Find Academic Supervisors

Eric T BradlowView profile

Professor

• Bayesian computation
• latent variable models
• marketing research methods
• +7 more

Eric T Bradlow, the K.P. Chao Professor at the University of Pennsylvania, is a multidisciplinary academic with appointments in Marketing, Economics, Education, and Statistics and Data Science . As Vice Dean of AI & Analytics and Chairperson of Wharton's Marketing Department , he bridges statistical rigor with business applications, focusing on Bayesian modeling , marketing research methods , and customer analytics . Key Affiliations: Wharton School, University of Pennsylvania Academic Roles: Professor, Vice Dean, Department Chair His research develops novel statistical frameworks for business challenges, including dual-network clustering for data granularity and household production theory for demand modeling. He has pioneered solutions for advertising response measurement and loyalty program optimization , published in journals like Marketing Science and Journal of Marketing Research . Professor Bradlow has received numerous teaching awards, including the Linback Award and multiple Wharton Excellence in Teaching Awards . He serves as Associate Editor and Editorial Board Member for leading journals and is a Fellow of the American Statistical Association and INFORMS Society for Marketing Science .

George J. LatellaView profile

Assistant Professor

• Food Marketing
• Sales Strategy
• Brand Management
• +4 more

George J. Latella is an Assistant Professor of Practice and Peck Fellow in the Department of Food, Pharma and Healthcare and Department of Management at the Haub School of Business, Saint Joseph's University. With over 30 years of teaching experience in Food Marketing, he integrates extensive industry expertise into his classroom instruction and has received multiple teaching and advising awards. His academic credentials include: MBA in Marketing from Saint Joseph's University Haub School of Business BS in Marketing from Drexel University Lebow School of Business Professor Latella's research centers on practical applications in food industry marketing, with emphasis on generational consumer behavior (Millennials and Baby Boomers), brand strategy, category management, and e-commerce. His work bridges academic theory with real-world sales and marketing challenges faced by retailers and manufacturers, drawing from his 35+ years of executive experience in the food sector. His publications reveal consistent focus on actionable marketing frameworks for the food industry, particularly addressing execution challenges, demographic targeting, and retailer-manufacturer dynamics. The articles demonstrate applied research connecting consumer insights with trade marketing strategies across diverse retail environments. He has received significant recognition for educational excellence: Distinguished Teaching Award (1998) Teaching Excellence Award (2006) Teaching Excellence Award (2012) Best Professor in the Haub School of Business (2016) Professor Latella actively mentors students through programs like the NGA Challenge (with multiple student wins over 15 years), conducts USDA-sponsored study tours for international scholars, and provides industry training for organizations including NASFT and Ferrero. He currently supports small food businesses through the Urban League of Philadelphia BRAP and developed SBDC pandemic-era webinars for food entrepreneurship. He maintains active industry engagement as partner in Bria & Latella's Italian Deli & Kitchen, consultant for Beacon Marketing Group and Latella & Associates, and board member for Food Industry Summit, CYO Board, Kristin's Krusade, and Food Marketing Association.

Rasoul EtesamiView profile

Associate Professor

• Game Theory
• Networked Control Systems
• Multiagent Decision-Making
• +7 more

Rasoul Etesami is an Associate Professor in the Department of Industrial and Systems Engineering and holds affiliate appointments in the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois Urbana-Champaign . He earned his Ph.D. and M.Sc. degrees in Electrical and Computer Engineering from UIUC in 2015 and was a Postdoctoral Research Fellow at Princeton University (2016-2017). Education Ph.D. Electrical and Computer Engineering, University of Illinois Urbana-Champaign (2015) M.Sc. Applied Mathematics, University of Illinois Urbana-Champaign (2015) M.Sc. Industrial and Systems Engineering, University of Illinois Urbana-Champaign (2012) Research Interests Dr. Etesami's work focuses on multiagent decision-making systems using tools from game theory, control theory, and optimization. His research spans networked control systems , dynamic games , distributed optimization , and cyber-physical security . He investigates social network dynamics and smart grid applications , with recent emphasis on federated learning and adversarial robustness in machine learning systems. Recent Publication Trends His 2024-2025 publications demonstrate expertise in: Dynamic games for social network influence and resource allocation Federated learning algorithms and poisoning attacks Optimization techniques for over-parameterized models and caching Control theory applications to smart grids and opinion dynamics Submodular optimization for social welfare maximization Bandit learning in constrained Colonel Blotto games Honors & Editorships NSF CAREER Award (2020) AFOSR Young Investigator Award (2023) James Franklin Sharp Teaching Award (2023) Associate Editor, IET Smart Grid (2018-2024) Conference chair/co-chair for AAAI, Allerton, and CDC events Academic Service Dr. Etesami actively participates in departmental governance as a member of: Faculty Hiring Committee Qualification Exam Committee Graduate Committee Research and Scholarly Enhancement Committee Seminar Chair Organizer

Huseyin TopalogluView profile

Professor

• Revenue Management
• Pricing Analytics
• Operations Research
• +4 more

Dr. Huseyin Topaloglu is the Howard and Eleanor Morgan Professor at the School of Operations Research and Information Engineering at Cornell University and Cornell Tech . He holds a B.S. in Industrial Engineering from Bogazici University (1997), an M.A. in Operations Research from Princeton University (1999), and a Ph.D. in Operations Research from Princeton University (2001). Education: B.S. Industrial Engineering (1997) – Bogazici University M.A. Operations Research (1999) – Princeton University Ph.D. Operations Research (2001) – Princeton University His research focuses on revenue management , pricing analytics , assortment optimization , and stochastic dynamic programming . Recent work includes advancements in multinomial logit models , network revenue management , and dynamic inventory allocation . He has published extensively in leading journals such as Operations Research , Management Science , and M&SOM , often collaborating with researchers like Y. Bai , P. Rusmevichientong , and O. El Housni . His 15 most recent publications (2024–2003) demonstrate expertise in revenue management using multinomial logit models , dynamic programming , and stochastic optimization . Key subfields include assortment planning , network revenue , pricing under uncertainty , and approximation algorithms for complex systems like ambulance redeployment and airline capacity control . He has also authored books like Revenue Management and Pricing Analytics (2019) and Fundamentals of Linear Optimization (2021).

Giovanni ModaneseView profile

Professor

• Quantum Field Theory
• Classical Field Theory
• General Relativity
• +3 more

Giovanni Modanese is a Professor in the Department of Mathematical Physics at the Free University of Bolzano, Faculty of Engineering. He earned his PhD in theoretical physics from the University of Pisa (1992) and held postdoctoral positions at MIT, the Max-Planck Institute (Munich), and ECT* (Trento). Education: PhD in Theoretical Physics (University of Pisa, 1992) Positions: Contract Lecturer (2000), Researcher, then Professor at Free University of Bolzano His research spans quantum and classical field theory, general relativity, and complex systems in social sciences and economics. Recent work includes Aharonov-Bohm electrodynamics, gauge waves, and diffusion models on networks. Key trends in his publications include quantum coherence in macroscopic systems, nonlocal effects in electromagnetism and gravity, and stochastic/agent-based modeling of economic and social phenomena. He has contributed to experimental proposals for detecting anomalous electromagnetic and gravitational effects.

Sridhar SeshadriView profile

Professor

• Stochastic modeling
• Supply chain management
• Revenue management
• +3 more

Sridhar Seshadri is the Alan J. and Joyce D. Baltz Endowed Professor and Professor of Business Administration at the University of Illinois at Urbana-Champaign. He has served as Area Chair of Information, Operations, Supply Chain, and Analytics (2018-2022) and Health Innovation Professor at the Carle Illinois College of Medicine. His affiliations span operations management, supply chain analytics, and healthcare innovation. Educational Background: PhD in Management Science, University of California at Berkeley (1993) Postgraduate Diploma in Management, Indian Institute of Management Ahmedabad (1980) Bachelor of Technology in Mechanical Engineering (with distinction), Indian Institute of Technology Madras (1978) Research Interests focus on stochastic modeling and its applications in queueing systems , supply chain management , and revenue management . His work addresses dual sourcing inventory systems, epidemic modeling for supply chains, AI-augmented healthcare workflows, and the intersection of operations and financial markets. Recent publications include analyses of commodity trading under uncertainty , AI in neurology , and pandemic response strategies . Key Scientific Contributions are recognized through: 2022 INFORMS Revenue Management and Pricing Section Practice Award Fellow of the Production and Operations Management Society (2018-present) James F. Towey Faculty Fellow (2018) Grants and Leadership include projects on Health Access Points in Rural Illinois (2024-2026), Business Analytics Collaboratory (2021-2023), and Dynamic Resource Management for Pandemics (2020-2021). He has served as Department Editor for Production & Operations Management and Associate Editor for Management Science .

• Industrial Organization
• Behavioral Economics
• Contract Theory
• +3 more

Heiko Karle is an Associate Professor of Microeconomics at the Economics & Law Department of Frankfurt School of Finance & Management since August 2019. Previously, he held a Junior Professorship (equivalent to Assistant Professor) at the same institution from September 2016 to August 2019. He is also a CEPR Research Fellow and CESifo Research Fellow . His career includes a postdoctoral position at the Center for Law and Economics of ETH Zurich (2012–2016), research at the European Centre for Advanced Research in Economics and Statistics (ECARES) of the Université Libre de Bruxelles (2009–2012), and graduate training at the University of Mannheim (PhD, 2012; Diplom in Economics, 2007). He has held visiting positions at the Toulouse School of Economics (PhD studies) and the University of California, Berkeley (undergraduate studies). His research focuses on Industrial Organization and Behavioral Economics , particularly on how loss aversion and psychological switching costs influence consumer behavior and firm strategies in competitive markets. His work explores topics such as advertising dynamics , platform competition , contract design , and pre-purchase information . He has published extensively in top-tier journals including the Journal of Political Economy , RAND Journal of Economics , and Management Science . Recent publications highlight trends in behavioral pricing , digital market design , and psychological incentives in economic interactions. His 2025 Management Science article on Imperfect Targeting and Advertising Strategies demonstrates how firms optimize marketing under consumer loss aversion. Scientific Awards & Affiliations: CEPR Research Fellow CESifo Research Fellow He leads the DFG-FWF funded research group “Consumer Preferences, Consumer Mistakes, and Firms’ Response,” which secured several million EUR in grants and filled three full-time postdoc positions starting in Fall 2023. His teaching includes Microeconomics and Decision Theory at the undergraduate level.

Mike Mingcheng WeiView profile

Associate Professor

• Supply Chain Management
• Revenue Management and Dynamic Pricing
• Consumer-Oriented Operations Models
• +2 more

Dr. Mike Mingcheng Wei is an Associate Professor of Operations Management at the School of Management, University at Buffalo. He specializes in optimizing business operations through advanced analytical methods, with a focus on supply chain management, revenue management, and machine learning applications. His research bridges theoretical models and real-world decision-making challenges in dynamic pricing, consumer behavior analysis, and transportation networks. Dr. Wei holds a PhD from Washington University in St. Louis, an MS from Pennsylvania State University, and a BBA from Fudan University in China. His academic journey reflects expertise in both quantitative methodologies and practical operational strategies. His research interests span multiple domains: (1) Supply Chain Innovation - developing responsive pricing strategies for fashion products and C2C marketplaces; (2) Machine Learning Applications - advancing algorithms for high-dimensional data analysis and online decision-making; (3) Transportation Networks - exploring congestion pricing mechanisms and traffic bottleneck solutions through financial derivatives. His work frequently integrates game theory, stochastic modeling, and big data analytics. Professional memberships include INFORMS, MSOM, POMS, and DSI, reflecting his engagement with leading operations research communities. His research has been published in top-tier journals like Management Science and Transportation Research Part B, as well as conferences such as ICML. Dr. Wei’s office is located at 326C Jacobs Management Center, Buffalo, NY, where he advises students on operations and strategy topics. Though no specific grants or labs are highlighted in the provided text, his work demonstrates a commitment to advancing analytical frameworks for contemporary business challenges.

Ali AouadView profile

Assistant Professor

• Operations Research
• Computer Science
• Economics
• +7 more

Ali Aouad is an Assistant Professor at MIT Sloan School of Management (Department of Management Science and Operations) and holds an Associate Professor title (on leave) at London Business School . He earned a PhD in Operations Research from MIT and MS/BS in Applied Mathematics from École Polytechnique (Paris). His research focuses on algorithms and decision processes at the intersection of operations, computer science, and economics, with applications to supply/demand management, market design, public sector operations (e.g., food security), and online platforms. Education: BSc & MSc (École Polytechnique), PhD (MIT) Work Experience: Applied Scientist at Uber Technologies (2017-2018), consultant at Boston Consulting Group (Paris/Casablanca), and collaborations with tech firms. His research interests span algorithmic market design, stochastic optimization, approximation algorithms, and digital platform mechanisms . Recent work explores layout optimization for cultural institutions, food subsidy efficacy in underserved communities, and dynamic pricing in matching systems. He co-advises PhD students at MIT and collaborates internationally. Awards: Multiple student paper competitions (POMS, INFORMS, IBM), Nicholson Prize finalist, and JFIG Paper Competition winner. Grants & Labs: Collaborates with industry partners (e.g., Uber) and leads research teams in public sector operations and matching systems design.

Gerardo IñiguezView profile

Associate Professor

• Network Science
• Computational Social Science
• Data Science
• +8 more

Gerardo Iñiguez is an Associate Professor in Complex Systems at Tampere University's Faculty of Information Technology and Communication Sciences. He holds visiting positions at Central European University (Department of Network and Data Science), Aalto University (Computer Science), and UNAM (Center of Complexity Science). His research focuses on network science, computational social science, and data science, particularly studying social dynamics, contagion processes, and urban mobility. He co-founded the data science startup Predify (Mexico) and is involved in initiatives like the European Humane AI Net project. Iñiguez also contributes to interdisciplinary fields through game development and science communication. Education: PhD in Computational Science from Aalto University (Finland), MSc and BSc in Physical Sciences and Physics from UNAM (Mexico). His work bridges academia and industry, with a focus on applying network theory to real-world problems. Notable achievements include studies on rank dynamics, social network evolution, and the impact of deception in social systems. He actively publishes in top journals like Nature Communications and Science Advances. Research themes include complex contagion models, opinion dynamics, and the interplay between social structure and behavior. He explores how generic mechanisms shape hierarchy formation and uses statistical inference to validate models against empirical data. Current projects aim to optimize urban policies and understand polarization in digital societies.

• Operations Research
• Inventory Management
• Stochastic Optimization
• +3 more

Xin Chen is an Adjunct Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign. His research focuses on stochastic systems, inventory management, pricing strategies, and reinforcement learning applications in operations research. He collaborates internationally on projects involving stochastic optimization, supply chain dynamics, and decision-making under uncertainty. His work bridges theoretical advancements with practical applications in logistics, finance, and algorithm design. Key research areas include: stochastic inventory control, dynamic pricing models with reference price effects, convex optimization techniques in operations, and causal analysis in reinforcement learning. He has explored topics such as asymptotic policy optimality, robust optimization frameworks, and algorithmic solutions for complex systems with uncertain parameters. Recent publications emphasize multi-agent systems, network revenue management, and decision-making under stochastic constraints. His work on market selection and inventory control integration highlights innovative approaches to multi-objective optimization. Collaborations span institutions globally, with notable contributions to the theory of L-convexity and its applications in operations. Xin Chen’s research also addresses practical challenges such as cold start problems in online advertising, vaccine distribution under scarcity, and resilience strategies for power systems through spectral graph clustering.

• Economic Theory
• Decision-Making Under Uncertainty
• Social Learning
• +3 more

Mira Frick is a Professor in the Department of Economics at Princeton University. Her research focuses on decision-making under uncertainty, social learning dynamics, mechanism design, and game theory applications. She collaborates extensively with researchers like Ryota Iijima and Yuhta Ishii, producing influential work in top journals such as Econometrica, the American Economic Review, and the Journal of Political Economy. Her recent research explores topics including contagious ambiguity in strategic interactions, welfare implications of biased learning processes, and the efficiency of multi-agent information systems. She also examines how dispersed social behaviors arise in assortative societies and the fragility of collective learning when agents misinterpret others’ information. Key contributions include foundational work on dynamic random utility models and objective rationality frameworks for ambiguity preferences. Her methodologies often combine rigorous mathematical analysis with insights from behavioral economics and information theory. No scientific awards are explicitly listed in the provided materials. While no advising relationships or grants are detailed, her prolific collaboration network suggests significant contributions to academic mentorship and research initiatives. She is actively engaged with the economics community through publications, conferences, and editorial roles.

Adam ElmachtoubView profile

Associate Professor

• Operations Research
• Machine Learning
• Revenue Management
• +5 more

Adam Elmachtoub is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia University, where he holds affiliations with the Data Science Institute and serves as an Amazon Visiting Academic. He earned his B.S. in Operations Research from Cornell University and his Ph.D. in Operations Research from the Massachusetts Institute of Technology (MIT), followed by a postdoctoral position at IBM's T.J. Watson Research Center focusing on Smarter Commerce technologies. His research bridges machine learning, analytics, and operations, with dual specializations: (1) developing ML-driven personalization methods for retail, logistics, and travel industries; and (2) creating novel models for revenue and supply chain optimization in e-commerce and service systems. His work emphasizes practical applications of optimization under uncertainty, data-driven decision-making, and algorithmic fairness. Elmachtoub's publication record demonstrates consistent focus on pricing analytics, revenue management, and operational efficiency, with recent work exploring fairness constraints, reusable resources, and pandemic-responsive systems. His research integrates stochastic modeling, statistical learning, and economic theory to solve complex industrial problems. Notable scientific recognitions include: NSF CAREER Award for foundational contributions to data-driven optimization IBM Faculty Award for industry-relevant research 1st place in INFORMS JFIG Paper Competition Great Teacher Award from the Society of Columbia Graduates Forbes 30 Under 30 in Science honor He maintains active research funding through NSF and IBM collaborations. While specific student advisees are unnamed in available sources, his teaching recognition suggests significant mentoring activities. No explicit details regarding dedicated labs or research teams are documented.

Vineet GoyalView profile

Professor

• Machine Learning
• Optimization
• Operations Research
• +4 more

Vineet Goyal is a Professor in the Department of Industrial Engineering and Operations Research at Columbia University's School of Engineering and Applied Science. His research focuses on Machine Learning & Analytics Optimization , with applications to energy markets, revenue management, and healthcare systems. Education: Bachelor's in Computer Science (IIT Delhi, 2003) Ph.D. in Algorithms, Combinatorics and Optimization (Carnegie Mellon, 2008) Postdoctoral research at MIT's Operations Research Center (2008-2010) Professor Goyal develops data-driven algorithms for large-scale dynamic optimization problems, particularly addressing robustness in uncertain environments. His work bridges theoretical advances in optimization with practical implementations in: Energy market resource allocation Revenue management systems Healthcare decision support Recent publications show a strong focus on multi-armed bandits , assortment optimization , and proactive medical interventions , with methodologies spanning from robust optimization to online learning. His research has been supported by: NSF CAREER Award (2014) Google Faculty Research Award (2013) NSF grants for dynamic optimization Professor Goyal also contributes to fundamental algorithmic research with theoretical advances in: LP-based approximation techniques Adjustable robust optimization Stochastic reward modeling Value iteration acceleration methods

Brett HollenbeckView profile

Associate Professor

• Empirical Industrial Organization
• Quantitative Marketing
• Antitrust Policy
• +7 more

Brett Hollenbeck is an Associate Professor of Marketing at the Anderson School of Management, University of California, Los Angeles (UCLA). His research focuses on empirical industrial organization and quantitative marketing, with notable work on digital platform regulation, retail market dynamics, and consumer behavior analytics. Ph.D. in Economics from University of Texas at Austin M.S. and B.S. in Economics from University of Texas at Austin and George Washington University His research interests span online review manipulation , retail market structure , loyalty program optimization , and policy analysis in emerging markets like cannabis regulation. Recent work examines LLM adoption impacts on digital behavior and market dynamics. Key article trends include: E-Commerce fraud detection Platform trust mechanisms Market concentration effects Consumer preference modeling Scientific awards include the 2022 Marketing Science Service Award . His work appears in top journals like Management Science , Marketing Science , and PNAS , with press coverage in The Economist, New York Times, and Harvard Business Review.