Announcing the ICLR 2021 Invited Speakers

By ICLR 2021 Senior Program Chair Katja Hofmann (Microsoft Research, Cambridge, UK) and Program Co-Chairs Naila Murray (Facebook AI Research), Alice Oh (KAIST), Ivan Titov (U. Edinburgh)

It’s time for a big drum-roll … today we are announcing our invited speakers for ICLR 2021! Much activity precedes this exciting moment. First, we collect a long list of candidate speakers, many of whom are suggestions from the community, and especially our ICLR Area Chairs. Second, we in the program committee consider all suggestions to take into account how each would contribute to widening the range of perspectives at the event. Finally, we invite speakers (and cross our fingers for them to accept our invitation), before confirming talk details with each one of them.

Without further ado, we are incredibly honored to have the following amazing speakers contribute their unique perspectives to ICLR 2021:

Invited speakers for ICLR 2021
  • Lourdes Agapito
    Perceiving the 3D World from Images and Video
  • Michael Bronstein
    Geometric Deep Learning: the Erlangen Programme of ML
  • Kyu Jin Cho
    Soft bodied robots for human centered design of robots for everyday life
  • Yejin Choi
    Commonsense Intelligence: Cracking the Longstanding Challenge in AI
  • Alexei (Alyosha) Efros
    Self-Supervision for Learning from the Bottom Up
  • Timnit Gebru
    Moving beyond the fairness rhetoric in machine learning
  • Kate Saenko
    Is My Dataset Biased?
  • Manuela Veloso
    AI in Finance: Scope and Examples

At ICLR 2021, each invited speaker will give a half-hour talk, followed by a live Q&A. This is a unique opportunity to learn from our speakers’ expertise and insights, understand their perspective on key challenges and opportunities, and get inspired to seize these opportunities.

I personally cannot wait to see the invited talks and participate in the live Q&A for all speakers at ICLR 2021 from May 3 to May 7, 2021. To join the event, register now at: https://iclr.cc/Register/view-registration.

See you at the conference!

Read on to learn more about each of our invited speakers:

Lourdes Agapito
Perceiving the 3D World from Images and Video

Lourdes Agapito holds the position of Professor of 3D Vision at the Department of Computer Science, University College London (UCL). Her research in computer vision has consistently focused on the inference of 3D information from single images or videos acquired from a single moving camera. She received her BSc, MSc and PhD degrees from the Universidad Complutense de Madrid (Spain). In 1997 she joined the Robotics Research Group at the University of Oxford as an EU Marie Curie Postdoctoral Fellow. In 2001 she was appointed as Lecturer at the Department of Computer Science at Queen Mary University of London. From 2008 to 2014 she held an ERC Starting Grant funded by the European Research Council to focus on theoretical and practical aspects of deformable 3D reconstruction from monocular sequences. In 2013 she joined the Department of Computer Science at University College London and was promoted to full professor in 2015. She now heads the Vision and Imaging Science Group, is a founding member of the AI centre and co-director of the Centre for Doctoral Training in Foundational AI. Lourdes serves regularly as Area Chair for the top Computer Vision conferences (CVPR, ICCV, ECCV) was Program Chair for CVPR 2016 and will serve again for ICCV 2023. She was keynote speaker at ICRA 2017, the top robotics conference, and is a member of the executive committee of the British Machine Vision Association. In 2017 she co-founded Synthesia, the London based synthetic media startup responsible for the AI technology behind the Malaria no More video campaign that saw David Beckham speak 9 different languages to call on world leaders to take action to defeat Malaria.

Michael Bronstein
Geometric Deep Learning: the Erlangen Programme of ML

Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. He also heads ML research in Project CETI, a TED Audacious Prize-winning collaboration aimed at understanding the communication of sperm whales. Michael received his PhD from the Technion in 2007. He has held visiting appointments at Stanford, MIT, and Harvard, and has also been affiliated with the Institute for Advanced Study at TUM (as a Rudolf Diesel Fellow, 2017–2019) and Harvard (as a Radcliffe fellow, 2017–2018). Michael is the recipient of the Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019). He has previously served as Principal Engineer at Intel Perceptual Computing and was one of the key developers of the Intel RealSense technology.

Kyu Jin Cho
Soft bodied robots for human centered design of robots for everyday life

Kyu Jin Cho is a Professor and the Director of Soft Robotics Research Center and Biorobotics Lab at Seoul National University. He received his Ph.D. in mechanical engineering from MIT and his B.S and M.S. from Seoul National University. He was a postdoctoral fellow at Harvard Microrobotics Laboratory before joining SNU in 2008. He has been exploring novel soft bio-inspired robot designs, including a water jumping robot, various shape changing robots and soft wearable robots for the disabled. He has received the 2014 IEEE RAS Early Academic Career Award for his fundamental contributions to soft robotics and biologically inspired robot design. He has published a Science paper on the water jumping robot and several papers in Science Robotics with novel robot designs. He serves RAS as associate VP of Publication Activities Board, and will serve as VP of the RAS Technical Activities Board next year.

Yejin Choi
Commonsense Intelligence: Cracking the Longstanding Challenge in AI

Yejin Choi is a Brett Helsel associate professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and also a senior research manager at AI2 overseeing the project Mosaic. Her research interests include language grounding with vision, physical and social commonsense knowledge, language generation with long-term coherence, conversational AI, and AI for social good. She is a co-recipient of the AAAI Outstanding Paper Award in 2020, a recipient of Borg Early Career Award (BECA) in 2018, among the IEEE’s AI Top 10 to Watch in 2015, a co-recipient of the Marr Prize at ICCV 2013, and a faculty advisor for the Sounding Board team that won the inaugural Alexa Prize Challenge in 2017. Her work on detecting deceptive reviews, predicting the literary success, and interpreting bias and connotation has been featured by numerous media outlets including NBC News for New York, NPR Radio, New York Times, and Bloomberg BusinessWeek. She received her Ph.D. in Computer Science from Cornell University.

Alexei (Alyosha) Efros
Self-Supervision for Learning from the Bottom Up

Alexei (Alyosha) Efros is a professor of computer science at UC Berkeley and member of the BAIR lab. Prior to that, he was nine years on the faculty of Carnegie Mellon University, and has also been affiliated with École Normale Supérieure/INRIA and University of Oxford. His research is in the area of computer vision and computer graphics, especially at the intersection of the two. He is particularly interested in using data-driven techniques to tackle problems where large quantities of unlabeled visual data are readily available. Efros received his PhD in 2003 from UC Berkeley. He is a recipient of the Sloan Fellowship (2008), Guggenheim Fellowship (2008), Okawa Grant (2008), SIGGRAPH Significant New Researcher Award (2010), 3 PAMI-TC Helmholtz Test-of-Time Prizes (1999,2003,2005), the ACM Prize in Computing (2016), and Diane McEntyre Award for Excellence in Teaching Computer Science (2019). He likes Paris and gelato.

Timnit Gebru
Moving beyond the fairness rhetoric in machine learning

Timnit Gebru was recently fired by Google for raising issues of discrimination in the workplace. Prior to that she was a co-lead of the Ethical AI research team at Google Brain. She received her PhD from the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li, and did a postdoc at Microsoft Research, New York City in the FATE (Fairness Accountability Transparency and Ethics in AI) group, where she studied algorithmic bias and the ethical implications underlying projects aiming to gain insights from data. Timnit also co-founded Black in AI, a nonprofit that works to increase the presence, inclusion, visibility and health of Black people in the field of AI.

Kate Saenko
Is My Dataset Biased?

Kate Saenko is an Associate Professor of Computer Science at Boston University and a consulting professor for the MIT-IBM Watson AI Lab. She received a PhD from MIT and was a postdoc at UC Berkeley and Harvard. Her research is on representation learning for visual and language data, with a focus on generalizable, explainable, data-efficient and power-efficient AI models.

Manuela Veloso
AI in Finance: Scope and Examples

Manuela M. Veloso is the Head of J.P. Morgan AI Research, which pursues fundamental research in areas of core relevance to financial services, including data mining and cryptography, machine learning, explainability, and human-AI interaction. J.P. Morgan AI Research partners with applied data analytics teams across the firm as well as with leading academic institutions globally. Professor Veloso is on leave from Carnegie Mellon University as the Herbert A. Simon University Professor in the School of Computer Science, and the past Head of the Machine Learning Department. With her students, she had led research in AI, with a focus on robotics and machine learning, having concretely researched and developed a variety of autonomous robots, including teams of soccer robots, and mobile service robots. Her robot soccer teams have been RoboCup world champions several times, and the CoBot mobile robots have autonomously navigated for more than 1,000km in university buildings. Professor Veloso is the Past President of AAAI, (the Association for the Advancement of Artificial Intelligence), and the co-founder, Trustee, and Past President of RoboCup. Professor Veloso has been recognized with multiple honors, including being a Fellow of the ACM, IEEE, AAAS, and AAAI. She is the recipient of several best paper awards, the Einstein Chair of the Chinese Academy of Science, the ACM/SIGART Autonomous Agents Research Award, an NSF Career Award, and the Allen Newell Medal for Excellence in Research. Professor Veloso earned a Bachelor and Master of Science degrees in Electrical and Computer Engineering from Instituto Superior Tecnico in Lisbon, Portugal, a Master of Arts in Computer Science from Boston University, and Master of Science and PhD in Computer Science from Carnegie Mellon University. See www.cs.cmu.edu/~mmv/Veloso.html for her scientific publications.

Please note that all talk details are preliminary and may change as the talks and program are finalized.

International Conference on Learning Representations

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