About

Aram Bahrini is a Teaching Assistant Professor and Deloitte Scholar at the Gies College of Business, University of Illinois at Urbana-Champaign. He holds a Ph.D. in Systems Engineering from the University of Virginia, an M.S. in Mathematics from the University of British Columbia, and an M.S. in Industrial & Manufacturing Systems Engineering from Kansas State University. His professional experience includes roles as a Lecturer in Systems Engineering at the University of Virginia, a Teacher of AP-Statistics, and a Research Affiliate at the Systems Engineering-Link Lab. Bahrini has also served as a 2nd Lieutenant in the Logistics Unit of the Army Ground Forces. His research focuses on systems engineering, decision-making, and data science, with recent publications covering topics such as intelligent negotiation frameworks, sustainable supply chains, supply chain competition, and applications of machine learning and deep learning in various fields. Bahrini has contributed to the academic community through numerous articles, conference proceedings, and peer reviews, demonstrating a strong engagement in advancing knowledge in his areas of expertise. He has been recognized with awards such as the Deloitte Scholar and Teachers Ranked as Excellent, and he is actively involved in service roles including ad hoc reviewing for multiple scientific journals.

Research topics

• Artificial Intelligence
• Machine Learning
• Computer Science
• Mathematics
• Geography
• Engineering
• Statistics

Selected publications

• An analytics-based structural modeling study of circular practices in sustainable supply chains

  Supply Chain Analytics · 2026-01-26

  articleOpen accessSenior author

  The importance of sustainability has significantly increased for many companies in recent years. However, limited studies have explored the integrated impact of circular economy practices, dynamic capabilities, and fields of action on supply chain sustainability. This study addresses this gap by applying circular economy principles within retail‑equipment supply chains to evaluate their impact on the economic, environmental, and social dimensions of sustainability. A comprehensive review of the relevant literature on circular economy, dynamic capabilities, fields of action, and sustainable supply chains facilitated the identification of 51 key indicators. To fulfill the research objectives, a retail-equipping manufacturing company was selected as the focal point of analysis, and data was collected through a structured questionnaire. We analyzed the data via partial least squares structural equation modeling (PLS-SEM) in SmartPLS 3, estimating a reflective–reflective hierarchical component model on n = 131 responses to a 51-item instrument covering circular economy fields, dynamic capabilities, and sustainability outcomes. Results support seven hypotheses: both fields of action (H1–H3) and dynamic capabilities (H4–H6) positively affect economic, environmental, and social performance, and their joint effect is strong (H7: β = 0.73; R2 = 0.54). The study presents an integrated, empirically validated model that links circular economy dynamic capabilities and fields of action, thereby extending dynamic capabilities theory into the sustainability domain. The findings suggest that circular economy principles not only support environmental protection and cost efficiency but also enhance organizational agility, stakeholder satisfaction, and resilience. While the specific outcomes may vary across industries, this study offers a robust foundation for future research and practical insights to guide strategic decision-making for companies aiming to transition to more sustainable, circular supply chains. The model provides actionable diagnostics to prioritize investments in design, reverse logistics, and capability development aligned with triple-bottom-line goals in manufacturing.

  PublisherDOI

• An Intelligent Negotiation Framework for Industry 5.0: Integrating Deep Reinforcement Learning, Natural Language Processing, and MCDM

  Group Decision and Negotiation · 2026-04-03

  article1st authorCorresponding
  PublisherDOI

• A Hybrid Data-Driven and Fuzzy MCDM Approach for Employee Selection

  2025-05-02 · 1 citations

  articleSenior author

  Employee selection, a cornerstone of human resource management, critically shapes organizational performance and long-term effectiveness. While traditional approaches primarily rely on expert-based evaluations, this study proposes a novel hybrid framework that integrates Multi-Criteria Decision-Making methods with data mining techniques to reduce the dimensionality of the number of criteria or variables considered. By integrating backward regression with fuzzy Multi-Criteria Decision-Making methods, our framework reduces model complexity and captures criteria interdependencies, while fuzzy logic addresses ambiguity in expert judgment, a gap often overlooked in prior research. The methodology first uses backward regression modeling with the employee attrition rate as the response variable to identify core criteria. Subsequently, the fuzzy Decision-Making Trial and Evaluation Laboratory analyzes interrelationships between criteria, followed by the fuzzy Analytic Network Process for weighting criteria and ranking candidates. We validate our approach using real-world recruitment data—including expert interview scores and historical attrition—from a company specializing in electronic attendance systems. The AI-generated rankings are benchmarked against these expert-based evaluations to assess alignment with human judgment. Initially, 17 criteria were systematically reduced to 11 core factors, resulting in a streamlined yet robust evaluation system. Our findings emphasize that “Time-of-service,” “Requested-wage,” “Teamwork,” and “Leadership” are the most critical criteria influencing effective IT personnel selection.

  PublisherDOI

• An annotated bibliography for comparative prime number theory

  Expositiones Mathematicae · 2025-01-01 · 4 citations

  articleOpen access

  The goal of this annotated bibliography is to record every publication on the topic of comparative prime number theory together with a summary of its results. We use a unified system of notation for the quantities being studied and for the hypotheses under which results are obtained.

  PublisherDOI

• Charging Ahead or Fueling Change? Decoding the Green Future of the United States

  2025-05-02

  articleSenior author

  The US faces increasing uncertainty in its clean energy transition amid recent policy reversals on net-zero goals. This study explores the strategic question of whether electric vehicles or ethanol-based biofuels offer a more viable path toward sustainable transportation. We conduct a comparative analysis of EVs and biofuels by integrating lifecycle assessment, time-series forecasting, and predictive analytics. Environmental impacts, economic feasibility, and adoption trajectories are evaluated through a US-focused lens while referencing global policy benchmarks and consumer behavior trends. Our model incorporates emissions across all scopes, infrastructure requirements, and policy sensitivity, offering a multifaceted view of these competing technologies. Preliminary findings highlight distinct trade-offs. EVs benefit from lower operational emissions and policy momentum but face challenges in battery production and grid dependency. Biofuels, especially ethanol, offer near-term scalability and lower infrastructure costs but struggle with market penetration and lifecycle efficiency. This research delivers evidencebased insights to guide policy and investment decisions. By clarifying the advantages and constraints of each approach, the study contributes to shaping a more resilient and effective clean energy future.

  PublisherDOI

• Two-period competition between traditional and online supply chains with strategic customers and different pricing strategies

  Computers & Industrial Engineering · 2025-10-30 · 1 citations

  articleCorresponding
  PublisherDOI

• Beyond Traditional Biases in AI Hiring: Exposing the Hidden Systemic Challenges in Resume Screening

  2025-05-02 · 2 citations

  articleSenior author

  From automatically curating your music playlist to guiding life-changing hiring decisions, artificial intelligence now discreetly shapes every corner of our world, including how we recruit. In theory, using AI in recruitment streamlines processes and saves valuable time for hiring teams. Yet, concerns remain about whether automated methods are truly fair and equitable. AI can inadvertently encode biases tied to demographic factors such as race and gender, prompting organizations to explore Explainable AI for greater transparency. However, hidden biases may persist undetected, even when personal information is removed. These details can threaten the integrity of our hiring practices and often slip beneath our awareness. Our work explores these overlooked biases, offering insights and strategies to foster more ethical, inclusive recruitment processes. In this paper, we conduct a controlled experiment using a Large Language Modelsbased recruitment tool to quantify how subtle resume variations can influence candidate evaluations. By isolating factors such as “career gaps” and “keyword usage”, we highlight overlooked biases that can significantly alter AIdriven hiring outcomes. Our work explores these overlooked biases, offering insights and strategies to foster more ethical, inclusive recruitment processes. Keywords- Algorithmic bias, AI fairness, LLM-based hiring, Explainable AI, Ethical AI in recruitment

  PublisherDOI

• A novel approach for multi-objective truck scheduling problems in a cross-docking center

  International Journal of Systems Assurance Engineering and Management · 2024-10-17 · 4 citations

  article
  PublisherDOI

• Improving churn prediction using imperialist competitive algorithm for feature selection in telecom

  International Journal of Business Information Systems · 2024-01-01

  articleSenior author

  Customer churn prediction is often formulated as a binary classification task. Feature selection is a significant preprocessing step that can improve the performance of the resulted churn prediction model. Its principal objective is to find a minimum set that eliminates irrelevant or redundant features and increases the performance of learning techniques. This study proposes a new feature selection method that exploits the imperialist competitive algorithm to select the optimal feature set. To evaluate the usefulness of the proposed method, three state-of-the-art filter feature selection methods are selected. Also, we develop a wrapper feature selection method that works based on the genetic algorithm. We conduct the experiments using two churn datasets of the telecommunication industry. The experiments show that the proposed feature selection method considerably improves the performance of the generated models.

  PublisherDOI

• Equity in Emergency Care: A Predictive Analysis of Cultural and Racial Impacts on Health Outcomes

  2024-05-03 · 1 citations

  articleSenior author

  This study addresses the critical issue of health disparities in emergency departments, motivated by the necessity to understand how cultural and racial identities influence patient behaviors and outcomes. We examine the interrelation between these identities and various factors, such as hospital stay duration, mode of arrival, and biases, collectively contributing to the disparities in emergency healthcare results. Utilizing a predictive analytical framework, our methodology involves a comprehensive analysis of emergency department patient data to uncover patterns and correlations that signify the impact of cultural and racial factors. This approach facilitates a deeper understanding of the challenges and identifies opportunities for enhancing healthcare equity in emergency settings. We anticipate revealing significant insights into how cultural and racial identities affect health outcomes in the emergency department. This strategy is pivotal in narrowing the health disparities gap and elevating the quality of care for all patients, underscoring the crucial role of incorporating cultural and racial awareness in fostering equitable healthcare outcomes.

  PublisherDOI

Frequent coauthors

• Hossein Abbasimehr

  Azarbaijan Shahid Madani University

  14 shared

• Robert J. Riggs

  University of Virginia

  12 shared

• Lihua Cai

  South China Normal University

  8 shared

• Anna Baglione

  University of Virginia

  8 shared

• Mehdi Boukhechba

  Janssen (United States)

  8 shared

• Reza Paki

  Politecnico di Milano

  6 shared

• Kimberly Dowdell

  University of Virginia

  5 shared

• Karen Measells

  University of Virginia Health System

  5 shared

Labs

• Gies Business Research LabPI

Education

• Teaching and Research Assistant, Mathematics

  The University of British Columbia

  2019

Awards & honors

• Deloitte Scholar, University of Illinois at Urbana-Champaign…
• Teachers Ranked as Excellent, UIUC Center for Innovation in…
• Teachers Ranked as Excellent, UIUC Center for Innovation in…
• Specialized Faculty Council, Gies College of Business, 2025-…