I'm a third-year graduate student at Stanford University. I'm studying computer science, interested in systems research, and am advised by Matei Zaharia and Peter Bailis. My most recent project was Willump (MLSys 2020), a statistically-motivated end-to-end optimizer for machine learning inference. Before Willump, I worked on MacroBase (VLDB 2019) and Arachne (OSDI 2018). I did my undergrad at Harvard, where I majored in computer science and worked with Professor Margo Seltzer on the Automatically Scalable Computation (ASC) project, writing my senior thesis on my work on it. I also worked with Professor Alexander Rush on predicting congressional voting records from bill text (EMNLP 2016).
Ph.D. in Computer Science• Eventually
I'm in my third year at Stanford, advised by Matei Zaharia and Peter Bailis. My most recent project was Willump, a statistically-motivated end-to-end optimizer for machine learning inference, which will appear in MLSys 2020. I've made Willump's source code available on GitHub. Before Willump, I worked on MacroBase with Peter and Matei in work which appeared at VLDB 2019, as well as on Arachne with John Ousterhout and Henry Qin in work presented at OSDI 2018. I also worked on Slicer while on an internship at Google.
Bachelor's in Computer Science • 2017
I concentrated in Computer Science at Harvard, graduating in 2017. I did my senior thesis with Margo Seltzer on the Automatically Scalable Computation (ASC) project, winning the Hoopes Prize for excellence in undergraduate research. I also worked with Alexander Rush on a project predicting the results of congressional votes from the text of the bills voted on, which was published in EMNLP 2016.
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference.
Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia.
Conference on Machine Learning and Systems (MLSys) 2020.
DIFF: A Relational Interface for Large-Scale Data Explanation.
Firas Abuzaid, Peter Kraft, Sahaana Suri, Edward Gan, Eric Xu, Atul Shenoy, Asvin Ananthanarayan, John Sheu, Erik Meijer, Xi Wu, Jeff Naughton, Peter Bailis, Matei Zaharia.
International Conference on Very Large Data Bases (VLDB) 2019.
Arachne: Core-Aware Thread Management.
Henry Qin, Qian Li, Jacqueline Speiser, Peter Kraft, John Ousterhout.
Symposium on Operating Systems Design and Implementation (OSDI) 2018.
Automatically Scalable Computation That Is More Scalable and Automatic.
Harvard University Senior Thesis. 2017.
Improving Supreme Court Forecasting Using Boosted Decision Trees.
Aaron Kaufman, Peter Kraft, Maya Sen.
Political Analysis. 2019.
An Embedding Model For Predicting Roll-Call Votes.
Peter Kraft, Hirsh Jain, Alexander Rush.
Empirical Methods for Natural Language Processing (EMNLP) 2016.