Kamalika Chaudhuri is a Professor at the University of California, San Diego and a Research Scientist at FAIR in Meta. She received a Bachelor of Technology degree in Computer Science and Engineering in 2002 from Indian Institute of Technology, Kanpur, and a PhD in Computer Science from University of California at Berkeley in 2007. She received an NSF CAREER Award in 2013 and a Hellman Faculty Fellowship in 2012. She has served as the program co-chair for AISTATS 2019 and ICML 2019, and as the General Chair for ICML 2022. Kamalika’s research interests lie in trustworthy machine learning – or machine learning beyond accuracy, which includes problems such as learning from sensitive data while preserving privacy, learning under sampling bias, in the presence of an adversary, and from off-distribution data.

Preethi Lahoti is a Senior Research Scientist at Google Deepmind, driving research efforts on building safe, helpful, fair and inclusive large language models. She has worked extensively on safety modeling techniques for Gemini to align AI models and improve their safety in downstream tasks. Previously, she earned her PhD in Computer Science at the Max Planck Institute for Informatics and Saarland University in Germany, and her master’s degree in Machine Learning from Aalto University in Finland."

Fanny Yang is an Assistant Professor in the Computer Science Department (D-INFK) at ETH Zurich. Previously she was a postdoctoral Scholar at Stanford University working with John Duchi and Percy Liang and a Junior Fellow at the Institute for Theoretical Studies at ETH Zurich working with Nicolai Meinshausen. Before that, she was a PhD student at the EECS department of UC Berkeley advised by Martin Wainwright.

Asma Ghandeharioun, M.Sc., Ph.D., is a senior research scientist with the People + AI Research team at Google DeepMind. She works on aligning AI with human values through better understanding and controlling (language) models, uniquely by demystifying their inner workings and correcting collective misconceptions along the way. While her current research is mostly focused on machine learning interpretability, her previous work spans conversational AI, affective computing, digital health, and, more broadly, human-centered AI. She holds a doctorate and master’s degree from MIT and a bachelor’s degree from the Sharif University of Technology. She has been trained as a computer scientist/engineer and has research experience at MIT, Google Research, Microsoft Research, EPFL, and in collaboration with medical professionals from Harvard, renowned hospitals in the Boston area, and abroad. Her work has been published in premier peer-reviewed machine learning and digital health venues such as ICLR, NeurIPS, ICML, EMNLP, AAAI, ACII, AISTATS, Frontiers in Psychiatry, and Psychology of Well-being. She has received awards at NeurIPS and her work has been featured in Wired, Wall Street Journal, and New Scientist.

Dr. Golnoosh Farnadi is an Assistant Professor at the School of Computer Science at McGill University and an Adjunct Professor at University Montréal. She is a visiting faculty researcher at Google, a core academic member at MILA (Quebec Institute for Learning Algorithms) and holds Canada CIFAR AI chair. She is a co-director of McGill’s Collaborative for AI & Society (McCAIS), and the founder and principal investigator of the EQUAL lab at Mila/McGill University. EQUAL lab (EQuity & EQuality Using AI and Learning algorithms) is a cutting-edge research laboratory dedicated to advancing the fields of algorithmic fairness and responsible AI. With a mission to promote equity and equality in AI systems, Equal Lab harnesses the power of advanced learning algorithms and AI technologies to tackle the pressing issues surrounding bias and discrimination in AI and machine learning models.

Luciana Benotti received a PhD in Computing from the Université de Lorraine in France for her work on Natural Language Processing (NLP) at the Institut National de Recherche en Informatique et en Automatique (INRIA). She also has an Erasmus Mundus joint Masters degree in Computational Logics from the University of Bolzano in Italy and the Universidad Politécnica de Madrid, and a Masters in Computer Science from the Universidad del Comahue in Argentina.  She served as the chair of the executive board of the Association for Computational Linguistics until January 2024 and is currently a member of the board. He has been an invited professor at the University of Stanford, US; Imperial College, London; INRIA, France; and CIMEC, Italy. She is currently an Associate Professor in Computer Science at the Universidad Nacional de Córdoba and a researcher at the National Research Council in Argentina. She works with the NGOs Fundación Vía Libre to investigate NLP technology from a human rights perspective. Her research focuses on the areas of misunderstandings with conversational agents, natural language processing, error and bias analysis in language models, and the social impact of artificial intelligence.

Emma Pierson is the Andrew H. and Ann R. Tisch Assistant Professor at Cornell Tech, the Jacobs Technion-Cornell Institute, and the Technion, and an incoming assistant professor at Berkeley in EECS affiliated with the Computational Precision Health program. She develops data science and machine learning methods to study inequality and healthcare. Her work has been recognized by best paper, poster, and talk awards, an NSF CAREER award, a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, MIT Technology Review 35 Innovators Under 35, Forbes 30 Under 30 in Science, AI2050 Early Career Fellowship, and Samsung AI Researcher of the Year. Her research has been published at venues including ICML, KDD, WWW, Nature, and Nature Medicine, and she has also written for The New York Times, FiveThirtyEight, Wired, and various other publications.

Irina Rish is a Full Professor at the Université de Montréal (UdeM), where she leads the Autonomous AI Lab, and a core faculty member of MILA - Quebec AI Institute. She holds Canada Excellence Research Chair (CERC) and a CIFAR Chair. Dr. Rish completed her MSc and PhD in AI at the University of California, Irvine, and also holds an MSc in Applied Mathematics from Moscow Gubkin Institute. Irina is the recipient of the INCITE compute grant by the US Department of Energy and currently leads an INCITE project on Scalable Foundation Models on Summit & Frontier supercomputers at the Oak Ridge Leadership Computing Facility, focusing on developing open-source large-scale AI models (a.k.a. Foundation Models). She is also a co-founder and the Chief Science Officer of nolano.ai, a company focused on both development of large-scale foundation models and providing a range of model services, including  compression, inference acceleration, and evals. 

Negar Rostamzadeh is a Staff Research Scientist at Google Research, where she studies the intersection of Fundamental Machine Learning research and Responsible AI. Prior to that, Negar was a research scientist at Element AI, where she worked on efficient sampling and labeling approaches in a variety of multimedia and computer vision problems. Negar obtained her PhD from the University of Trento, where she was advised by Dr. Nicu Sebe. Her main area of research during her PhD was on large scale video understanding problems. During her PhD, she spent two years at MILA, where she worked on attention mechanisms in videos, video captioning and generation under the supervision of Dr. Aaron Courville. Negar was a co-founder of Women in Deep Learning (WiDL) in 2016, and a co-organizer of WiML, WiCV and WiDL in 2017. She served as a DEI chair for ICCV 2021. She was also co-founder and chair of multiple workshop series in CVPR, such as Computer Vision for Fashion, Art and Design, Ethical Considerations in Creative Applications of Computer Vision, and Responsible Generative AI, as well as Science meets Engineering of Deep Learning workshop series at ICLR, NeurIPS and FAccT.