I am a second year Ph.D. student in Computer Science at Columbia University. I study machine learning theory under the supervision of Professor Rocco Servedio and Professor Daniel Hsu. I graduated from Brown University in May 2018 with an joint Sc.B. in Applied Mathematics and Computer Science. In my "gap year" between Brown and Columbia, I lived in San Francisco as an Associate Data Scientist at LinkedIn and a Software Engineering Intern at Lumi Labs.
My goal in research is to make machine learning a more trustworthy and equitable tool built on rigorous mathematical foundations. To do so, I'm interested in studying why neural networks and other machine learning models work so well in practice, including work on generalization (how well a learning algorithm adapts to new data) and approximation (what kinds of learning rules can be represented by neural networks). I'm also interested in any topics on the overlap between dynamical systems and machine learning. (I briefly dabbled with dynamical systems research with Professor Bjorn Sandstede at Brown.)
I currently co-coordinate the CS Theory Student Seminar at Columbia.
I am grateful for funding from an NSF GRFP fellowship, which I received in March 2021.
D. Hsu*, C. Sanford*, R. Servedio*, E. Vlatakis*. "On the Approximation Power of Two-Layer Networks of Random ReLUs." 2021.
C. Sanford. "Applying Rademacher-Like Bounds to Combinatorial Samples and Function Selection." Honors Thesis, Brown Department of Computer Science, 2018.
K. Cygan*, C. Sanford*, W. Fairbrother. "Spliceman2 - A Computational Web Server That Predicts Sequence Variations in Pre-mRNA Splicing." Bioinformatics 33 (18), 2017.
J. Gross*, C.Sanford*, G. Kocks*. "Projected Water Needs and Intervention Strategies in India." Undergraduate Mathematics and its Applications 37 (2), 2016.
Software Engineering Intern
April 2019 - August 2019
Associate Data Scientist
August 2018 - April 2019
Data Analytics Intern
June 2017 - August 2017
Ph.D. Student in Computer Science
August 2019 -
GPA: 4.0 (out of 4.0)
M.S. in Computer Science
August 2019 - February 2021
Randomized Algorithms; Optimization; ML Theory; Economics, AI, and Optimization; Computation and the Brain
Sc.B. in Applied Mathematics-Computer Science
September 2014 - May 2018
GPA: 3.9 (out of 4.0)
Algorithms; Advanced Algorithms Seminar; ML; Information Theory; Prescriptive Analytics; Abstract Algebra; Cryptography; Real Analysis
COMS 4252: Computational Learning Theory
CSCI 1570: Design and Analysis of Algorithms