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Lunch Break and Posters
Xingyou Song · Elad Hoffer · Wei-Cheng Chang · Jeremy Cohen · Jyoti Islam · Yaniv Blumenfeld · Andreas Madsen · Jonathan Frankle · Sebastian Goldt · Satrajit Chatterjee · Abhishek Panigrahi · Alex Renda · Brian Bartoldson · Israel Birhane · Aristide Baratin · Niladri Chatterji · Roman Novak · Jessica Forde · YiDing Jiang · Yilun Du · Linara Adilova · Michael Kamp · Berry Weinstein · Itay Hubara · Tal Ben-Nun · Torsten Hoefler · Daniel Soudry · Hsiang-Fu Yu · Kai Zhong · Yiming Yang · Inderjit Dhillon · Jaime Carbonell · Yanqing Zhang · Dar Gilboa · Johannes Brandstetter · Alexander R Johansen · Gintare Karolina Dziugaite · Raghav Somani · Ari Morcos · Alfredo Kalaitzis · Hanie Sedghi · Lechao Xiao · John Zech · Muqiao Yang · Simran Kaur · Qianli Ma · Yao-Hung Hubert Tsai · Ruslan Salakhutdinov · Sho Yaida · Zachary Lipton · Daniel Roy · Michael Carbin · Florent Krzakala · Lenka Zdeborová · Guy Gur-Ari · Ethan Dyer · Dilip Krishnan · Hossein Mobahi · Samy Bengio · Behnam Neyshabur · Praneeth Netrapalli · Kris Sankaran · Julien Cornebise · Yoshua Bengio · Vincent Michalski · Samira Ebrahimi Kahou · Md Rifat Arefin · Jiri Hron · Jaehoon Lee · Jascha Sohl-Dickstein · Samuel Schoenholz · David Schwab · Dongyu Li · Sang Keun Choe · Henning Petzka · Ashish Verma · Zhichao Lin · Cristian Sminchisescu

Sat Dec 14 12:00 PM -- 02:00 PM (PST) @ None

Since we are a small workshop, we will hold the poster sessions during the day, including all the breaks as the authors wish.

Author Information

Xingyou Song (Google Brain)
Elad Hoffer (Technion)
Wei-Cheng Chang (Carnegie Mellon University)
Jeremy Cohen (Carnegie Mellon University)
Jyoti Islam (Georgia State University)
Yaniv Blumenfeld (Technion)
Andreas Madsen (Computationally Demanding)
Jonathan Frankle (MIT)
Sebastian Goldt (Institut de Physique Théorique, CNRS, Paris)
Satrajit Chatterjee (Google AI)
Abhishek Panigrahi (Microsoft Research India)
Alex Renda (MIT)
Brian Bartoldson (Florida State University)
Israel Birhane (Mila)

MSE Student , Programmer, Researcher and Robotics Architect

Aristide Baratin (Université de Montreal)
Niladri Chatterji (UC Berkeley)
Roman Novak (Google Brain)
Jessica Forde (Brown University)
YiDing Jiang (Google Research)
Yilun Du (MIT)
Linara Adilova (Fraunhofer IAIS)
Michael Kamp (Fraunhofer IAIS)
Berry Weinstein (IDC)
Itay Hubara (Technion)
Tal Ben-Nun (ETH Zurich)
Torsten Hoefler (ETH Zurich)
Daniel Soudry (Technion)

I am an assistant professor in the Department of Electrical Engineering at the Technion, working in the areas of Machine learning and theoretical neuroscience. I am especially interested in all aspects of neural networks and deep learning. I did my post-doc (as a Gruss Lipper fellow) working with Prof. Liam Paninski in the Department of Statistics, the Center for Theoretical Neuroscience the Grossman Center for Statistics of the Mind, the Kavli Institute for Brain Science, and the NeuroTechnology Center at Columbia University. I did my Ph.D. (2008-2013, direct track) in the Network Biology Research Laboratory in the Department of Electrical Engineering at the Technion, Israel Institute of technology, under the guidance of Prof. Ron Meir. In 2008 I graduated summa cum laude with a B.Sc. in Electrical Engineering and a B.Sc. in Physics, after studying in the Technion since 2004.

Hsiang-Fu Yu (Amazon)
Kai Zhong (Amazon)
Yiming Yang (CMU)
Inderjit Dhillon (UT Austin & Amazon)
Jaime Carbonell (CMU)
Yanqing Zhang (Georgia State University)
Dar Gilboa (Columbia University)
Johannes Brandstetter (LIT AI Lab / University Linz)
Alexander R Johansen (DTU)
Gintare Karolina Dziugaite (Element AI)
Raghav Somani (University of Washington)

Theoretical Machine Learning enthusiast majorly interested in Optimization and Statistics.

Ari Morcos (Facebook AI Research)
Alfredo Kalaitzis (Element AI)
Alfredo Kalaitzis

Freddie is a part-time Senior Research Fellow, and Theme Lead of ML for Earth Observation and Remote Sensing, in the Oxford Applied and Theoretical Machine Learning lab (led by Yarin Gal) of Oxford University. He's also an ML & Project Lead at NASA's Frontier Development Lab (FDL), and the (part-time) ML Lead of Trillium Technologies , the R&D production company behind FDL. Since FDL US 2020, Freddie has been a ML & Project Lead for project Waters Of The United States (WOTUS), in partnership with the USGS, Planet, Maxar, Google Cloud and NVIDIA, towards the ultimate vision for mapping all flowing water on Earth , at near real-time, by fusing LiDAR sensors and daily very high resolution (VHR) satellite imagery. He started his journey with FDL 2019 as a mentor , helping teams super-resolve solar magnetograms and predict GPS disruptions induced by solar weather . Until April 2020, he was an Applied Research Scientist in the AI for Good lab (led by Julien Cornebise) of Element AI in London, focusing on applications of ML and statistics that enable NGOs and nonprofits. During this work, he led the Multi-Frame Super-Resolution research collaboration with Mila Montréal , which was awarded by ESA for topping the PROBA-V Super-Resolution challenge . He also co-authored the technical report written with Amnesty International, on the first large-scale study of online abuse against women on Twitter , whose front-page coverage in the Financial Times led to Twitter working with Amnesty to better protect the rights of vulnerable users online . Also with Amnesty, he has co-authored a technical blog-post on using VHR satellite imagery to detect evidence of genocide in rural areas of Darfur, Sudan. Prior to Element AI, he was a Senior Data Scientist in Digital Shadows, a Data Scientist in Microsoft's Xbox EMEA team, and a postdoc in Bayesian statistics (with Ricardo Silva) at the Statistical Science department of University College London . He has a PhD in Computer Science from the University of Sheffield (supervised by Neil Lawrence), and a MSc in Artificial Intelligence from the University of Edinburgh . His PhD research led to contributions in probability methods for dimensionality reduction of data and developed methods for gene-expression time-series to discover genetic factors of disease. Off work, he occasionally mentors teenagers of ages 12-18 for Teens in AI, and he is the founder of Well-Being in ML , the first official social event at NeurIPS advocating for healthy well-being and mental practice in academia and industry of ML .

Hanie Sedghi (Google Brain)
Hanie Sedghi

I am a senior research scientist at Google Brain, where I lead the “Deep Phenomena” team. My approach is to bond theory and practice in large-scale machine learning by designing algorithms with theoretical guarantees that also work efficiently in practice. Over the recent years, I have been working on understanding and improving deep learning. Prior to Google, I was a Research Scientist at Allen Institute for Artificial Intelligence and before that, a postdoctoral fellow at UC Irvine. I received my PhD from University of Southern California with a minor in mathematics in 2015.

Lechao Xiao (Google Brain)
John Zech (Icahn School of Medicine)
Muqiao Yang (Carnegie Mellon University)
Simran Kaur (Carnegie Mellon University)
Qianli Ma (Carnegie Mellon University)
Yao-Hung Hubert Tsai (Carnegie Mellon University)
Ruslan Salakhutdinov (Carnegie Mellon University)
Sho Yaida (Facebook AI Research)
Zachary Lipton (Carnegie Mellon University)
Daniel Roy (Univ of Toronto & Vector)
Michael Carbin (MIT)
Florent Krzakala (École Normale Supérieure)
Lenka Zdeborová (CEA)
Guy Gur-Ari (Google)
Ethan Dyer (Google)
Dilip Krishnan (Google)
Hossein Mobahi (Google Research)
Samy Bengio (Google Research, Brain Team)
Behnam Neyshabur (New York University)

I am a staff research scientist at Google. Before that, I was a postdoctoral researcher at New York University and a member of Theoretical Machine Learning program at Institute for Advanced Study (IAS) in Princeton. In summer 2017, I received a PhD in computer science at TTI-Chicago where I was fortunate to be advised by Nati Srebro.

Praneeth Netrapalli (Microsoft Research)
Kris Sankaran (Mila)
Julien Cornebise (Element AI)
Yoshua Bengio (Mila)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.

Vincent Michalski (Université de Montréal)
Samira Ebrahimi Kahou (McGill University)
Md Rifat Arefin (University of Montreal)
Jiri Hron (University of Cambridge)
Jaehoon Lee (Google Brain)
Jascha Sohl-Dickstein (Google Brain)
Samuel Schoenholz (Google Brain)
David Schwab (ITS, CUNY Graduate Center)
Dongyu Li (Carnegie Mellon University)
Sang Keun Choe (Carnegie Mellon University)
Henning Petzka (Lund University)
Ashish Verma (IBM Research)
Zhichao Lin (Element AI)
Cristian Sminchisescu (Google Research)

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