediahuomiossa
Petro Poutanen (31.1.2015): Onko laskennallinen yhteiskuntatiede viimein saapumassa Suomeen?

Break in HIIT production networks in Otaniemi area in between 2016-03-10 10:00 and 2016-03-14 13:27

pmtonter — Thu, 10 Mar 2016 08:00:00 +0000

Schedule:

2016-03-10 10:00 to 2016-03-14 13:27

Duration:

51h 27min

Affected services:

HIIT production networks in Otaniemi area.

Description:

There will be a short break in HIIT's production networks in Otaniemi area due to them being configured to be ran over an MPLS tunnel instead of a light-path. This is done to ensure more redundant L2 configuration.

Update at 2016-03-10 09:55 by Pekka Tonteri: Break's schedule was updated from 9-12 to 10-14.

Update at 2016-03-14 13:15 by Markus Nuorento: There are problems with connections between Kumpula and Otaniemi. Quick fix before we get them fixed is to drop MTU of network interface to 1428.

Update at 2016-03-14 13:27 by Markus Nuorento: The break is over. Connection with standard MTU setting (1500B) works normally. Jumbo frames (9000B) don't work.

Break in Kumpula-Otaniemi light-paths at 2016-03-09 14:22 - 2016-03-10 02:20

pmtonter — Wed, 09 Mar 2016 12:22:00 +0000

Schedule:

2016-03-09 14:22 to 2016-03-10 02:20

Duration:

11h 58min

Affected services:

Network connections running over Kumpula-Otaniemi light-paths.

Description:

The following services are affected by a broken light-path in between Kumpula and Otaniemi:

HIIT's network connections in Otaniemi region, including Open Innovation House (OIH), T- and TUAS- building.
Testbed.

The schedule to fix the broken fibre is not available.

To remediate the situation and to gain redundancy to production network connections, all other VLANs except the testbed will be relocated from the Kumpula-Otaniemi light-path to MPLS tunnel at at 2016-03-10. The MPLS tunnel will be ran from University of Helsinki to Aalto university and it will carry the traffic over IP using FUNET links of both universities. FUNET links are redundant, a.k.a. there are two (2) of them in each university so breakage of one link will not cause a break in connections.

Update at 2016-03-10 03:10 by Pekka Tonteri: Light-paths seem to function again and the break is over. :) The production network will see some downtime in the morning due to MPLS configuration. That break should, however, be quite short. A separate break report will be issued due to MPLS configuration.

Update at 2016-03-10 03:23 by Pekka Tonteri: A break report for MPLS configuration has been created.

References:

FUNET break report.

Break in Open Innovation House's network connections at 2016-02-22 13:36 - 2016-02-23 12:05

pmtonter — Mon, 22 Feb 2016 11:36:00 +0000

Schedule:

2016-02-22 13:36 to 2016-02-23 12:05

Duration:

22h 29min

Affected services:

Network connections of Open Innovation House

Description:

A lightpath in between Kumpula and Otaniemi is broken. No estimate about the duration of the break currently exists.

More information from the FUNET break report.

Update at 2016-02-23 by Pekka Tonteri: The light path has been fixed at 12:05.

References:

FUNET break report.

Break in shell.hiit.fi at 2016-02-19 13:30 - 13:45

nuorento — Fri, 19 Feb 2016 09:30:34 +0000

Schedule:

2016-02-19 13:30 to 13:45

Duration:

15min

Affected services:

shell.hiit.fi

Description:

Shell.hiit.fi need to be rebooted due to glibc library security update.

References:

https://www.debian.org/security/2016/dsa-3481

Mitre's CVE dictionary: CVE-2015-7547, CVE-2015-8776, CVE-2015-8778, CVE-2015-8779.

Break in eomer.it.hiit.fi at 2016-02-19 09:00 - 09:04

nuorento — Fri, 12 Feb 2016 10:19:56 +0000

Schedule:

2016-02-19 09:00 to 09:04

Duration:

4min

Affected services:

wiki.hiit.fi

Description:

There is a short break in faramir.it.hiit.fi as we reboot the server to enable non-critical kernel updates.

Update at 2016-02-19 09:05 by Markus Nuorento: Server succesfully booted. The break is over.

Break in range.hiit.fi at 2016-02-18 10:00 - 11:00

nuorento — Sun, 07 Feb 2016 11:28:04 +0000

Schedule:

2016-02-18 10:00 to 11:00

Duration:

Affected services:

range.hiit.fi

Description:

There will be one hour break in range.hiit.fi due to the OS upgrade. Range.hiit.fi will be upgraded to Debian GNU/Linux v8.3.

Break in eye.hiit.fi at 2016-02-17 10:00 - 11:00

nuorento — Sat, 06 Feb 2016 11:28:04 +0000

Schedule:

2016-02-17 10:00 to 11:00

Duration:

Affected services:

eye.hiit.fi

Description:

There will be one hour break in eye.hiit.fi due to the OS upgrade. Eye.hiit.fi will be upgraded to Debian GNU/Linux v8.3.

HIIT - helsinki1.jpg

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Break in LDAP logins using secondary LDAP server at 2016-11-15 15:57 - 2016-11-16 10:20

Break in faramir.it.hiit.fi at 2016-10-13 16:34 - 16:38

Break in shell.hiit.fi at 2016-10-20 11:11 - 11:13

Break in faramir.it.hiit.fi at 2016-10-13 16:23 - 16:25

Break in Drupal sites on faramit.it.hiit.fi

Research assistant (computational social science)

^{Spring 2018}