ISE assistant professors Karmel S. Shehadeh and Shengbo Wang, and associate professor Andrés Gómez.
Faculty members from USC’s Daniel J Epstein Department of Industrial and Systems Engineering featured prominently during the 2025 awards of the INFORMS Annual Meeting in Atlanta, Georgia. The event featured over 6,000 academic scholars, industry leaders and experts spanning operations research analytics and artificial intelligence.
Three USC Viterbi School of Engineering ISE faculty members — associate professor Andrés Gómez, and assistant professors Karmel S. Shehadeh and Shengbo Wang — were recognized with key awards at the event.
“I’m incredibly proud to congratulate Andrés, Karmel, and Shengbo on these well-deserved honors,” said Maged Dessouky, Tryon Chair in Industrial and Systems Engineering and chair of the Epstein Department at USC Viterbi. “These awards reflect the exceptional caliber and breadth of research excellence within our department, spanning optimization theory, location analysis, and applied probability. Epstein Department faculty are advancing both the theoretical foundations and practical applications of industrial and systems engineering.”
Andrés Gómez wins prestigious 2025 ICS Prize for Operations Research and Computer Science
The INFORMS Computing Society (ICS) Prize is awarded annually to the best English-language paper or group of related papers that advance the state of the art in the interface of operations research and computer science.
Epstein Family Early Career Chair in Industrial and Systems Engineering Andrés Gómez was announced as the winner of the 2025 ICS Prize, along with his co-authors Alper Atamtürk and Shaoning Han. Han is a former ISE Ph.D. student supervised by Gómez, whose papers were part of his Ph.D. dissertation. Later, he was also a USC Viterbi postdoctoral researcher under the supervision of Professor Jong-Shi Pang.
The ICS award was presented for a collection of six papers from the research team. Their work tackles a common challenge in both optimization and machine learning: many problems we care about—like selecting the best features in a regression model, building interpretable predictors, or managing investment portfolios with risk—are naturally expressed with “on/off” decisions, for example, whether to keep or discard a variable or to invest or not invest in an asset. These problems are hard to solve directly because of nonconvexities, meaning standard optimization tools struggle. The authors’ central idea was to find clever ways to “convexify” these problems—reformulating them so that they become mathematically friendlier while still preserving their essential structure. Starting with special cases like certain quadratic systems (M-matrices) and then generalizing to broader families of conic quadratic problems, they demonstrated how concepts like submodularity—a kind of mathematical diminishing returns property—can be harnessed to create tighter, more efficient formulations.
The impact of Gómez and his co-authors’ work is shown in faster, more reliable methods for tasks like variable selection, risk management, and signal processing, showing how deep mathematical insights can translate into practical advances across optimization and data science.
Gómez joined the Epstein Department in 2019 with a focus on developing new theories and tools for challenging optimization problems, particularly in finance, machine learning and statistics. Prior to joining USC, Gómez worked as an assistant professor in the Department of Industrial Engineering at the University of Pittsburgh.
Gómez has received numerous prior honors, including a 2024 Young Investigator Program Award of $450,000 from the Air Force Office of Scientific Research, for the development of new methodological and computational tools to enhance algorithmic decision-making and AI. He was named a 2023 finalist in the INFORMS Junior Faculty Information Group Paper Competition, and he was an awardee of the Google Research Scholar Program in 2022.
In 2023, Gómez and his collaborators received a $700,000 AFOSR grant to establish state-of-the-art GPU Clusters within the Epstein Department.
Karmel S. Shehadeh Honored with the Chuck ReVelle Rising Star Award
WiSE Gabilan Assistant Professor in the Epstein Department Karmel S. Shehadeh was announced as the 2025 recipient of the prestigious Chuck ReVelle Rising Star Award from INFORMS Section on Location Analysis (SOLA). The award is announced every other year at the SOLA business meeting at the INFORMS conference, honoring an early career researcher who has made a “significant contribution to location analysis research and displays the potential to continue to do so.” This award is regarded as an extremely high honor within the locational analysis community. Shehadeh was recognized for her track record in addressing emerging location problems in healthcare and humanitarian fields, among others.
Shehadeh is an expert in data-driven optimization under uncertainty and (mixed) integer programming. Prior to joining USC, Shehadeh was an assistant professor of ISE at Lehigh University.
Shehadeh’s research pushes the boundaries of the theory and applications of stochastic and distributionally robust optimization methodologies and their applications. Her work addresses challenging real-world problems across several application domains, including facility location, transportation systems, and healthcare operations and analytics. Most recently, Shehadeh has developed data-driven models to reduce delays in elective surgery and streamline hospital scheduling. Her upcoming project, “Advancing Contextual Stochastic Optimization via Distributionally Robust Optimization Techniques,” is supported by a grant from the Air Force Office of Scientific Research (AFOSR) under its Mathematical Optimization program.
Shehadeh has received a number of previous awards and honors including the 2022 INFORMS Minority Issues Forum (MIF) Paper Award, 2024 INFORMS MIF Early Career Award (honorable mention), a Junior Faculty Interest Group (JFIG) Paper Prize (finalist), a JFIG Excellence Teaching award (third place), a Service Science Best Cluster Paper Award (finalist), and the 2023 Alfred Nobel Robinson Faculty Award at Lehigh University.
Shengbo Wang Receives Best Paper Award for Stochastic Differential Equations Research
Assistant professor in the Epstein Department Shengbo Wang was honored at the event with a Best Student Paper award by the INFORMS Applied Probability Society. The award recognized his paper, An Efficient High-dimensional Gradient Estimator for Stochastic Differential Equations, published in Neurips 2024.
The paper addresses the challenge of optimizing large-scale, complex dynamic decision-making systems that evolve randomly over time. Many such systems in engineering, operations, management, and machine learning applications can be modeled using a mathematical framework known as parameterized stochastic differential equations (SDEs). Modern large-scale systems often require SDE models with millions or even billions of tuning parameters. Systematically finding the optimal parameter values typically necessitates computing a high-dimensional gradient, which has traditionally been computationally intensive. Wang’s paper introduces a novel approach to efficiently compute gradients using simulation methods. The proposed algorithm exhibits a remarkable property: it requires roughly the same amount of computation time regardless of the number of parameters.
Wang is an expert in a wide range of areas within applied probability, machine learning, and simulation. He received his Ph.D. from the Department of Management Science and Engineering (MS&E) at Stanford University.
Wang’s research focuses on the design and analysis of algorithms for learning and controlling dynamic engineering systems, with applications in machine learning, management science, and operations research. Key areas of his work include the development of statistically tractable data-driven models and scalable algorithms for reliable dynamic policy optimization and reinforcement learning (RL). A central theme of Wang’s research is robustness and scalability in modern data-driven policy learning.
Wang has been honored with previous awards, including the 2024 Meritorious Reviewer Award Mathematics of Operations Research, and the 2020 Merrill Presidential Scholar at Cornell University.
Published on November 19th, 2025
Last updated on January 16th, 2026