General Chair
Sanmi Koyejo
Stanford University / Virtue AI
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.
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.
General Chair
Shakir Mohamed
Senior Staff Scientist
DeepMind
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.
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.
Program Chair
Alekh Agarwal
Google Research
Program Chair
Danielle Belgrave
GSK.ai
Program Chair
Kyunghyun Cho
Genentech / NYU
Kyunghyun Cho - Glen de Vries Professor of Health Statistics, NYU; Executive Director of Frontier Research, Prescient Design, Genentech Cho's work spans machine learning and natural language processing. He co-developed the Gated Recurrent Unit (GRU) architecture and has contributed to neural machine translation and sequence-to-sequence learning. He is a CIFAR Fellow of Learning in Machines & Brains and received the 2021 Samsung Ho-Am Prize in Engineering. He served as program chair for ICLR 2020, NeurIPS 2022, and ICML 2022.
Kyunghyun Cho - Glen de Vries Professor of Health Statistics, NYU; Executive Director of Frontier Research, Prescient Design, Genentech Cho's work spans machine learning and natural language processing. He co-developed the Gated Recurrent Unit (GRU) architecture and has contributed to neural machine translation and sequence-to-sequence learning. He is a CIFAR Fellow of Learning in Machines & Brains and received the 2021 Samsung Ho-Am Prize in Engineering. He served as program chair for ICLR 2020, NeurIPS 2022, and ICML 2022.
Program Chair
Alice Oh
Associate Professor
KAIST
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.
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.
Workshop Chair
Sungjin Ahn
Assistant Professor
KAIST
Workshop Chair
Hsuan-Tien Lin
National Taiwan University
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 in KDDCup 2011, the champion of track 2 in KDDCup 2012, and the double-champion of the two tracks in KDDCup 2013.
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 in KDDCup 2011, the champion of track 2 in KDDCup 2012, and the double-champion of the two tracks in KDDCup 2013.
Workshop Chair
Tristan Naumann
Microsoft Research
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).
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).
Workshop Chair
Hanie Sedghi
Research Scientist
Google Deepmind
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 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.
Tutorial Chair
Adji Bousso Dieng
Graduate Student
Princeton University & Google AI
Tutorial Chair
Jessica Schrouff
Research Scientist
GSK
I am a Director of Responsible AI at GSK since 2025. Previously, I was at Alphabet (Google Research, Google Deepmind) 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.
I am a Director of Responsible AI at GSK since 2025. Previously, I was at Alphabet (Google Research, Google Deepmind) 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.
Tutorial Chair
Andrew Gordon Wilson
New York University
Competition Chair
Jake Albrecht
Bristol Myers Squibb
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.
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.
Competition Chair
Marco Ciccone
Phd student
Vector Institute
Competition Chair
Gustavo Stolovitzky
IBM Research
Datasets and Benchmarks Chair
Deepti Ghadiyaram
Research Scientist
Runway
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.
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.
Datasets and Benchmarks Chair
Joaquin Vanschoren
Asst. Professor
Eindhoven University of Technology, Google DeepMind
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.
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.
Diversity, Inclusion & Accessibility Chair
Ignatius Ezeani
Dr
Lancaster University
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.
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.
Diversity, Inclusion & Accessibility Chair
Erin Grant
New York University
Affinity Chair
Kehinde Aruleba
Mr
University of Leicester
Affinity Chair
Sunipa Dev
Research Assistant
Google Research
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.
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.
Affinity Chair
Arjun Subramonian
Meta
Expo Chairs
Ismini Lourentzou
Virginia Tech
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. Her research has received support from NSF, DARPA, Amazon, and CCI. Currently, Lourentzou’s PLAN Lab competes in the Alexa Prize TaskBot Challenge 2.
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. Her research has received support from NSF, DARPA, Amazon, and CCI. Currently, Lourentzou’s PLAN Lab competes in the Alexa Prize TaskBot Challenge 2.
Expo Chairs
Wenming Ye
Sr. Solutions architect
Boson.ai
Outreach Chair
Jessica Forde
Brown University
Outreach Chair
Matthew Wang
University of California, Los Angeles (UCLA)
Outreach Co-Chair, M.S. in CS at UCLA. Passionate about CS Ed, PL + HCI, and open-source. Say hi!
Outreach Co-Chair, M.S. in CS at UCLA. Passionate about CS Ed, PL + HCI, and open-source. Say hi!
Ethics Review Chair
William Isaac
DeepMind
Ethics Review Chair
Sasha Luccioni
Hugging Face
Ethics Review Chair
Cherie Poland
Data & Machine Learning Engineer
Complex Adaptive Systems Research | Virginia Tech
Applied Complex Systems Engineering
Applied Complex Systems Engineering
Ethics Review Chair
Deborah Raji
University of Toronto
Communication Chair
Animesh Garg
Assistant Professor
Georgia Tech, Univ of Toronto
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.
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.
Communication Chair
Sahra Ghalebikesabi
Google DeepMind
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.
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.
Social Chair
Jung-Woo Ha
Ph.D
NAVER Cloud AI Lab
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
- 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
Social Chair
Freddie Kalaitzis
Ph.D.
University of Oxford
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.
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.
Journal Chair
Tegan Maharaj
Mila (HEC)
Journal Chair
Koustuv Sinha
FAIR, Meta
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.
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.
Hybrid Workflow Chair
Hendrik Strobelt
IBM Research / MIT-IBM Ai Lab
Workflow Manager
Zhenyu (Sherry) Xue
NeurIPS
Logistics and IT
Terri Auricchio
VP
Logistics and IT
Brad Brockmeyer
NeurIPS Staff
Logistics and IT
Lee Campbell
Bioinformatics
NeurIPS Staff
Logistics and IT
Brian Nettleton
NeurIPS Staff
Logistics and IT
Mary Ellen Perry
Executive Director
Level 5 Events
Logistics and IT
Max Wiesner
IT
NeurIPS Staff
Logistics and IT
Stephanie Willes
NeurIPS Staff
