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CHONGLI QIN
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Hi there! My name is Chongli Qin. My aim is to do what I can to use AI
technologies for good and reduce harm. I was previously a Senior Research
Scientist at Google DeepMind. My research is largely split between AI safety as
well as AI for sciences pushing the frontiers of methodologies for both red
teaming/adversarial attacks as well as leveraging ML techniques for sciences
such as AlphaFold. My papers have been published in major journals such as
Nature and PNAS, also spotlight papers at major ML conferences such as NeurIPS.



RESEARCH

Achieving robustness in the wild via adversarial mixing with disentangled
representations [2020], Sven Gowal, Chongli Qin, Po-Sen Huang, Taylan Cemgil,
Krishnamurthy Dvijotham, Timothy Mann, Pushmeet Kohli, Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition Efficient neural
network verification with exactness characterization [2020], Krishnamurthy Dj
Dvijotham, Robert Stanforth, Sven Gowal, Chongli Qin, Soham De, Pushmeet Kohli,
Uncertainty in artificial intelligence Improved protein structure prediction
using potentials from deep learning [2020], Andrew W Senior, Richard Evans, John
Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin
Žídek, Alexander WR Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen
Simonyan, Steve Crossan, Pushmeet Kohli, David T Jones, David Silver, Koray
Kavukcuoglu, Demis Hassabis, Nature Uncovering the limits of adversarial
training against norm-bounded adversarial examples [2020], S. Gowal, Chongli
Qin, Jonathan Uesato, Timothy Mann, Pushmeet Kohli. In Proceedings of the 1st
Conference on Myths in the Universe. Training generative adversarial networks by
solving ordinary differential equations [2020], Chongli Qin, Yan Wu, Jost Tobias
Springenberg, Andy Brock, Jeff Donahue, Timothy Lillicrap, Pushmeet Kohli.
Advances in Neural Information Processing Systems On a continuous time model of
gradient descent dynamics and instability in deep learning [2022], Mihaela
Rosca, Yan Wu, Chongli Qin, Benoit Dherin, Transactions on Machine Learning
Research Scalable verified training for provably robust image classification
[2019], Sven Gowal, Krishnamurthy Dj Dvijotham, Robert Stanforth, Rudy Bunel,
Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, Pushmeet Kohli
Proceedings of the IEEE/CVF International Conference on Computer Vision
Verification of Non-linear Specifications [2019], Chongli Qin, Brendan
O’Donoghue, Rudy Bunel, Robert Stanforth, Sven Gowal, Jonathan Uesato, Grzegorz
Swirszcz, Pushmeet Kohli International Conference on Learning Representations
Adversarial Robustness through Local Linearization [2019], Chongli Qin, James
Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi,
Soham De, Robert Stanforth, Pushmeet Kohli Advances in Neural Information
Processing Systems Power Law Tails in Phylogenetic Systems [2018], Chongli Qin,
Lucy Colwell Proceedings of the National Academy of Sciences



PUBLIC TALKS

Effective Altruism 2020: Invited talk “Ensuring Safety and Consistency in the
Age of Machine Learning”

DeepMind / UCL Deep Learning Lecture Series 2020: Guest Lecture “Responsible
Innovation”

Conference on Neural Information Processing Systems 2020: Spotlight Talk
“Training Generative Adversarial Networks by Solving Ordinary Differential
Equations”



WORKSHOPS

Continuous Time Perspective on Machine Learning

Mihaela Rosca · Chongli Qin · Julien Mairal · Marc Deisenroth.



CONTACT

Email

LinkedIn