Senem Işık

Hi! I'm Senem, a first-year PhD student at Stanford University supported by Stanford Management Science & Engineering fellowship. My first year-advisors are 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 originally from Istanbul, Türkiye. I pronounce my full name as Senæm ɯˈʃɯk, and here's a place that's close to my heart 🧡

Email: senemi [at] stanford [dot] edu

Research

Value of Bayesian Learning in Online Decision Making with Alireza AmaniHamedani, Ali Aouad, and Amin Saberi, preliminary version presented at The Market Design in the Age of AI Conference, 2026

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, preprint]

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

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

Internships

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

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

Stanford ML group, AI for Climate Change Bootcamp Active learning for geospatial data (Summer 2023)

Meta, Software Engineer Intern Stream processing (Summer 2022)

Teaching

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

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

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

AddisCoder 🧡 taught by Prof. Jelani Nelson (Summer 2025) Check this out! And if teaching in Ethiopia interests you, APPLY!

Reviewer: Theoretical Computer Science (journal)

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)

I gave quite a lot of presentations lately (mainly for CS 331X: AI for Algorithmic Reasoning and Optimization taught by Prof. Ellen Vitercik and MS&E 319: Matching Theory taught by Prof. Amin Saberi): Incentive Aware Machine Learning: Robustness with(out) predictions, Thickness and Information in Dynamic Matching Markets, Learning to design voting rules, Unintended Memorization in Neural Networks, (Primal Dual) Neural Algorithmic Reasoning, In-Context Learning: Literature Review, Optimal Stopping with Predictions, Optimizing Solution-Samplers for Combinatorial Problems...

More (from undergraduate): Semi-direct products, Arzela Ascoli Theorem