Dr. Ashkan Rahimi-Kian has 24+ years of professional experiences in Smart Energy Grids, Power Systems Planning, Operations and Optimal Control, DERMS, Operations Research, Predictive Analytics, Game Theory & Multi-Agent Systems (MAS), and Transactive Energy Markets by means of Blockchain. He has served as the V.P. of Engineering at Genscape Inc. (2001 to 2002); Senior Research Associate at School of ECE, Cornell University (2003); Assistant Professor (2004 to 2008) and Associate Professor (2009 to 2014) at School of ECE, College of Engineering, University of Tehran; Senior Research Associate, ECE Dept., University of Toronto (2015); Founder and CEO, I-EMS Group Ltd., Toronto, Canada (2015 to present); and Senior Data Scientist & Energy Market Analyst, Opus One Solutions, Toronto, Canada (2016 to 2018). He is a registered Professional Engineer in Ontario, Canada and a Senior Member of IEEE. He has published over 150 peer-reviewed papers about Smart Energy Grids, Energy Markets, Power Systems' Load & Price Estimation, Home and Building Intelligent Energy Management Systems. Ashkan is the main inventor of two US patents, "Integrated Distribution Planning" and “A WEB-BASED SaaS with LOAD/PV FORECASTING, Power Distribution System Simulation/Optimization (DERMS), and Blockchain-based Transactive Energy Platform SERVICES FOR Smart Grids”. Ashkan has designed and managed the development of several software solutions for power systems/smart grids’ optimal planning, operations, monitoring and control for the past 18 years.
Dr. Yalda Mohsenzadeh is an Assistant Professor in the Department of Computer Science and a core member of the Brain and Mind Institute at Western University, London, ON, Canada. She is also a faculty affiliate with Vector Institute for Artificial Intelligence, Toronto, ON, Canada. Before joining Western, she was a postdoctoral associate in the Computer Science and Artificial Intelligence Lab (CSAIL) and McGovern Institute for Brain Research at MIT, Cambridge, MA, USA. Prior to that, she was a postdoctoral fellow in the Center for Vision Research at York University, Toronto, ON, Canada. Yalda received her PhD in statistical machine learning in 2014 from Amirkabir University of Technology, Tehran, Iran. Her research is interdisciplinary, spanning machine learning, computer vision and their application in cognitive neuroscience and medical imaging with a successful track record of collaboration with industry sectors.
ABSTRACT:
Everyday, we are bombarded with hundreds of images on our smart phone, on television, or in print. Recent work shows that images differ in their memorability, some stick in our mind while others are fade away quickly, and this phenomenon is consistent across people. While it has been shown that memorability is an intrinsic feature of an image, still it's largely unknown what features make images memorable. In this talk, I will present a series of our studies which aim to address this question by proposing a fast representation learning approach to modify and control the memorability of images. The proposed method can be employed in photograph editing applications for social media, learning aids, or advertisement purposes.
