Machine Learning Coffee Seminars2019-09-18T11:05:09+03:00

About Us

Machine Learning Coffee seminars are weekly seminars organized by Finnish Center for Artificial Intelligence (FCAI) and are 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.

Please see FCAI’s YouTube channel for video recordings of past talks.

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

Upcoming Talks (2019)

October 2019

FCAI Highlight on easy modeling tools: The case of prior predictive distribution

Date: October 28, 2019

Abstract: One of the goals for FCAI is to make development of probabilistic AI solutions easier and faster, by combining fundamental research on modeling principles with open-source tools that are being used by the industry and academia. This talk provides an overview to the activities related to this, and in particular drills into recent research on how the prior predictive distribution can be used for eliciting expert knowledge and for automatic parts of the modeling process.

Bio: Arto Klami is an assistant professor of computer science at University of Helsinki. He leads the Multi-source Probabilistic Inference group and is coordinating the FCAI Highlight on Easy and privacy-preserving modeling tools. He received his PhD from Aalto University in 2008 and worked as Academy Research Fellow in 2013-2019. He has published more than 60 scientific articles and his main research area is statistical machine learning, with contributions for example in data integration and approximate inference. He has worked on wide range of applications from computational neuroscience to modeling human activity, and is currently focusing on AI-driven ultrasonic cleaning and hyperspectral imaging.

Speaker: Arto Klami
Affiliation: Assistant Professor , Department of Computer Science Helsinki Institute for Information Technology

Place of Seminar: Lecture Hall Exactum D122, University of Helsinki

November 2019

FCAI Simulator-based inference: AI-assisted vaccine development

Date: November 04, 2019

Abstract: Several recent studies suggest that negative frequency-dependent selection (NFDS) acting through accessory genome loci is a strong force influencing the population evolution of major human bacterial pathogens. Such models can be fitted using likelihood- free inference for simulator-based statistical models, for example using the ELFI software (elfi.ai). Here we review some of these results and combine them with machine learning to introduce a practical method for optimizing vaccines by jointly using data from genomic population surveys and epidemiological studies. The results may lead to largely improved workflows to design new vaccines.

Bio: Jukka Corander is a professor at the Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Norway, also a professor at the Department of Mathematics and Statistics, University of Helsinki, Finland, and an honorary faculty member at the Wellcome Sanger Institute, Cambridge, UK. He has won two ERC grants, an ERC StG (2009-2014) from the mathematics and statistics panel and an ERC AdG (2017-2022) from the infection and immunity panel. He has published nearly 250 scientific peer-reviewed articles and his main research interests are microbial evolution and transmission modeling, statistical machine learning, population genomics, and inference algorithms. The statistical methods introduced by his research group have led to numerous discoveries on the evolution, resistance, virulence, and transmission of pathogenic bacteria and viruses.
Speaker: Jukka Corander
Affiliation: Professor of Statistics Department of Mathematics and Statistics, University of Helsinki

Place of Seminar: Lecture Hall T5, Konemiehentie 2, Aalto University

FCAI Research Programme on Interactive AI

Date: November 11, 2019

Abstract: How to create machines that understand people and can work with them as partners? This talk provides an overview of FCAI’s research on interactive AI and AI-aided design. The research program builds on simulation-based AI, which offers an exciting way to combine first principles-based and data-driven methods. I will review the methodology, present applications, and discuss open challenges.

Bio: Antti Oulasvirta leads the User Interfaces research group at Aalto University and the FCAI Research Programme on Interactive AI. Prior to joining Aalto, he was a Senior Researcher at the Max Planck Institute for Informatics and the Cluster of Excellence on Multimodal Computing and Interaction at Saarland University. He received his doctorate in Cognitive Science from the University of Helsinki in 2006, after which he was a Fulbright Scholar at the School of Information in the University of California-Berkeley in 2007-2008 and a Senior Researcher at Helsinki Institute for Information Technology HIIT in 2008-2011. He was awarded the ERC Starting Grant (2015-2020) for research on computational design of user interfaces. Dr. Oulasvirta serves as an associate editor for ACM TOCHI and frequently participates in the paper committees of HCI conferences, including the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI). His work has been awarded the Best Paper Award and Best Paper Honorable Mention at CHI twelve times between 2008 and 2019. He has held keynote talks on computational user interface design at NordiCHI’14, CoDIT’14, EICS’16, IHCI’17, ICWE’19, and Chinese CHI ’19. In 2019, he was invited to the Finnish Academy of Science and Letters.

Speaker: Antti Oulasvirta
Affiliation: Associate Professor, User interfaces, Department of Communications and Networking, Aalto University

Place of Seminar: Lecture Hall Exactum D122, University of Helsinki

Machine learning in drone remote sensing

Date: November 18, 2019

Abstract: Utilization of unmanned aerial vehicles, drones, has increased explosively during the past couple of years. Integrating analytics and actuation either utilizing individual drones, drone teams or drone swarms will enable robotization of various environmental operations in the future. This presentation focuses on drone based remote sensing in agricultural and forestry applications. Drones become intelligent when coupled with sensors and analytics. Imaging sensors (color, hyperspectral and multispectral cameras) enable exploration of objects’ 3D structure, and reflection and absorption characteristics. Essential data preprocessing steps are georeferencing, 3D object reconstruction and radiometric calibration. Machine learning plays a fundamental role in building the analytics process. The physical observations measured in field or in laboratory are related to the remote sensing observations and models are built to extract information. Then in analytics applications remote sensing data are fed to the analyzer and the information are extracted utilizing the models. This presentation deals with remote sensing sensor, data calibration and machine learning and presents selected case studies in forest inventory, tree health analysis and grass quality and quantity assessment.

Bio: Dr. Eija Honkavaara is Research Manager at the Finnish Geospatial Research Institute in the National Land Survey (FGI) and Docent at the Aalto University in the field of Automatic Systems in Photogrammetry. Her current research focuses on drone based geoinformation and AI technologies for environmental remote sensing. The DroneFinland-team led by Dr Honkavaara has developed several generations of high-performance drone remote sensing systems with cameras, hyperspectral sensors, analytics applications as well as inhouse data processing chains. She has a wide international research network, is a leader of working group “Hyperspectral Imaging Applications” in the International Society for Photogrammetry and Remote Sensing (ISPRS) and has led several national and international projects on UAV remote sensing.

Speaker: Eija Honkavaara
Affiliation: Finnish Geospatial Research Institute, FGI

Place of Seminar: Lecture Hall T5, Konemiehentie 2, Aalto University

Networked Exponential Families for Big Data over Networks

Date: November 25, 2019

Abstract: The data arising in many important applications consists of high-dimensional data points that are related by an intrinsic network structure. We introduce networked exponential families to jointly leverage the information in the topology as well as the high-dimensional attributes (features or labels) of networked data points. Networked exponential families provide a flexible probabilistic model for heterogeneous datasets with intrinsic network structure. These models can be learnt efficiently using network Lasso, which implicitly pools or clusters the data points according to the intrinsic network structure and the local likelihood. The resulting method can be formulated as a non-smooth convex optimization problem which we solve using a primal-dual splitting method.
This primal-dual method is appealing for big data applications as it can be implemented as a highly scalable message-passing algorithm.
Paper: https://arxiv.org/abs/1905.09056

Bio: Alexander Jung received the Diplom-Ingenieur and Dr. techn. degrees in electrical engineering/signal processing from Vienna University of Technology, Vienna, Austria, in 2008, and 2011, respectively. After Post-Doc stays at TU Vienna and ETH Zurich, he joined Aalto in 2015. He is currently assistant professor for machine learning within the Department of Computer Science at Aalto University. His research interests are in statistical signal processing and machine learning for big data with emphasis on sparse models as well as trade-offs between accuracy and computational complexity of learning algorithms. He has taught the main courses on machine learning and artificial intelligence attracting several thousands of students from and beyond Aalto.

Speaker: Alex Jung
Affiliation: Assistant Professor of Computer Science, Aalto University

Place of Seminar: Lecture Hall Exactum D122, University of Helsinki

December 2019

TBA

Date: December 02, 2019

Speaker: Teemu Roos
Affiliation: Associate Professor of Computer Science
,Machine Learning and Data Science, Helsinki University

Place of Seminar: Lecture Hall T5, Konemiehentie 2, Aalto University

Organizers

Samuel Kaski Professor of Computer Science, Aalto University
Laura Ruotsalainen Professor of Computer Science, University of Helsinki
Khaoula El Mekkaoui Doctoral Student, Aalto University