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Networking Lunch (provided) + Poster Session
Abraham Stanway · Alex Robson · Aneesh Rangnekar · Ashesh Chattopadhyay · Ashley Pilipiszyn · Benjamin LeRoy · Bolong Cheng · Ce Zhang · Chaopeng Shen · Christian Schroeder · Christian Clough · Clement DUHART · Clement Fung · Cozmin Ududec · Dali Wang · David Dao · di wu · Dimitrios Giannakis · Dino Sejdinovic · Doina Precup · Duncan Watson-Parris · Gege Wen · George Chen · Gopal Erinjippurath · Haifeng Li · Han Zou · Herke van Hoof · Hillary A Scannell · Hiroshi Mamitsuka · Hongbao Zhang · Jaegul Choo · James Wang · James Requeima · Jessica Hwang · Jinfan Xu · Johan Mathe · Jonathan Binas · Joonseok Lee · Kalai Ramea · Kate Duffy · Kevin McCloskey · Kris Sankaran · Lester Mackey · Letif Mones · Loubna Benabbou · Lynn Kaack · Matthew Hoffman · Mayur Mudigonda · Mehrdad Mahdavi · Michael McCourt · Mingchao Jiang · Mohammad Mahdi Kamani · Neel Guha · Niccolo Dalmasso · Nick Pawlowski · Nikola Milojevic-Dupont · Paulo Orenstein · Pedram Hassanzadeh · Pekka Marttinen · Ramesh Nair · Sadegh Farhang · Samuel Kaski · Sandeep Manjanna · Sasha Luccioni · Shuby Deshpande · Soo Kim · Soukayna Mouatadid · Sunghyun Park · Tao Lin · Telmo Felgueira · Thomas Hornigold · Tianle Yuan · Tom Beucler · Tracy Cui · Volodymyr Kuleshov · Wei Yu · yang song · Ydo Wexler · Yoshua Bengio · Zhecheng Wang · Zhuangfang Yi · Zouheir Malki

Fri Jun 14 12:00 PM -- 01:30 PM (PDT) @ None

Catered sandwiches and snacks will be provided (including vegetarian/vegan and gluten-free options). Sponsored by Element AI.

Author Information

Abraham Stanway (Amperon)
Alex Robson (Invenia Labs)
Aneesh Rangnekar (Rochester Institute of Technology)
Ashesh Chattopadhyay (Rice University)
Ashley Pilipiszyn (Stanford University)
Benjamin LeRoy (Carnegie Mellon University)

I am a third year PhD student in the Statistics & Data Science Department at Carnegie Mellon University. My work involves leveraging structure information in diverse application fields to improve algorithms and develop better tools to quantify uncertainty in these settings. My interests include high dimensional statistics, statistical machine learning, and data visualization.

Bolong Cheng (SigOpt)

I am a research engineer at [SigOpt](www.sigopt.com), an Intel company. Currently, I work on productionizing Bayesian optimization, and more broadly, sequential decision making problems. Prior to SigOpt, my research focused on [approximate dynamic programming](http://adp.princeton.edu) and stochastic optimization, with an application in controlling grid-level battery storage.

Ce Zhang (ETH Zurich)
Chaopeng Shen (Pennsylvania State University)

Solving water resources and climate resilience problem with AI and process based models. Study streams, soil moisture, groundwater, etc.

Christian Schroeder (University of Oxford)
Christian Clough (Planet)
Clement DUHART (MIT Medialab)
Clement Fung (University of British Columbia)
Cozmin Ududec (Invenia Labs)
Dali Wang (Oak Ridge National Lab)
David Dao (ETH Zurich / UC Berkeley)

I'm a PhD student at ETH Zurich building AI and Data Systems for Sustainable Development. I'm leading the Climate + AI initiative at DS3Lab, mapping the ethical use of AI, and directing the Kara research project with Stanford and UC Berkeley. I'm also the founder of GainForest, a non-profit grantee of Microsoft’s AI for Earth program, which leverages decentralized technology to prevent deforestation. Previously, I was an engineer in Silicon Valley and a research fellow at Berkeley AI Research (BAIR), Stanford University and Broad Institute of MIT and Harvard. I'm a Global Shaper at World Economic Forum, a Core Member of Climate Change AI, a Climate Leader at Climate Reality Project and organized conferences with thousands of attendees in Germany, Silicon Valley, and at Harvard.

di wu (McGill University)
Dimitrios Giannakis (New York University)
Dino Sejdinovic (University of Oxford)
Doina Precup (McGill University / DeepMind)
Duncan Watson-Parris (University of Oxford)
Gege Wen (Stanford University)
George Chen (Carnegie Mellon University)
Gopal Erinjippurath (Planet Labs Inc)
Haifeng Li (Central South University)
Han Zou (University of California, Berkeley)
Herke van Hoof (University of Amsterdam)
Hillary A Scannell (University of Washington)
Hiroshi Mamitsuka (Kyoto University / Aalto University)
Hongbao Zhang (Petuum Inc)
Jaegul Choo (Korea University)
James Wang (Penn State University)
James Requeima (University of Cambridge)
Jessica Hwang (Stanford University)
Jinfan Xu (Zhejiang University)
Johan Mathe (Froglabs)
Jonathan Binas (Mila, Montreal)
Joonseok Lee (Google Research)
Kalai Ramea (Palo Alto Research Center)
Kate Duffy (Northeastern University)
Kevin McCloskey (Google)
Kris Sankaran (Mila)
Lester Mackey (Microsoft Research)
Lester Mackey

Lester Mackey is a machine learning researcher at Microsoft Research, where he develops new tools, models, and theory for large-scale learning tasks driven by applications from healthcare, climate, recommender systems, and the social good. Lester moved to Microsoft from Stanford University, where he was an assistant professor of Statistics and (by courtesy) of Computer Science. He earned his PhD in Computer Science and MA in Statistics from UC Berkeley and his BSE in Computer Science from Princeton University. He co-organized the second place team in the \$1M. Netflix Prize competition for collaborative filtering, won the \$50K Prise4Life ALS disease progression prediction challenge, won prizes for temperature and precipitation forecasting in the yearlong real-time \$800K Subseasonal Climate Forecast Rodeo, and received a best student paper award at the International Conference on Machine Learning.

Letif Mones (Invenia Labs)
Loubna Benabbou (University of Quebec UQAR)
Lynn Kaack (ETH Zurich)
Matthew Hoffman (Google)
Mayur Mudigonda (UC Berkeley)
Mehrdad Mahdavi (Pennsylvania State University)
Michael McCourt (SigOpt)

My research focuses on reproducing kernel Hilbert spaces as applied within meshfree approximation theory. Currently, I am working at SigOpt to adapt theory and strategies from functional/numerical analysis to be used in Bayesian optimization.

Mingchao Jiang (Rice University)
Mohammad Mahdi Kamani (The Pennsylvania State University)
Neel Guha (Carnegie Mellon University)
Niccolo Dalmasso (Carnegie Mellon University)
Nick Pawlowski (Imperial College London)
Nikola Milojevic-Dupont (Mercator Research Institute on Global Commons and Climate Change)
Paulo Orenstein (Stanford University)
Pedram Hassanzadeh (Rice University)
Pekka Marttinen (Aalto University)
Ramesh Nair (Planet Labs Inc)
Sadegh Farhang (Pennsylvania State University)
Samuel Kaski (Aalto University)
Sandeep Manjanna (McGill University)
Sasha Luccioni (Mila)

I am currently a postdoctoral researcher working with Yoshua Bengio on the Climate Change AI (CCAI) project. The goal of the project is to develop an interactive website to depict accurate, vivid, and personalized outcomes of climate change, which will bring the future closer in the mind of the viewer and will demonstrate specific actions they can take to improve the environment. In my previous studies and research, I have worked on various domains, namely Natural Language Processing and AI in Education (AIED).

Shuby Deshpande (Carnegie Mellon University)
Soo Kim (Lawrence Livermore National Laboratory)
Soukayna Mouatadid (University of Toronto)
Sunghyun Park (Aamzon)
Tao Lin (Zhejiang University)
Telmo Felgueira (Instituto Superior Técnico)
Thomas Hornigold (University of Oxford)
Tianle Yuan (NASA GSFC)
Tom Beucler (Columbia University & UCI)
Tracy Cui (Google NYC)
Volodymyr Kuleshov (Stanford University / Afresh)
Wei Yu (University of Toronto)
yang song (oak ridge national lab)

I am an earth system modeler which great interest in studying the interactions between environmental change and terrestrial biosphere processes, including land surface energy balance, carbon, nitrogen, and phosphorus cycles, and hydrological cycle. In particular, I am interested in (1) using machine learning approaches to identify metagenomics-informed soil enzyme functional groups and their climate response at the regional and global scale; (2)Using metagenomic information to improve the representation of microbially-mediated soil carbon, nitrogen, and phosphorus biogeochemical cycles in the Earth system models; and Developing big data and decision support tools for agriculture and bioenergy industry.

Ydo Wexler (Amperon)
Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio is recognized as one of the world’s leading experts in artificial intelligence and a pioneer in deep learning. Since 1993, he has been a professor in the Department of Computer Science and Operational Research at the Université de Montréal. He is the founder and scientific director of Mila, the Quebec Institute of Artificial Intelligence, the world’s largest university-based research group in deep learning. He is a member of the NeurIPS board and co-founder and general chair for the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains and is Fellow of the same institution. In 2018, Yoshua Bengio ranked as the computer scientist with the most new citations, worldwide, thanks to his many publications. In 2019, he received the ACM A.M. Turing Award, “the Nobel Prize of Computing”, jointly with Geoffrey Hinton and Yann LeCun for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing. In 2020 he was nominated Fellow of the Royal Society of London.

Zhecheng Wang (Stanford University)
Zhuangfang Yi (Development Seed)

Zhuangfang Yi is a machine learning engineer at Development Seed. As a machine learning engineer and a formerly trained research scientist Zhuangfang can quickly script algorithms and tools that help translate scientific methodologies to solve real-world problems. She has over 10 years of experience conducting geospatial analysis and image processing. Zhuangfang has led and developed multiple open-source tools for applying machine learning at scale either for object-based detection or pixel decoding.

Zouheir Malki (Polytechnique Montreal)

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