Michael I. Jordan

About

Michael I. Jordan is Senior Researcher at Inria, Paris, and the Pehong Chen Distinguished Professor Emeritus in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters in Mathematics from Arizona State University and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive, biological, and social sciences. Prof. Jordan is a member of several prestigious academies including the National Academy of Sciences, the National Academy of Engineering, the American Academy of Arts and Sciences, the Royal Society as a Foreign Member, and the Chinese Academy of Sciences as a Foreign Member. He is also a Fellow of the American Association for the Advancement of Science. Throughout his career, he has been recognized with numerous awards such as the BBVA Foundation Frontiers of Knowledge Award in Information and Communication Technologies in 2025, the inaugural World Laureates Association Prize in 2022, the Ulf Grenander Prize from the American Mathematical Society in 2021, the IEEE John von Neumann Medal in 2020, the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015, and the ACM/AAAI Allen Newell Award in 2009. He has delivered several distinguished lectures including the Inaugural IMS Grace Wahba Lecture in…

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Mathematics
• Mathematical optimization
• Applied mathematics
• Data Mining
• Algorithm
• Geology
• Mathematical analysis

Selected publications

• Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

  Molecular Systems Biology · 2021 · 661 citations

  As the number of single-cell transcriptomics datasets grows, the natural next step is to integrate the accumulating data to achieve a common ontology of cell types and states. However, it is not straightforward to compare gene expression levels across datasets and to automatically assign cell type labels in a new dataset based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint re…

  DOI

• HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

  2022 IEEE Symposium on Security and Privacy (SP) · 2020 · 596 citations

  The goal of a decision-based adversarial attack on a trained model is to generate adversarial examples based solely on observing output labels returned by the targeted model. We develop HopSkipJumpAttack, a family of algorithms based on a novel estimate of the gradient direction using binary information at the decision boundary. The proposed family includes both untargeted and targeted attacks optimized for ℓ and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.or…

  DOI

• Skilful nowcasting of extreme precipitation with NowcastNet

  Nature · 2023 · 376 citations

  . We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a neural-network framework with end-to-end forecast error optimization. On the basis of radar observations from the USA and China, our model produces physically plausible precipitation nowcasts with sharp multiscale patterns over regions of 2,048 km × 2,048 km and with lead times of up to 3 h. In a systematic evaluation by 62 professional m…

  DOI

• Understanding the acceleration phenomenon via high-resolution differential equations

  Mathematical Programming · 2021 · 124 citations

  Abstract Gradient-based optimization algorithms can be studied from the perspective of limiting ordinary differential equations (ODEs). Motivated by the fact that existing ODEs do not distinguish between two fundamentally different algorithms—Nesterov’s accelerated gradient method for strongly convex functions (NAG-) and Polyak’s heavy-ball method—we study an alternative limiting process that yields high-resolution ODEs . We show that these ODEs permit a general Lyapunov function framework for t…

  DOI

• ML-LOO: Detecting Adversarial Examples with Feature Attribution

  Proceedings of the AAAI Conference on Artificial Intelligence · 2020 · 88 citations

  Senior authorCorresponding

  Deep neural networks obtain state-of-the-art performance on a series of tasks. However, they are easily fooled by adding a small adversarial perturbation to the input. The perturbation is often imperceptible to humans on image data. We observe a significant difference in feature attributions between adversarially crafted examples and original examples. Based on this observation, we introduce a new framework to detect adversarial examples through thresholding a scale estimate of feature attributi…

  PublisherOA PDFDOI

Recent grants

• Flexible Machine Learning

  NSF · $210k · 2004–2007

• RI: Medium: Collaborative Research: Algorithmic High-Dimensional Statistics: Statistical Optimality, Computational Barriers, and High-Dimensional Corrections

  NSF · $755k · 2019–2024

Frequent coauthors

• Martin J. Wainwright

  Massachusetts Institute of Technology

  135 shared

• Nir Yosef

  University of California, Berkeley

  65 shared

• Peter L. Bartlett

  61 shared

• Yee Whye Teh

  60 shared

• Tamara Broderick

  Western Caspian University

  51 shared

• Tianyi Lin

  Columbia University

  51 shared

• Ion Stoica

  49 shared

• Aldo Pacchiano

  48 shared

Education

• Ph.D.

  University of California, Berkeley

• M.S.

  Massachusetts Institute of Technology (MIT)

• B.S.

  University of California, Berkeley

Awards & honors

• BBVA Foundation Frontiers of Knowledge Award in Information…
• ICBS Frontiers of Science Award (with John Duchi and Martin…
• ICBS Frontiers of Science Award (with Yuchen Zhang, Mingshen…
• Laureate Distinguished Fellow, International Engineering and…
• Academy Award, National Academy of Artificial Intelligence (…

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