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 …

A Principal Research Scientist in Machine Learning at Data61❤CSIRO, and a Honorary Associate Professor (level D) at the University of New South Wales (UNSW) and Australian National University (ANU). Between 2013-2015, he was a postdoctoral researcher in the team LEAR, INRIA, Grenoble. He received his BSc in Telecommunications and Software Engineering in 2004 from the Warsaw University of Technology, Poland, and completed his PhD in Computer Vision in 2013 at CVSSP, University of Surrey, UK. His current interests include contrastive and few-shot learning. He has received awards such as the Sang Uk Lee Best Student Paper Award from ACCV'22, the Runner-up APRS/IAPR Best Student Paper Award from DICTA'22, outstanding Area Chair by ICLR 2021--2023. He has served as a Workshop Program Co-Chair for NeurIPS'23 and WWW'25, and Senior Area Chair for several NeurIPS, ICLR, ICML and AISTATS conferences.

Dr. Marzyeh Ghassemi is an Assistant Professor at MIT in Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES), and a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair. She holds MIT affiliations with the Jameel Clinic and CSAIL. Professor Ghassemi holds a Herman L. F. von Helmholtz Career Development Professorship, and was named a CIFAR Azrieli Global Scholar and one of MIT Tech Review’s 35 Innovators Under 35. During her education she was awarded a Goldwater Scholarship and Marshall Scholarship.

Nancy F. Chen is an ASTAR fellow, who leads the Multimodal Generative AI group, heads the Artificial Intelligence for Education (AI4EDU) programme at I2R (Institute for Infocomm Research) and is a principal investigator at CFAR (Centre for Frontier AI Research), ASTAR. Dr. Chen’s recent work in large language models have won honors at ACL 2024, including Area Chair Award and Best Paper Award for Cross-Cultural Considerations in Natural Language Processing. Dr. Chen consistently garners best paper awards for her AI research applied to diverse applications. Examples include IEEE ICASSP 2011 (forensics), APSIPA 2016 (education), SIGDIAL 2021 (social media), MICCAI 2021 (neuroscience), and EMNLP 2023 (healthcare). Multilingual spoken technology from her team has led to commercial spin-offs and has been deployed at Singapore’s Ministry of Education to support home-based learning. Dr. Chen has supervised 100+ students and staff. She has won professional awards from USA National Institute of Health, IEEE, Microsoft, P&G, UNESCO, and L’Oréal.

She servers as Program Chair of NeurIPS 2025, APSIPA Board of Governors (2024-2026), IEEE SPS Distinguished Lecturer (2023-2024), Program Chair of ICLR 2023, Board Member of ISCA (2021-2024), and is honoured as Singapore 100 Women in Tech (2021). Prior to A*STAR, she worked at …

I am a Ph. D. student in the Graduate Institute of Networking and Multimedia at National Taiwan University. I’m also the Associate Learner in CLLab, advised by Prof. Hsuan-Tien Lin. Before joining CLLab, I was a researcher in Big Data Laboratory at Chunghwa Telecom Laboratories. My research interests include:- Active Learning, Preference Alignment, Diffusion Models, and In-Context Learning.

I’m a PhD student at Nanyang Technological University, focusing on Trustworthy AI. It’s a pleasure to serve as an Assistant PC for NeurIPS 2025.

My main interest is the intersection between (human) language and computing, I focus on Machine Learning, Artificial Intelligence and Natural Language Processing.

Co-Founder and Chief Medical AI Officer

Theodore Papamarkou is the founder of PolyShape, a startup company with the mission to develop health intelligence for innovative healthcare solutions, while currently holding the title of distinguished professor at Zhejiang Normal University. Prior to his current role, Theodore held the positions of professor in the mathematics of data science at The University of Manchester, of strategic hire in artificial intelligence at the Oak Ridge National Laboratory, and of assistant professor at the University of Glasgow. In early stages of his career, he worked as a post-doctoral researcher at the University of Warwick, at University College London, and at the University of Cambridge. Theodore’s research interests span categorical probabilistic inference, Bayesian deep learning, topological deep learning, and applications of these in biomedical research and healthcare.

Pascale Fung (馮雁) (born 1966 in Shanghai, China) is a professor in the Department of Electronic & Computer Engineering and the Department of Computer Science & Engineering at the Hong Kong University of Science & Technology(HKUST). She is the director of the newly established, multidisciplinary Centre for AI Research (CAiRE) at HKUST. She is an elected Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for her “contributions to human-machine interactions”[1] and an elected Fellow of the International Speech Communication Association for “fundamental contributions to the interdisciplinary area of spoken language human-machine interactions”.

Ponciano Jorge Escamilla-Ambrosio received the B.Sc. degree in Mechanical Electrical Engineering and the M.Sc. degree, with honours, in Electrical Engineering, both from the National Autonomous University of Mexico (UNAM) in 1995 and 2000, respectively. He received his Ph.D. degree from the University of Sheffield, UK, in January 2004. From 2003 to 2010, he was a research associate at the University of Bristol, UK, within the Aerospace Engineering and Computer Science departments. From 2010 to 2011, he was a research associate in the Department of Electronics, National Institute of Astrophysics, Optics, and Electronics, Mexico. From 2011 to 2013, Dr. Escamilla-Ambrosio was the General Director of Innovation and Development at the Scientific Division of the Secretariat of the Interior, Mexico. Since 2013, he has been a Researcher at the Computing Research Center of the National Polytechnic Institute (IPN), Mexico. Dr. Escamilla-Ambrosio has more than 120 publications in journals, conference proceedings, and book chapters. Dr. Escamilla-Ambrosio's research interests include Internet of Things, Smart Cities, Sensors/Data Fusion, Cybersecurity, Wireless Sensor Networks, Neuro-Fuzzy Networks, and Intelligent Control. He is a Senior Member of the IEEE and a Member of the Mexican System of Researchers, level 2. Dr. Escamilla-Ambrosio has been a member of the organizing …

Irene Chen an Assistant Professor in UC Berkeley and UCSF’s Computational Precision Health program with a joint appointment in Berkeley EECS. She received her PhD from MIT EECS as a member of the Clinical Machine Learning group. She received a joint AB/SM degree from Harvard University, and she has worked at Dropbox and Microsoft Research.

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 Google DeepMind NYC where I work on Data Excellence for AI. My team DEER (Data Excellence for Evaluating Responsibly) is part of the Responsible AI (RAI) organization. Our work is focused on developing metrics and methodologies to measure the quality of human-labeled or machine-generated data. The specific scope of this work is for gathering and evaluation of adversarial data for Safety evaluation of Generative AI systems. I received MSc in Computer Science from Sofia University, Bulgaria, and PhD from Twente University, The Netherlands. I am currently serving as a co-chair of the steering committee for the AAAI HCOMP conference series and I am a member of the DataPerf working group at MLCommons for benchmarking data-centric AI. Check out our data-centric challenge Adversarial Nibbler supported by Kaggle, Hugging Face and MLCommons. Prior to joining Google, I was a computer science professor heading the User-Centric Data Science research group at the VU University Amsterdam. Our team invented the CrowdTruth crowdsourcing method jointly with the Watson team at IBM. This method has been applied in various domains such as digital humanities, medical and online multimedia. I also guided the human-in-the-loop strategies as a Chief Scientist at a …

Meg is a lead at Kaggle with a focus on datasets, models, competitions, evaluation, benchmarks, open source, developer tooling, and community. Her academic background is in linguistics. She has Master's degrees from UCLA and North Carolina State University where she studied sociophonetics and language variation. She is a co-chair for the 2025 NeurIPS Datasets & Benchmarks track.

Alessandra Tosi is a Senior Scientist at Mind Foundry, an Oxford University spin out company. Her research interest falls in the area of probabilistic models, probabilistic geometries, Bayesian Optimisation and model interpretability. Alessandra focuses on how to integrate cutting edge AI solution into the business process in order to enhance human-AI interactions and Responsible AI solution.

I am the Director of Artificial Intelligence and Machine Learning at New York City's Office of Technology and Innovation. I am responsible for work related to the city's AI Action Plan, being New York City's public commitment to lead the responsible use of innovative artificial intelligence (AI) technology, particularly at the level of local governments. I bring my years of experience in global AI governance and AI risk management to the role, having previously founded Responsible AI LLC, a startup, and expertise in ML for financial services and academic research.

I remain active in academic research and am published in leading venues. I am currently an Ethics Chair of the NeurIPS machine learning conference and Area Chair for the ACM FAccT conference on Fairness, Accountability and Transparency.

Hima Lakkaraju is an Assistant Professor at Harvard University focusing on explainability, fairness, and robustness of machine learning models. She has also been working with various domain experts in criminal justice and healthcare to understand the real world implications of explainable and fair ML. Hima has recently been named one of the 35 innovators under 35 by MIT Tech Review, and has received best paper awards at SIAM International Conference on Data Mining (SDM) and INFORMS. She has given invited workshop talks at ICML, NeurIPS, AAAI, and CVPR, and her research has also been covered by various popular media outlets including the New York Times, MIT Tech Review, TIME, and Forbes. For more information, please visit: https://himalakkaraju.github.io/

Luba Elliott is a curator, artist and researcher specialising in artificial intelligence in the creative industries. She is currently working to educate and engage the broader public about the latest developments in creative AI through monthly meetups, talks and tech demonstrations. As curator, she organised workshops and exhibitions on art and AI for The Photographers’ Gallery, the Leverhulme Centre for the Future of Intelligence and Google. Prior to that, she worked in start-ups, including the art collector database Larry’s List. She obtained her undergraduate degree in Modern Languages at the University of Cambridge and has a certificate in Design Thinking from the Hasso-Plattner-Institute D-school in Potsdam.

Priya Prakash is a designer-inventor and CPTO of D4SC (Design for Social Change), named by the Financial Times as one of the top 3 in UK Tech. A graduate of NID and RCA, she won an Apple Design Award and holds patents for BBC iPlayer and Nokia Asha phones. She’s shipped industry firsts for BBC, Samsung, HP, and EE - from repairable phones, smart homes to making Elle UK online and launching London’s first digital lifestyle showroom, Digital Wellbeing Labs. A cycling accident led to found Changify, a people-powered intelligence platform for cities that won Shanghai’s Open Data award and was recognised in the Queen’s Honours. At Smartly and HERE Technologies, she defined the categories Intelligent Creative and Location Intelligence, recognised by Forrester. She’s now building - NatureOS, a nature based autopoietic system for climate using LLMs, RAG and agentic AI. Priya advises PE and VC firms on scaling portfolio companies, is a trustee at D&AD, a judge for Cannes Lions and The One Show, and Creative AI Co-Chair at NeurIPS 2025. She plays basketball and forages in nature.

Lam M. Nguyen is a Staff Research Scientist at IBM Research, Thomas J. Watson Research Center working in the intersection of Optimization and Machine Learning/Deep Learning. He is also the PI of ongoing MIT-IBM Watson AI Lab projects. Dr. Nguyen received his B.S. degree in Applied Mathematics and Computer Science from Lomonosov Moscow State University in 2008; M.B.A. degree from McNeese State University in 2013; and Ph.D. degree in Industrial and Systems Engineering from Lehigh University in 2018. Dr. Nguyen has extensive research experience in optimization for machine learning problems. He has published his work mainly in top AI/ML and Optimization publication venues, including ICML, NeurIPS, ICLR, AAAI, AISTATS, Journal of Machine Learning Research, and Mathematical Programming. He has been serving as an Action/Associate Editor for Journal of Machine Learning Research, Machine Learning, Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, and Journal of Optimization Theory and Applications; an Area Chair for ICML, NeurIPS, ICLR, AAAI, CVPR, UAI, and AISTATS conferences. His current research interests include design and analysis of learning algorithms, optimization for representation learning, dynamical systems for machine learning, federated learning, reinforcement learning, time series, and trustworthy/explainable AI.

Jean Kossaifi leads research at NVIDIA in the field of AI for Scientific Simulation, where he advances new algorithmic paradigms to solve complex physics-based problems. His core research focuses on fundamental algorithms, including combining tensor methods with deep learning, to develop efficient and powerful neural architectures. To help democratize advanced computational techniques and accelerate scientific discovery, he created two widely used open-source libraries: TensorLy, for tensor methods, and NeuralOperator, for scientific machine learning. Prior to NVIDIA, Jean was a founding member of the Samsung AI Center in Cambridge. His academic foundation includes a French Engineering Diploma in Mathematics, Computer Science, and Finance and a BSc in advanced mathematics. Jean then completed my PhD in Artificial Intelligence at Imperial College London. For more on his work, including his publications and open-source projects, please visit his personal website and Google Scholar profile.

Alex Lu is a Senior Researcher at Microsoft Research New England, in the BioML group. His research focuses on machine learning methods that enable biologists to discover new hypotheses from big biological datasets. Facets of his research program include self-supervised representation learning towards the goal of characterizing previously unknown classes or patterns, foundation models towards the goal of making machine learning accessible to biologists working with more restricted datasets, and robustness and domain generalization to ensure that methods detect biological signal and not technical confounders.

She is a postdoctoral researcher in the AutoML group at Eindhoven University of Technology, contributing to the OpenEuroLLM project. Her current focus is on designing scalable model architectures for LLMs and efficient post-training techniques. She received her Ph.D. in machine learning from Halmstad University, Sweden, where she was part of the Center for Applied and Intelligent Systems Research (CAISR). Her doctoral research centered on meta-learning with the goal of improving generalization across diverse tasks, especially in low-data scenarios. Her broader research interests include few-shot learning, continual learning, self-supervised learning, and in-context learning. She is passionate about advancing methods that push the boundaries of efficient, generalizable, and scalable AI.

Dr. Tao Qin (秦涛) is a Senior Principal Researcher/Manager at Microsoft Research AI4Science. His research fields encompass deep learning (with applications to machine translation, speech synthesis, healthcare, scientific discovery), and reinforcement learning (with applications to games and real-world problems). Recently, he has been concentrating on AI for science, specifically in areas such as molecular modelling and design, drug discovery, biochemistry, and more. He holds both a PhD and Bachelor's degree from Tsinghua University. Additionally, he is a senior member of ACM and IEEE, and serves as an Adjunct Professor (PhD advisor) at the University of Science and Technology of China.

Under his leadership, his team has made significant achievements. They helped Microsoft reach human parity in Chinese-English machine translation in 2018, clinched first place for eight translation tasks in WMT 2019, and developed the world's best Mahjong AI, named Suphx, which achieved a 10 DAN ranking on the Tenhou platform in mid-2019.

Leobardo Morales

first year PhD Student on Computer Science Neurisp first-time attendance I have been chair of many AI Conferences in Mexico, good in fundraising. I would like to help in the conference, want to be volunteer LatinX affinity group Part of the board of Mexican Society in Artificial Intelligence https://smia.mx/smiaMesaDirectiva.php

David Rolnick is an Assistant Professor and Canada CIFAR AI Chair in the School of Computer Science at McGill University and at Mila – Quebec AI Institute. He is a Co-founder and Chair of Climate Change AI and serves as Scientific Co-director of Sustainability in the Digital Age and co-lead of the Global Center on AI and Biodiversity Change (ABC). Dr. Rolnick is a Sloan Research Fellow and an AI2050 Early Career Fellow and was named to the MIT Technology Review’s 2021 list of “35 Innovators Under 35” for his work in building the field of AI and climate change. He received his Ph.D. in Applied Mathematics from MIT and is a former Fulbright Scholar, NSF Graduate Research Fellow, and NSF Mathematical Sciences Postdoctoral Research Fellow.

Hi, I’m Jonas. I am a research group leader in Tübingen, where I’m building a group for safety- & efficiency- aligned learning (🦭). Before this, I’ve spent time at the University of Maryland and the University of Siegen.

I am mostly interested in questions of safety and efficiency in modern machine learning. There are a number of fundamental machine learning questions that come up in these topics that we still do not understand well. In safety, examples are questions about the principles of data poisoning, the subtleties of water-marking for generative models, privacy questions in federated learning, or adversarial attacks against large language models. Can we ever make these models “safe”, and how do we define this? Are there feasible technical solutions that reduce harm?

Further, I am interested in questions about the efficiency of modern AI systems, especially for large language models. How efficient can we make these systems, can we train strong models with little compute? Can we extend the capabilities of language models with recursive computation? How do efficiency modifications impact the safety of these models?

Miguel González Mendoza holds a PhD degree and a Postdoc in Artificial Intelligence from INSA and LAAS-CNRS Toulouse, France, in 2003 and 2004 respectively. Since 2004 he works as research professor at Tecnologico de Monterrey, Mexico.
Miguel González Mendoza’s research activities are focused on machine learning, semantic web and big data applications, areas in which has supervised 9 PhD and 21 MSc. Theses, published more than 100 peer reviewed scientific publications, participated and conducted more than 20 national (CONACYT founded) and international (European founded) research and innovation projects, and chaired 4 international Congresses. President of the Mexican Society for Artificial Intelligence (2017-2018), Member of the Mexican National Research System (SNI) rank II (Jan 2016), member since 2006. Head of the Graduate Programs on Computer Sciences at Tecnologico de Monterrey, Mexico 2005-2016. Invited as Young Scientist at the World Economic Forum for New Champions in Tianjin China in September 2012.

I am a recent PhD graduate in Information Systems and a Bachelor's degree in Computer Science. I have 5 years of work experience in industry and academia. My doctoral research in sociotechnical computer science has cultivated me as a scientist dedicated to devising pragmatic solutions and possessing a utilitarian perspective. I am enthusiastic about volunteering for causes related to race and gender equality. I am driven by a desire to continually enhance my skills, make substantial contributions to AI for good initiatives, and advance professionally within a forward-thinking company.