I am a first year Ph.D. student in Computer Science at Columbia University. I study computational learning theory under the supervision of Professor Rocco Servedio and Professor Daniel Hsu. I recently 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 am interested in studying the problems of generalization (how well a learning algorithm adapts to new data), fairness (how an algorithm treats sub-populations relative to others), and interpretability (how algorithms justify their predictions to human observers). 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 coordinate the CS Theory Student Seminar at Columbia.

Email: clayton [AT] cs.columbia.edu
LinkedIn: claytonsanford
Github: chsanford

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.


Lumi Labs

Software Engineering Intern

April 2019 - August 2019


Associate Data Scientist

August 2018 - April 2019

Data Analytics Intern

June 2017 - August 2017


Columbia University

Ph.D. Student in Computer Science

August 2019 -

Brown University

Sc.B. in Applied Mathematics-Computer Science

September 2014 - May 2018

GPA: 3.90 (out of 4.00)


  • Computer Science Senior Prize
  • Magna Cum Laude
  • Departmental Honors

Selected Coursework

Algorithms, Advanced Algorithms Seminar, Machine Learning, Probabilistic Methods in CS, Information Theory, Prescriptive Analytics, Models of Computation, Abstract Algebra, Cryptography, Real Analysis


Head Teaching Assistant

CSCI 1570: Design and Analysis of Algorithms

Fall 2017

Undergraduate Teaching Assistant

APMA 1360: Topics in Chaotic Dynamics

Spring 2017

CSCI 1010: Theory of Computation

Fall 2016

CSCI 220: Discrete Structures and Probability

Spring 2016

CSCI 190: Accelerated Intro to CS

Fall 2015

A few random things...