About

Md Noor E Alam is a tenured Associate Professor in the Department of Mechanical & Industrial Engineering at Northeastern University and a Core AI Faculty Member at the Institute for Experiential AI. His research focuses on Operations Research, Machine Learning, and Causal AI to develop interpretable solutions for public health, energy, and manufacturing sectors. Dr. Alam's work involves designing scalable computational approaches for large-scale optimization problems, particularly in robust causal inference, to support policy decisions in healthcare, manufacturing, and service industries. Prior to his current position, he was a Postdoctoral Research Fellow at the Sloan School of Management at MIT. He has received notable recognitions including the National Science Foundation CAREER Award in 2021 and the COE Faculty Fellow in 2025. Dr. Alam has served as a board member of the Logistics and Supply Chain Division of the Institute of Industrial and Systems Engineers (IISE), and as an associate editor for prominent journals such as INFORMS Journal on Computing and INFORMS Journal on Applied Analytics. His educational background includes a PhD in Engineering Management from the University of Alberta, Canada, and master's and bachelor's degrees in Industrial & Production Engineering from Bangladesh University of Engineering & Technology.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Computer Security
• Business
• Algorithm
• Operations research
• Engineering
• Computer network
• Medicine
• Mathematical optimization
• Mathematical analysis
• Internal medicine
• Pharmacology
• Applied mathematics
• Operations management
• Mathematics

Selected publications

• Computational approaches for solving two-echelon vehicle and UAV routing problems for post-disaster humanitarian operations

  Expert Systems with Applications · 2023 · 57 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Security
  • Computer Science
  PublisherDOI

• Sampling Kaczmarz-Motzkin method for linear feasibility problems: generalization and acceleration

  Mathematical Programming · 2021 · 24 citations

  Senior authorCorresponding
  • Computer Science
  • Mathematics
  • Applied mathematics
  DOI

• A machine learning framework to predict the risk of opioid use disorder

  Machine Learning with Applications · 2021 · 37 citations

  Senior authorCorresponding
  • Machine Learning
  • Artificial Intelligence
  • Machine Learning

  Opioid overdose epidemic is a national public health crisis in the US. Little is known about how large-scale data analytics can be leveraged to help physicians predict whether a prescription opioid user will develop opioid use disorder. To that end, we proposed a machine learning framework for identifying potential risk factors of opioid use disorder from a large-scale healthcare claims data. These risk factors identified by the proposed framework can be used to predict which patient will be at higher risk of opioid use disorder following an opioid prescription. We utilized clinical diagnosis and prescription histories from Massachusetts commercially insured individuals who were prescribed opioids. We performed several feature selection techniques on a class imbalanced analytic sample to identify patient-level demographic and clinical features that were influential predictors of opioid use disorder. We, then compared the predictive power of four well-known machine learning algorithms: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting to predict the patients’ risk of opioid use disorder. The study results showed that the Random Forest model achieved superior predictive performance in terms of AUC and recall. Alongside the higher predictive accuracy, the random forest model identified clinical features, some of which were fairly consistent with prior clinical findings. In addition, our proposed framework is capable of extracting some other clinical features, which are predictive of opioid use disorder and indicative as the proxies of patients’ health status. We anticipate that the findings of our study will potentially help reduce in-appropriate and over prescription of opioids.

  DOI

Recent grants

• CAREER: Robust Matching Algorithms for Causal Inference in Large Observational Studies

  NSF · $500k · 2021–2027

Frequent coauthors

• Tasnim Ibn Faiz

  20 shared

• Gary J. Young

  Northeastern University

  16 shared

• Md Sarowar Morshed

  15 shared

• Md Mahmudul Hasan

  Sylhet Agricultural University

  13 shared

• Chrysafis Vogiatzis

  University of Illinois Urbana-Champaign

  11 shared

• Md Saiful Islam

  Independent University

  7 shared

• Alicia Modestino

  7 shared

• Leonard D. Young

  Massachusetts Department of Public Health

  7 shared

Labs

• Decision Analytics LabPI

Awards & honors

• COE Faculty Fellow (2025)
• National Science Foundation CAREER Award (2021)
• 1st Place Winners: 2019 Association for Public Policy Analys…
• Analytics Best Track Paper Award, 2016 IEOM Detroit Conferen…
• Postdoctoral Fellowship, Natural Sciences and Engineering Re…

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