21 June (Wednesday)

22 June (Thursday)

23 June (Friday)


Keynote 2

Keynote 3

Oral session 1

Oral session 4


Oral session 2

Oral session 5

Keynote 1

Oral session 3

Oral session 6

Opening reception

Gala dinner


Keynote Speakers

Randy McIntosh
Randy McIntosh
Dr. Randy McIntosh is vice-president of Research at Baycrest and director of Baycrest's Rotman Research Institute, and Professor of Psychology at the University of Toronto. Dr. McIntosh is a pioneer in the study of how different parts of the brain work together to bring about the wide range of human mental operations. He is leading a team of international scientists on the development of The Virtual Brain (thevirtualbrain.org), which has the potential to revolutionize how clinicians assess and treat various brain disorders, including cognitive impairment caused by stroke and Alzheimer’s disease. The computerized model will deliver the first real, usable and open simulation of the human brain. For researchers, surgeons, neuroscientists and therapists, The Virtual Brain promises improved patient outcomes by letting clinicians simulate cognitive interventions – right from a Web browser.

Rajeev Raizada
Rajeev Raizada
Dr. Rajeev Raizada is an Assistant Professor in the Department of Brain & Cognitive Sciences at the University of Rochester, and also a member of the university's Center of Excellence in Data Science. His research uses pattern recognition algorithms to investigate the structure of neural representations in the human brain, concentrating especially on visual object recognition and linguistic meaning. More information about the research in his lab can be found at http://raizadalab.org and on Twitter at @RaizadaLab.

Irina Rish
Dr. Irina Rish is a researcher at the AI Foundations department of IBM T.J. Watson Research Center. She received MS in Applied Mathematics from Moscow Gubkin Institute, Russia, and PhD in Computer Science from the University of California, Irvine. Her areas of expertise include artificial intelligence and machine learning, with a particular focus on probabilistic graphical models, sparsity and compressed sensing, active learning, and their applications to various domains, ranging from diagnosis and performance management of distributed computer systems ("autonomic computing") to predictive modeling and statistical biomarker discovery in neuroimaging and other biological data. Irina has published over 60 research papers, several book chapters, two edited books, and a monograph on Sparse Modeling, taught several tutorials and organized multiple workshops at machine-learning conferences, including NIPS, ICML and ECML. She holds 24 patents and several IBM awards. As an adjunct professor at the EE Department of Columbia University, she taught several advanced graduate courses on statistical learning and sparse signal modeling.