So, this happened. My alma mater chose me as the Alumnus of the Year! As a proud alumni of Aalto University, this is my immense honour to be selected as one. See the announcement in Suomi and English. Aalto University (in particular School of Science within) and Finland just keep on giving, and I feel like I continue to receive without giving anything back. I will have to think of some way for me to pay back all that I have received from them. Kiitos paljon! Of course, the whole event was virtual, and due to the time difference, I
Note: see this announcement on the Mila’s homepage Earlier this month (Nov 2020) at the Samsung AI Forum 2020 I was one of the five recipients of the inaugural Samsung AI Researcher of the Year Award by the Samsung Advanced Institute of Technology (SAIT). Samsung has been supporting my research ever since I was a postdoc at Mila in Montreal, and without their support I wouldn’t have been able to support all my PhD students (NSF, i’m looking at you!) Because of this prolonged support, I had been already grateful to Samsung even before this award, and I am even
I am happy to share the news that Cristina Savin and I have been selected to receive the Google Faculty Research Award this year in the area of computational neuroscience with the topic on <Online Meta-Learning>. See https://research.google/outreach/past-programs/faculty-research-awards/ for the list of awardees.
The extended abstract version of <Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening> has received the best paper award at the AI for Social Good Workshop co-located with ICML’19 last week in Long Beach, CA. Congratulations to the first author Nan who is a PhD student at the NYU Center for Data Science, the project lead Krzysztof who is an assistant professor at NYU Radiology, and all the other members of this project!
Keunwoo Choi, George Fazekas, Mark Sandler and I have received the Best Paper Award at the 18th Annual International Society for Music Information Retrieval Conference (ISMIR). The paper is <Transfer Learning for Music Classification and Regression Tasks> which investigates different ways to exploit the knowledge captured within a deep convolutional network trained to tag a song for other relevant tasks. The main idea is to use not only the final hidden activation vector (as has been usual in computer vision) but to use the activations from all the layers, as some target tasks may require low-level details. Check it out