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Afternoon Coffee Break & Poster Session
Heidi Komkov · Stanislav Fort · Zhaoyou Wang · Rose Yu · Ji Hwan Park · Samuel Schoenholz · Taoli Cheng · Ryan-Rhys Griffiths · Chase Shimmin · Surya Karthik Mukkavili · Philippe Schwaller · Christian Knoll · Yangzesheng Sun · Keiichi Kisamori · Gavin Graham · Gavin Portwood · Hsin-Yuan Huang · Paul Novello · Moritz Munchmeyer · Anna Jungbluth · Daniel Levine · Ibrahim Ayed · Steven Atkinson · Jan Hermann · Peter Grönquist · · Priyabrata Saha · Yannik Glaser · Lingge Li · Yutaro Iiyama · Rushil Anirudh · Maciej Koch-Janusz · Vikram Sundar · Francois Lanusse · Auralee Edelen · Jonas Köhler · Jacky H. T. Yip · jiadong guo · Xiangyang Ju · Adi Hanuka · Adrian Albert · Valentina Salvatelli · Mauro Verzetti · Javier Duarte · Eric Moreno · Emmanuel de Bézenac · Athanasios Vlontzos · Alok Singh · Thomas Klijnsma · Brad Neuberg · Paul Wright · Mustafa Mustafa · David Schmidt · Steven Farrell · Hao Sun

Sat Dec 14 03:20 PM -- 04:20 PM (PST) @
in Machine Learning and the Physical Sciences »

Author Information

Heidi Komkov (University of Maryland)
Stanislav Fort (Stanford University / Google Research)
Zhaoyou Wang (Stanford University)
Rose Yu (Northeastern University)
Ji Hwan Park (Brookhaven National Lab.)
Samuel Schoenholz (Google Brain)
Taoli Cheng (MILA, Université de Montréal)
Ryan-Rhys Griffiths (University of Cambridge)
Chase Shimmin (Yale University)
Surya Karthik Mukkavili (Mila)
Philippe Schwaller (IBM Research Zurich / UniBe)
Christian Knoll (Graz University of Technology)
Yangzesheng Sun (University of Minnesota)
Keiichi Kisamori (National Institute of Advanced Industrial Science and Technology (AIST))
Gavin Graham (Total SA)
Gavin Portwood (Los Alamos National Laboratory)

I am a post-doc researcher at Los Alamos National Lab. My expertise is in fluid turbulence simulation and modeling. Current research is focused on developing turbulence models with various machine learning approaches.

Hsin-Yuan Huang (California Institute of Technology)
Paul Novello (French Alternative Energies and Atomic Energy Commission / INRIA Paris Saclay)
Moritz Munchmeyer (Perimeter Institute for Theoretical Physics)
Anna Jungbluth (University of Oxford)
Daniel Levine (Schrodinger, Inc.)
Ibrahim Ayed (Sorbonne Université)
Steven Atkinson (GE Research)
Jan Hermann (FU Berlin)
Peter Grönquist (ETHZ)
Priyabrata Saha (Georgia Institute of Technology)
Yannik Glaser (University of Hawaii at Manoa)

Graduate student working on applying Machine Learning models to problems from the natural sciences.

Lingge Li (UC Irvine)
Yutaro Iiyama (CERN)
Rushil Anirudh (Lawrence Livermore National Laboratory)
Maciej Koch-Janusz (ETH Zurich)
Vikram Sundar (University of Cambridge)
Francois Lanusse (UC Berkeley)
Auralee Edelen (SLAC / Stanford)
Jonas Köhler (Free University of Berlin)
Jacky H. T. Yip (The Chinese University of Hong Kong)
jiadong guo (peng cheng laboratory)

My research focus on ML in finance.

Xiangyang Ju (Lawrence Berkeley National Laboratory)
Adi Hanuka (SLAC, Stanford)
Adrian Albert (Terrafuse, Inc.)

Dr. Adrian Albert is an expert in machine learning science, physics, and energy systems. He leads Terrafuse’s machine learning research and development. Previously, he was a machine learning research scientist at Lawrence Berkeley National Lab, where he conducted research on physics-enabled machine learning for physical science applications. He completed postdoctoral research at MIT working on deep learning for remote-sensing imagery and urban science applications and obtained his PhD in Electrical Engineering at Stanford with a thesis on machine learning for energy grids. He was one of the first machine learning scientists at the startup C3.ai, where he helped build C3’s and the industry’s first AI product for large-scale predictive maintenance for energy and industrial systems. He was part of the founding team for, and is currently an advisor at, EdTech startup Myriad Sensors, makers of multifunctional sensors for STEM.

Valentina Salvatelli (IQVIA & NASA Frontier Development Lab)

Valentina is a Senior Applied Scientist at Microsoft Research. In her current role in the AI for Health team at MSR she builds advanced deep learning models to predict genetic mutations from medical images. Previously at IQVIA she worked to support the cure of rare diseases by develping ML models based on electronic health records. She has a background as astrophysicist, in her PhD she researched how to use astronomical data and bayesian inference to understand the evolution of the universe.  Valentina is also affiliated to the SETI Institute and the Frontier Development Lab, with which she works on ML for satellite instruments (solar telescopes and cubesats).

Mauro Verzetti (CERN)

High energy physicist with over nine years of experience in data science and data mining at the Large Hadron Collider. Specialised in statistical modelling of the data, both through ansatz fitting and machine learning. Skilled software developer, fluent in both python and C++. Currently leading a team of ~50 people focused on object classification and interacting with all the major stakeholders of my experiment.

Javier Duarte (UC San Diego)

I am an Assistant Professor in experimental high energy physics at UC San Diego and a member of the CMS collaboration at CERN. My research interests include measuring the properties and couplings of the Higgs boson and searching for beyond-the-standard-model particles in LHC data. I am interested in developing machine learning algorithms, real-time trigger systems (with applications to embedded devices), and heterogenous computing architectures for the next generation of high energy physics experiments.

Eric Moreno (California Institute of Technology)
Emmanuel de Bézenac (Sorbonne Université)
Athanasios Vlontzos (Imperial College London)

PhD student at Imperial College, BioMedIAICL Formerly ML Research at Apple, Zeit Medical, NASA FDL, GE Healthcare

Alok Singh (LBNL)
Thomas Klijnsma (Fermilab)
Brad Neuberg (NASA Frontier Development Lab/SETI Institute)

My research interests are machines that see, hear, and plan in order to augment people & society’s capabilities. I am a machine learning software engineer, with a focus on deep learning. I am currently a Staff Machine Learning Engineer with Planet, applying machine and deep learning to remote sensing imagery of Earth's surface. Planet images the entirety of the Earth daily to monitor changes and pinpoint trends. The ultimate goal is to enable a Queryable Earth, indexing physical change on Earth and making it searchable for all. I have a decade and a half experience as a software engineer across such companies as Google & Dropbox, startups, and the open source world. I also have a Machine Learning Research Scientist affiliation with the SETI Institute and NASA’s Frontier Development Lab, applying deep learning to space science and space exploration. Before the SETI Institute I was a Machine Learning Engineer at Dropbox, doing industrial R&D to ship deep learning-powered products to millions of users and across billions of files. I've worn many hats in my career, whether as a tech lead, a product engineer, a startup founder, or a full stack software engineer. I have a long tradition of untraditional, cross-disciplinary innovation across fields. Earlier work includes having started Coworking, which grew into an international grassroots movement to establish a new kind of workspace for the self-employed, with more than 15,000 coworking spaces now open globally. At a startup named Inkling I founded Inkling Habitat, re-imagining interactive digital textbooks for higher education and how they are published by adopting ideas from computer science — Inkling Habitat turned into a multi-million dollar business that was adopted by the world’s major educational publishers, including Pearson & Elsevier. At Google I helped the web blossom into a true application deployment platform through efforts like HTML5. Finally, I worked with Douglas Engelbart, the inventor of the computer mouse & hypertext, on the National Science Foundation-funded HyperScope project to use advanced hypertext to support collaborative teams.

Paul Wright (Stanford University)
Mustafa Mustafa (Berkeley Lab)
David Schmidt (University of Massachusetts Amherst)
Steven Farrell (Lawrence Berkeley National Laboratory)
Hao Sun (CUHK)

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