History of Previous Talks in Spring 2020

A maximum likelihood approach for modelling viral evolution using genome sequence data

Date: January 20, 2020

Abstract: Viruses evolve very rapidly, on clinically relevant timescales. HIV evolves around repeated attacks from the host immune system to eventually cause AIDS. Seasonal influenza strains change from year to year, requiring updates to the influenza vaccine. New influenza strains can evolve from viruses that infect other species, causing global pandemic disease. Genome sequence data can provide an insight into all of these processes. Where evolution proceeds over a sufficient period of time, phylogenetic methods provide a framework for the analysis of data, but for shorter periods such approaches are inadequate. We here outline a maximum likelihood approach, using short-read data to evaluate the state, and over time the evolution, of a viral population. We illustrate the use of the method in understanding the potential evolution of pandemic influenza and its application to sequence data from a case of long-term influenza infection.

Speaker: Dr Chris Illingworth

Affiliation: Professor of Department of Genetics, University of Cambridge

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

Content Based Recommendation Engines’ tech building blocks, with particular focus on word embedding models and user experience

Date: Jan 17, 2020 (9:15 – 11:00)

Abstract: Iris.ai is tackling the problem of an exponentially expanding corpus of scientific knowledge, and limited human ability to manually sort, navigate and review it. Old school solutions are based on limiting key words and human-made taxonomies, revolving around a biased citation system and with results presented in endless and unstructured lists. The Iris.ai technology, using key concept extraction, contextual synonym enrichment, neural topic modeling and word importance-based document similarity, allows us to build intuitively meaningful content-based indexes. These indexes and their corresponding content are turned into intuitive, helpful visualizations for improved overview and navigation. This poses a novel and more efficient way of performing systematic research landscape mappings, reducing manual labor by 78%.

Bio: Victor Botev is the CTO and Co-Founder of Iris.ai, preciously a researcher from Chalmers University of Technology. He studied individual Master’s degrees in Artificial Intelligence
and Computer Systems and Networks at Sofia University St. Kliment Ohridski and Chalmers, respectively. After his degrees he stayed at Chalmers, where he conducted research on clustering and predictive neural network models and the usage of signal processing
techniques in studying Big Data. Victor has put his unique combination of AI research, software development lead and ambitious vision to the ultimate test at Iris.ai.

Speaker: Victor Botev
Affiliation: CTO Iris AI

Place of Seminar: Lecture Hall T2, Konemiehentie 2, Aalto University

A new look into teaching AI and Machine Learning: Lessons from the Elements of AI

Date: January 13, 2020

Abstract: AI and machine learning are some of the hottest topics both inside the academia and in the “real world”. These topics have been taught for decades and there exist standard syllabi for introductory courses. However, as AI is shaping every aspect of our society, we need to change the ways in which it is taught. The Elements of AI is an exceptional AI course because it doesn’t require any prerequisites in computing or mathematics beyond basic arithmetics.

When we started the Elements of AI, there was no guarantee that our approach would be viable. Perhaps we would only end up exacerbating the hype and spreading misconceptions instead of resolving them. After 1.5 years, five language versions (soon to be >20), and over 300 000 users, we can conclude that yes, the approach works: it is possible to learn to understand AI without knowing any programming.

In this talk, I describe the basic concept, our pedagogical thinking, and some of the lessons learned. The talk hopefully encourages others to engage, one way or another, in similar initiatives.

Speaker: Teemu Roos
Affiliation: Professor of Computer Sciences, Helsinki University

Place of Seminar: Lecture Hall T6, Konemiehentie 2, Aalto University