Find Academic Supervisors

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

Minseok Ryu is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial & Operations Engineering from the University of Michigan (2020). Prior to ASU, he was a postdoctoral appointee at Argonne National Laboratory’s Mathematics and Computer Science Division. His research focuses on optimization methodologies for decentralized and stochastic decision-making distributed algorithms for machine learning and operations research applications in healthcare systems and energy grids Teaching responsibilities include courses on applied deterministic operations research (IEE 574), optimization (IEE 622), and research practicums (IEE 792). His work emphasizes computational challenges in decision-making under uncertainty, with recent projects addressing nurse staffing optimization, federated learning frameworks (e.g., APPFL/APPFLX), and resilient power grid systems. He has no recorded academic awards but actively contributes to open-source software and cross-disciplinary research collaborations. Research interests bridge theory and practice, targeting social goods through optimization techniques like distributionally robust optimization, federated learning, and heuristic algorithms for energy and healthcare systems.

Michael A OsborneView profile

Professor

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +3 more

Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML

• Business Process Management
• Process Mining
• Software Engineering
• +3 more

Marlon Dumas is a Professor of Information Systems at the University of Tartu's Faculty of Science and Technology, with a 20-year academic career spanning Estonia and Australia. He holds a PhD in Computer Science from the University of Grenoble 1, France, and has served as Head of Chair and Programme Director in Software Engineering programs. Specializes in Business Process Management (BPM) and Process Mining Current research focuses on prescriptive process monitoring, simulation modeling, and data privacy Recipient of 25+ scientific awards, including multiple Test of Time Awards and the Estonian National Research Award in Technical Sciences Editorial leadership: Area Editor for Information Systems (Elsevier) and ERC Starting Grant Panel Chair His work bridges theoretical advancements in BPM with practical applications in financial services and anti-money laundering. He has developed tools like SIMOD, Kronos, and Kairos for process optimization and analysis. His research integrates AI/ML techniques (reinforcement learning, causal inference) with traditional process modeling. Key trends in his recent publications include: Prescriptive monitoring systems combining causal inference and machine learning Privacy-preserving process mining techniques (differential privacy, anonymization) Resource availability modeling and multi-objective process optimization LLM applications for process analysis and intervention policies Scientific honors include: 2024 BPM Best Paper & Prototype Awards 2023 ICPM Best Prototype Award 2019 ERC Advanced Grantee 2017 Estonian National Research Award in Technical Sciences 2019 MODELS Test of Time Award 2017 BPM Best Prototype Award He has mentored PhD candidates as thesis examiner and contributed to 30+ conference program committees, including General Co-Chair roles at ESEC-FSE 2019 and CAiSE 2018. His work has been supported by European Research Council grants and Estonian Science Foundation projects.

• Economics of IT Labor
• Technological Change and Reskilling
• Algorithms and AI in HR and Management
• +5 more

Prasanna (Sonny) Tambe is a Professor at the Wharton School of the University of Pennsylvania, specializing in the economics of technology and labor markets. His research explores AI’s impact on workforce dynamics, HR algorithms, and the gender wage gap in tech industries. Education: Ph.D. in Managerial Science and Applied Economics (Wharton, UPenn); S.B. and M.Eng. in Electrical Engineering and Computer Science (MIT). His work leverages internet-scale data from job platforms and patent databases to analyze trends in skill acquisition, remote work diversity, and algorithmic bias in hiring. Recent studies examine AI’s role in HR decision-making, the economics of emerging technologies, and labor market responses to IT innovation. Scientific Awards: Best Undergraduate Professors (Poets & Quants, 2020) Best Paper Awards (Management Science, Information Systems Research) ISS Sandra A. Slaughter Early Career Award (2016) Tambe co-directs Wharton Human-AI Research, focusing on ethical AI integration in organizations. His teaching includes award-winning courses on AI’s societal implications and data-driven business strategies.

Sanghyun HongView profile

Assistant Professor

• Trustworthy AI
• Machine Learning
• Efficient Machine Learning
• +5 more

Sanghyun Hong is an Assistant Professor at Oregon State University's School of Electrical Engineering and Computer Science , focusing on Trustworthy AI and Cybersecurity . He holds a Ph.D. in Computer Science from the University of Maryland, College Park (2021) and a B.S. in Electrical Engineering and Computer Science from Seoul National University (2015). His research bridges machine learning , security , and privacy-preserving systems . Current research themes: Robustness of AI systems to adversarial attacks Privacy-preserving machine learning Security of pre-trained and large language models Hardware fault vulnerabilities in neural networks Cybersecurity workforce development Publication Trends (15 most recent): Focus on adversarial machine learning (jailbreaking LLMs, membership inference) Advances in physics-informed neural networks and time series forecasting Key contributions to AI security and malware detection Interdisciplinary work in visualization design and tsunami warning systems Scientific Accolades : Google Faculty Research Award (2023) Samsung Global Research Award (2022, 2023, 2024) DARPA Riser (2022) NSF SFS Award (co-PI, 2023) USENIX Enigma Speaker (2021) Academic Leadership : Mentors 5 Ph.D. students and has graduated 8 M.S. and B.S. students. Currently developing next-generation auditing frameworks for AI systems while on medical leave until Winter 2026.

• Operations Management
• Manufacturing Systems
• Supply Chain Management
• +5 more

Sunder Kekre is the Vasantrao Dempo Professor of Operations Management at Carnegie Mellon University’s Tepper School of Business , where he has held academic appointments since 1984. His research focuses on manufacturing systems, global supply chains, and healthcare operations management. Research Expertise : New product development structures, strategic costing, lean innovation, knowledge sharing in enterprise networks, and business analytics for coordinated supply chains. Key Publications : Contributions to journals like Management Science , Operations Research , and Production and Operations Management , with work on LNG storage valuation, RFID logistics, and healthcare disparities. Scientific Recognition : Dempo Chair Professorship Bosch Chair Professorship Best Paper Award (1995) Teaching : Courses in Operations Management, Strategic Management of the Enterprise, and Information Systems Project. Non-Academic Experience : Prior engineering roles at Tata Steel (1974-1980) and consulting engagements with Fortune 500 firms like Bosch, Caterpillar, and IBM.

• Labor Economics
• Corporate Governance
• Personnel Policy
• +4 more

Solntsev Sergey Andreevich is an Associate Professor at the Department of Applied Economics within the Faculty of Economic Sciences at the National Research University Higher School of Economics (HSE). He also serves as Deputy Head of the Laboratory of Labor Market Research. With over 23 years of scientific and teaching experience since joining HSE in 2004, he specializes in labor economics, corporate governance, and personnel policy. Candidate of Economic Sciences (2006), Lomonosov Moscow State University Master's degree in Economics (2003), Lomonosov Moscow State University Bachelor's degree in Economics (2001), Lomonosov Moscow State University His research focuses on labor market dynamics, particularly examining wage structures, personnel policies in Russian companies, top management labor markets, and the relationship between higher education and labor market outcomes. He has conducted extensive empirical research on how Russian firms adjust wages, recruit employees, and manage executive turnover, with a particular emphasis on corporate governance mechanisms. His work often utilizes unique Russian enterprise and household survey data to provide insights into labor market functioning in the Russian context. His publication portfolio demonstrates a consistent focus on Russian labor market issues, with particular attention to higher education outcomes, wage setting practices, and executive labor markets. His research combines rigorous empirical methodology with practical policy implications, contributing significantly to understanding labor market dynamics in Russia during periods of economic transition and crisis. Letter of thanks from the Rector of HSE (December 2022) Gratitude from the Faculty of Economic Sciences of HSE (February 2020) Academic Work Allowance (2014-2015, 2008-2009) Bonus for publication in a List B journal (2023-2025) Professor Solntsev teaches multiple courses including Labor Economics, Labor and Personnel Economics (in Russian and English), and Russian Economy at both bachelor's and master's levels. His teaching reflects his research expertise, providing students with insights into contemporary labor market issues in Russia. His research has been supported through institutional mechanisms at HSE, including participation in the Laboratory of Labor Market Research activities and various research projects. As Deputy Head of the Laboratory of Labor Market Research at HSE's Faculty of Economic Sciences, he contributes to one of Russia's leading research centers focused on labor market analysis. The laboratory conducts empirical studies on various aspects of the Russian labor market, often in collaboration with government agencies like Rostруд (Federal Labor and Employment Service). Professor Solntsev works closely with colleagues including Professor Sergey Roshchin, the laboratory head, on multiple research initiatives.

Lynn WuView profile

Associate Professor

• Artificial Intelligence
• Enterprise Social Media
• Innovation
• +6 more

Lynn Wu is an Associate Professor at the Wharton School of the University of Pennsylvania, focusing on the intersection of artificial intelligence, analytics, and innovation. She teaches MBA, undergraduate, and doctoral courses on emerging technologies' transformative impact on business and society. Education: B.S. in Finance and Computer Science, MIT M.S. in Computer Science, MIT Ph.D. in Management Science, MIT Sloan Her research explores how AI and digital platforms reshape productivity, workforce dynamics, and innovation strategies, with applications in antitrust policy and startup ecosystems. Key trends in her publications include AI's role in post-IPO innovation, robotics' impact on managerial roles, and social media's ability to mitigate funding disparities. Scientific Awards: Kauffman Best Paper Award (2019) Sandy Slaughter Early Career Award (2019) AIS Early Career Award (2018) ISR Best Published Paper (2014) Best Paper Awards at ICIS (2009), HICSS (2013), etc. She has collaborated with IBM, Google, Meta, and advised the U.S. Department of Justice and World Bank, with her work cited by The New York Times , The Economist , and Harvard Business Review .

Maya BalakrishnanView profile

Assistant Professor

• Operations Management
• Artificial Intelligence
• Supply Chain Management
• +3 more

Maya Balakrishnan is an Assistant Professor of Operations Management at the Jindal School of Management (JSOM), University of Texas at Dallas. She holds a PhD in Business Administration from Harvard Business School (2024) and a BS in Computer Science from Stanford University (2016). Her primary research focuses on Human-AI collaboration, Corporate Social Responsibility, and Behavioral Operations Management. She teaches courses such as AI in Supply Chain Management (OPRE 4393) and Advanced AI in Supply Chain Management (OPRE 6383). Her research explores how humans interact with algorithms in operational contexts and the ethical implications of workforce diversity disclosures on consumer behavior. Recent work emphasizes trust-building through operational design and mitigating risks in human-AI systems. Her awards include multiple first-place recognitions in behavioral operations competitions and a best presentation award at the Advances in Decision Analysis Conference. Awards: 2024 Production and Operations Management Junior Scholar Paper Competition (1st Place) 2023 INFORMS Behavioral Operations Working Paper Competition (2nd Place) 2022 Best PhD Blitz Presentation (Advances in Decision Analysis) Dr. Balakrishnan is actively involved in professional organizations such as INFORMS and the Manufacturing and Service Operations Management Society (MSOM). Her work bridges behavioral insights with operational systems, addressing real-world challenges in AI ethics and supply chain innovation.

• Organizational Behavior
• Interpersonal Trust
• Workplace Emotions
• +3 more

Daniel J McAllister is an Associate Professor at the National University of Singapore Business School , Department of Management and Organisation. His research explores interpersonal relationships in organizations , with particular emphasis on social emotions , trust dynamics , and their implications for organizational citizenship behavior and ethical leadership . He has published extensively in top-tier journals such as Academy of Management Review, Journal of Applied Psychology, and Academy of Management Journal. Academic Focus: Organizational Behavior, Trust/Distrust, Workplace Emotions Teaching Interests: Technical Knowledge, Practical Application, Ethical Decision-Making Key Courses: MNO2007 (Undergraduate), BMA5004A (MBA), MNO6012A (PhD) McAllister's research spans workplace underdog trajectories, awe in leadership, abusive supervision, and cross-cultural management in China. His work examines how emotions like contempt, envy, and schadenfreude influence organizational outcomes. He emphasizes creating a safe learning environment that integrates theoretical knowledge ( technical ), real-world application ( practical ), and ethical judgment ( wisdom ). McAllister has received consistently positive student feedback for his engaging teaching style , with recent evaluations highlighting improvements in time management and practical relevance. He distributes course materials post-class and avoids rote memorization, prioritizing conceptual understanding. His 2025 work on workplace underdogs and awe-driven leadership continues to shape contemporary organizational theory.

John Z. AyanianView profile

Professor

• Health Equity
• Health Care Disparities
• Access to Care
• +4 more

John Z. Ayanian serves as the Alice Hamilton Distinguished University Professor of Medicine and Healthcare Policy at the University of Michigan, holding joint appointments as Professor of Internal Medicine in the Medical School, Professor of Health Management and Policy in the School of Public Health, and Professor of Public Policy in the Gerald R Ford School of Public Policy. As inaugural Director of the Institute for Healthcare Policy and Innovation (IHPI), he leads a consortium of 700 faculty members across 15 schools and maintains clinical practice as a general internist at Michigan Medicine. His academic foundation includes a Bachelor of Arts in history and political science from Duke University (1982), medical degree from Harvard Medical School (1987), and master's in public policy from Harvard Kennedy School (1987), followed by residency and fellowship at Brigham and Women’s Hospital and post-doctoral training in health services research at Harvard School of Public Health. Dr. Ayanian's research program investigates health equity, access to care, and quality of care with particular attention to social determinants including race/ethnicity, gender, socioeconomic status, and insurance coverage. His work critically examines Medicaid expansion impacts, Medicare Advantage disparities, and policy responses to health inequities, often utilizing large-scale claims databases and cross-institutional collaborations. Current projects include the federally-authorized evaluation of Michigan's Medicaid expansion program serving over 700,000 adults. Analysis of his 15 most recent publications (2025) reveals three dominant research thrusts: (1) Medicare Advantage vs Traditional Medicare comparisons across diverse clinical conditions, (2) Medicaid policy evaluation including unwinding impacts and expansion effects, and (3) innovative measurement of health equity through new indices and AI applications. His work consistently emphasizes methodological rigor in health services research while maintaining strong policy relevance. His scientific honors include: Election to the National Academy of Medicine Master status in the American College of Physicians John Eisenberg National Award for Career Achievement in Research Distinguished Investigator Award from AcademyHealth Election to Alpha Omega Alpha and Association of American Physicians Dr. Ayanian leads the federally-funded Healthy Michigan Plan evaluation team of 15 faculty members and serves as founding Editor-in-Chief of JAMA Health Forum, previously holding editorial positions at the New England Journal of Medicine. His research receives substantial federal support focused on health policy evaluation, with particular emphasis on vulnerable populations. He actively mentors students and junior faculty across multiple disciplines. As Director of IHPI, he fosters interdisciplinary collaboration across 15 schools at the University of Michigan. His leadership extends to center memberships in AI and Digital Health Innovation, Caswell Diabetes Institute, and Center for Global Health Equity, where he promotes data-driven solutions to health disparities through cross-campus partnerships and innovative research methodologies.

Mor ArmonyView profile

Professor

• Operations Management
• Stochastic Modeling
• Service Operations
• +7 more

Mor Armony is the Vice Dean for Faculty and Research, Harvey Golub Professor of Business Leadership, and Professor of Technology, Operations & Statistics at the Leonard N. Stern School of Business, New York University. She has been a key faculty member since 1999 and is a leading researcher in stochastic modeling and service operations. Ph.D. in Operations Research, Stanford University (1999) M.S. in Operations Research, Stanford University (1997) M.S. in Statistics, Hebrew University of Jerusalem (1996) B.S. in Mathematics and Statistics, Hebrew University of Jerusalem (1993) Her research focuses on large-scale service systems, particularly in healthcare and contact centers. She investigates patient flow in hospitals, optimization of customer experience, and control of stochastic processing systems using advanced queueing models and operations research techniques. Her work bridges theoretical rigor with practical applications in service operations management. The recent articles reflect a strong trend toward integrating behavioral aspects into operations models, such as customer impatience, strategic patient behavior, and the impact of online reviews on physician demand. Her research spans healthcare operations, call center optimization, and dynamic routing in heterogeneous systems, consistently published in top journals like Management Science , Operations Research , and Production and Operations Management . Scientific recognition includes being named the Harvey Golub Professor of Business Leadership, a distinguished title at NYU Stern. Harvey Golub Professor of Business Leadership She actively advises research projects and collaborates with scholars on topics including staffing, routing, and capacity management. Her work has been supported by ongoing academic engagement and publication, with recent projects addressing appointment scheduling with no-shows, strategic capacity withholding, and co-sourcing in call centers. She leads research in data-driven queueing science applied to hospital operations and is involved in empirical studies on digital health platforms. Her research group, the Operations Management Group at Stern, focuses on developing analytical models for complex service systems. She contributes to interdisciplinary efforts in healthcare operations and collaborates with medical researchers on improving critical care delivery and outpatient scheduling.

Nicholas NelsonView profile

Associate Professor

• Astrophysics
• Dynamical Chaos
• Solar Physics
• +3 more

Dr. Nicholas Nelson is an Associate Professor in the Department of Physics at California State University, Chico. His research spans interdisciplinary areas including astrophysics, dynamical chaos, and medical education curriculum development. He specializes in stellar evolution models, solar convection dynamics, and magnetic field generation in stars. His work bridges physics and healthcare, addressing structural competency in medical training and social determinants of health through innovative curricula. Research interests include: solar magnetic loop formation, chaotic dynamics in celestial bodies, and integrating social determinants of health into residency programs. His publications reflect a dual focus on computational astrophysics and healthcare equity. Notable contributions include studies on knuckleball aerodynamics, early career challenges in astrophysics, and curriculum design for addressing health disparities. Though no awards are explicitly listed, his work demonstrates impactful cross-disciplinary engagement. No advising relationships or grant information was provided in the source material. His office is located in PHSC 121B on campus.

• Parallel Computing
• Scientific Computing
• Adaptive Mesh Refinement
• +3 more

Martin Berzins is a Professor of Computer Science at the University of Utah, affiliated with the School of Computing and the Scientific Computing and Imaging (SCI) Institute. His research focuses on parallel scientific computing, numerical methods for partial differential equations, and high-performance computing frameworks. He is a leading developer of the Uintah framework, a scalable simulation tool used for large-scale engineering and scientific problems. Research Interests : Parallel algorithms, adaptive mesh refinement, material point method (MPM), exascale computing, computational fluid dynamics, and performance portability. His work emphasizes scalable software solutions for complex multiscale and multiphysics simulations, with applications in environmental modeling, explosive detonation analysis, and computational mechanics. Recent articles highlight advancements in Uintah's portability to exascale systems, error estimation in MPM, and high-order numerical methods. Berzins has contributed significantly to the development of task-based parallelism strategies and heterogeneous computing optimizations. His research bridges theoretical numerical analysis with practical large-scale computational challenges. Collaborations include DOE projects on hazard analysis and exascale computing. He has pioneered the integration of runtime systems like Hedgehog with Uintah to enhance scalability on modern architectures. His work ensures computational frameworks remain viable for emerging hardware trends, emphasizing both algorithmic innovation and software engineering rigor.

John W. O'NeillView profile

Professor

• Strategic Management
• Real Estate
• Asset Management
• +5 more

Professor John W. O'Neill is a Professor of Hospitality Management at Pennsylvania State University's School of Hospitality Management. He serves as Director of the Hospitality Real Estate Strategy Group, focusing on real estate, asset management, and strategic management in the hotel industry. His research bridges financial analysis with operational strategies in hospitality. Education : B.S. in Hotel Administration from Cornell University M.S. in Real Estate from New York University Ph.D. in Business Administration from University of Rhode Island O'Neill's research explores hotel financial performance, debt servicing, brand affiliation, work-family dynamics, and market disruption from platforms like Airbnb. His work combines empirical analysis with strategic frameworks to address industry challenges. Leadership and Research Groups : Director, Hospitality Real Estate Strategy Group Active researcher in hotel valuation and operational risk