جست‌وجوی استاد و پژوهشگر

Sara Amarمشاهده پروفایل

استادیار

• Multi-Criteria Decision Making
• Operations Research
• Industry 5.0
• +۷ مورد دیگر

Dr. Sara Amar is an Assistant Professor of Industrial and System Engineering at Bowling Green State University (BGSU), part of the College of Technology, Architecture and Applied Engineering. She holds a Ph.D. in Industrial and Logistics Engineering and a Master's in Logistics and Transportation. Her research focuses on Multi-Criteria Decision Making, Operations Research, Industry 5.0, Sustainable Development, and Urban Logistics. Dr. Amar actively contributes to academic conferences as a reviewer and committee chair, emphasizing practical applications of theoretical frameworks in industrial and logistical challenges. Her educational background includes advanced degrees in logistics and engineering, complemented by a strong publication record spanning topics from facility layout optimization to emotional factors in supply chain digitalization. Research highlights include sustainable urban planning frameworks, emotional intelligence integration in decision-making models, and bi-criteria optimization approaches for industrial problems. Dr. Amar's work bridges theoretical models with real-world applications in transportation, energy systems, and socio-economic policy design. Her publications demonstrate a focus on interdisciplinary solutions, combining mathematical modeling with practical case studies. Notable contributions include studies on energy transportation cost optimization using nonlinear programming and interactive facility layout design methodologies. While no specific grants or awards are listed, her active conference participation underscores her engagement with the academic community.

Chase Murrayمشاهده پروفایل

دانشیار

• Operations Research
• Autonomous Vehicles
• Vehicle Routing & Logistics
• +۵ مورد دیگر

Chase Murray is an Associate Professor in the Department of Industrial and Systems Engineering at the University at Buffalo (SUNY). He directs the SOAR Drone Facility and Optimator Lab , focusing on operations research applications for autonomous systems. His work includes drone logistics, routing optimization, and AI integration in delivery networks. Education: PhD, Industrial & Systems Engineering, University at Buffalo, 2010 ME, Industrial Engineering, Texas A&M University, 2000 BS, Industrial Engineering, Texas A&M University, 1998 Research Interests: Dr. Murray’s research emphasizes leveraging autonomous vehicles for logistics and surveillance. Key areas include: - Routing algorithms for drone-truck systems - Weather and traffic impact modeling - Integer programming for problem classification - FAA-certified drone pilot with hands-on SOAR facility testing . Publications: Over 30 peer-reviewed articles focus on drone coordination, vehicle routing, and optimization frameworks. Recent work includes graph-based ILP classification and weather-aware delivery systems. Labs & Software: SOAR : Drone test facility with 3D digital twin simulations Optimator Lab : Develops tools like VeRoViz (routing visualization) and tex2solver (LaTeX-to-code translator) Open-source projects: Ground Control Station software, ROS-enabled web interfaces Grants & Advising: Advised PhD students Ritwik Raj (traffic congestion modeling) and Lan Peng (weather-impact scheduling). Active in collaborative projects on AI-driven logistics systems.

Evrim Dalkiranمشاهده پروفایل

دانشیار

• Global Optimization
• Reformulation-Linearization Technique (RLT)
• Mixed-integer Nonlinear Programming (MINLP)
• +۹ مورد دیگر

Dr. Evrim Dalkiran is an Associate Professor in the Department of Industrial and Systems Engineering at Wayne State University's College of Engineering. She holds a Ph.D. in Industrial and Systems Engineering from Virginia Tech (2011), and B.S./M.S. from Bogazici University (2003, 2006). Her work focuses on global optimization and decision analysis with applications in healthcare operations and supply chain management. B.S., Industrial Engineering, Bogazici University, Turkey M.S., Industrial Engineering, Bogazici University, Turkey Ph.D., Industrial and Systems Engineering, Virginia Tech Her research spans Global Optimization , Reformulation-Linearization Technique , Mixed-Integer Programming , and Multi-Objective Optimization , with application domains in Healthcare Operations , Emergency Patient Flow , Supply Chain Management , and Diabetes Risk Modeling . Current research includes real-time coordination tools for emergency departments and inverse optimization models. Her publications demonstrate expertise in polynomial programming , RLT implementations , and healthcare logistics , with recent work on automotive sustainability and smart grid allocation . Articles span journals like European Journal of Operational Research and Operations Research . Best Paper Award, Journal of Global Optimization, 2012 Outstanding Faculty Award, WSU, 2016 Pritsker Dissertation Award Finalist, 2012 IIE Student Paper Competition Advisor, multiple awards As faculty advisor, she mentors students in projects related to emergency department optimization and supply chain modeling . Her grant history includes multiple Veterans Administration projects on patient flow and Goodyear-funded supply chain optimization. She teaches advanced optimization courses and maintains professional affiliations with INFORMS and Mathematical Optimization Society.

Purushothaman Damodaranمشاهده پروفایل

استاد

• Industrial Engineering
• Systems Engineering
• Operations Research
• +۷ مورد دیگر

Purushothaman Damodaran is a Presidential Teaching Professor at the Department of Industrial and Systems Engineering at Northern Illinois University . With expertise in large-scale optimization , logistics , simulation , scheduling , Lean Manufacturing , and Six Sigma , he develops mixed-integer linear formulations and novel algorithms for real-world applications in electronics manufacturing, healthcare, and logistics. Ph.D. , Industrial Engineering, Texas A&M University (2002) M.S. , Industrial Engineering, Northern Illinois University (1997) B.E. , Mechanical Engineering, University of Madras (1994) His research focuses on optimization models for batch processing machines , parallel machine scheduling , and simulation-based decision support systems . He has collaborated with industries like 3M , Caterpillar , Royal Caribbean Cruise Lines , and IBM , addressing challenges in manufacturing systems , warehouse design , and service operations . Recent publications highlight heuristic approaches (e.g., GRASP , Simulated Annealing , Particle Swarm Optimization ) for flow shop scheduling , job shop optimization , and material slitting problems . His work often integrates Lean Six Sigma principles and simulation-optimization for real-time manufacturing improvements . Scientific Awards : Best Paper Award, IISE Annual Conference (2021) National Science Foundation Grant (2008-2009) for server manufacturing research As a faculty teaching mentor at NIU, he teaches undergraduate and graduate courses in Six Sigma , Warehousing , and Advanced Lean Manufacturing , emphasizing real-life projects and student engagement .

Keely Croxtonمشاهده پروفایل

استاد

• Supply Chain Management
• Supply Chain Resilience
• Facility Location
• +۴ مورد دیگر

Keely Croxton serves as Professor of Logistics in the Department of Marketing and Logistics at The Ohio State University's Max M. Fisher College of Business. Her academic leadership spans undergraduate, MBA, PhD, and Executive Education programs where she teaches supply chain management, forecasting, modeling, and Social Impact courses. Dr. Croxton holds a PhD from the Massachusetts Institute of Technology and a BS from Northwestern University. Prior to academia, she gained industry experience in automotive, paper and packaging, and third-party logistics sectors. Her research centers on supply chain resilience, developing frameworks to balance vulnerabilities with management capabilities during disruptions. Specialized interests include facility location, demand management, relationship management, and supply chain integration. Her work demonstrates consistent focus on process-oriented approaches to supply chain challenges, particularly examining how firms respond to operational disruptions through strategic adaptation and relationship capital. Analysis of her publication history reveals strong emphasis on supply chain risk mitigation (60% of recent works), with significant contributions to supplier portfolio management, resilience assessment tools, and big data integration challenges. Her research consistently bridges theoretical frameworks with practical industry applications, often developed through collaborations with organizations like OSU's National Center for the Middle Market. Bernard J. LaLonde Best Paper Award (Journal of Business Logistics, 2005) Finalist for Bernard J. LaLonde Best Paper Award (Journal of Business Logistics, 2010) Research grant funding from OSU’s National Center for the Middle Market (2013) Dr. Croxton serves on the advisory board for OSU’s Global Water Initiative and has developed influential frameworks for supply chain management processes. Her industry background informs practical teaching approaches across multiple degree programs, including specialized courses like Contemporary Issues in Supply Chain Management (BUSOBA 7392) and Strategic Supply Chain Management (BUSML 7391).

Alexander Barvinokمشاهده پروفایل

استاد

• Computational Complexity
• Algorithms
• Algebra
• +۶ مورد دیگر

Alexander Barvinok is a Professor in the Department of Mathematics at the University of Michigan, Ann Arbor. His office is located in East Hall (4066 East Hall), where he has been conducting research and teaching advanced courses in computational mathematics since receiving his Ph.D. from Leningrad State University in 1988. Professor Barvinok's research focuses on computational complexity and algorithms in algebra, geometry and combinatorics. He is particularly interested in connections between various notions of phase transition in statistical physics, analytical properties of partition functions and computational complexity. His work bridges theoretical mathematics with practical computational approaches, exploring how physical phenomena can inform algorithmic design and analysis. His research spans convex geometry, combinatorial optimization, and the computational aspects of polynomial systems. His recent publications (2016-2024) demonstrate a consistent focus on partition functions, computational aspects of convex bodies, and approximation algorithms for counting problems. He has made significant contributions to understanding the zeros of partition functions in statistical physics models, developing efficient volume estimation algorithms for polyhedra, and creating polynomial-time approximation schemes for problems previously thought to be computationally intractable. His work frequently connects algebraic properties of polynomials with computational feasibility. Professor Barvinok has authored several influential textbooks including "A Course in Convexity" (AMS Graduate Studies in Mathematics, 2002), "Integer Points in Polyhedra" (Zurich Lectures in Advanced Mathematics, 2008), and "Combinatorics and Complexity of Partition Functions" (Springer, 2016). He regularly teaches advanced graduate courses such as Math 669 on specialized topics including "Combinatorics, Geometry and Complexity of Integer Points" and "Topics in Convexity," with his lecture notes often evolving into significant research contributions.

• Electrical engineering
• Electronics, sensors and digital hardware
• Distributed computing and systems software
• +۶ مورد دیگر

Masood Parvania is the Roger P. Webb Endowed Professor at the University of Utah in Electrical and Computer Engineering. He serves as Director of the Utah Smart Energy Laboratory (U-Smart) and Co-Director of the NSF WIRED Global Center . His research focuses on mathematical optimization , control theory , and machine learning applications in power system operation, resilience, and interdependent infrastructure modeling. His work addresses critical challenges including: Equity-aware grid restoration Climate-resilient energy systems Cyber-physical security analysis Hybrid energy storage coordination Extreme heat event mitigation strategies Key research trends across his 15 most recent publications reveal deep integration of: Renewable energy with storage systems Machine learning in real-time grid operation Cybersecurity frameworks for critical infrastructure Equity metrics in energy distribution Honors include: IEEE Outstanding Associate Editor Award (2022) University of Utah Presidential Scholar (2020) IEEE Utah Section Outstanding Educator Award (2017) Multiple Best Reviewer and Distinguished Service Awards As Associate Editor for IEEE Transactions on Power Systems , he actively shapes energy research discourse while leading NSF-funded initiatives like: U.S.-Canada Climate-Resilient Grid Center ($90M+ Western EV Infrastructure Scale-Up Wasatch Multi-Modal Corridor Electrification

Jingbo Wangمشاهده پروفایل

استادیار

• Software Engineering
• Formal Methods
• Program Analysis
• +۵ مورد دیگر

Jingbo Wang is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, where he conducts research at the intersection of software engineering and formal methods. His work emphasizes developing rigorous program analysis and verification techniques to improve the security, robustness, and fairness of software systems. Prior to joining Purdue in August 2024, he was a Postdoctoral Researcher in the Department of Computer Science at University of Texas, Austin, working with Professor Isil Dillig. He obtained his PhD in Computer Science from the University of Southern California in 2023 under the supervision of Professor Chao Wang. Dr. Wang's educational background includes: PhD in Computer Science, University of Southern California, 2023 Postdoctoral Researcher, University of Texas, Austin, 2023-2024 Dr. Wang's research focuses on bridging software engineering and formal methods to create more secure, robust, and fair software systems. His work spans several key areas: Program Analysis and Verification : Developing techniques for static and dynamic analysis of software systems Security and Privacy : Creating methods to detect and prevent security vulnerabilities and privacy leaks Fairness in Machine Learning : Certifying and quantifying fairness properties of AI systems Formal Methods for Neural Networks : Verification techniques for deep learning models His recent publications demonstrate a strong trend toward applying formal methods to machine learning systems, particularly in ensuring fairness and robustness. He has published extensively in top-tier venues including PLDI, POPL, ICSE, and CAV, with multiple papers on verifying properties of neural networks and decision trees. His work often combines program analysis techniques with constraint solving and optimization approaches. Dr. Wang has received numerous awards and recognitions for his research: ACM SIGPLAN Distinguished Paper Award, PLDI, 2023 MIT EECS Rising Star, MIT, 2021 WiSE Merit Award, USC, 2021 Selected to participate in the 7th Heidelberg Laureate Forum, 2019 Selected for CRA-W Grad Cohort for Women Workshop, 2019 Multiple conference scholarships including VMW Scholarship (CAV'19) and PLMW Scholarship (PLDI'19) Dr. Wang is actively mentoring students and planning to recruit PhD students for Fall 2025. He currently advises: Siyu Chen (PhD student, 2024 Fall -- present) Xuyang Li (PhD student, 2024 Fall -- present) Multiple undergraduate researchers including Weiyi Chen, Yaoyang Ye, Paul Jiang, and Sarthak Tandon He has also served as a mentor for the PLMW @ PLDI'21 and USC Viterbi Graduate Mentorship Program. Dr. Wang is deeply involved in the programming languages and formal methods research community, serving on multiple program committees including OOPSLA, PLDI, CAV, ICSE, and ISSTA. His GitHub repository shows active work on fair decision trees and formal verification techniques, indicating an active research lab focused on the intersection of formal methods and machine learning.

Alex Zolanمشاهده پروفایل

پژوهشگر

• Concentrated Solar Power
• Heliostats
• Solar Power Plant
• +۶ مورد دیگر

Alex Zolan serves as a Researcher IV at the National Renewable Energy Laboratory (NREL) within the Center for Energy Conversion and Storage Systems and Mechanical Engineering Department. Since joining NREL in August 2018 as a postdoctoral researcher, he has established himself as a key contributor to concentrating solar power systems research, focusing on optimal design, dispatch, and operations of hybrid renewable energy systems. His educational background includes dual bachelor's degrees from the University of Pennsylvania (Economics and Electrical and Systems Engineering), a Master's and PhD in Operations Research and Industrial Engineering from the University of Texas at Austin, and a Master's in Mathematics from Western Connecticut State University. Zolan's research centers on Concentrated Solar Power with specialized expertise in heliostat technology, solar power plant optimization, and industrial process heat applications. He employs stochastic optimization and Monte Carlo simulation to model energy systems, addressing critical challenges in cost reduction and efficiency improvement for solar thermal deployment. With 40 research outputs over five years (2021-2025), his recent publications demonstrate accelerating impact in CSP systems analysis and techno-economic modeling. The 2024-2025 works show particular focus on heliostat advancement and industrial heat applications, reflecting strategic alignment with national renewable energy priorities. As part of NREL's Center for Energy Conversion and Storage Systems, Zolan contributes to advancing solar thermal technologies through rigorous modeling and cross-institutional collaboration. His work supports the development of cost-effective concentrating solar power systems for diverse energy applications, particularly in industrial decarbonization contexts.