About Me

I am rising third year PhD candidate at the University of Michigan focusing on the intersection of machine learning and healthcare. My research interests include causal inference, survival analysis, sports analytics, and combining machine learning with wearable sensors to prevent physiological harm. My other hobbies include sports, video games, music, and movies.

Contact Details

(937) 499-4885


University of Michigan, Ann Arbor

PhD in Computer ScienceTBD

University of Michigan, Ann Arbor

Masters in Computer ScienceAugust 2020

University of California, Berkeley

B.A. Computer Science, Mathematics, Statistics, Campus Outstanding GSI Award May 2018

Centerville High School

Honors Diploma May 2014

Relevant Undergraduate Coursework: Structure and Interpretation of Computer Programs, Multivariable Calculus, Data Structures, Discete Mathematics, Linear Algebra and Differential Equations, Introduction to Real Analaysis, Introduction to Artificial Intelligence, Concepts of Probability, Advanced Linear Algebra, Introduction to Complex Analysis, Efficient Algorithms and Intractable Problems, Advanced Linear Algebra, Statistics in Game Theory, Introduction to Machine Learning, Concepts of Statistics, Abstract Algebra, Elementary Algebraic Topology, Concepts of Statistics, Time Series, Introduction to Database Systems.
Relevant Graduate Coursework: Graduate Machine Learning, Reinforcement Learning, Data Mining, Advanced Artificial Intelligence, Microarchitecture.
Extracurriculars: Computer Science and Engineering Graduate Student Board, Computer Science Mentors, Intramural Sports, Smash at Berkeley


University of Michigan

Graduate Student Research Assistant August 2018 -

I am a rising third year PhD student working under the supervision of Professor Jenna Wiens. I work on projects related to survival analysis, causal inference, sports analytics, and combining machine learning with wearable sensors to prevent physiological harm.

University of Michigan

Graduate Student Instructor September 2019 - December 2019

I was part of the course staff for EECS 492, an undergraduate course which introduces important topics in artificial intelligence. My main responsibilities included teaching discussion sections, holding office hours, managing grading, and creating and revising assignments. I have given two lectures to the course on game theory and search algorithms.

University of Michigan

Big Data Summer Institute Lecturer June 2019 -

Developed a Python tutorial and taught it to public health undergraduate students for the big data summer institute (BDSI) program.

Computer Science and Engineering Graduate Student Group

Relations Chair June 2019 -

I serve on the board of the group that represents the graduate student body of the University of Michigan's computer science and engineering department. My past role focused on hosting weekly community gatherings for graduate students to socialize, and my current role focuses on facilitating graduate student communications with the department, faculty, staff, and various companies.

University of California Berkeley

Summer Instructor for Foundations of Data Science May 2018 - August 2018

I was one of two co-instructors responsible for maintaining and leading a University of California, Berkeley official summer course. The course had 250 students enrolled, and my main responsibiities including creating lecture material, giving lectures, training and organizing staff, maintaining online lab assessments, homeworks, and projects, creating written exams, compiling final grades, and day to day logistical tasks.

University of California Berkeley

Teaching Assistant for Foundations of Data Science January 2016 - May 2016, August 2016 - December 2016, August 2017 - May 2018

Teaching Assistant for Introduction to Artificial Intelligence June 2016 - August 2016

Teaching Assistant for Data Structures and Algorithms January 2017 - May 2017

I have been hired as a 50% GSI each time I have served as an undergraduate teaching assistant. My responsibilities have included teaching sections, holding office hours, creating worksheets and exams, and miscellaneous backend software development tasks to keep the classes running smoothly. Some specifics include creating the course website, compiling final grades, maintaining the auto-grading of assignments, and organization of other teaching assistants. I have also been a Head TA for the Data Science course four times.

UC Berkeley EECS Department

Research March 2016 - Present

Researched the use of deep learning techniques on medical data to determine abnormalities in heartbeats with two PhD mentors. Specifically, we worked towards adapting a state of the art generative model (Wavenet) to learn representations for the task of detecting heartbeat abnormalities in physiological data.

Computer Science Mentors

President May 2016 - May 2017

After two years of mentoring, I became president of an organization aimed to ease the transition into rigrorous introductory computer science courses. The relatively new organization now serves over 1200 students whle also allowing over 100 students the ability to practice their teaching.

84.51° (Kroger/Ralphs)

Data Analayst Intern May 2017 - August 2017

Interned at a data analytics firm owned by the supermarket Kroger. I used machine learning models for variable selections to assist better match customers with coupon offers. Moreover, I helped introduce Natural Language Processing to the company, both creating an introductory guide to teach beginners while also creating a script which reads in user comments and extracts general themes.

Mathematics Department

Peer Advisor August 2016 - May 2017

Peer advisor for the mathematics advisor, where my primary job was holding drop-in office hours for students who had questions about scheduling and the math department as a whole. Some other responsibilities included holding large scale advising sessions with other advisors and giving input for the administration of the department.

UC Berkeley Decal: Practical Data Science Skills for Internships

Machine Learning Lecturer August 2017 - Present

This decal focuses on three aspects; Collaboration (github/latex), Database Querying (SQL), and Machine Learning. I lectured on the machine learning portion, focusing away from the mathematical and statistical complexities, and instead on applications in a business and societal sense that anyone can implement. I focused on some of the biggest ML concepts and their current use.

Publications and Preprints

[1] Caleb Belth, Fahad Kamran, Donna Tjandra, and Danai Koutra. "When to remember where you came from: node representation learning in higher-order networks." In 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 222-225. IEEE, 2019.

[2] Karandeep Singh, Thomas S. Valley, Shengpu Tang, Benjamin Y. Li, Fahad Kamran, Michael W. Sjoding, Jenna Wiens et al. "Validating a Widely Implemented Deterioration Index Model Among Hospitalized COVID-19 Patients." medRxiv (2020).