Priscila de Azevedo Drummond

I study how organizations with limited resources decide where to intervene, especially when outcomes are hard to measure, and the available data is unreliable.

I have worked on improving resource allocation under similar constraints in a variety of settings, including a middle school, a soccer league, a network of women’s crisis centers, a border checkpoint, Department of Labor inspections, and a group of anti-trafficking agencies working on the same cases. Each time, the setting and method changed, but the main question stayed the same.

I am now a PhD candidate in Industrial Engineering at Northeastern University, in the ORSJ lab, working with Prof. Kayse Lee Maass. I have scheduled my defense for December 2, 2026. My dissertation develops network interdiction models for multi-agency coordination. I look at what happens to enforcement decisions when several agencies act on the same part of a network, and their actions are not independent. The main application is labor trafficking. I aim to help agencies working against labor trafficking make better decisions with limited, hidden information and very few resources.

What connects all of my work is a focus on decisions where the costs and benefits affect different people. For example, a model that identifies the farms most likely to involve labor trafficking, so that Department of Labor inspections can be sent there, or one that shows when enforcement operations should work with social services and where they should not. It is not just a technical problem. It depends on the decision-maker’s perspective, and that is at the heart of the modeler’s challenge.

I came to engineering after earning an Industrial Design - Product Design degree, working in finance, teaching myself new skills, and finding my own path and my joy along the way. This experience has shaped how I approach teaching.

Research interests: Coordinated decision-making across organizations · Supply Chain Management · Prescriptive analytics for multi-stakeholder intervention · Resource allocation in constrained service and supply systems · Supply chain compliance and labor risk · Measurement design for contested and multi-dimensional outcomes · Responsible analytics and the consequences of optimized decisions · Analytics and operations pedagogy

Methods: Network optimization and interdiction · Integer and mixed-integer programming · Bayesian decision analysis · Multilevel and hierarchical regression · Data envelopment analysis · Expert elicitation · Scoping and systematic review · Qualitative and participatory methods

Selected Highlights

🏆 2026 COE Outstanding Graduate Teaching Award — Northeastern University College of Engineering College-level teaching award.

📖 Instructor of Record — OR 6205: Deterministic Operations Research, Northeastern University (Spring 2026) Rated above departmental means for enthusiasm, sufficiency of feedback, inclusive learning environment, and organization of course materials.

🛠️ Course redesign leadership — IE 4516: Quality Assurance, Northeastern University Led a complete redesign of the course teaching materials, with Prof. Hugh McManus and Dr. Rehab Ali, around experimental, simulation-based, and project-based learning.

📊 Student-ranked teaching — IE 5617: Lean Concepts and Applications In an end-of-term poll ranking all 17 class sessions, the two modules I taught placed 3rd and 7th, at 4.81 and 4.67 on a 5-point scale — higher than the instructor’s own scores for the same sessions.

📄 Published in PLOS ONE (2024) — Multilevel regression model for detecting H-2A labor violations in U.S. agriculture With Jafari, Bhimani, Nishigaya, Ding, Farrell & Maass. DOI: 10.1371/journal.pone.0302960

🎤 INFORMS Annual Meeting 2025 — Session Chair, Cooperation and Partnership in Public Services, Atlanta, GA

🎓 Post-Secondary Teaching Certification — Northeastern University, completed Spring 2026

Research

Job market paper

Collaboration in Anti-Trafficking Efforts: A Network Interdiction Problem with Multiple Dependent Attacks (with Kayse Lee Maass)

Anti-trafficking agencies — a federal investigator, a labor inspector, a social service provider — often work the same operation without knowing what the others are doing. The literature almost always assumes either that only one actor intervenes on a given element, or that multiple interventions combine independently. Neither matches how task forces actually work, and getting it wrong changes where the budget should go.

The paper gives an exact linearization with dual bounds, and asks two questions aimed at a management audience: what does a decision-maker lose by assuming independence, and how large is the coordination gap between a centralized task force and the same agencies spending the same total separately?

The wider program

My dissertation comprises four papers designed to inform each other: a PRISMA-ScR scoping review establishing the gap, the model above, an algorithm and computational study, and a theory and complexity paper.

Earlier work developed a Bayesian decision model with cost–benefit analysis for U.S. ports of entry, determining when officers should escalate a screening for possible trafficking. At UFRGS in Brazil, I applied data envelopment analysis, alongside structured expert judgment, to the network of services for survivors of domestic and gender-based violence across 450+ municipalities — mapping provider flows, survivor pathways, and information pathways through the system. The finding that mattered was about portfolio rather than volume: the range of services available constrained effectiveness more than the total amount invested.

Teaching

I have taught eight university courses across four universities and more than 300 students, and before that spent three and a half years teaching middle school in Brazil — from students learning boatbuilding in an underserved community in Rio de Janeiro, to graduate students working through the mathematics of linear programming in Boston.

In Spring 2026 I was instructor of record for OR 6205 (Deterministic Operations Research) at Northeastern, and that year received the College of Engineering Outstanding Graduate Teaching Award. My approach centers on:

• Backward design — learning objectives drive every assessment and activity
• Active learning — collaborative board work, case studies, and real-world LP modeling
• Transparent assessment — detailed rubrics and rework opportunities so students own their learning
• Inclusive engagement — multiple feedback channels, anonymous and named, to reach all learners

“I did like that she was very happy to teach this course cause I could tell that she loves this topic so much, and I like that she was able to listen to our feedback to make the course better.” — OR 6205 student, Spring 2026 (TRACE evaluation)

“Your communication is articulate and you have a natural warmth that makes us feel comfortable. It’s clear that you care about teaching and helping us understand.” — IE 5617 student, Summer 2025

See the Teaching page for syllabi, course maps, and teaching materials.