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Jonathan Frankle
Jonathan Frankle is Chief Scientist at MosaicML, where he leads the company's research team toward the goal of developing more efficient algorithms for training neural networks. In his PhD at MIT, he empirically studies deep learning with Prof. Michael Carbin, specifically the properties of sparse networks that allow them to train effectively (his "Lottery Ticket Hypothesis"). He will be joining the faculty of the School of Engineering and Applied Sciences at Harvard in the fall of 2023 as an assistant professor. He earned his BSE and MSE in computer science at Princeton and has previously spent time at Google Brain, Facebook AI Research, Microsoft, and Georgetown Law.
Olga Saukh
Olga Saukh is associate professor and group leader Embedded Information Processing at TU Graz, Institute of Technical Informatics and a faculty member at the Complexity Science Hub Vienna. She holds a Ph.D. in Computer Science from the University of Bonn, Germany, and she did her postdoctoral training at ETH Zurich, Switzerland in the Computer Engineering and Networks Laboratory. Her research focuses on low-power sensing, embedded machine learning and edge computing. She is interested in both theoretical beauty of algorithm engineering and in solving real-world challenges, mainly in the environmental domain.
Mostafa Dehghani
Mostafa is a research scientist at Google Brain. He has been working on scaling neural networks for language, vision, and robotics. Besides large scale models, he works on improving the allocation of compute in neural networks, in particular Transformers, via adaptive and conditional computation. Mostafa obtained his Ph.D. in machine learning from the University of Amsterdam where he worked on training neural networks with imperfect supervision.
Radu Grosu
Radu Grosu is a full Professor, the Head of the Cyber-Physical-Systems Division, and the former Head of the Computer-Engineering Institute, at the Faculty of Informatics, Technische Universität Wien, Austria. Radu Grosu is also a Research Professor at the Department of Computer Science, of the State University of New York at Stony Brook, USA. The research interests of Radu Grosu include the modeling, the analysis and the control of cyber-physical systems and of biological systems. The applications focus of Radu Grosu includes smart-mobility, Industry 4.0, smart-buildings, smart-agriculture, smart-health-care, smart-cities, IoT, cardiac and neural networks, and genetic regulatory networks.
Radu Grosu is the recipient of the National Science Foundation Career Award, the State University of New York Research Foundation Promising Inventor Award, the Association for Computing Machinery Service Award, and is an elected member of the International Federation for Information Processing, Working Group 2.2.
Dan Alistarh
Dan Alistarh is a Professor at IST Austria. Previously, he was a Researcher at Microsoft Research, a Postdoctoral Associate at MIT CSAIL, and received his PhD from the EPFL. His research focuses on algorithms and data structures, with a focus on applications in machine learning and high-performance computing. He is the recipient of an ERC Starting Grant focusing on scalable machine learning, and more recently of the best paper award at the 2021 International Symposium on Distributed Computing.
Amirhossein Habibian
Amir is a senior staff researcher and manager at Qualcomm AI Research in Amsterdam. His research interest includes efficient video perception, model compression, conditional computation, and generative modeling. He obtained his Ph.D. in AI from the University of Amsterdam (in 2015) on learning multimodal video representations.
Sara Hooker
Sara Hooker is currently working on efforts to broaden access to machine learning research. Prior to this, Sara was a research scientist at Google Brain for the previous 5 years working on training models that fulfill multiple desiderata. Her main research interests gravitate towards interpretability, model compression, and security. In 2014, she founded Delta Analytics, a non-profit dedicated to bringing technical capacity to help non-profits across the world use machine learning for good.
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