Mike Dusenberry
Mike Dusenberry is a Research Software Engineer at Google Cloud AI Research. Previously, he was AI Resident at Google Brain and Google Health Research. Before joining Google, Mike obtained a B.S. in Computer Science, studied in medical school as an M.D. candidate for two years prior to leaving to focus on machine learning, and worked as a machine learning engineer at IBM focused on research and open-source software for distributed systems and healthcare. Mike's work is focused on research in Bayesian deep learning (neural nets, probability, decisions, uncertainty), and applications to medicine and other high-stakes problems where uncertainty, reliability, and robustness matter.
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Morse Neural Networks for Uncertainty Quantification
Clara Huiyi Hu
ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling (2023)
Plex: Towards Reliability using Pretrained Large Model Extensions
Du Phan
Mark Patrick Collier
Zi Wang
Zelda Mariet
Clara Huiyi Hu
Neil Band
Tim G. J. Rudner
Karan Singhal
Joost van Amersfoort
Andreas Christian Kirsch
Rodolphe Jenatton
Honglin Yuan
Kelly Buchanan
D. Sculley
Yarin Gal
ICML 2022 Pre-training Workshop (2022)
Combining Ensembles and Data Augmentation Can Harm Your Calibration
Yeming Wen
Ghassen Jerfel
Rafael Rios Müller
International Conference on Learning Representations (2021)
A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan
Joel Shor
Arkady Epshteyn
Ashwin Sura Ravi
Beth Luan
Chun-Liang Li
Daisuke Yoneoka
Dario Sava
Hiroaki Miyata
Hiroki Kayama
Isaac Jones
Joe Mckenna
Johan Euphrosine
Kris Popendorf
Nate Yoder
Shashank Singh
Shuhei Nomura
Thomas Tsai
npj Digital Medicine (2021)
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
Edward Choi
Zhen Xu
Yujia Li
Gerardo Flores
Association for the Advancement of Artificial Intelligence (AAAI) (2020)
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Ghassen Jerfel
Yeming Wen
Yian Ma
International Conference on Machine Learning (ICML) (2020)
Analyzing the Role of Model Uncertainty for Electronic Health Records
Edward Choi
Jeremy Nixon
Ghassen Jerfel
ACM Conference on Health, Inference, and Learning (ACM CHIL) (2020)
Bayesian Layers: A Module for Neural Network Uncertainty
Mark van der Wilk
Danijar Hafner
Neural Information Processing Systems (NeurIPS) (2019)
