Bio
I specialize in building Machine Learning applications. I enjoy coming up with simple solutions to complex problems, whether as a manager or an individual contributor. From 2016 to 2021, I worked in Amazon's display advertising business, where I led a team of scientists and engineers to develop and launch applications in automated ad budget management, response prediction, and ad ranking and allocation. I learned a lot about monitoring and operating large ML systems during this time. We built a real-time analytics system to monitor our algorithms at fine grain. I got to experience first hand why metrics are not just a mathematical definition.
Other than working at Amazon, I wrote books on feature engineering and evaluating machine learning models. From 2013-2016, I worked at Turi (also known as GraphLab), a small Seattle-based startup that built a Machine Learning Platform for non-ML experts. The company was acquired by Apple in 2017. From 2007-2013, I worked as a researcher in the Machine Learning Group at Microsoft Research. Prior to that, I was a postdoc at Carnegie Mellon University's Auton Lab and the Parallel Data Lab. I received B.A.s in Mathematics and Computer Science and a Ph.D. in Electrical Engineering from U. C. Berkeley in Prof. Michael Jordan's lab.
I live in Seattle. When not working, I meditate daily and do yoga occasionally. I'm also training in Reiki and energy intuition.
Talks
The How and Why of Feature Engineering.
Strata + Hadoop World, San Jose, CA. March 2016.
Evaluating Machine Learning Models—A Beginner's Guide.
Seattle Data Science MeetUp. Sep, 2015.
Understanding Feature Space in Machine Learning.
Rich Data Summit, SF, CA. 2015.
The Challenges of Bringing Machine Learning to the Masses.
Alice Zheng and Sethu Raman. NIPS Workshop on Software Engineering for Machine Learning. Montreal, Quebec, Canada. 2014.
What the #*($! is Big Data?—A Holistic View of Data and Algorithms.
Strata Conference, Santa Clara, CA. Feb 2014.
SlidesVideo
Research Papers
Collaborative Denoising Auto-Encoders for Top-N Recommender Systems.
Yao Wu, Christopher DuBois, Alice X. Zheng, Martin Ester.
In Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM 2016), San Francisco, 2016.
Gradient Boosted Feature Selection.
Zhixiang (Eddie) Xu, Gao Huang, Kilian Q.Weinberger, Alice X. Zheng.
In Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), New York, 2014.
Lazy Paired Hyper-Parameter Tuning.
Alice X. Zheng and Mikhail Bilenko.
In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), Beijing, China, 2013.
WebpageSlidesFast Image Tagging.
Minmin Chen, Alice Zheng, Kilian Q. Weinberger.
In Proceedings of the 30th International Conference on Machine Learning (ICML 2013), Atlanta, GA, USA, 2013.
Fast Top-K Similarity Queries Via Matrix Compression.
Yucheng Low and Alice X. Zheng.
In Proceedings of the Twenty-First ACM International Conference on Information and Knowledge Management (CIKM 2012), 2012.
pdf (short)pdf (long)Active Graph Reachability Reduction for Network Security and Software Engineering.
Alice X. Zheng, John Dunagan, Ashish Kapoor.
In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI), 2011.
Diagnosing performance changes by comparing request flows.
Raja R. Sambasivan, Alice X. Zheng, Michael De Rosa, Elie Krevat, Spencer Whitman, Michael Stroucken, William Wang, Lianghong Xu, Gregory R. Ganger.
In 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2011.
There's an app for that, but it doesn't work. Diagnosing Mobile Applications in the Wild.
Sharad Agarwal, Ratul Mahajan, Alice Zheng, Victor Bahl.
In ACM HotNets IX, October 2010.
Practical Performance Models for Complex, Popular Applications: A Feasibility Study.
Eno Thereska, Bjoern Doebel, Alice X. Zheng, Peter Nobel.
In International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2010.
A Survey of Statistical Network Models.
Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg, Edoardo M. Airoldi.
Foundations and Trends in Machine Learning, 2, 2, pp 129-233, 2009.
Heat-ray: Combating Identity Snowball Attacks Using Machine Learning, Combinatorial Optimization and Attack Graphs.
John Dunagan, Alice X. Zheng, Daniel R. Simon.
ACM Symposium on Operating Systems Principles (SOSP), 2009.
Categorizing and Differencing System Behaviours.
Raja R. Sambasivan, Alice X. Zheng, Eno Thereska, Gregory R. Ganger.
Second Workshop on Hot Topics in Autonomic Computing. June 15, 2007. Jacksonville, FL.
Modeling the Relative Fitness of Storage.
Michael Mesnier, Matthew Wachs, Raja R. Sambasivan, Alice Zheng, Gregory R. Ganger.
International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS). San Diego, CA. June 12-14, 2007. ACM. Awarded Best Paper.
A Generative Model for Dynamic Contextual Friendship Networks.
Alice X. Zheng and Anna Goldenberg.
CMU tech report, 2006.
Exploratory Study of a New Model for Evolving Networks.
Anna Goldenberg and Alice X. Zheng.
Proceedings of the Workshop on Statistical Network Analysis: Models, Issues, and New Directions at ICML-06.
Statistical Debugging: Simultaneous Isolation of Multiple Bugs.
Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken.
Proceedings of ICML-06.
Statistical Software Debugging.
Alice X. Zheng.
Doctoral dissertation, U.C. Berkeley, 2006.
Efficient Test Selection in Active Diagnosis via Entropy Approximation.
Alice X. Zheng, Irina Rish, Alina Beygelzimer.
Proceedings of UAI-05.
Scalable Statistical Bug Isolation.
Ben Liblit, Mayur Naik, Alice X. Zheng, Alex Aiken, Michael I. Jordan.
ACM SIGPLAN 2005 Conference on Programming Language Design and Implementation (PLDI 2005).
Public Deployment of Cooperative Bug Isolation.
Ben Liblit, Mayur Naik, Alice X. Zheng, Alex Aiken, Michael I. Jordan.
Workshop on Remote Analysis and Measurement of Software Systems (RAMSS), 2004.
Failure Diagnosis Using Decision Trees.
Mike Chen, Alice X. Zheng, Jim Lloyd, Michael I. Jordan, Eric Brewer.
International Conference on Autonomic Computing (ICAC-04), 2004.
Statistical Debugging of Sampled Programs.
Alice X. Zheng, Michael I. Jordan, Ben Liblit, Alex Aiken.
Advances in Neural Information Processing Systems 16, 2003.
Sampling User Executions for Bug Isolation.
Ben Liblit, Alex Aiken, Alice X. Zheng, Michael I. Jordan.
Workshop on Remote Analysis and Measurement of Software Systems (RAMSS), May 9, 2003.
Bug Isolation via Remote Program Sampling.
Ben Liblit, Alex Aiken, Alice X. Zheng, Michael I. Jordan.
ACM SIGPLAN 2003 Conference on Programming Language Design and Implementation (PLDI 2003).
Learning a Gaussian Process Prior for Automatically Generating Music Playlists.
John C. Platt, Christopher J.C. Burges, Steven Swenson, Christopher Weare, Alice Zheng.
Advances in Neural Information Processing Systems 14, 2001.
Link analysis, eigenvectors, and stability.
Andrew Y. Ng, Alice X. Zheng, Michael I. Jordan.
Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), 2001.
Stable methods for link analysis.
Andrew Y. Ng, Alice X. Zheng, Michael I. Jordan.
Proceedings of the Twenty-Fourth Annual Internation ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2001), 2001.
Fast Multiple Antenna Differential Decoding.
Kenneth L. Clarkson, Wim Sweldens, Alice Zheng.
IEEE Transactions on Communications, Vol. 49, Nr. 2, pp. 253-261, 2001.