Stochastic approximation
Finite-time analysis of iterative algorithms under random noise.
Industrial & Systems Engineering
Bulut
Boru
Ph.D. Student · University of Minnesota
I study stochastic approximation algorithms 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. I am especially interested in 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.
Algorithm design
Adaptive sampling, projection, and variance-reduction methods.
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Education
2023–Present
Ph.D. in Industrial and System Engineering
University of Minnesota · Minneapolis, Minnesota
GPA: 3.88
Philosophy minor
Advisor: Martin Zubeldia
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
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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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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.
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, applied probability, and related research.