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 and approximate inference algorithms. I am supported by the NSF GRFP grant.
I completed my undergrad in computer science at Yale University ('16) where I worked with Jessi Cisewski on astrostatistics and Brian Scasselati on human-robot collaboration. I also spent time at University of Oxford working on probabilistic programming with Frank Wood, and at Harvard University working on interpretable deep learning with Finale Doshi-Velez.
I took a year off before starting graduate school, where I worked at Facebook in the applied machine learning group. I also helped found a startup building a probabilistic progamming language in Excel called Invrea. In my free time, I like to play tennis and do hackathons.
+ 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)
+ 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)