Welcome to my homepage. I work in on ML research and AI products at scale. The culture of building effective and scalable systems enables AI creation and assistance. The use and application of AI should be safe and trusting. Researchers may also see me at conferences and curiously working on AI papers. Right now, I'm open to working with engineers, investors on new innovations.
With interest in deep learning and scalable production ML systems, my resarch highlights include action recognition with Independent Subspace Analysis, and object detection with convolutional networks. With natural languages, we took the word-embedding idea to machine translation. I also worked simulation of cognition of approximate visual number sense and heterogeneous causal learning for contextual bandits.
Obtained B.A. and M.Eng. from University of Cambridge, Ph.D. at Stanford University. During PhD, I had the fortune to work with the Stanford AI Lab, NLP Group, Stanford Psychology, and NEC Labs America Media Analytics Group on deep learning applications to computer vision.
In industry, I have worked with many wonderful people and teams over the years.
Learning Hierarchical Spatio-temporal Features for Action Recognition with Independent Subspace Analysis. A First Hack of On-line Education for High-school students
Publications
Publish with Will Y. Zou
Neural Inhibition Improves Dynamic Routing and Mixture of Experts arxiv paper 2025 [PDF]
Realistic CDSS Drug Dosing with End-to-end Recurrent Q-learning for Dual Vasopressor Control
Will Y. Zou, Jean Feng, Alexandre Kalimouttou, Jennifer Yuntong Zhang, Christopher W. Seymour, Romain Pirracchio.
[PDF] Neurips 2025 Workshop on TS4H
Deep Learning of Continuous and Structured Policies for Aggregated Heterogeneous Treatment Effects
Jennifer Y. Zhang, Shuyang Du, Will Y. Zou.
arxiv paper 2025 [PDF]
Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects.
Will Y. Zou, Shuyang Du, Smitha Shyam, Mingshi Wang, Jan Pedersen, Zoubin Ghahramani.
[arxiv paper][code]
Alternative/previous versions:
[1] Aggregating Counterfactual Queries with Neural Architectures to Learn Complex Action Policies arxiv paper 2021[PDF]
[2] Heterogeneous Causal Learning for Optimizing Aggregated Functions in User Growth arxiv paper 2025 [PDF]
[3] Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing. [arxiv paper]
RapidScorer: Fast Tree Ensemble Evaluation by Maximizing Compactness in Data Level Parallelization.
Ting Ye, Hucheng Zhou, Will Y. Zou, Bin Gao, Ruofei Zhang.
ACM SIGKDD (KDD) 2018 [Paper]
Invariance for Perceptual Recognition through Deep Learning.
Will Y. Zou.
Ph.D. Thesis, Stanford University.
Numerosity discrimination in deep neural networks: Initial competence, developmental refinement and experience statistics.
Will Y. Zou, Alberto Testolin, James L. McClelland.
Developmental Science Journal [Paper]
Unsupervised learning with neural networks in the formation of abstract number features in deep layers.
Time-course of Development and Factors Affecting Sensitivity to Visual Numbers Through Unsupervised Learning.
Will Y. Zou, James L. McClelland.
NCPW, 2014. [Abstract] [Stoianov & Zorzi]
Generic Object Detection with Dense Neural Patterns and Regionlets.
Will Y. Zou, Xiaoyu Wang, Miao Sun, Yuanqing Lin.
British Machine Vision Conference (BMVC), 2014.
arXiv preprint 1404.4316, April 2014.
[Abstract] [preprint PDF] [arXiv] [bib]
Competitive and very fast generic object detection (64x Caffe), combining cascaded boosting classifiers with convolutional nets.
ImageNet Detection Results
Bilingual Word Embeddings for Phrase-based Machine Translation.
Will Y. Zou, Richard Socher, Daniel Cer, Christopher D. Manning.
Empirical Methods in Natural Language Processing (EMNLP), 2013.
[PDF] [bib] [Poster] [Bilingual Embeddings] [Project Page] [Slides] [Word Similarity Dataset]
The first application of deep learning word embeddings in machine translation.
Progressive Development of the Number Sense in a Deep Neural Network.
Will Y. Zou, James L. McClelland.
Annual Conference of the Cognitive Science Society (CogSci), 2013.
[Poster] [bib] [Link]
Deep Learning of Invariant Features via Simulated Fixations in Video.
Will Y. Zou, Shenghuo Zhu, Andrew Y. Ng, Kai Yu.
Neural Information Processing Systems (NIPS), 2012.
[PDF] [bib] [Poster]
[Demo code]
[Invariance visualizations]
Illustration of applying slowness in neural networks to learn invariant features on still images.
Unsupervised Learning of Visual Invariance with Temporal Coherence.
Will Y. Zou, Kai Yu, Andrew Y. Ng
Neural Information Processing Systems (NIPS) Workshop on Deep Learning and Unsupervised Feature Learning, 2011
[PDF] [bib]
Haptic Belt with Pedestrian Detection [Tech Demo].
Quoc V. Le, Morgan Quigley, Jean Feng, Justin Chen, Will Y. Zou, Marc Rasi, Tiffany Low, Andrew Y. Ng
Neural Information Processing Systems , 2011 (Demonstrations).
[Poster] [Video]
Real-time, 360 degree pedestrian detection on a GPU-equipped back-pack with 'third-sense' haptic vibration belt.
Real-time People Detection with Convolutional Neural Networks in a Haptic Device.
Will Y. Zou, Tao Wang, Morgan Quigley, Andrew Y. Ng
TEDx Stanford, 2012.
Quoc V. Le, Will Y. Zou, Serena Y. Yeung, Andrew Y. Ng.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. (Oral presentation)
[PDF]
[Appendix] [Multi-resolution Code (~53%AP)] [Simple Code (~50.5%AP)] [Notes] [Link to Hollywood2].
State-of-the-art action recognition with unsupervised feature learning.
[Example Actions on Youtube]
[Features] already trained on Hollywood2
[Here] is some code to extract cool ISA features on images instead of videos, e.g. this paper.
US Patents
Distributed Cache-buffer System for Training of Deep Learning Models on CPU-based Cloud-computing system.
US Patent under review
With Alibaba Group/AliCloud US.
Fine-grained Categorical Query Semantic Similarity for Query Auto-completion Ranking via Deep Neural Networks
US Patent under review
With Alibaba Group/Taobao.com US.
Regionlets with Shift Invariant Neural Patterns for Object Detection.
US Patent Granted
With NEC Labs America in Cupertino, CA.
System and Method for Detecting Platform Anomalies Through Neural Networks.
US Patent Granted
With Apcera Inc. in San Francisco, CA.
Professional Services
Area Chair: Advanced Machine Learning Methods, KDD 2025
Reviewer, ICLR 2026
Reviewer, KDD Research Track 2026
Reviewer, KDD Research Track 2025
Reviewer, AAAI 2020
Reviewer, ICML 2020
Reviewer, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Reviewer, IEEE Signal Processing Letters
Program committee member, NIPS 2014
Program committee member, NIPS 2015
Reviewer, Neural Computation
Program committee member, ACM Multimedia 2014
Program committee member, NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011
Past Research Projects
A continuous gesture human-computer interface Dasher
with David J. C. MacKay
Cavendish Laboratory, Cambridge, 2009.
Efficient implementation of Bayesian Sets on Netflix Dataset.
with Zoubin Ghahramani
[M.Eng. Thesis]
Machine Learning Group, Cambridge, 2008.
An ADMM Solution to the Sparse Coding Problem
[Stanford Technical Report]
EE364B Convex Optimization, 2011.
Teaching
Stanford CS229 - Machine Learning, Fall 2010, Fall 2013
Stanford CS228 - Probablistic Graphical Models, Spring 2013, Spring 2014
Leisure
Blog on Tech & Research
Deep learning pointers for general interest.
Deep learning course by Geoff Hinton.
