The Speakers

Cliff Young is a software engineer in Google Research, where he works on codesign for deep learning accelerators. He is one of the designers of Google’s Tensor Processing Unit (TPU) and one of the founders of the MLPerf benchmark. Previously, Cliff built special-purpose supercomputers for molecular dynamics at D. E. Shaw Research and was a Member of Technical Staff at Bell Labs. Cliff holds AB, MS, and PhD degrees in computer science from Harvard University. Cliff is a member of ACM and IEEE.

Gintare Karolina Dziugaite is a Lead Research Scientist at Element AI, a ServiceNow company. She is also an associate member at Mila, the Quebec AI Institute. Her research combines theoretical and empirical approaches to understanding deep learning, with a focus on generalization and network compression. Before joining Element AI, she obtained her Ph.D. in machine learning from the University of Cambridge, under the supervision of Zoubin Ghahramani. Prior to that, she studied Mathematics at the University of Warwick and read Part III in Mathematics at the University of Cambridge, receiving a Masters of Advanced Study (MASt) in Applied Mathematics. In 2020, Karolina was a member of the Institute for Advanced Study,, participating in the special year on Optimization, Statistics, and Theoretical Machine Learning. In 2019, she was a Simons Fellow during the Foundations of Deep Learning program at the Simons Institute for the Theory of Computing at the University of Berkeley from programs. She was also a long-term participant at the Simons Institute in 2017 and 2020 during programs on theoretical machine learning and interpretable machine learning.

Rosanne Liu is a research scientist in Google Brain, and co-founder and executive director of ML Collective, a nonprofit organization for open collaboration and accessible mentorship. Before that she was a founding member of Uber AI. She has published research at NeurIPS, ICLR, ICML, Science and other top venues, and her work has been covered by WIRED, MIT Tech Review, Fortune and others. She obtained her PhD in Computer Science at Northwestern University; while at school she used neural networks to help discover novel materials, and to optimize fuel efficiency in hybrid vehicles. Outside of research, she supports underrepresented communities, and organizes symposiums, workshops, and a weekly reading group “Deep Learning: Classics and Trends” since 2018. She serves as the Diversity Equity & Inclusion co-chair of ICLR 2022.

Torsten is a Professor of Computer Science at ETH Zürich, Switzerland. He is also a key member of the Message Passing Interface (MPI) Forum where he chairs the "Collective Operations and Topologies" working group. His research interests revolve around the central topic of "Performance-centric System Design" and include scalable networks, parallel programming techniques, and performance modeling. Torsten won best paper awards at the ACM/IEEE Supercomputing Conference SC10, SC13, SC14, SC19, EuroMPI'13, HPDC'15, HPDC'16, IPDPS'15, and other conferences. He published numerous peer-reviewed scientific conference and journal articles and authored chapters of the MPI-2.2 and MPI-3.0 standards. He received the Gordon Bell Prize, the Latsis prize of ETH Zurich, as well as ERC starting and consolidator grants. Additional information about Torsten can be found on his homepage at htor.inf.ethz.ch.

Friedemann Zenke is a junior group leader in computational neuroscience at the Friedrich Miescher Institute for Biomedical Research (FMI) in Basel, Switzerland. He is broadly interested in information processing in biological neural networks, focusing on learning and plasticity, continual learning, and spiking neural networks. His group combines both analytical and computational approaches from computational neuroscience, neuromorphic engineering, and machine learning. Friedemann studied physics at the University of Bonn, Germany, and the Australian National University in Canberra, Australia, initially focusing on experimental hadron physics. He then shifted to computational neuroscience for his Ph.D. with Wulfram Gerstner at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, where he worked on the theory of synaptic and homeostatic plasticity in spiking neural networks. Subsequently, Friedemann joined Surya Ganguli’s group at Stanford as a post-doc to study the role of complex synaptic dynamics as a remedy for catastrophic forgetting in deep neural networks. Later he moved to the University of Oxford as a Sir Henry Wellcome fellow, where he worked with Tim Vogels. During this time, he further developed functionally inspired learning rules for spiking neural networks.

Mitchell is a PhD student at the University of Washington interested in understanding and improving neural networks. Recent research has focused on neural network sub-networks, sub-spaces, and robustness. Mitchell is also a member of ML Collective, and previously worked at the Allen Institute for Artificial Intelligence.