Find Academic Supervisors

• Entrepreneurship
• Innovation Management
• Strategic Management
• +3 more

Joel A. Ryman serves as Associate Professor of Management at California State University, Monterey Bay (CSUMB), where he joined the faculty in August 2019. He directs the capstone program and teaches strategic management, building on prior experience teaching global business and strategic management at East Tennessee State University. Education: B.A. in Economics, Goshen College (1982) Master of International Management, American Graduate School of International Management (1988), specializing in international finance and Japanese language Ph.D. in Business Administration, University of Tennessee (1999), focused on strategic management Dr. Ryman's research centers on innovation processes within small and medium-sized enterprises, examining how entrepreneurs navigate uncertainty through effectuation theory. His work extends to strategic thinking frameworks, the influence of religious contexts on entrepreneurial behavior (particularly Mennonite entrepreneurship), and business model innovation. Through CSUMB's Institute for Innovation and Economic Development, he actively collaborates with regional entrepreneurs to study real-world innovation dynamics. His publication record reveals a cohesive research trajectory spanning strategic management, entrepreneurship, and innovation. Recent works demonstrate increasing focus on effectuation theory in SME contexts, interdisciplinary connections between cultural factors and business performance, and pedagogical applications for fostering innovation. The scholarship consistently bridges academic theory with practical entrepreneurial challenges, particularly in resource-constrained environments. As capstone director, Dr. Ryman mentors student teams in strategic management projects while maintaining active research engagement through the Institute for Innovation and Economic Development. His work facilitates knowledge exchange between academic research and the Monterey Bay entrepreneurial ecosystem.

Antonio NorelliView profile

Research Fellow

• Artificial Intelligence
• Deep Learning
• Explainable AI
• +3 more

Antonio Norelli is a Research Fellow at Oxford University and a PhD student in Computer Science at Sapienza University of Rome, specializing in AI and deep learning within the GLADIA research group. His academic journey includes a BSc in Physics, an MSc in Computer Science, and the SSAS interdisciplinary honors program. He has held research internships at Spiketrap (San Francisco) and Amazon Lablet in Tübingen. His research focuses on bridging the gap between human and artificial intelligence, emphasizing symbolic reasoning, explainable AI, and multimodal learning. Notable contributions include the ASIF framework for unsupervised multimodal alignment and Olivaw, an Othello-playing AI that achieves expert-level performance without human knowledge. His key research interests span artificial scientific discovery, neural network explainability, and the theoretical foundations of intelligence. Recent work explores relative representation learning and zero-shot latent space communication, with applications in cross-modal retrieval and efficient model adaptation. Norelli’s interdisciplinary approach combines insights from philosophy of science (Popperian critical rationalism) with machine learning to develop systems capable of symbolic reasoning and hypothesis generation. Publications highlight advancements in: (1) Multimodal learning without training through coupled data alignment, (2) Game AI strategies for Othello using minimal resources, (3) Neuro-symbolic architectures for interpretable decision-making. His work has been presented at NeurIPS and IEEE Transactions on Games, among others. Current efforts aim to formalize intelligence as information-processing mechanisms grounded in symbol manipulation and semantic alignment.

Raffaella RavinettoView profile

Professor

• Pharmaceutical Policies
• Equitable Access to Medicines
• Research Ethics
• +3 more

Raffaella Ravinetto is a Professor and Head of the Department of Public Health at the Institute of Tropical Medicine (ITM) in Antwerp, Belgium. She holds a PharmD and PhD in Biomedical Sciences. Her roles include chairing ITM’s Institutional Review Board, MSF’s Ethics Review Board, and serving as an extraordinary professor at the University of Western Cape, South Africa. She has extensive experience in pharmaceutical policies, equitable access to medicines, and research ethics in low- and middle-income countries. Her career spans humanitarian work with Médecins Sans Frontières (MSF), leadership in clinical trials and global health initiatives, and advocacy for quality-assured health products. Education: University Master Degree in Pharmacy, University of Torino, Italy (1986–1991) Postgraduate Diploma in Tropical Medical Biology, ITM Antwerp, Belgium (2001–2002) PhD in Biomedical Sciences, KU Leuven, Belgium (2012–2016) Research Interests: Focuses on pharmaceutical policies, access to quality medicines, research ethics in vulnerable populations, and global health equity. Her work addresses challenges in low-resource settings, including substandard medicines, ethical clinical trials, and health system strengthening. Teaching and Mentorship: Lecturer at ITM and international institutions, supervising over 30 postgraduate and doctoral students. Co-developed courses on pharmaceutical policies and ethics. Organized training programs in Good Clinical Practices (GCP) and ethics review capacities in Africa. Grants and Projects: Leads initiatives such as the ConPharm project on antibiotic resilience in Syria and the SECRET project enhancing ethics review in Uganda and Ethiopia. Collaborates on WHO prequalification programs and quality assurance strategies. Lab/Teams: Involved in QUAMED (Quality Medicines for All), Switching the Poles Network, and the WHO Prequalification Program. Active in global health partnerships to improve medicine quality and access.

Salvador BarberàView profile

Professor

• Social Choice Theory
• Mechanism Design
• Game Theory
• +7 more

Salvador Barberà is a Professor of Economics at the Autonomous University of Barcelona since 1986, Director of the MOVE research consortium, and a Research Professor at the Barcelona Graduate School of Economics. His academic contributions span multiple roles in research institutions and government agencies, including leadership positions at ICREA and the Spanish Ministry of Science and Education. Research Focus: Social Choice Theory, Mechanism Design, Game Theory, and Public Economics. Leadership Roles: Past-president of the Social Choice and Welfare Society and ASSET; served on journal editorial boards (Econometrica, Games and Economic Behavior). His research investigates strategy-proof rules, constitutional design, domain restrictions, coalition formation, and decision-making under uncertainty. Key themes include preference aggregation, institutional design, and the interplay between individual and collective rationality. Recent publications address foundational topics in social choice and mechanism design, with a focus on voting systems, committee structures, and incentive-compatible allocation rules. These works bridge theoretical rigor with applications in political economy and public policy. Scientific Awards 2011: Fellow of the Society for the Advancement of Economic Theory 2008: Corresponding member of the Academia Nacional Argentina de Ciencias Económicas 2008: Rey Jaime I Award 2007: Doctor Honoris Causa, Universidad Pablo de Olavide de Sevilla 2006: Fellow of the Spanish Economic Association 1988: Fellow of the Econometric Society 2010: Spanish National Research Prize “Pascual Madoz” in Social Sciences and Economics 2007: Fundació Catalana per a la Recerca i la Innovació Prize (FCRI) 1996: Rey Juan Carlos Award 2007: Encomienda con Placa de la Orden Civil de Alfonso X “El Sabio” 2006: Commandeur de l’Ordre National Du Mérite Barberà has held visiting positions at Caltech, Stanford University, and the Université des Sciences Sociales de Toulouse. He has influenced economic policy through roles like Secretary General for Scientific and Technological Policies in Spain’s Ministry of Science and Education.

Nisheeth SrivastavaView profile

Associate Professor

• Cognitive Science
• Artificial Intelligence
• Computational Modeling
• +7 more

Nisheeth Srivastava is an Associate Professor in the Department of Computer Science & Engineering and Cognitive Science at the Indian Institute of Technology, Kanpur. His research bridges computational models of cognition with real-world applications in AI and local problem-solving. Cognitive Science : Mathematically modeling human behavior inattention, memory, and decision-making. Artificial Intelligence : Integrating cognitive insights into AI agent design for human-like behavior. Solving Local Problems : Applying technical knowledge to address challenges in resource-constrained environments. His recent work explores predictive modeling limitations , probability inference from sparse data , and decentralized multi-agent systems , with trends spanning decision theory, machine learning, and user behavior analysis. Lab affiliates include Visiting Research Scientist Muhammad Aurangzeb Ahmed, focusing on agent-based simulations of religious affiliation.

• Innovation Management
• Lean Management
• Knowledge Management
• +4 more

Dr. Jan Janiszewski serves as an Assistant Professor in the Innovation Management Department at Warsaw School of Economics (SGH), Poland's premier business university. His academic work bridges theoretical innovation frameworks with practical business applications across diverse sectors including healthcare, manufacturing, and retail. His research focuses on Innovation Management , Lean Methodologies , and Sustainable Business Practices , with particular emphasis on frugal innovation under resource constraints, tax policy impacts on entrepreneurial activity, and knowledge management systems. Recent work examines pandemic-era leadership challenges in lean implementation and eco-innovation strategies for Polish enterprises. Analysis of his publication trajectory reveals three dominant trends: (1) Practical adaptation of lean/kaizen principles to Polish institutional contexts; (2) Critical examination of functional stupidity in organizational behavior; (3) Development of tax incentive frameworks to boost innovation competitiveness. His healthcare management studies demonstrate lean methodology's effectiveness in optimizing resource allocation within constrained public systems.

Stuart RussellView profile

Professor

• Artificial Intelligence
• AI Safety
• Ethics in AI
• +5 more

Stuart Russell is a Distinguished Professor of Computer Science, Cognitive Science, and Computational Precision Health at the University of California, Berkeley, and holds the Michael H. Smith and Lotfi A. Zadeh Chair in Engineering. He is also Honorary Fellow of Wadham College, Oxford, and affiliated with the UCSF Department of Computational Precision Health. His research focuses on AI safety, ethics, reinforcement learning, and the societal implications of artificial intelligence. Education: B.A. (Physics, 1st Class Honors, University of Oxford, 1982); Ph.D. (Computer Science, Stanford University, 1986). Research interests include foundational AI theory, provably beneficial AI systems, and global AI governance. He co-authored the seminal textbook Artificial Intelligence: A Modern Approach (4th ed., 2020) and authored Human Compatible: AI and the Problem of Control (2019), recognized as a bestseller and influential work in AI ethics. Key contributions include pioneering work on rational metareasoning, Bayesian networks, and probabilistic AI. He advises governments and international bodies on AI policy and has testified before U.S. Senate and U.K. Parliament committees. Notable awards include the ACM/AAAI Allen Newell Award (2022), IJCAI Research Excellence Award (2022), and Officer of the British Empire (2021). Advising and grants: As chair of Berkeley’s Computer Science Division and leader of the Human-Compatible AI Center, he oversees cutting-edge research programs. His work intersects academia, industry, and policy to address AI’s societal challenges. Labs/teams: Director of the Center for Human-Compatible AI (CHAI), co-founder of the Kavli Center for Ethics, Science, and the Public, and member of the Berkeley AI Research (BAIR) Lab.

Qipeng Phil ZhengView profile

Professor

• Operations Research
• Network Optimization
• Stochastic Optimization
• +6 more

Qipeng Phil Zheng is a Professor at the University of Central Florida specializing in Operations Research, Stochastic Optimization, and Energy Systems optimization. His research bridges Network Optimization, Integer Programming, and Supply Chain Management with practical applications in Transportation Planning, Logistics, and Dynamic Traffic Assignment. Ph.D. Industrial & Systems Engineering, University of Florida (2010) M.S. Automation, Tsinghua University (2005) B.S. Industrial Automation, North China University of Technology (2001) His recent research focuses on stochastic energy systems modeling, social network influence dynamics, and multi-agent sustainability frameworks. Key methodologies include decomposition algorithms, machine learning integration, and risk-constrained optimization. Publications from 2020-2025 demonstrate consistent contributions to power grid resilience, blockchain-enabled energy trading, and social media cascading behavior analysis. Active in interdisciplinary applications, Zheng’s work spans from technical energy optimization to social network modeling, emphasizing practical implementation and algorithm development. His 2025 article introduces bounded rationality frameworks for demand response systems, while 2024 research addresses risk-aware energy maintenance scheduling.

Marcos VasconcelosView profile

Assistant Professor

• Control Systems
• Networked Robotics
• Distributed Optimization
• +3 more

Marcos Vasconcelos is an Assistant Professor in the Department of Electrical & Computer Engineering at the Florida A&M University–Florida State University College of Engineering. His research focuses on distributed systems, multi-agent coordination, and networked control with applications in robotics, cybersecurity, and bio-inspired systems. He holds a PhD in Electrical Engineering and has published extensively on topics such as remote estimation over adversarial networks, strategic information disclosure, and data-driven classification of communication signals. Key research areas include optimizing coordination efficiency in robotic teams, developing robust estimation strategies under denial-of-service attacks, and advancing distributed optimization algorithms for constrained networks. His work bridges theoretical foundations in control systems and game theory with practical implementations in cyber-physical systems and networked robotics. Notable contributions include neuroscheduling techniques for real-time systems, strategic multi-agent games with bounded rationality, and bio-inspired models for decentralized decision-making. His research has been supported by grants focusing on resilient communication protocols and distributed machine learning frameworks.

Rad NiazadehView profile

Associate Professor

• Online Algorithms
• Algorithmic Mechanism Design
• Data-Driven Decision Making
• +4 more

Rad Niazadeh is an Associate Professor of Operations Management at the University of Chicago Booth School of Business and holds a courtesy appointment at the Toyota Technological Institute at Chicago (TTIC) . He previously served as a visiting researcher at Google Research NYC and a Motwani postdoctoral fellow at Stanford University's Computer Science Department. He earned his PhD in Computer Science (with a minor in Applied Mathematics) from Cornell University. Research Interests: Focus on data-driven online decision-making and algorithmic mechanism design for platforms and non-profit operations. Key areas include fair resource allocation, dynamic matching algorithms, and socially-aware AI. Recent Publications: 15+ works on topics spanning online algorithms (e.g., refugee resettlement, reusable resource matching), fairness in AI (e.g., equity-constrained search), and market design (e.g., pricing with cancellations, combinatorial Bernoulli factories). Scientific Awards: Recipient of the Asness Junior Faculty Fellowship (2023), multiple INFORMS Rothkopf Junior Researcher Prizes , and IJCAI Distinguished Paper Award (2024). His co-authored works have won best paper honors at INFORMS and MSOM. Advising: Mentoring PhD students Mohammad Reza Aminian (awarded INFORMS Service Science Best Student Paper), Soonbong Lee (INFORMS AMD First Place), and Yueyang Zhong . Collaborates with principal researchers Yiding Feng and Pranav Nuti .

Stefan BucherView profile

Assistant Professor

• Behavioral Economics
• Information Economics
• Cognition
• +2 more

Stefan Bucher is an Assistant Professor at the Faculty of Economics , University of Cambridge. His research bridges behavioral economics, cognitive science, and machine learning to address computational approaches for relaxing cognitive constraints in economic decision-making. Education: PhD in Economics (New York University), BSc & MSc in Computational Science and Engineering (ETH Zürich), MSc in Economics (London School of Economics and Political Science) Research Interests: Focus on Behavioral & Information Economics , Cognition , and Machine Learning , with particular emphasis on: Algorithmic choice architecture for bounded rationality Computational modeling of cognitive costs in decision-making Neural coding mechanisms in economic environments Information processing under constraints AI applications to behavioral economics Equity implications of attentional biases in policy contexts Publication Trends show a focus on cognitive economics, computational modeling, and machine learning intersections. His work explores divisive normalization, base-rate neglect, and algorithmic solutions for constrained decision-making. Laboratory: The Computational Cognitive Economics Lab develops interdisciplinary frameworks combining behavioral experiments with algorithmic analysis.

Anil AswaniView profile

Associate Professor

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

Anil Aswani serves as an Associate Professor and Head Undergraduate Advisor in the Department of Industrial Engineering and Operations Research (IEOR) at the University of California, Berkeley's College of Engineering. His work centers on developing statistical and optimization techniques for big data to model human behavior in complex systems, enabling better system design and management across healthcare, energy, and social domains. Education: Ph.D. in Electrical Engineering and Computer Sciences, UC Berkeley (2010) Research interests include operations research, machine learning, optimization, and statistical modeling, with applications in healthcare systems (e.g., personalized disease management, mechanical ventilation), energy systems (e.g., EV charging, HVAC), and human behavior analytics. His methods integrate causal inference, reinforcement learning, and tensor completion to address real-world challenges in resource allocation and system optimization. Analysis of recent publications reveals dominant trends in applying reinforcement learning to healthcare (mechanical ventilation, disease management), contract design for end-of-life care and cybersecurity, and fair decision-making frameworks. Key methodological themes include off-policy evaluation, tensor completion for high-dimensional data, and optimization under uncertainty across dynamic systems. Scientific Awards: NSF CAREER Award (2019) for "Data-Driven Personalized Chronic Disease Management" As Head Undergraduate Advisor, Aswani guides academic planning and curriculum development for IEOR students. His research is primarily funded by the NSF CAREER award, focusing on data-driven healthcare optimization, with additional support for interdisciplinary projects like food assistance program analysis and medical data privacy. Collaborations span public health, medicine, and engineering domains. His interdisciplinary team bridges operations research, computer science, and domain-specific applications, evidenced by joint studies on nutritional assistance programs, NICU admissions prediction, and step-tracker re-identification. Current work emphasizes scalable methods for personalized interventions in complex socio-technical systems.

• Stochastic Modeling of Communication, Information and Service Systems
• Queueing Theory
• Stochastic Processes
• +9 more

Alexander Stolyar is Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign , where he has taught courses on stochastic processes, queueing systems, and service engineering. Before joining Illinois in January 2017, he was the Timothy J. Wilmott Endowed Chair Professor at Lehigh University (2014–2017) and spent nearly two decades in industrial research at Bell Laboratories , Motorola, and AT&T Labs-Research. Stolyar’s research centers on stochastic modeling of large-scale communication, information, and service systems , including cloud data centers, communication networks, and call/contact centers. He develops mathematical frameworks for dynamic resource allocation, scheduling, congestion control, and distributed algorithms , leveraging tools from queueing theory, stochastic processes, fluid and diffusion limits, large deviations, and mean-field analysis . His recent publications (2021–2025) reveal sustained focus on asymptotic optimality in complex systems: designing lightweight algorithms that become provably optimal as system scale grows. Key themes include greedy and randomized policies for inventory systems with stochastic lead times , parallel-server systems with redundancy and cancel-on-completion , particle systems with mean-field interactions exhibiting traveling-wave behavior , and packing-constrained service systems motivated by cloud data-center operations . Scientific honors include election as INFORMS Fellow (2021) , the INFORMS Applied Probability Society Best Publication Award (2004) , and the ACM SIGMETRICS Best Simulation Paper Award (1996) . He serves or has served on the editorial boards of Queueing Systems (Editor, 2008–present), IEEE Transactions on Information Theory (2019–2021), Operations Research (2006–2011), Advances in Applied Probability / Journal of Applied Probability (2008–2015), and as Technical Program Committee member for leading conferences such as SIGMETRICS and IFIP Performance . Stolyar’s research has generated more than 30 U.S. patents spanning wireless network scheduling, cloud resource allocation, dynamic load balancing, and packet-data network control—many of which have been incorporated into commercial cellular and cloud systems. His work is supported by NSF and industry grants, and he has advised numerous PhD students and postdocs who now hold faculty and research positions worldwide.

Shachar KarivView profile

Professor

• Experimental Economics
• Behavioral Economics
• Decision-Making Under Risk and Uncertainty
• +6 more

Shachar Kariv is the Benjamin N. Ward Professor of Economics at the University of California, Berkeley, where he has served since 2003. He directs the UC Berkeley Experimental Social Science Laboratory (Xlab) and affiliates with the Choice Lab at NHH Norwegian School of Economics. Educated at Tel Aviv University (B.A.) and New York University (M.A., Ph.D.), Kariv has held visiting roles at Princeton's Institute for Advanced Study, European University Institute, Nuffield College Oxford, Stanford, and Cambridge. Ph.D., Economics, New York University (2003) M.A., Economics, New York University (2000) B.A., Economics, Tel Aviv University (1998) His research spans experimental and behavioral economics, focusing on decision-making under risk and uncertainty, social preferences, network economics, and public goods. He investigates how individuals balance fairness versus self-interest and equality versus efficiency trade-offs, extending to physician behavior, tax policy, and economic development. His work combines traditional economic theory with innovative data collection methods, including graphical interfaces for preference elicitation. Recent publications examine the transferability of economic models across risk domains, policy preferences of government officials, and the relationship between physician altruism and healthcare outcomes. His experimental studies on social learning in networks have theoretical implications for coordination in incomplete information environments, recognized through multiple awards including the Sloan Research Fellowship and UC Berkeley teaching honors. Sloan Research Fellowship (2009-10) UC Berkeley Cheit Award for Excellence in Teaching (2012) NYU Outstanding Teaching Awards (2001, 2002) NYU Outstanding Dissertation Award (2003) Kariv's leadership roles include Department Chair at Berkeley (2014-17, 2021-22) and directing Xlab (2008-2014). He serves on editorial boards of top journals and has received grants from the Alfred P. Sloan Foundation and Center for Equitable Growth. His work at the intersection of theory and experiment continues to shape understanding of economic rationality and social behavior.

• Probabilistic Machine Learning
• Statistical Inference
• Computational Neuroscience
• +4 more

Luigi Acerbi is an Associate Professor at the Department of Computer Science, University of Helsinki, leading the Machine and Human Intelligence research group. He is affiliated with the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning, statistical inference methods (e.g., amortized and surrogate-based approaches), and computational and cognitive neuroscience, including Bayesian models of perception and resource-constrained rationality. Previously, he held postdoctoral positions at the University of Geneva and New York University. He earned his PhD from the Doctoral Training Centre in Computational Neuroscience at the University of Edinburgh, working with Sethu Vijayakumar and Daniel Wolpert. His work includes developing open-source tools like BADS (Bayesian Adaptive Direct Search) and VBMC (Variational Bayesian Monte Carlo), widely used for optimization and Bayesian inference in MATLAB/Python. He actively contributes to the academic community through teaching (e.g., BAMB! 2022 summer school tutorials on model fitting) and software development (GitHub repositories for optimization, inference, and AI tools like Athanor). His research bridges machine learning, neuroscience, and cognitive science, emphasizing robust and efficient statistical methods.