Mike Wu
334 Jordan Hall
CV | github | google scholar | blog

I'm a second year PhD student in Computer Science at Stanford University advised by Noah Goodman. My research area is in deep generative models, approximate inference, computational education, and numerical methods for fluid dynamics. I am supported by the NSF GRFP grant.

I did my undergrad in CS at Yale ('16) where I worked with Jessi Cisewski on astrostatistics. I also spent time at Oxford working on probabilistic programming with Frank Wood, and at Harvard working on explainable AI with Finale Doshi-Velez.

I took a year off before starting graduate school, where I worked at Facebook Research. I also helped found a startup building a PPL in Excel called Invrea. In my free time, I like to play tennis!

In Submission

+ Optimizing for Interpretability in Deep Neural Networks with Simulable Decision Trees
Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez. Submitted 2019. (draft)

+ Generative Grading: Neural Approximate Parsing for Automated Student Feedback
Ali Malik (*), Mike Wu (*), Vrinda Vasavada, Jinpeng Song, John Mitchell, Noah Goodman, Chris Piech. Submitted 2019. (draft)
(*) equal contribution

+ 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. Submitted 2019. (draft)

+ Meta-Amortized Variational Inference and Learning
Kristy Choi (*), Mike Wu (*), Noah Goodman, Stefano Ermon. ArXiv. (report)
(*) equal contribution


+ 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). (paper).

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

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

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

+ Tree Regularization of Deep Models for Interpretability
Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez. AAAI 2018. (paper) (spotlight)

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

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

+ 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. Intel ISEF Semifinalist. (paper)

Conference Presentations

+ Modeling contextual flexibility in visual communication
Judith Fan, Robert X.D. Hawkins, Mike Wu, Noah Goodman. VSS 2018.

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