Curriculum Vitae

Research Interests

Optimization and learning for high-stakes decision-making under uncertainty; equitable resource allocation; cost-aware and mixed-fidelity bandit learning; reinforcement learning with human feedback; human-centered operations research; human–AI collaboration.

Education

• Massachusetts Institute of Technology (MIT), Cambridge, MA
  PhD in Operations Research; expected completion, June 2028
  Advisor: Swati Gupta

• Georgia Institute of Technology, Atlanta, GA
  B.S. in Industrial and Systems Engineering; May 2023
  Valedictorian, College of Engineering

Honors and Fellowships

• 2025–2028
  National Science Foundation Graduate Research Fellowship (GRFP)

• 2023–2024
  Henry Gabbay MIT Ph.D. Fellowship
  Awarded to one promising incoming first-year ORC student each year.

• Spring 2023
  Institute of Industrial & Systems Engineering (IISE) National Capstone Competition – 1st Place
  National capstone award for a supplier selection decision-support tool utilizing Thompson sampling frameworks.

• Spring 2023
  Valedictorian, College of Engineering, Georgia Institute of Technology (Davidson Family Tau Beta Pi Award)
  Awarded to the best graduate in the College of Engineering (across 11 majors) at Georgia Tech.

• Spring 2022
  Adobe Women in Technology Scholarship
  $10,000 scholarship awarded nationally to 10 women for demonstrated skills in computing.

• 2021–2023
  Georgia Tech IISE Awards:
  • Best 2nd/3rd Year Student in Industrial Engineering (2021).
  • Best Undergraduate Researcher in Industrial Engineering (2022).
  • Best Display of Leadership in the GT Industrial Engineering Department (2023).
  • Best Graduating Senior in Industrial Engineering (2023).
• 2021, 2022
  Georgia Tech Undergraduate Research Awards:
  • 3rd Place Poster, School of Engineering (2021).
  • 2nd Place Oral Presentation, School of Engineering (2022).

• Fall 2021, Fall 2022
  Society of Women Engineers Undergraduate Research Competition Finalist
  Top 5 women based on demonstrated research in engineering.

• Fall 2021
  INFORMS Undergraduate Travel Scholarship
  Competitive scholarship to attend the INFORMS Annual Meeting based on academic record, research, and leadership.

• 2021
  Brooke Owens Fellowship
  Competitive fellowship for internships and mentorship in aerospace for women and gender-minority students.

• Fall 2020
  Literature Media & Communications First Year Writing Award
  Best first-year research composition at Georgia Tech (out of ~4,500 students).

Publications

1. Pollack, M., Piansky, R., Gupta, S., & Molzahn, D. (2025).
   Equitably allocating wildfire resilience investments for power grids: The curse of aggregation and vulnerability indices.
   Applied Energy.
   DOI:10.1016/j.apenergy.2025.125501

2. Kim, C., Moondra, J., Verma, S., Pollack, M., Kong, L., Tambe, M., & Gupta, S. (2025).
   Navigating the Social Welfare Frontier: Portfolios for Multi-Objective Reinforcement Learning.
   International Conference on Machine Learning (ICML).
   PMLR v267

Preprints

1. Pollack, M., & Steimle, L. N. (2024).
   The implications of state aggregation in deteriorating Markov Decision Processes with optimal threshold policies.
   arXiv preprint arXiv:2405.12912.

In Preparation

1. Pollack, M., Daillak, T., Toner, M., Uygun, K., Yeh, H., & Gupta, S. (2026).
   When Should AI Defer? Responsible Automation for Deceased Donor Kidney Allocation.

2. Pollack, M., Gupta, S., & Martinez Duvall, D. (2025).
   Cost-Averse Learning with Mixed-Fidelity Bandit Feedback.

Talks

• When Should AI Defer? Responsible Automation for Deceased Donor Kidney Allocation
  • 2026 INFORMS Annual Meeting, San Francisco, CA (upcoming)
  • 2026 MIT-MGB Symposium, Cambridge, MA (upcoming)
  • 2026 INFORMS Healthcare Conference, Raleigh, NC (July 2026)
• Learning Over Mixed Fidelity Feedback
  • 2025 INFORMS Annual Meeting, Atlanta, GA (Session chair and speaker)
  • 2025 Analytics Edge course, MIT Sloan School of Management
• The Curse of Aggregation and Vulnerability Indices in Climate Resource Allocation
  • Guest lecture, ECN 310, Syracuse University (April 2025)
  • Guest lecture, Deterministic OR 2, Virginia Tech (December 2024)
  • 2024 INFORMS Annual Meeting, Seattle, WA
  • Energy Justice Analysis Division, U.S. Department of Energy (April 2024)
  • 2023 INFORMS Annual Meeting, Phoenix, AZ
• Online Learning for Supplier Selection: A Georgia Tech Capstone
  • 2023 IISE Annual Conference & Expo, New Orleans, LA (May 2023)
• State Space Design for Markov Decision Processes
  • 2022 INFORMS Annual Meeting, Indianapolis, IN

Research Experience

• 2023–Present
  Graduate Research Assistant
  Massachusetts Institute of Technology, Cambridge, MA
  Advisor: Prof. Swati Gupta
  Projects:
  1. Responsible automation and interpretable, uncertainty-aware AI for deceased donor kidney allocation.
  2. Cost-averse learning with mixed-fidelity bandit feedback.
  3. Portfolios for multi-objective reinforcement learning.

• 2022–2023
  Undergraduate Research Assistant
  Georgia Institute of Technology, Atlanta, GA
  Supervisors: Prof. Swati Gupta and Prof. Daniel Molzahn
  Project: Optimizing infrastructure investment decisions for powerline undergrounding to prevent power loss, decrease wildfire risk, and ensure equitable benefit across socioeconomic groups.

• 2020–2023
  Undergraduate Research Assistant
  Georgia Institute of Technology, Atlanta, GA
  Supervisor: Prof. Lauren Steimle
  Project: Optimal aggregation of the state spaces of Markov Decision Processes to balance precision of state space definition with accuracy of estimated transition probabilities.

Teaching Experience

• Fall 2026
  Teaching Assistant, 15.C57 Optimization Methods
  MIT Sloan School of Management, Cambridge, MA
  • Lead recitations, hold office hours, and assist with grading.
• August 2024–Present
  Lecturer and Project Lead
  BlendED, Cambridge, MA
  • Designed an 8-class seminar-style series introducing undergraduate engineering students to the field of operations research.
  • Serve as a research mentor for class projects and ongoing projects.

Industry Experience

• Summer 2026
  Strategy & Analytics Intern
  Morgan Stanley Wealth Management, New York, NY
  • Formulated a mixed-integer optimization model selecting which client to call, when, and which product to recommend for Relationship Managers, combining adoption propensities, expected net new assets, margins, and pickup probabilities.
  • Forecast inbound call demand to derive outbound capacity; showed quarterly contact targets were infeasible (~24 weeks needed for 90% coverage) and extended the model to a rolling-horizon dynamic optimization with a contact-risk augmented value score.
  • Achieved ~40% higher expected profit and ~5× lower opportunity cost than AUM-based ranking across 25 simulation replications, validated across multiple RMs.
  • Integrated the optimized call queue into RM Client Pulse, an agentic AI prototype, and scoped LLM translation of RM preferences into optimization constraints.
• Fall 2022
  Industrial Engineering Intern
  Tesla Motors, Fremont, CA
  • Built and deployed Markov Decision Process models that generated $12.5M in annual savings through optimized headcount planning across manufacturing operations.
  • Developed Multiple Linear Regression and stochastic decision models to quantify cycle-time bottlenecks, forecast staffing needs, and drive high-impact workforce allocation strategies.
  • Partnered with cross-functional engineering teams to translate analytical insights into actionable factory-floor process improvements.
• Fall 2021
  Flight Sciences Engineering Intern
  Hermeus Corporation, Atlanta, GA
  • Developed Python-based analysis and optimization tools, including an automated performance optimizer, to maximize vehicle efficiency across Mach regimes and flight profiles.
  • Prototyped machine-learning-driven analysis scripts to accelerate aerodynamic model evaluation and data processing.
• Summer 2021
  Brooke Owens Fellow
  Space Angels, New York, NY
  • Sourced deals and participated in founder introductions with prospective portfolio companies.
  • Wrote three content pieces on early-stage investment methodology, best practices for founders hiring their first twenty employees, and a research report on the state and direction of the InsurTech sector.

Skills

• Programming and Optimization: Julia, Python, Gurobi
• Methods: Optimization, reinforcement learning, bandits, simulation, machine learning
• Tools: Git, Windows, Microsoft Office
• Professional Skills: Technical writing, project management, interdisciplinary collaboration, presentations