Machine Learning Coffee Seminars

Spring 20192019-02-12T09:18:31+00:00

History of Previous Talks in Spring 2019

Machine Learning For Tomography

Date: February 11, 2019

Abstract: Tomography refers to imaging methods where one attempts to recover the internal structure of a physical body from non-invasive boundary measurements. The most famous example is X-ray tomography used in hospitals. The mathematics of inverse problems focuses on extracting information from indirect data, and tomography is a central research topic in the field. Recently, machine learning has offered new data-driven possibilities for image reconstruction. Some of the results using, for example, the “U-net” are truly stunning. However, they are largely “black boxes,” and especially in medical imaging there is a great need for interpretability. This talk presents some ideas on using traditional inverse problems mathematics for calculating nonlinear features that are then used as inputs for machine learning. This way one could increase interpretability, reduce the size of networks needed for learning, allow the use of smaller training data sets, and increasing the robustness of the network (the image formation tasks in ill-posed inverse problems of tomography are very sensitive to noise).

Speaker: Samuli Siltanen

Affiliation: Professor of Industrial Mathematics, University of Helsinki

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

Probabilistic Modelling With the Experts

Date: February 4, 2019

Abstract: I will discuss multiple-data-source prediction problems typical of omics-based precision medicine. What is less typical is that some of the data sources are expert users, whose time is costly, changing the problem to active learning or experimental design for prediction. We have addressed this setup as a probabilistic modelling problem, where different types of sources need different modelling assumptions. I will demonstrate that promising results can be achieved in treatment effectiveness prediction tasks in restricted settings, even by explaining human variation with noise models. Richer behaviour requires richer models that draw from cognitive science.

Speaker: Samuel Kaski

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: Seminar Room T6, Konemiehentie 2

Gray Box Models for Controllable and Transparent Interactive AI

Date: January 28, 2019

Abstract: Gray box modelling combines first principles -based white box models with a data-driven approach to strike a balance between representation power and ontrollability. I discuss interactive AI and computational design as application areas for gray box models. So-called light gray models learn psychological parameters from data. Dark gray models, on the other hand, are data-driven models pretrained with white-box models. As a promising approach in intelligent user interfaces, I discuss models of human performance and cognition, which can predict the human consequences of a design decision. These models can 1) represent population and individual characteristics in a psychologically meaningful way and 2) predict the adaptive behavioral response of a person. However, previously their use has been limited because of lack of appropriate likelihood inference methods. I discuss the use of probabilistic machine learning methods for learning model parameters from real world data and taking decisions in the light of confidence levels.

Speaker: Antti Oulasvirta

Affiliation: Professor of Computer Science, Aalto University

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

Can Physiological Signals be used for Implicit Interaction with Information?

Date: January 21, 2019

Abstract: Would it be nice if computers would be able to empathize with us effortlessly and understand what we are interested in or what is entertaining to us while looking for information and consuming content ? The talk will introduce physiological signals as input to computing systems outside of the medical domain. A definition of implicit interaction will be presented along with roles it can have in improving the experience of users. Several recent cases will be present to assess how far we are from using physiological signals including reflecting on current challenges.

Speaker: Giulio Jacucci

Affiliation: Professor of Computer Science, University of Helsinki

Place of Seminar: Aalto University, Seminar Room T6

Machine learning in cancer research and oncology

Date: January 14, 2019

Abstract: Medicine in general and oncology in particular is experiencing a paradigm shift where several molecularly targeted therapies have become available to patients; and many more will become in the future. This change is driven by the technological advances that have reduced the barriers to measure large amounts of data at pathophysiological and molecular levels from a cancer patient. Finding right drug to the right patient requires a joint effort where machine learning experts collaborate with translational and clinical experts. In this presentation I focus on on-going cancer research projects in the Faculty of Medicine where collaboration with the local machine learning community can lead to scientific breakthroughs and medical benefits.

Speaker: Sampsa Hautaniemi

Affiliation: Professor of Systems Biology, University of Helsinki

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