Kick-off event of HiData - Helsinki Centre for Data Science

Event type: 
Event
Event time: 
2017-12-01 13:00 to 16:30
Place: 
Auditorium A111, Exactum, Kumpula campus, Gustaf Hällströmin katu 2b
Description: 
The kick-off event will present plans for HiData as well as a range of examples of data science within the participating universities. It is also a great opportunity to network with other academics with interest in data science. Anyone interested in data science research or education is welcome to join the HiData network! Also join the open Facebook group for HiData.

Data science, i.e., extraction of knowledge and insights from data, is important across many fields of science. HiData, Helsinki Centre for Data Science, aims to create a world-class research and research-based education hub of data science in Helsinki, as a joint effort between the University of Helsinki and Aalto University. HiData builds on the existing, strong research in various areas of data science, and aims to provide novel synergies across disciplines. HiData is funded in 2017-2021 as part of the profiling measures of the Academy of Finland, the University of Helsinki and Aalto University, and will get to full speed during 2018.

Please register by Nov 23th

Welcome!

More information:
Prof. Hannu Toivonen
Director of HiData
University of Helsinki 

AI Day

Event type: 
Event
Event time: 
2017-12-13 11:00 to 17:00
Place: 
Dipoli, Otakaari 24, 02150 Espoo
Description: 

Welcome to Dipoli on 13 December 2017!

The current, top-level AI research and education in Finland will be displayed during our half-day event.

Academics, welcome to present your results and network with potential private and  public sector partners!Companies, come tell about your problems that need solving!

We offer intriguing matchmaking opportunities for companies and the public sector – you are welcome to come and discuss how AI is changing the world and to discover new opportunities for your business. Meet the people at the leading edge of AI research and combine your efforts to get a head start for your organisation. 

Keynote speakers

Professor Moncef Gabbouj, Tampere University of Technology

Senior Lecturer Michael U. Gutmann, University of Edinburgh

Professor Giulio Jacucci, University of Helsinki

Professor Samuel Kaski, Aalto University

Professor Teemu Roos, University of Helsinki

See the full program here.

Registration to the event

  • For academic contributions (posters, demos, pitches) the registration deadline is 19 November.Due to time and space limitations, the organisers reserve the right to select the contributions, if necessary.
  • For reserving a small rollup demo space with table (width max 1m), please contact Terhi Kajaste at terhi.kajaste@aalto.fi for further information by 30 November.
  • For other participants the registration deadline is 5 December.

Link to the registration >>

Machine Learning Coffee seminar "Learning of Ultra High-Dimensional Potts Models for Bacterial Population Genomics"

Lecturer : 
Jukka Corander
Event type: 
HIIT seminar
Doctoral dissertation
Respondent: 
Opponent: 
Custos: 
Event time: 
2017-11-13 09:15 to 10:00
Place: 
Exactum D122, Kumpula
Description: 

Jukka Corander, Professor of Statistics, University of Helsinki and University of Oslo

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.

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. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Next talks:

Nov 20, Otaniemi: Perttu Hämäläinen "Towards Intelligent Exergames"

Nov 27, Kumpula: Erik Aurell

Welcome!

 

Machine Learning Coffee seminar "Learning Markov Equivalence Classes of Directed Acyclic Graphs: an Objective Bayes Approach"

Lecturer : 
Guido Consonni
Event type: 
HIIT seminar
Doctoral dissertation
Respondent: 
Opponent: 
Custos: 
Event time: 
2017-10-30 09:15 to 10:00
Place: 
Exactum D122, Kumpula
Description: 

Guido Consonni, Professor of Statistics, Universita Cattolica del Sacro Cuore

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.

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. Talks will begin at 9:15 am and porridge and coffee will be served from 9:00 am.

Next talks:

Nov 6, Otaniemi: Pekka Marttinen "Efficient and accurate approximate Bayesian computation"

Nov 13, Kumpula: Jukka Corander "Learning of Ultra High-Dimensional Potts Models for Bacterial Population Genomics"

Nov 20, Otaniemi: Perttu Hämäläinen "Towards Intelligent Exergames"

Welcome!

Helsinki Algorithms Seminar: "Delegatable and error-tolerant algorithms" Petteri Kaski, Aalto

Event type: 
HIIT seminar
Event time: 
2017-10-19 16:15 to 17:00
Place: 
Exactum B222, Gustaf Hällströmin katu 2B, Helsinki
Description: 

Speaker: Petteri Kaski
Assistant Professor, Aalto University

Delegatable and error-tolerant algorithms

Abstract:

Is it possible to delegate a computation to an unreliable and more powerful counterparty? Can we design algorithms in such a way that not only can their execution be delegated, but a controlled number of adversarial errors can take place during the execution and yet one can recover the desired result? This talk will review theory and engineering efforts to bring such algorithm designs to the computing practice, including some of our recent work.

**

Helsinki Algorithms Seminar is a weekly meeting of researchers in the Helsinki area interested in the art of algorithms and algorithm design, broadly interpreted to cover both theoretical ideas and algorithm engineering on concrete computing platforms. In most cases we have a presentation prepared for each meeting to communicate an idea, a recent result, work-in-progress, or demo, but this should not be at the expense of discussion and simply having fun with algorithms.

Our affiliations are with Aalto University and the University of Helsinki, and accordingly our activities alternate between the Otaniemi Campus of Aalto University and the Kumpula Campus of University of Helsinki, catalyzed by the Helsinki Institute for Information Technology HIIT, under the Algorithmic Data Analysis (ADA) programme.

Welcome!

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