Hi! I'm Senem, a first-year PhD student at Stanford University supported by Stanford 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
Random Lipschitz functions on graphs with weak expansion with Jinyoung Park submitted, preliminary version presented at Joint Mathematics Meetings, 2025 (slides, poster)
Local Limits of Small-World Networks with Yeganeh Alimohammadi and Amin Saberi submitted, preliminary version appeared at a workshop at ACM Conference on Economics and Computation, 2024 (poster)
Improving the convex program for antidote allocation and Theoretical analyses of epidemiological processes on graphs with Uma Dayal, Margalit Glasgow, and Mary Wootters Stanford CURIS (Computer Science Research) Poster Session, 2021 (Outstanding Poster Award)
Genesis Therapeutics, Machine Learning Research Intern VQ-VAEs for encoding molecular structures (2025)
IMC Trading, Quantitative Trader Intern Auctions on single stock options (2023)
Stanford ML group, AI for Climate Change Bootcamp Active learning for geospatial data (2023)
Meta, Software Engineer Intern Stream processing (2022)
Stanford CS 269I (Incentives in Computer Science) taught by Prof. Aviad Rubinstein
Stanford CS 161 (Design and Analysis of Algorithms) taught by Prof. Aviad Rubinstein
Stanford MS&E 211DS (Introduction to Optimization: Data Science) taught by Prof. Amin Saberi
AddisCoder 🧡 taught by Prof. Jelani Nelson (Check this out! And if teaching in Ethiopia interests you, APPLY!)
Theoretical Computer Science (journal)
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 on broad range of papers and future directions (mainly for CS 331X: AI for Algorithmic Reasoning and Optimization and MS&E 319: Matching Theory)
Incentive Aware Machine Learning: Robustness with(out) predictions
Thickness and Information in Dynamic Matching Markets
Learning to design voting rules
More: 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