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Lunch + Poster Session
Frederik Gerzer · Bill Yang Cai · Pieter-Jan Hoedt · Kelly Kochanski · Soo Kyung Kim · Yunsung Lee · Sunghyun Park · Sharon Zhou · Martin Gauch · Jonathan Wilson · Joyjit Chatterjee · Shamindra Shrotriya · Dimitri Papadimitriou · Christian Schön · Valentina Zantedeschi · Gabriella Baasch · Willem Waegeman · Gautier Cosne · Dara Farrell · Brendan Lucier · Letif Mones · Caleb Robinson · Tafara Chitsiga · Victor Kristof · Hari Prasanna Das · Yimeng Min · Alexandra Puchko · Alexandra Luccioni · Kyle Story · Jason Hickey · Yue Hu · Björn Lütjens · Zhecheng Wang · Renzhi Jing · Genevieve Flaspohler · Jingfan Wang · Saumya Sinha · Qinghu Tang · Armi Tiihonen · Ruben Glatt · Muge Komurcu · Jan Drgona · Juan Gomez-Romero · Ashish Kapoor · Dylan J Fitzpatrick · Alireza Rezvanifar · Adrian Albert · Olya (Olga) Irzak · Kara Lamb · Ankur Mahesh · Kiwan Maeng · Frederik Kratzert · Sorelle Friedler · Niccolo Dalmasso · Alex Robson · Lindiwe Malobola · Lucas Maystre · Yu-wen Lin · Surya Karthik Mukkavili · Brian Hutchinson · Alexandre Lacoste · Yanbing Wang · Zhengcheng Wang · Yinda Zhang · Victoria Preston · Jacob Pettit · Draguna Vrabie · Miguel Molina-Solana · Tonio Buonassisi · Andrew Annex · Tunai P Marques · Catalin Voss · Johannes Rausch · Max Evans

Sat Dec 14 12:00 PM -- 02:00 PM (PST) @
in Tackling Climate Change with ML »

Author Information

Frederik Gerzer (fortiss/TU Munich)
Bill Yang Cai (Government Technology Agency of Singapore)
Pieter-Jan Hoedt (LIT AI Lab / Unversity Linz)

PhD student at the Institute for Machine Learning at the Johannes Kepler University Linz. I am always eager to learn new things, so feel free to approach me with any fun fact you happen to know!

Kelly Kochanski (University of Colorado Boulder)

Earth science researcher using machine learning to make better predictions about natural hazards and climate change.

Soo Kyung Kim (Lawrence Livermore National Laboratory)
Yunsung Lee (Korea University)
Sunghyun Park (Korea University)
Sharon Zhou (Stanford University)
Martin Gauch (University of Waterloo)
Jonathan Wilson (Haverford College)
Joyjit Chatterjee (University of Hull)

Joyjit is a PhD. Computer Science Student- Researcher at the University of Hull, Yorkshire, United Kingdom. A Chartered Engineer, MIET, MIEEE, AMIE, Jr. Member of Isaac Newton Institute for Mathematical Sciences, Cambridge & Life Member of Indian Science Congress Association (Govt. of India), Joyjit did his Bachelor of Technology in Electronics and Communication Engineering from Amity University, Noida, India, where he was the Gold Medalist and University Topper. A rare amalgamation of dedication and intelligence, Joyjit has already published an array of research papers in Computer Science & Electronics Engineering and filed three patents. He has co-authored papers with reputed professors and academicians from across the globe, including Taiwan, Germany, China, India and UK in the area of Signal Processing, Machine Learning and Artificial Intelligence. Joyjit was also a Visiting Research Scholar at Tamkang University, Taiwan on a Research Project funded by the Ministry of Education, Taiwan. He has a sound understanding of the industry and has done various internships, including being a System Verilog HEP Trainee at Mentor Graphics, Noida, India (A Siemens Business). An established writer, Joyjit has authored a book "Thoughts of a Young Man", a collection of short poems, inspirational quotes and random thoughts, which was highly applauded by people, especially youngsters. Joyjit is the recipient of several honours and awards including, Gold Medal from Amity University, Noida, India, India Book of Records Holder, Merit Scholarship from Amity University, Noida, India, Letter of Appreciation from Ministry of HRD, Govt. of India, Amul Vidya Bhushan Award and the like.Joyjit's research interests include, but are not limited to Machine Learning, Artificial Intelligence, Data Analytics, Electronic Design & Automation and Signal Processing. He can be reached at joyjitece@gmail.com

Shamindra Shrotriya (Carnegie Mellon University)
Dimitri Papadimitriou (University of Antwerp)
Christian Schön (Saarland Informatics Campus)
Valentina Zantedeschi (GE Global Research)
Gabriella Baasch (University of Victoria)
Willem Waegeman (Ghent University)
Gautier Cosne (Mila, Universite de Montreal)

Machine Learning Scientist: Highly motivated by optimizing, modeling and forecasting real-world problems.

Dara Farrell (University of Washington (2019 graduate))
Brendan Lucier (Microsoft Research)
Letif Mones (Invenia Labs)
Caleb Robinson (Georgia Institute of Technology)
Tafara Chitsiga (University of the Witwatersrand)

I am a Chemical Engineer passionate about tackling Climate Change by use of Carbon Capture and Storage. I currently work as a Researcher and Process Engineering consultant. My research work entails developing new materials for Carbon capture, testing them and scaling up for possible industry application. As part of material testing and scaling up for industry application, I am using machine learning techniques to build models that can predict carbon capture capacity of materials at different operating conditions.

Victor Kristof (EPFL)
Hari Prasanna Das (University of California Berkeley)
Yimeng Min (MILA)
Alexandra Puchko (Western Washington University)
Alexandra Luccioni (Mila + Université de Montréal)
Kyle Story (Descartes Labs)

Kyle Story is a Computer Vision Engineer at Descartes Labs, where he develops machine learning approaches to understand satellite imagery at scale. Kyle has a PhD in astrophysics, where he studied the early universe and the cosmic microwave background.

Jason Hickey (Google Inc.)
Yue Hu (Vanderbilt University)

am a PhD student in computer science at Vanderbilt University and the Institute for Software Integrated Systems. I earned my M.S. in Systems Engineering at the Department of Civil and Environmental Engineering in University of California at Berkeley. I earned my B.S. in Civil and Environmental Engineering from Tongji University, China. My current work is focused on machine learning and optimization techniques for outlier detections, with application on traffic extreme event detection. My general research interest lies in machine learning, transportation cyber physical systems and sustainable resilient urban systems.

Björn Lütjens (Massachusetts Institute of Technology)
Zhecheng Wang (Stanford University)
Renzhi Jing (Princeton University)
Genevieve Flaspohler (Massachusetts Institute of Technology)
Jingfan Wang (Stanford University)
Saumya Sinha (University of Colorado, Boulder)
Qinghu Tang (Tsinghua University)
Armi Tiihonen (Massachusetts Institute of Technology)
Ruben Glatt (Universidade de São Paulo)
Muge Komurcu (MIT)

I study climate change and its impacts at regional and local scales through high-resolution modeling and inter-disciplinary collaborations. Most recently, I created the first high-resolution (3km horizontal resolution) climate projections for the Northeastern United States for 60 years with more than 200 climate variables saved at hourly intervals ( 2 Petabytes) and analyzed differences between the coarse Earth System Model projections and my high-resolution projections. I have a Ph.D. in meteorology from Penn State University and completed my postdoctoral training in Earth System Models at Yale University. I also have a master's degree in Meteorological Engineering and dual bachelor's degrees in Meteorological Engineering and Environmental Engineering from Istanbul Technical University.

Jan Drgona (Pacific Northwest National Laboratory)

I am a data scientist in the Physics and Computational Sciences Division (PCSD) at Pacific Northwest National Laboratory, Richland, WA. My current research interests fall in the intersection of model-based optimal control, constrained optimization, and machine learning.

Juan Gomez-Romero (Universidad de Granada)
Ashish Kapoor (Microsoft)
Dylan J Fitzpatrick (Carnegie Mellon University)

Dylan Fitzpatrick is a PhD candidate in Machine Learning and Public Policy at Carnegie Mellon University, where he researches novel statistical machine learning algorithms for pattern detection. As PhD student, Dylan works mostly in the domains of public health and criminology, exploring new methods for disease outbreak detection and crime forecasting in spatial data. Most recently, Dylan has focused on individual-level opioid use monitoring, developing techniques for assessing risk of opioid misuse in patients and identifying unsafe opioid prescribing practices from prescription data. Dylan participated in University of Chicago's Data Science for Social Good program, where he used machine learning methods to identify instances of collusion and corruption in bidding for World Bank-funded development projects around the world. While working for IBM Research, Dylan developed a framework for active imitation learning using generative adversarial networks (GANs), in which an optimal policy for decision-making is learned by selectively querying an expert agent. Dylan earned a BA in Economics from Middlebury College and an MS in Computer Science from Carnegie Mellon University. Outside of research, Dylan enjoys spending time outdoors with his dog, playing guitar, practicing yoga, and baking rhubarb pies. Dylan is an avid Minnesota Vikings fan who often wishes he had a choice in the matter.

Alireza Rezvanifar (university of victoria)
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.

Olya (Olga) Irzak (Frost Methane Labs)
Kara Lamb (University of Colorado Boulder)

I received my Ph.D. in Physics from the University of Chicago in 2015. I am currently a Research Scientist at the Cooperative Institute for Research in Environmental Sciences, where I work at NOAA's Chemical Sciences Division in the Chemical Composition and Processes Group. My current research focuses on the sources, optical properties, atmospheric lifetime, and climate impacts of absorbing aerosols in the Earth's atmosphere. I am interested in how machine learning can be applied to address problems in the physical sciences, and I participated in NASA's Frontier Development Lab AI Research Sprint this past summer.

Ankur Mahesh (ClimateAi)
Kiwan Maeng (Carnegie Mellon University)
Frederik Kratzert (LIT AI Lab / University Linz)
Sorelle Friedler (Haverford College)
Niccolo Dalmasso (Carnegie Mellon University)
Alex Robson (Invenia Labs)
Lindiwe Malobola (University of the Witwatersrand)

I'm a technology consultant based in South Africa. I mainly specialize in Intelligent Process Automation (IPA), an application of artificial intelligence which uses computer vision; cognitive automation and machine learning to robotic process automation. I am very passionate about Data Science and particularly curious about Deep Learning and Machine Learning. My other research interest include Topological Data Analytics, Topological Machine Learning and Dynamical Systems. I am looking forward to learn and contribute.

Lucas Maystre (EPFL)
Yu-wen Lin (UC Berkeley)

A second-year PhD student interested in optimization, control, machine learning and their applications to smart buildings.

Surya Karthik Mukkavili (Mila)
Brian Hutchinson (Western Washington University)
Alexandre Lacoste (Element AI)
Yanbing Wang (Vanderbilt University)

Yanbing Wang is a doctoral student in Civil Engineering and the Institute for Software Integrated Systems at Vanderbilt University. She earned a B.S. in Civil and Environmental Engineering in 2018 from University of Illinois at Urbana-Champaign. During her undergraduate studies, she co-founded a non-profit organization, Bridges to Prosperity, and helped rural communities in Guatemala and Panama construct pedestrian bridges that allow safe access to local amenities. Prior to Vanderbilt, Yanbing actively involved herself in a range of research topics including life cycle assessment on wastewater treatment infrastructure, fleet assignment and optimal scheduling in public transit, and most recently, the application of computer vision techniques to driver-assistance devices. Yanbing's current research focuses on modeling and estimating traffic flows that are composed of mixed human-operated and automated vehicles.

Zhengcheng Wang (Tsinghua University)
Yinda Zhang (Princeton university)
Victoria Preston (Massachusetts Institute of Technology)
Jacob Pettit (Lawrence Livermore National Laboratory)

B.S. in Scientific Computing at Florida State University. I'm currently working on machine learning research at Lawrence Livermore National Laboratory. Long term, I'd like to understand cognition and develop intelligent machines. I'm also interested in meta-learning, knowledge representation, and reinforcement learning (RL).

Draguna Vrabie (Pacific Northwest National Laboratory)

Draguna Vrabie is a chief data scientist at Pacific Northwest National Laboratory, in the Data Sciences and Machine Intelligence group where she serves as team leader for the Autonomous Learning and Reasoning team. Her work is at the intersection of control system theory and machine learning and is aimed at the design of adaptive decision and control systems. Her current focus is on deep learning methodologies and algorithms for design and operation of high-performance, cyber-physical systems. Prior to joining PNNL in 2015, she was a senior scientist at United Technologies Research Center, in East Hartford, Connecticut. She published two books on optimal control and reinforcement learning, and more than fifty journal and conference papers, with more than five thousand citations. She has chapters in the Control Systems Handbook and in the Handbook on Computational Intelligence. She has served on the editorial board of IEEE Transactions on Control Systems Technology and IEEE Transactions of Neural Networks and Learning Systems, as a program committee member for international symposia on control, computing, and machine learning, and as a technical reviewer for conferences, journals and government funding programs. Vrabie holds a doctorate in electrical engineering from the University of Texas at Arlington, and an ME and BE in automatic control and computer engineering from Gheorghe Asachi Technical University, Iaşi, Romania.

Miguel Molina-Solana (Universidad de Granada)
Tonio Buonassisi (Massachusetts Institute of Technology)
Andrew Annex (Johns Hopkins University)
Tunai P Marques (University of Victoria)
Catalin Voss (Stanford University)
Johannes Rausch (ETH Zurich)
Max Evans (ClimateAI)

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