Shakir Mohamed is a senior staff scientist at DeepMind in London. Shakir's main interests lie at the intersection of approximate Bayesian inference, deep learning and reinforcement learning, and the role that machine learning systems at this intersection have in the development of more intelligent and general-purpose learning systems. Before moving to London, Shakir held a Junior Research Fellowship from the Canadian Institute for Advanced Research (CIFAR), based in Vancouver at the University of British Columbia with Nando de Freitas. Shakir completed his PhD with Zoubin Ghahramani at the University of Cambridge, where he was a Commonwealth Scholar to the United Kingdom. Shakir is from South Africa and completed his previous degrees in Electrical and Information Engineering at the University of the Witwatersrand, Johannesburg.

Sanmi Koyejo is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign and a research scientist at Google AI in Accra. Koyejo's research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally, Koyejo focuses on applications to biomedical imaging and neuroscience. Koyejo co-founded the Black in AI organization and currently serves on its board.

I am a professor at KAIST in the School of Computing with joint appointment in the Graduate School of AI. My research interests are in developing and applying machine learning models for natural language processing. In our research group, we look at various data such as news, social media, Wikipedia, and programming education.

Kyunghyun Cho is an associate professor of computer science and data science at New York University and a research scientist at Facebook AI Research. He was a postdoctoral fellow at the Université de Montréal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.

Tristan Naumann is a Principal Researcher in the Real World Evidence (RWE) group at Microsoft Research’s Health Futures. His research focuses on problems at the intersection of machine learning (ML) and health, specifically exploring relationships in complex, unstructured health data using techniques from natural language processing (NLP) and unsupervised learning. He values supporting the broader ML community through academic service and has served as a General Chair, and a variety of other roles, for NeurIPS, AHLI Conference on Health, Inference, and Learning (CHIL), and Machine Learning for Health (ML4H).

Professor Hsuan-Tien Lin received a B.S. in Computer Science and Information Engineering from National Taiwan University in 2001, an M.S. and a Ph.D. in Computer Science from California Institute of Technology in 2005 and 2008, respectively. He joined the Department of Computer Science and Information Engineering at National Taiwan University as an assistant professor in 2008 and has been promoted to full professor in 2017. Between 2016 and 2019, he worked as the Chief Data Scientist of Appier, a startup company that specializes in making AI easier for marketing. Currently, he keeps growing with Appier as its Chief Data Science Consultant. From the university, Prof. Lin received the Distinguished Teaching Awards in 2011 and 2021, the Outstanding Mentoring Award in 2013, and five Outstanding Teaching Awards between 2016 and 2020. He co-authored the introductory machine learning textbook Learning from Data and offered two popular Mandarin-teaching MOOCs Machine Learning Foundations and Machine Learning Techniques based on the textbook. He served in the machine learning community as Progam Co-chair of NeurIPS 2020, Expo Co-chair of ICML 2021, and Workshop Chair of NeurIPS 2022 and 2023. He co-led the teams that won the champion of KDDCup 2010, the double-champion of the two tracks …

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.

I am a Senior Research Scientist at DeepMind since 2022. I joined Alphabet in 2019 as part of Google Research working on trustworthy machine learning for healthcare. Before that, I was a postdoctoral researcher at University College London and Stanford University studying machine learning for neuroscience. My current interests lie at the intersection of trustworthy machine learning and causality.

Dr. Jake Albrecht is the Director of Challenges and Benchmarking at Sage Bionetworks, responsible for managing the strategic and operational activities of benchmarking projects and data challenges, including the DREAM Challenges. Jake has a Ph.D. from MIT and B.S. from the University of Nebraska, both in Chemical Engineering.

Marco Ciccone is an ELLIS Postdoctoral Researcher in the VANDAL group at Politecnico di Torino and UCL. His current research interests are in the intersection of meta, continual, and federated learning with a particular focus on modularity and models re-use to scale the training of agents with heterogeneous data, and mitigate the effect of catastrophic forgetting and interference across tasks, domains, and devices. He has been NeurIPS Competiton Track co-chair in 2021, 2022 and 2023.

Joaquin Vanschoren is Associate Professor in Machine Learning at the Eindhoven University of Technology. He holds a PhD from the Katholieke Universiteit Leuven, Belgium. His research focuses on understanding and automating machine learning, meta-learning, and continual learning. He founded and leads OpenML.org, a popular open science platform with over 250,000 users that facilitates the sharing and reuse of machine learning datasets and models. He is a founding member of the European AI networks ELLIS and CLAIRE, and an active member of MLCommons. He obtained several awards, including an Amazon Research Award, an ECMLPKDD Best Demo award, and the Dutch Data Prize. He was a tutorial speaker at NeurIPS 2018 and AAAI 2021, and gave over 30 invited talks. He co-initiated the NeurIPS Datasets and Benchmarks track and was NeurIPS Datasets and Benchmarks Chair from 2021 to 2023. He also co-organized the AutoML workshop series at ICML, and the Meta-Learning workshop series at NeurIPS. He is editor-in-chief of DMLR (part of JMLR), as well as an action editor for JMLR and machine learning moderator for ArXiv. He authored and co-authored over 150 scientific papers, as well as reference books on Automated Machine Learning and Meta-learning.

I am a Research Scientist at Facebook AI Applied Research (FAIAR) where I work on Computer Vision, Image and Video Processing, and Machine Learning. I work on problems such as perceptual image and video quality, large-scale video action recognition, fairness and inclusivity.

Prior to joining Facebook AI, I obtained my PhD at the University of Texas at Austin in 2017 where I worked with Alan Bovik on perceptual image and video quality assessment for real-world content.

Dr Ignatius Ezeani A Senior Teaching/Research Associate with the Data Science Group at Lancaster University. I'm interested in the application of NLP techniques in building resources for low-resource languages especially African languages, but my interests span other related areas like corpus linguistics, distributional semantics, machine learning, deep neural models and general AI.

Computing Innovation Fellow 2020, Research Assistant at University of Utah, Postdoctoral Fellow at UCLA starting Jan 2020. Research interests are Responsible and Interpretable AI, NLP and Algorithmic Fairness.

Ismini Lourentzou is an Assistant Professor at Virginia Tech's Computer Science Department, where she leads the Perception and LANguage (PLAN) Lab. She is a core faculty member of the Sanghani Center for Artificial Intelligence and Data Analytics, an affiliate faculty of the National Security Institute (VT NSI), and an affiliate faculty of the Center for Advanced Innovation in Agriculture (VT CAIA). Her primary research focus is multimodal machine learning, particularly the intersection of vision and language in settings with limited supervision, and its applications in embodied AI, video understanding, healthcare, etc. Lourentzou obtained her Ph.D. in Computer Science from the University of Illinois at Urbana - Champaign and has previously worked as a Research Scientist at IBM Research. She has served as Expo Co-Chair of NeurIPS 2022, Workshop Co-Chair of NeurIPS 2023, Associate PC Chair of ACM PETRA 2021 and 2023, Doctoral Consortium Chair of ACM PETRA 2022, and assumed editorial and Area Chair roles for top-tier journals and conferences (ACL'23, MICCAI'23, PLOS Digital Health Computer Vision Section Editor, etc.). Lourentzou received a 2023 Outstanding New Assistant Professor Award from Virginia Tech College of Engineering, a 2019 IBM Invention Plateau, and was also selected as a 2019 EECS Rising Star. …

Outreach Co-Chair, M.S. in CS at UCLA. Passionate about CS Ed, PL + HCI, and open-source. Say hi!

Applied Complex Systems Engineering

Animesh Garg is a Stephen Fleming Early Career Professor at the School of Interactive Computing at Georgia Tech as well as on the faculty at University of Toronto and Vector Institute . He leads the People, AI, and Robotics (PAIR) research group. Animesh is also a Senior Researcher at Nvidia Research. Animesh earned a Ph.D. from UC Berkeley and was previously a postdoc at the Stanford AI Lab. His work aims to build Generalizable Autonomy which involves a confluence of representations and algorithms for reinforcement learning, control, and perception — all in the purview of embodied systems.

I am a Research Scientist at Google DeepMind. I have obtained my PhD from the University of Oxford, supervised by Chris Holmes. I have interned at DeepMind London and Microsoft Research Cambridge, and my research has also received the Microsoft Research PhD Fellowship. My research focusses on generative modelling for robustness, differential privacy and interpretability.

• Head, AI Innovation, NAVER Cloud
• Research Fellow, NAVER AI Lab
• Datasets and Benchmarks Co-Chair, NeurIPS 2023
• Socials Co-Chair, ICML 2023
• Socials Co-Chair, NeurIPS 2022
• BS, Seoul National University
• PhD, Seoul National University

Freddie is a Senior Research Fellow at the Dept. of Computer Science, University of Oxford, investigating topics mainly in AI for Earth Observation. He is the principal investigator of OpenSR, a €1M government contract with ESA, to increase the safety of Super-Resolution technology for the Sentinel-2 archive. He is also an independent consultant, involved in projects where he leads teams in the Frontier Development Lab (FDL), a private-public partnership between NASA, SETI, and Trillium Technologies. His recent FDL projects were funded by NASA SMD to investigate the use of SAR imagery for disaster detection, and by the USGS to develop near-real-time water stream mapping from daily PlanetScope imagery. His most recent work is a survey on the State of AI for Earth Observation, in collaboration with Satellite Applications Catapult.

Research Scientist at Meta AI NYC. PhD from McGill University / Mila, advised by Dr Joelle Pineau.

I primarily work on logical language understanding, systematic generalization, logical graphs and dialog systems.

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