Marzyeh Ghassemi
Associate Professor, MIT
Dr. Marzyeh Ghassemi is an Associate 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. Previously, she was a Visiting Researcher with Alphabet’s Verily and an Assistant Professor at University of Toronto. Prior to her PhD in Computer Science at MIT, she received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University. Professor Ghassemi has previously served as a NeurIPS Workshop Co-Chair and General Chair for the ACM Conference on Health, Inference and Learning (CHIL). She also founded the non-profit Association for Health Learning and Inference. Professor Ghassemi has published across computer science and clinical venues, including NeurIPS, KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, Nature Medicine, Nature Translational Psychiatry, and Critical Care. Her work has been featured in popular press such as Fortune, MIT News, NVIDIA, and The Huffington Post.

Shakir Mohamed
Research Director - AI, Science and Society, Google DeepMind
Shakir Mohamed is a scientist and engineer working at the intersection of statistical machine learning and artificial intelligence. His research blends multiple disciplines, guided by three pillars: probabilistic foundations of intelligence, addressing global challenges, and transformative social impact. He develops methods for probabilistic reasoning and agent-based decision-making, with applications in healthcare, the environment, and initiatives that foster diversity, responsibility, and freedom. He also writes about the interplay of computational, epistemological, and social paradigms on his blog: blog.shakirm.com. Shakir is a Director of Research at Google DeepMind in London, where he has been since its early startup days in 2013. He is a founder and Chair of the Board of Trustees of the Deep Learning Indaba, a non-profit dedicated to strengthening African AI and ML communities. He also holds academic positions as an Associate Fellow at the Leverhulme Centre for the Future of Intelligence (University of Cambridge) and Honorary Professor at University College London (UCL). He has played key organizational roles in leading ML conferences: Programme Chair for DALI 2019 and ICLR 2019, Senior Programme Chair for ICLR 2020, General Chair for ICLR 2021, and co-General Chair for NeurIPS 2022. He serves on the boards of ICML, ICLR, and NeurIPS, and is a member of the Royal Society’s Diversity and Inclusion Committee (2020–2025). Previously, he was a Junior Research Fellow at CIFAR in the Neural Computation and Adaptive Perception program, based at UBC with Nando de Freitas. He earned his PhD under Zoubin Ghahramani at the University of Cambridge as a Commonwealth Scholar and completed his undergraduate studies in Electrical and Information Engineering at the University of the Witwatersrand, Johannesburg, South Africa.

Suleiman Ali Khan
Sr. Applied Scientist, Amazon, Seattle
Suleiman is a Sr. Applied Scientist at Amazon, and the science lead for Amazon Catalog's flagship generative AI. He is a fellow at Turku University of Applied Sciences where he is a member of the steering committee for Privacy Preserving ML. He did his PhD in Bayesian Machine Learning with Prof. Samuel Kaski in 2014 from Aalto University, Finland, and shapes his research interests around generative models and federated learning. He has published 50+ peer-reviewed articles with 3000+ citations, including Nature Biotechnology, ICML, AISTATS, KDD, ECML, Lancet Oncology, and Machine Learning. He is a reviewer at IEEE TPAMI, TSP, JMBI, Biostatistics, Scientific Reports and ISMB. He has organized multiple scientific meetings, 2x European Bioinformatics meetings, and 11x ML workshops at Amazon.

Ahmed Youssef
Ph.D. Candidate, University of Cincinnati
Ahmed Youssef is a Ph.D. Candidate in the High Energy Theory Group at the University of Cincinnati, where he applies machine learning to tackle challenges in particle physics. Supported by the Department of Energy (DOE) and the National Science Foundation (NSF), his research focuses on improving simulation accuracy and efficiency in modeling particle collisions. Ahmed collaborates with researchers affiliated with leading institutions, including Berkeley, Fermilab, CERN, and MIT, and has presented at NeurIPS and various international venues. He served as a convener at Division of Particles & Fields (DPF) and Phenomenology Symposium (Pheno) 2024, co-organized the Muslim in ML Affinity Workshop at NeurIPS 2024, and reviewed submissions for the NeurIPS ML and Physical Science Workshop. He received several awards for his research including the URC Fellowship, Lab2Market Fellowship, and the Deutschland Scholarship.

Gasser Elbanna
PhD Candidate, Harvard University
Gasser Elbanna is a PhD Candidate in Speech and Hearing, Bioscience and Technology program at Harvard University. He currently works at the Laboratory for Computational Audition at MIT where he studies the underlying perceptual and neural mechanisms of speech perception using artificial neural networks. Gasser holds a masters degree in Neuroscience and Neuro-engineering from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He completed his undergraduate degree in Systems and Biomedical Engineering at Cairo University, Egypt. During his masters, Gasser was awarded the Bertarelli Fellowship to work as a graduate research intern at MIT and Harvard Medical School working in the Senseable Intelligence Group at the McGovern Institute for Brain and Cognitive Sciences.

Ehsaneddin Asgari
Research Faculty, Qatar Computing Research Institute
Ehsanoddin Asgari is a scientist at the Qatar Computing Research Institute (QCRI), working on multilingual and multimodal language processing, with applications spanning the processing of world's low-resource natural languages to language modeling of biological sequences and chemical compounds. Ehsan completed his Ph.D. at the University of California, Berkeley, and his master's from the École Polytechnique Fédérale de Lausanne (EPFL). Before joining QCRI, he was an NLP technical lead at the Volkswagen Group at Data:Lab and conducted part-time postdoctoral research at the Helmholtz Research Center for Infection Research. His previous roles include research positions at MIT's CSAIL, MIT Brain and Cognitive Sciences, the NLP group at LMU Munich, ABB Research, and the University of Illinois at Urbana-Champaign's Singapore research center (ADSC)

Azmine Toushik Wasi
Graduate, Shahjalal University of Science and Technology
Azmine Toushik Wasi is a recent engineering graduate from Bangladesh, working on Biomedical AI, Generative AI, LLMs, Reasoning, Graph Neural Networks and interdisciplinary research in Humans, AI and Language with publications in top venues like ICLR, WWW, COLING, DASFAA, ACCV, and CSCW, along with workshops at NeurIPS, AAAI, ACL, EMNLP, ICML, and CHI. He collaborates with leading institutions, including Harverd, HYU, KSU, Mila Quebec, CMU, and Cohere Labs, on LLMs, molecular ML, MedAI, and NLP-HCI. He also serves as a reviewer for almost all top ML conferences and workshops. He is also a Kaggle Grandmaster and has 3 years of experience in AI-driven product/content automation and product and project management.

Rian Adam Rajagede
PhD Student, University of Central Florida
Rian is a PhD student at the University of Central Florida, researching Secure and Reliable Machine Learning. He is also a Lecturer at Universitas Islam Indonesia (currently inactive during PhD study). He has various ML project experience in Indonesia including developing an Automatic Essay Scoring system for the Education Assessment Center at the Ministry of Education and Culture and creating deep learning-based detection models for Nestle Indonesia's on-shelf product availability monitoring.

Nasik Muhammad Nafi
Postdoctoral Research Associate at Oak Ridge National Lab
Nasik Muhammad Nafi is a Postdoctoral Research Associate at Oak Ridge National Laboratory (ORNL). He completed his Ph.D. in Computer Science at Kansas State University under the supervision of Dr. William Hsu. His research focuses on deep reinforcement learning and intelligent visual perception, aiming to build agents that learn from visual input and generalize across task variations through robust representation learning. Nasik’s broader interests include meta-learning, transfer learning, and sequential decision-making in complex environments. His prior experience includes research assistantship at the Knowledge Discovery in Databases (KDD) lab, and internships at DEKA Research and Development and C2FO. He holds an MS from Kansas State University and a B.Sc. in Computer Science and Engineering from BUET. His applied research spans agriculture, sports safety, and disaster response.

Yousra Farhani
PhD Student, INSA Lyon and the École Supérieure d’Informatique, ESI Algiers
Yousra Farhani is a Quantum Machine Learning and Optimisation researcher affiliated with INSA Lyon and the École Supérieure d’Informatique, ESI Algiers. Recipient of the Quantum Rising Star Award 2024 for Women in Quantum and the prestigious Arab Young Pioneers Award in Quantum Computing.

Muhammad Irfan Khan
Faculty, Turku University of Applied Sciences
Researcher in Health-Tech lab with research focus on Image analysis in distributed optimization, particularly on brain tumor segmentation in federated settings, Publications in MICCAI, ICONIP. Reviewer for MICCAI, ICONIP. Part of organizing team in 3rd Musiml in NeurIPS 2024.

Kamran Soomro
Faculty, UWE Bristol, UK
Associate Professor (Reader) of Artificial Intelligence in the School of Computing and Creative Technologies at UWE Bristol, UK. Dr Soomro has over 15 years of experience in research and academia. His research interests include the use of ICT technologies for smart cities and urban management, knowledge management, artificial intelligence, big data, and natural language processing. Dr Soomro has previously co-organized MusiML 2024 as well as co-chaired the Fourth and Fifth International Workshops on Smart City Clouds: Systems, Technologies and Applications in 2017 and 2018. Dr Soomro will be participating online.