Russ Salakhutdinov is a UPMC Professor of Computer Science in the Department of Machine Learning at CMU. He received his PhD in computer science from the University of Toronto. After spending two post-doctoral years at MIT, he joined the University of Toronto and later moved to CMU. His primary interests lie in deep learning, reinforcement learning, embodied AI, and large-scale optimisation. He is an action editor of the Journal of Machine Learning Research, served as a director of AI research at Apple, served on the senior programme committee of several top-tier learning conferences including NeurIPS and ICML, was a program co-chair for ICML 2019, and is a general chair for ICML 2024. He has authored/co-authored over 200 research papers and his work has received over 200,000 citations according to Google Scholar. He is an Alfred P. Sloan Research Fellow, Microsoft Research Faculty Fellow, a recipient of the Early Researcher Award, Google Faculty Award, and Nvidia's Pioneers of AI award.

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, and is a Turing AI Fellow leading work on trustworthy Machine Learning (ML). He is a Principal Research Fellow in ML at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. Previously, Adrian held senior roles in finance. He received a PhD in computer science from Columbia University, and an undergraduate degree in mathematics from Trinity College, Cambridge.

Dr. Nuria Oliver is co-founder and Director of the “Institute of Humanity-centric AI (Alicante ELLIS Unit); Chief Data Scientist at Data-Pop Alliance, Chief Scientific Advisor at the Vodafone Institute and co-founder and vicepresident of ELLIS (The European Laboratory for Learning and Intelligent Systems). She is a Telecommunications Engineer from the UPM and holds a PhD in Artificial Intelligence from the Massachusetts Institute of Technology (MIT). She also holds an honorary PhD from the Univ Miguel Hernandez.

She has over 25 years of research experience in the areas of human behavior modeling and prediction from data and human-computer interaction. She has been a researcher at Microsoft Research (Redmond, WA), the first female Scientific Director at Telefonica R&D for over 8 years and the first Director of Research in Data Science at Vodafone globally (2017-2019). Between March 2020 and April 2022, she served as Commissioner for the President of the Valencian Region on AI Strategy and Data Science to fight COVID-19. In that role, she co-led ValenciaIA4COVID, the winning team of the 500k XPRIZE Pandemic Response Challenge. Their work was featured in WIRED, Politico and MSNBC, among other media.

As an advisor, Dr. Oliver has been/is an advisor to several governments and …

Andrew Gordon Wilson is faculty in the Courant Institute and Center for Data Science at NYU. His interests include probabilistic modelling, Gaussian processes, Bayesian statistics, physics inspired machine learning, and loss surfaces and generalization in deep learning. His webpage is https://cims.nyu.edu/~andrewgw.

Dr. Bo Li is an assistant professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. She is the recipient of the IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, AI’s 10 to Watch, NSF CAREER Award, MIT Technology Review TR-35 Award, Dean's Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty Award, Intel Rising Star award, Symantec Research Labs Fellowship, Rising Star Award, Research Awards from Tech companies such as Amazon, Facebook, Intel, IBM, and eBay, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, which is at the intersection of machine learning, security, privacy, and game theory. She has designed several scalable frameworks for trustworthy machine learning and privacy-preserving data publishing. Her work has been featured by major publications and media outlets such as Nature, Wired, Fortune, and New York Times.

Finale Doshi-Velez is a Gordon McKay Professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences. She completed her MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School. Her interests lie at the intersection of machine learning, healthcare, and interpretability.

Selected Additional Shinies: BECA recipient, AFOSR YIP and NSF CAREER recipient; Sloan Fellow; IEEE AI Top 10 to Watch

Jonathan is an assistant professor in the Department of Computer Science and Department of Mathematics at NUS. His research interests are in information theory, machine learning, and high-dimensional statistics, particularly their intersection. He is a holder of the Singapore National Research Foundation (NRF) fellowship and the NUS Presidential Young Professorship. Previously, he did his post-doc at EPFL and his PhD at the University of Cambridge.

Jung-Woo Ha got his BS and PhD degrees in computer science from Seoul National University in 2004 and 2015. He got the 2014 Fall semester outstanding PhD dissertation award from Computer Science Dept. of Seoul National University. He worked as a research scientist and tech lead at NAVER LABS and research head of NAVER CLOVA. Currently, he works as the head of NAVER AI Lab in NAVER Cloud. He has contributed to the AI research community as Datasets and Benchmarks Co-chair for NeurIPS and Social Co-chair for ICML 2023 and NeurIPS 2022. Also, he has joined a senior technical program committee member, such as, Area chair for NeurIPS 2023 and 2022, Area chair for ICML 2023, and Senior area chair for COLING. His research interests include large language models, generative models, multimodal representation learning and their practical applications for real-world problems. In particular, he has mainly focused on practical task definition and evaluation protocol for continual learning in various domains.

Dan Alistarh is a Professor at IST Austria. His research focuses on high-performance algorithms for machine learning, and spans from purely theoretical results to practical implementations. Before ISTA, ge was a researcher at ETH Zurich and Microsoft Research, and a Postdoctoral Associate at MIT CSAIL. He received my PhD from the EPFL.

Dr. Lauren Oakden-Rayner, a radiologist and Senior Research Fellow at the Australian Institute for Machine Learning, concentrates on medical AI safety, addressing model robustness, generalization, evaluation, ethics and fairness.

Kiri Wagstaff is serving as a AAAS Congressional Fellow in Artificial Intelligence for Senator Mark Kelly in Washington D.C. She also teaches classes at Oregon State University. She previously worked at the NASA Jet Propulsion Laboratory, where she applied machine learning for space exploration. She published "Machine Learning that Matters" at ICML 2012 and is delighted to chair the new Position Paper Track at ICML 2024. She is passionate about keeping machine learning efforts relevant to our society's needs.

Amin Karbasi is currently a chief scientist at Robust Intelligence and an associate professor (on leave) at Yale University. Before that, he was a staff research scientist at Google. He has been the recipient of the National Science Foundation (NSF) Career Award, Office of Naval Research (ONR) Young Investigator Award, Air Force Office of Scientific Research (AFOSR) Young Investigator Award, DARPA Young Faculty Award, National Academy of Engineering Grainger Award, Amazon Research Award, Nokia Bell-Labs Award, Google Faculty Research Award, Microsoft Azure Research Award, Simons Research Fellowship, and ETH Research Fellowship. His work has also been recognized with a number of paper awards, including Graphs in Biomedical Image Analysis (GRAIL), Medical Image Computing and Computer Assisted Interventions Conference (MICCAI), International Conference on Artificial Intelligence and Statistics (AISTATS), IEEE ComSoc Data Storage, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), ACM SIGMETRICS, and IEEE International Symposium on Information Theory (ISIT). His Ph.D. thesis received the Patrick Denantes Memorial Prize from the School of Computer and Communication Sciences at EPFL, Switzerland.

PhD Student @ ELLIS Alicante. GNNs and Algorithmic Fairness