Senem Işık

Hi! I'm Senem Işık, a graduate student at Stanford University advised by Amin Saberi and Ellen Vitercik. Previously, I completed my Bachelor's with Honors in Mathematics and (concurrent) Master's in Computer Science (Theory & AI track) also at Stanford :)

I am broadly interested in theoretical and algorithmic foundations of learning and decision-making systems shaped by economic and societal forces. At Stanford, I am affiliated with the Theory and Operations Research groups, the Center for Computational Market Design, Language, Data, and Reasoning, SOAL (Society & Algorithms), and the SAIL (Stanford AI) Labs.

I am originally from Istanbul, Türkiye. I pronounce my full name as Senæm ɯˈʃɯk, and here are three places that are close to my heart 🧡

email: senemi [at] stanford [dot] edu

Research

Algorithmic perspective on learning and decision-making under uncertainty

Value of Learning in Online Decision Making: When does being Bayesian help? with Alireza AmaniHamedani, Ali Aouad, and Amin Saberi, preliminary version presented at The Stanford Market Design in the Age of AI Conference, 2026 [poster, preprint]

Improving the convex program for antidote allocation with Uma Dayal, Margalit Glasgow, and Mary Wootters, presented at Stanford CURIS (Computer Science Research), 2021 (Outstanding Poster Award) [poster]

Analyses of random graphs, processes and social, biological, and economic networks

Local Limits of Small-World Networks with Yeganeh Alimohammadi and Amin Saberi, preliminary version appeared at a workshop at ACM Conference on Economics and Computation, 2024 [poster, manuscript]

Random Lipschitz functions on graphs with weak expansion with Jinyoung Park, presented at Joint Mathematics Meetings, 2025 [slides, poster , manuscript]

Theoretical analyses of epidemiological processes on graphs with Uma Dayal, Margalit Glasgow, and Mary Wootters, presented at Stanford CURIS (Computer Science Research), 2021 [poster]

News

February, 2026. Presented our work on Bayesian online decision making at The Stanford Market Design in the Age of AI Conference

October, 2025. Presented our work on local properties of small world networks and weak expanders at TOCA-SV (Theory of Computing Associated-Silicon Valley)

January, 2025. Presented our work on weak expanders at the Joint Mathematics Meetings

Teaching

Stanford CS 269I (Incentives in Computer Science) taught by Aviad Rubinstein (Winter 2026)

Stanford CS 161 (Design and Analysis of Algorithms) taught by Aviad Rubinstein (Fall 2024)

Stanford MS&E 211DS (Introduction to Optimization: Data Science) taught by Amin Saberi (Winter 2025)

Jamcoders 🧡 taught by Jelani Nelson (2026) If teaching algorithms to high schoolers in Jamaica interests you, APPLY!

AddisCoder 🧡 taught by Jelani Nelson (Summer 2025) If teaching algorithms to high schoolers in Ethiopia interests you, APPLY!

Service: Theoretical Computer Science (journal)

Undergraduate Research Affiliations

Harvard CS Theory Group, Student Researcher Non-Adaptive Local Computation Algorithms (Summer 2025) Supervisor: Madhu Sudan

NYU Discrete Math REU, Student Researcher Lipschitz functions on expanders (Summer 2024) Supervisor: Jinyoung Park

Stanford Machine Learning group, AI for Climate Change Bootcamp Active learning for geospatial data (Winter-Spring 2023) Supervisor: Andrew Ng

Stanford CS Theory CURIS Research, Student Researcher Graph algorithms to combat epidemics (Summer 2021) Supervisor: Mary Wootters

Industry Internships

Genesis Therapeutics, Machine Learning Research Intern VQ-VAEs for encoding molecular structures (2025)

IMC Trading, Quantitative Trader Intern Auctions on single stock options (Summer 2023)

Meta, Software Engineer Intern Stream processing (Summer 2022)

Expository

A Random Walk through Probabilistic Graph Theory Honors Thesis submitted to the Stanford Department of Mathematics, advised by Tselil Schramm

Tackling the Traveling Salesman Problem with Graph Neural Networks Blog post published as part of Stanford course CS224W (Machine Learning with Graphs) by Jure Lescovec

Optimization Methods for Graphs: Thresholds, Matchings, and Dense Subgraphs Tutorial submitted as part of Stanford course EE 364B (Convex Optimization II) by Mert Pilanci

I gave quite a lot of presentations lately (mainly for CS 331X: AI for Algorithmic Reasoning and Optimization taught by Ellen Vitercik and MS&E 319: Matching Theory taught by Amin Saberi):

Learning to Combine Online Algorithms Online