General Chair
Been Kim
Google DeepMind
Been Kim is a staff research scientist at Google Brain. Her research focuses on helping humans to communicate with complex machine learning models: not only by building tools (and tools to criticize them) but also studying their nature compared to humans. She gave a talk at the G20 meeting in Argentina in 2019. Her work TCAV received UNESCO Netexplo award, was featured at Google I/O 19'. Her work was in a chapter of Brian Christian's book on "The Alignment Problem". Been gave keynote at ECML 2020, tutorials on interpretability at ICML, University of Toronto, CVPR and at Lawrence Berkeley National Laboratory. She was a co-workshop Chair ICLR 2019, and has been an (senior) area chair at NeurIPS, ICML, ICLR, AISTATS and others. She is a steering committee member of FAccT conference and former executive board member and VP of Women in Machine Learning. She received her PhD. from MIT.
Been Kim is a staff research scientist at Google Brain. Her research focuses on helping humans to communicate with complex machine learning models: not only by building tools (and tools to criticize them) but also studying their nature compared to humans. She gave a talk at the G20 meeting in Argentina in 2019. Her work TCAV received UNESCO Netexplo award, was featured at Google I/O 19'. Her work was in a chapter of Brian Christian's book on "The Alignment Problem". Been gave keynote at ECML 2020, tutorials on interpretability at ICML, University of Toronto, CVPR and at Lawrence Berkeley National Laboratory. She was a co-workshop Chair ICLR 2019, and has been an (senior) area chair at NeurIPS, ICML, ICLR, AISTATS and others. She is a steering committee member of FAccT conference and former executive board member and VP of Women in Machine Learning. She received her PhD. from MIT.
Senior Program Chair
Yisong Yue
Caltech & Asari AI
Program Chair
Swarat Chaudhuri
UT Austin
Program Chair
Katerina Fragkiadaki
CMU
Program Chair
Mohammad Emtiyaz Khan
RIKEN AIP, Tokyo
Program Chair
Yizhou Sun
Associate Professor
UCLA
Workshop Chair
Mercy Asiedu
Google, LLC
Workshop Chair
Stephanie Muller
DR
IBM
Workshop Chair
Luis Oala
Dotphoton
Head of Machine Learning @ Dotphoton
Head of Machine Learning @ Dotphoton
Workshop Chair
Girmaw Abebe Tadesse
Microsoft
Girmaw is a Principal Research Scientist and Manager at Microsoft AI for Good Research Lab which aims to develop AI solutions for critical problems across sectors including agriculture, healthcare, biodiversity, etc. Prior to that he was a Staff Research Scientist at IBM Research Africa working on detecting and characterizing systematic deviations in data and machine learning models. At IBM Research, Girmaw led multiple projects in trustworthy AI including evaluation of generative models, representation analysis in academic materials and data-driven insight extraction from public healthy surveys, with active collaborations with external institutions such as Bill & Melinda Gates Foundation, Stanford University, Oxford University and Harvard University. Previously, Girmaw also worked as a Postdoctoral Researcher at the University of Oxford, where he primarily developed deep learning techniques to assist diagnosis of multiple diseases, with collaborations with clinicians and hospitals in China and Vietnam. Girmaw completed his PhD at Queen Mary University of London, under the Erasmus Mundus Double Doctorate Program in Interactive and Cognitive Environments, with a focus on computer vision and machine learning algorithms for human activity recognition using wearable cameras. He has interned/worked in various research groups across Europe, including the UPC-BarcelonaTech (Spain), KU Leuven (Belgium), and INESC-ID (Portugal). Girmaw is an Executive Member for IEEE Kenya Section, and he is currently serving as a reviewer and program committee member for multiple top-tier AI focused journals and conferences.
Girmaw is a Principal Research Scientist and Manager at Microsoft AI for Good Research Lab which aims to develop AI solutions for critical problems across sectors including agriculture, healthcare, biodiversity, etc. Prior to that he was a Staff Research Scientist at IBM Research Africa working on detecting and characterizing systematic deviations in data and machine learning models. At IBM Research, Girmaw led multiple projects in trustworthy AI including evaluation of generative models, representation analysis in academic materials and data-driven insight extraction from public healthy surveys, with active collaborations with external institutions such as Bill & Melinda Gates Foundation, Stanford University, Oxford University and Harvard University. Previously, Girmaw also worked as a Postdoctoral Researcher at the University of Oxford, where he primarily developed deep learning techniques to assist diagnosis of multiple diseases, with collaborations with clinicians and hospitals in China and Vietnam. Girmaw completed his PhD at Queen Mary University of London, under the Erasmus Mundus Double Doctorate Program in Interactive and Cognitive Environments, with a focus on computer vision and machine learning algorithms for human activity recognition using wearable cameras. He has interned/worked in various research groups across Europe, including the UPC-BarcelonaTech (Spain), KU Leuven (Belgium), and INESC-ID (Portugal). Girmaw is an Executive Member for IEEE Kenya Section, and he is currently serving as a reviewer and program committee member for multiple top-tier AI focused journals and conferences.
Workshop Chair
Aisha Walcott
Google Research
Blog Track Chair
David Dobre
Mila
Blog Track Chair
Charlie Gauthier
Mila - Université de Montréal
Blog Track Chair
Gauthier Gidel
Mila, University of Montreal
I am a Ph.D student supervised by Simon Lacoste-Julien, I graduated from ENS Ulm and Université Paris-Saclay. I was a visiting PhD student at Sierra. I also worked for 6 months as a freelance Data Scientist for Monsieur Drive (Acquired by Criteo) and I recently co-founded a startup called Krypto. I'm currently pursuing my PhD at Mila. My work focuses on optimization applied to machine learning. More details can be found in my resume. My research is to develop new optimization algorithms and understand the role of optimization in the learning procedure, in short, learn faster and better. I identify to the field of machine learning (NIPS, ICML, AISTATS and ICLR) and optimization (SIAM OP)
I am a Ph.D student supervised by Simon Lacoste-Julien, I graduated from ENS Ulm and Université Paris-Saclay. I was a visiting PhD student at Sierra. I also worked for 6 months as a freelance Data Scientist for Monsieur Drive (Acquired by Criteo) and I recently co-founded a startup called Krypto. I'm currently pursuing my PhD at Mila. My work focuses on optimization applied to machine learning. More details can be found in my resume. My research is to develop new optimization algorithms and understand the role of optimization in the learning procedure, in short, learn faster and better. I identify to the field of machine learning (NIPS, ICML, AISTATS and ICLR) and optimization (SIAM OP)
Blog Track Chair
Fabian Pedregosa
Google DeepMind
Blog Track Chair
Leo Schwinn
Technical University Munich
Blog Track Chair
Claire Vernade
University of Tübingen
Diversity Equity & Inclusion Chair
Erin Grant
New York University / University of Alberta
Diversity Equity & Inclusion Chair
Rosanne Liu
ML Collective; Google DeepMind
Tiny Papers Chair
Tom Burns
Aleph Alpha Research
Tiny Papers Chair
Krystal Maughan
PhD student
University of Vermont
Current PhD student at the University of Vermont
Current PhD student at the University of Vermont
Mentor Chair
Surbhi Goel
University of Pennsylvania
Mentor Chair
Ellen Vitercik
Stanford University
Volunteer Chair
Francesco Locatello
ISTA
Volunteer Chair
Sara Magliacane
University of Amsterdam
Social Chair
Carlo D Eramo
University of Würzburg / TU Darmstadt
Workflow Chair
Zhenyu (Sherry) Xue
ICLR
