Mike Wu
334 Jordan Hall
wumike@stanford.edu
CV | github | google scholar | blog | notes

I'm a third year PhD student in Computer Science at Stanford University advised by Noah Goodman. I'm interested in more efficient learning algorithms using weak- and self- supervision. I am supported by the NSF GRFP grant.

I did my undergrad in CS at Yale ('16) where I worked on astrostatistics. Then, I took a year off before starting graduate school, where I worked at Facebook Research. I also helped found a startup building a probabilistic programming language in Excel called Invrea.

Past/Present Collaborators: Chris Piech, Stefano Ermon, Michael C. Hughes, Finale Doshi-Velez, Frank Wood

Preprints

Generative Grading: Neural Approximate Parsing for Automated Student Feedback
Ali Malik (*), Mike Wu (*), Vrinda Vasavada, Jinpeng Song, John Mitchell, Noah Goodman, Chris Piech
(*) equal contribution
ArXiv (pdf)

Optimizing for Interpretability in Deep Neural Networks with Simulable Decision Trees
Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez
ArXiv (pdf)

Publications

Regional Tree Regularization for Interpretability in Black Box Models
Mike Wu, Sonali Parbhoo, Michael C. Hughes, Ryan Kindle, Leo Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
AAAI 2020 (pdf) (oral)

Meta-Amortized Variational Inference and Learning
Mike Wu (*), Kristy Choi (*), Noah Goodman, Stefano Ermon
(*) equal contribution
NeurIPS 2019 Workshop on Bayesian Deep Learning (spotlight)
AAAI 2020 (pdf)

Pragmatic inference and Visual Abstraction Enable Contextual Flexibility during Visual Communication
Judith Fan, Robert X.D. Hawkins, Mike Wu, Noah Goodman
Computational Brain & Behavior 2019 (pdf) (journal)

Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference
Mike Wu, Noah Goodman, Stefano Ermon
AISTATS 2019 (pdf)

Zero Shot Learning for CodeEducation: Rubric Sampling with Deep Learning Inference
Mike Wu, Milan Mosse, Noah Goodman, Chris Piech
AAAI 2019 (pdf) (oral) (outstanding student paper award).

Multimodal Generative Models for Scalable Weakly Supervised Learning
Mike Wu, Noah Goodman
NeurIPS 2018 (pdf)

Spreadsheet probabilistic programming
Mike Wu, Yura Perov, Frank Wood, Hongseok Yang, William Smith
PROBPROG 2018 (pdf)

Tree Regularization of Deep Models for Interpretability
Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
NeurIPS 2017 Workshop on Transparent and Interpretable Machine Learning (spotlight)
AAAI 2018 (pdf) (oral)

Predicting intervention onset in the ICU with switching statespace models
Marzyeh Ghassemi, Mike Wu, Michael C. Hughes, Finale Doshi-Velez
CRI 2017 (pdf) (best informatics paper nomination)

Understanding Vassopressor Intervention and Weaning: Risk Prediction in a Public Heterogeneous Clinical Time Series Database
Mike Wu, Marzyeh Ghassemi, Mengling Feng, Leo Anthony Celi, Peter Szolovitz, Finale Doshi-Velez
JAMIA 2016 (pdf) (journal)

Edge-based Crowd Detection from Single Image Datasets
Mike Wu, Madhu Krishnan
IJCSI 2013 (pdf)

Autonomous Mapping and Navigation through Utilization of Edge-based Optical Flow and Time-to-Collision
Madhu Krishnan, Mike Wu, Young Kang, Sarah H. Lee
ARPN 2012 (pdf)
Intel ISEF Semifinalist 2012

Conference Abstracts

Modeling contextual flexibility in visual communication
Judith Fan, Robert X.D. Hawkins, Mike Wu, Noah Goodman
Vision Sciences Society (VSS) 2018

Investigating the Cosmic Web with Topological Data Analysis
Jessi Cisewski-Kehe, Mike Wu, Brittany Fasy, Wojciech Hellwing, Mark Lovell, Alessandro Rinaldo, Larry Wasserman
American Astronomical Society (AAS) Meeting 2018
Undergraduate Senior Thesis

Position and Vector Detection of Blind Spot motion with the Horn-Schunck Optical Flow
Stephen Yu, Mike Wu
XSEDE 2011 (pdf) (best high school poster)
Siemens Competition Semifinalist 2012
Intel ISEF Finalist, 3rd place in Computer Science 2011