Danielle Belgrave is a VP of AI/ML at GSK, where she leads the AI/ML Clinical Development team. Her experience spans conducting machine learning research and leading teams across academia and industry focused on scientific discovery and personalizing interventions in health. She has previously worked at Google DeepMind, Microsoft Research Cambridge, UK and Imperial College London. She has a B.Sc. in business mathematics and statistics from the London School of Economics and a master’s degree in statistics from University College London. Her PhD was at the University of Manchester in Machine Learning for Healthcare. Danielle is General Chair of NeurIPS 2025. She has previously served as Program Chair of NeurIPS 2022 and 2024 and Senior Program Chair of WiML 2017.

Maria Brbic (https://brbiclab.epfl.ch/) is an Assistant Professor of Computer Science and of Life Sciences at EPFL. Prior to joining EPFL, Maria was a postdoctoral fellow at Stanford University, Department of Computer Science. Her research interests are in the development of new AI methods that advance biomedical research. Among other recognitions and awards, she was named a Rising Star in EECS by MIT in 2021, received an Early Career Award by SIB in 2023, the SNSF Starting Grant in 2024 and was named a CIFAR Fellow in the Multiscale Human Program in 2025.

Ming C. Lin received her B.S., M.S., Ph.D. degrees in Electrical Engineering and Computer Science from the University of California, Berkeley. She is a Distinguished University Professor, Dr. Barry Mersky and Capital One E-Nnovate Endowed Professor, and former Elizabeth Stevinson Iribe Chair of Computer Science at the University of Maryland at College Park (2018-2022).  She is also John R. & Louise S. Parker Distinguished Professor, Emerita of Computer Science at the University of North Carolina (UNC) - Chapel Hill.  She is also an Amazon Scholar. She received several honors and awards, including NSF Young Faculty Career Award, Honda Research Initiation Award, UNC/IBM Junior Faculty Development Award and Hettleman Award for Scholarly Achievements, Beverly W. Long Distinguished Term Professor, IEEE VGTC VR Technical Achievement Award, Washington Academy of Sciences Distinguished Career Award, and many best paper awards. She is a Fellow of National Academy of Inventors, ACM, IEEE, Euro graphics, ACM SIGGRAPH Academy, and IEEE VR Academy.

Jenn Wortman Vaughan is a Senior Principal Research Manager at Microsoft Research, New York City, where she studies responsible AI with a focus on transparency, fairness, evaluation, and human-AI interaction. Originally trained in machine learning and algorithmic economics, she now often draws on methods from human-computer interaction to investigate how people engage with AI systems. Before joining MSR in 2012, Jenn completed her Ph.D. at the University of Pennsylvania and was an Assistant Professor of Computer Science at UCLA and a Computing Innovation Fellow at Harvard. Her work has been recognized with the NSF CAREER Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), and Penn’s Rubinoff dissertation award. Beyond her research, Jenn has helped shape the field through her mentorship of junior researchers and her leadership in roles including Program Co-Chair of NeurIPS and FAccT. But what she is most proud of is co-founding the Workshop on Women in Machine Learning (WiML), held annually since 2006.

Maartje Ter Hoeve is a Machine Learning Researcher at Apple's Machine Learning Research (MLR) team, led by Samy Bengio, in Cupertino, California. She obtained her PhD from the University of Amsterdam, where she worked with Maarten de Rijke and Julia Kiseleva. She completed her M.Sc. in Artificial Intelligence, and my BA in Linguistics, at the University of Amsterdam and partially at the University of Edinburgh.

She is also broadly interested in Human-Centered NLP, that is, centering the design and development of natural language technology around humans. Her research is mostly motivated from two angles, roughly summarized as: (i) who are the users of NLP systems, and what do they want?, and (ii) how can we use our knowledge of human language processing and acquisition when developing these NLP systems? 

Shweta Khushu is a Manager on the Al Engineering team at Vector Institute. In this role, she leads and manages the development of Vector’s innovative AI software, machine learning reference implementations, works with researchers to identify promising AI technologies, and helps build a diverse team of AI developers. In her previous role, Shweta held the position of Engineering Manager at SkySpecs Inc, where her team focused on applying Computer Vision techniques to solve real-world problems challenging the wind energy space. Additionally, she is a board member of Women in Machine Learning (WiML), a volunteer-run organization dedicated to supporting women in the field of machine learning through various events and programs. She also serves on the Technical Advisory Committee for ITU’s AI for Good - Innovate for Impact initiative. Shweta holds a Master of Science in Electrical and Computer Engineering from the University of Michigan.

Luisa Cutillo is an Associate Professor in the Department of Statistics at the School of Mathematics, University of Leeds. She received her PhD in applied mathematics from the University of Naples “Federico II” and her undergraduate degree in Mathematics from the University of Campania “Luigi Vanvitelli”, Italy. After her PhD she was a Lecturer (ricercatore) at the University of Naples “Parthenope” in the excellence department of Quantitative Methods and at the same time member of the Bioinformatics Core of the Telethon Institute of Genetics and Medicine in Naples. In 2015, she was awarded a 2 years Marie Skłodowska-Curie Individual Fellowship that she carried out in the research group of Professor Neil Lawrence both at the Department of Computer Science and Neuroscience (SITraN), University of Sheffield. She has been an academic at the University of Leeds since 2018 where her main role is to be Programme Manager for the following Masters programmes in the School of Mathematics:

• Data Science and Analytics M.Sc.

• Statistics M.Sc.

• Statistics with Applications to Finance M.Sc.

Hanna Megan Wallach is a computational social scientist and partner research manager at Microsoft Research. Her work makes use of machine learning models to study the dynamics of social processes. Her current research focuses on issues of fairness, accountability, transparency, and ethics as they relate to AI and machine learning. 

Sidhika Balachndar is a fourth-year Computer Science PhD student at UC Berkeley. She works on problems at the intersection of machine learning, fairness, and healthcare. She is advised by Emma Pierson and Nikhil Garg fortunate to be supported by the NSF GRFP.

She is interested in creating models in settings where outcome data is missing or biased (e.g., ICLR 2024). In particular, her research has focused on the following questions: What are the challenges of using outcome data collected from human decision makers or crowdsourced platforms? What identification approaches can we use to create models in these settings? Can we leverage external, domain specific information or data?

Marija Nedeljković is a recent MPhil Data Science graduate who’s passionate about data analysis and machine learning. Always curious, forever learning, and excited to take on whatever challenge comes next.

Diane Oyen develops machine learning methods that help scientists to understand complex data.

Specialties: Machine learning, probabilistic graphical models, scientific data mining, transfer learning, active learning, Bayesian networks, artificial intelligence, bioinformatics, interactive intelligent systems

Sarah loves leading early-stage technology startups and small and brilliant R&D teams. She wants to make exciting products that people will use. She has extensive experience bringing cross-functional teams together to solve challenging problems and create unique products.