Stochastic approximation
Finite-time analysis of iterative algorithms under random noise.
Industrial and Systems Engineering
Bulut
Boru
Ph.D. Student · University of Minnesota
I study stochastic approximation algorithms—including methods used in reinforcement learning—and the tradeoffs among bias, tail behavior, and computational efficiency.
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About
I am a fourth-year Ph.D. student in the Department of Industrial and Systems Engineering at the University of Minnesota.
My research uses probability and optimization to understand and improve stochastic algorithms, with a particular interest in reinforcement learning. I study concentration behavior, sample complexity, and algorithmic design for learning and decision systems.
My current work develops complementary ways to improve the tail behavior of stochastic approximation algorithms while quantifying the resulting transient bias and complexity.
Concentration
High-probability guarantees and bias-tail tradeoffs.
Reinforcement learning
Stochastic-approximation methods for learning and sequential decision-making.
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Education
2023–Present
Ph.D. in Industrial and Systems Engineering
University of Minnesota · Minneapolis, Minnesota
GPA: 3.88
Philosophy minor focused on philosophy of mathematics
Advisor: Martin Zubeldia
Selected doctoral-level coursework
- IE 8532 · Stochastic Processes and Queues A-
- MATH 8651 · Theory of Probability A
- MATH 8660 · Topics in Probability: Measure Theory A
2023
B.Sc. in Industrial Engineering
Koç University · Istanbul, Türkiye
CGPA: 3.83
Full scholarship · Vehbi Koç Honor Award
2023
B.Sc. in Computer Science and Engineering
Koç University · Istanbul, Türkiye
CGPA: 3.83
Full scholarship
Ph.D. progress
Four milestones toward graduation
A concise view of my progress from program requirements to the dissertation defense.
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Completed · Fall 2024
Qualifying examination
Passed the program’s qualifying examination.
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Completed
Credit requirement
Completed the program’s minimum requirement of 44 credits.
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In progress · Exam planned November 2026
Thesis proposal and preliminary examination
I am developing my thesis proposal. Passing the preliminary examination will advance me to Ph.D. candidacy.
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Future milestone
Dissertation defense
Defend my dissertation as the final major step before graduation.
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Publications
Bias–Tail Concentration Tradeoffs for Stochastic Approximation Algorithms
Develops adaptive-sampling and projection-based approaches that improve high-probability tail behavior while characterizing the resulting bias and sample-complexity tradeoffs.
In preparation for submission to Mathematics of Operations Research.
Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics
Data, 7(11), Article 166.
Identifies relationships between mobility data and daily COVID-19 case counts and develops a machine-learning model to forecast upcoming confirmed cases using Google mobility reports, daily testing data, and historical case counts.
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Talks & Presentations
Concentration of Variance-Reduced Stochastic Approximation
2025 INFORMS Annual Meeting
Georgia World Congress Center
Presented joint work with Sajad Khodadadian and Martin Zubeldia. This research was subsequently developed into the manuscript Bias–Tail Concentration Tradeoffs for Stochastic Approximation Algorithms.
Presented under the work’s original conference title.
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Research experience
2024–2025
Research Assistant
University of Minnesota
Research in stochastic approximation and concentration analysis, advised by Martin Zubeldia. Appointments in Summer and Fall 2024, and Summer and Fall 2025.
Oct 2021–May 2023
Long-Term Research Intern Part-time
Borda Technology · Istanbul, Türkiye
Worked with Professor Sibel Salman of Koç University and Professor Özgür M. Araz of the University of Nebraska–Lincoln.
Nov 2022–May 2023
Dynamic Operating Room Scheduling
Developed a model to dynamically optimize urgent operating-room schedules using dynamic programming and mathematical modeling, and applied machine-learning methods to predict operating times. The project was funded by TÜBİTAK.
Oct 2021–Oct 2022
Optimizing Patient and Resource Transportation Within Hospital Units
Optimized hospital transportation using mathematical models and genetic algorithms, formulated dynamic job-scheduling and capacitated routing problems, and developed machine-learning methods to assign transporter waiting locations.
Summer 2021
Summer Down Under: Research Internship
The University of Western Australia · Perth, Australia
Analyzed GPS drifter data to identify surface-ocean flow features, using Python and MATLAB for trajectory, velocity, and energy analysis. Supervised by Professor C. Pattiaratchi; full scholarship recipient.
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Teaching
Teaching assistantships across probability, machine learning, simulation, systems engineering, decision analysis, and statistics.
Fall 2026 Current
IE 3521 · Statistics, Quality, and Reliability
Teaching Assistant · Professor Martin Zubeldia
Spring 2026
IE 5545 · Decision Analysis
Teaching Assistant · Professor Kris Iyer
Spring 2025
IE 4011 · Stochastic Models
Teaching Assistant · Professor William L. Cooper
Spring 2024
IE 5080 · Machine Learning
Teaching Assistant · Professor Martin Zubeldia
Spring 2024
IE 5113 · Systems Engineering II
Teaching Assistant · Professor Kathryn Wust
Fall 2023
IE 3553/5553 · Simulation
Teaching Assistant · Professor Saumya Sinha
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Let’s connect.
I am always glad to discuss stochastic approximation, reinforcement learning, applied probability, and related research.