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Events for October 04, 2018
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CS Colloquium: Mohammad Soleymani (USC-ICT) - What Do Machines Learn in Emotion Recognition from EEG Signals?
Thu, Oct 04, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Mohammed Soleymani, USC-ICT
Talk Title: What Do Machines Learn in Emotion Recognition from EEG Signals?
Series: CS Colloquium
Abstract: Machines that are able to read our emotions and cognitive states make better companions. Emotions are often sensed by their external manifestations such as facial and vocal expressions. Additionally, studies in affective neuroscience have identified a set of emotional neural activities that can be captured by eletroencephalogram (EEG) signals, including asymmetric frontal brain activity and increase in information transfer. Motivated by these findings, a growing number of studies report developing EEG-based emotion recognition systems with promising results. In this talk, I first present my work on recognizing emotions of people watching videos. I then present a follow up study in which we aimed to better understand what machine learns in such scenarios. In the follow up work, we recorded a dataset which includes spontaneous emotions and posed expressions. Our analysis on the data collected in the follow up study demonstrates that the performance of existing EEG-based emotion recognition methods significantly decreases when evaluated across different corpora. We also found that models trained on spontaneous emotions perform well on recognizing mimicked expressions. Our results provide evidence that stimuli-related sensory information and facial electromyogram activities are the main components learned by machine learning models for emotion recognition using EEG signals.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.
Biography: Mohammed Soleymani is a research scientist with the USC Institute of Creative Technologies. He received his PhD in computer science from the University of Geneva in 2011. From 2012 to 2014, he was a Marie Curie fellow at Imperial College London. Prior to joining ICT, he was a research scientist at the Swiss Center for Affective Sciences, University of Geneva. His main line of research involves developing automatic emotion recognition and behavior understanding methods using physiological signals and facial expressions. He is also interested in understanding subjective attributes in multimedia content, e.g, predicting whether an image is interesting from its pixels or automatic recognition of music mood from acoustic content. He is a recipient of the Swiss National Science Foundation Ambizione grant and the EU Marie Curie fellowship. He has served on multiple conference organization committees and editorial roles, most notably as associate editor for the IEEE Transactions on Affective Computing and technical program chair for ACM ICMI 2018 and ACII 2017. He is one of the founding organizers of the MediaEval multimedia retrieval benchmarking campaign and the president elect for the Association for Advancement of Affective Computing (AAAC).
Host: David Traum
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Tech Talk: Parisa Mansourifard (Facebook) - Infrastructure Data Science Team at Facebook
Thu, Oct 04, 2018 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Parisa Mansourifard, Facebook
Talk Title: Infrastructure Data Science Team at Facebook
Series: Computer Science Colloquium
Abstract: In this talk, I will present what my team does at Facebook and what problems we aim to solve. Infrastructure Data Science partners with engineering teams to develop data-driven solutions for significant infrastructure challenges such as app and site performance, systems efficiency and reliability, resource allocation and long-term capacity forecasts. Infra Data Scientists use a range of tools, from A/B testing to machine learning, to help Facebook make decisions about operations and system design. The team contributes to all parts of a project's lifecycle, including scoping, data discovery, research, methodological design, code implementation, and reporting and interpreting final results. The teams' work varies, in line with the complex and diverse challenges of maintaining one of the largest and most advanced enterprise infrastructures in the world. We look for candidates with a wide range of backgrounds to join our team and help with this work.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Parisa Mansourifard is currently a data scientist at Infrastructure data science team at Facebook. Before joining Facebook, she was a data scientist at SupplyFrame Inc. and a part-time lecturer at CS department of University of Southern California teaching machine learning. She received the B.S. and M.S. in electrical engineering from Sharif university of technology, Tehran, Iran, in 2008 and 2010 respectively. She also got a M.S. in computer science and Ph.D. in electrical engineering from University of Southern California, Los Angeles, CA, USA, in 2015 and 2017, respectively. During her Ph.D. she held Viterbi Dean fellowships in 2011-2014 and AAUW dissertation completion fellowship in 2015-2016. She also got a best paper award for the operations research track at EU IEOM conference in Paris 2018.
Host: Computer Science Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
