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Morning Coffee Break & Poster Session
Eric Metodiev · Keming Zhang · Markus Stoye · Randy Churchill · Soumalya Sarkar · Miles Cranmer · Johann Brehmer · Danilo Jimenez Rezende · Peter Harrington · AkshatKumar Nigam · Nils Thuerey · Lukasz Maziarka · Alvaro Sanchez Gonzalez · Atakan Okan · James Ritchie · N. Benjamin Erichson · Harvey Cheng · Peihong Jiang · Seong Ho Pahng · Samson Koelle · Sami Khairy · Adrian Pol · Rushil Anirudh · Jannis Born · Benjamin Sanchez-Lengeling · Brian Timar · Rhys Goodall · Tamás Kriváchy · Lu Lu · Thomas Adler · Nathaniel Trask · Noëlie Cherrier · Tomohiko Konno · Muhammad Kasim · Tobias Golling · Zaccary Alperstein · Andrei Ustyuzhanin · James Stokes · Anna Golubeva · Ian Char · Ksenia Korovina · Youngwoo Cho · Chanchal Chatterjee · Tom Westerhout · Gorka Muñoz-Gil · Juan Zamudio-Fernandez · Jennifer Wei · Brian Lee · Johannes Kofler · Bruce Power · Nikita Kazeev · Andrey Ustyuzhanin · Artem Maevskiy · Pascal Friederich · Arash Tavakoli · Willie Neiswanger · Bohdan Kulchytskyy · sindhu hari · Paul Leu · Paul Atzberger

Sat Dec 14 09:40 AM -- 10:40 AM (PST) @

Author Information

Eric Metodiev (MIT)

I am interested in the intersection of machine learning and particle physics. I work on problems motivated by the Large Hadron Collider at CERN. My research uses insights from quantum field theory to develop new data-driven techniques for collider physics (and vice versa!).

Keming Zhang (UC Berkeley)
Markus Stoye (Reexen)

- Head of deep learning in REEXEN - Machine learning representative at CERN laboratory for CMS experiment

Randy Churchill (Princeton Plasma Physics Laboratory)
Soumalya Sarkar (United Technologies Research Center)
Miles Cranmer (Princeton University)

Miles Cranmer is an Astro PhD candidate trying to accelerate astrophysics with AI. Miles is from Canada and did his undergraduate in Physics at McGill. He is deeply interested in the automation of science, particularly aspects that are not yet tractable with existing machine learning, such as experiment planning, simulation, and theory. He works on symbolic regression, graph neural networks, normalizing flows, and learned simulation. He is hugely interested in symbolic ML, since, as he argues, symbolic models seem to be a surprisingly efficient basis for describing our universe.

Johann Brehmer (New York University)
Danilo Jimenez Rezende (Google DeepMind)
Peter Harrington (Lawrence Berkeley National Laboratory)
AkshatKumar Nigam (University Of Toronto)
Nils Thuerey (Technical University of Munich)
Lukasz Maziarka (Jagiellonian University)
Alvaro Sanchez Gonzalez (DeepMind)
Atakan Okan (New York University)
James Ritchie (University of Edinburgh)
N. Benjamin Erichson (University of California, Berkeley)
Harvey Cheng (SigOpt)

I am a research engineer at SigOpt. Currently, I am interested in problems in Bayesian optimization and reinforcement learning. I obtained my Ph.D. in electrical engineering from Princeton University, where I was advised by Prof. Warren B. Powell. My doctoral studies focused on approximate dynamic programming, stochastic optimization, and optimal learning, with an application in managing grid-level battery storage.

Peihong Jiang (Brown University)
Seong Ho Pahng (Harvard University)
Samson Koelle (University of Washington)
Sami Khairy (Illinois Institute of Technology)

Sami Khairy’s research interests span the broad areas of statistical learning, reinforcement learning, next generation AI powered wireless networks resource management and protocol design, and statistical signal processing. He received the M.S. degree in Electrical Engineering in 2016 from Illinois Institute of Technology, Chicago, IL, where he is currently working towards the Ph.D. degree. Sami received a Fulbright Predoctoral Scholarship from JACEE and the U.S. Department of State in 2015, and the Starr/Fieldhouse Research Fellowship from IIT in 2019. He is an IEEE student member and a member of IEEE ComSoc and IEEE HKN.

Adrian Pol (Université Paris Saclay / CERN)
Rushil Anirudh (Lawrence Livermore National Laboratory)
Jannis Born (ETH Zürich)
Benjamin Sanchez-Lengeling (Harvard University)
Brian Timar (Caltech)
Rhys Goodall (University of Cambridge)
Tamás Kriváchy (University of Geneva)

Machine Learning <3 Quantum Information

Lu Lu (Brown University)
Thomas Adler (LIT AI Lab / University Linz)
Nathaniel Trask (Sandia National Laboratories)
Noëlie Cherrier (CEA)
Tomohiko Konno (National Institute of Information and Commutations Technologies)

Ph.D. from The University of Tokyo. Worked Research Fellow at Princeton University. Deep learning Physics Game theory Complex networks Math

Muhammad Kasim (University of Oxford)
Tobias Golling (University of Geneva)
Zaccary Alperstein (University of British Columbia)
Andrei Ustyuzhanin (National Research University Higher School of Economics)
James Stokes (Simons Foundation)
Anna Golubeva (Perimeter Institute for Theoretical Physics)
Ian Char (Carnegie Mellon University)
Ksenia Korovina (Carnegie Mellon University)
Youngwoo Cho (Korea University)

Youngwoo Cho is a Ph.D. student in the Graduate School of Artificial Intelligence at KAIST. His interest is Deep Learning applied natural science.

Chanchal Chatterjee (Google)
Tom Westerhout (Radboud University)
Gorka Muñoz-Gil (ICFO)
Juan Zamudio-Fernandez (NYU)
Jennifer Wei (Google Research)
Brian Lee (Google)
Johannes Kofler (LIT AI Lab / University Linz)
Bruce Power (Chevron Energy Technology Company)
Nikita Kazeev (The Sapienza University of Rome)
Andrey Ustyuzhanin (NRU HSE)
Artem Maevskiy (National Research University Higher School of Economics)
Pascal Friederich (University of Toronto)
Arash Tavakoli (Imperial College London)
Willie Neiswanger (Carnegie Mellon University)
Bohdan Kulchytskyy (1qbit)
sindhu hari (Quantiphi Analytics)
Paul Leu (University of Pittsburgh)
Paul Atzberger (University of California Santa Barbara)

Paul J. Atzberger studied mathematics at the Courant Institute at New York University where he received his PhD in 2003. Subsequently, from 2003 - 2006 he was a postdoctoral fellow at Rensselaer Polytechnic Institute. He joined the faculty at the University of California Santa Barbara in 2006. His research is in the area of stochastic analysis and computational methods for diverse applications.

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