جست‌وجوی استاد و پژوهشگر

Tammy Cowartمشاهده پروفایل

استاد

• Intellectual Property Law
• Employment Law
• Corporate Governance and Compliance
• +۳ مورد دیگر

Tammy Cowart is a Professor of Business Law at the Soules College of Business , University of Texas at Tyler. She serves as Chair of the Accounting, Finance & Business Law department and holds the Bart Brooks Professor of Ethics and Leadership title. Her academic career spans over two decades, with teaching and research focusing on legal and ethical frameworks in business contexts. J.D., Texas Tech University B.S., Texas A&M University Dr. Cowart's research interests center on intellectual property , corporate governance , privacy law , and ethical leadership . Her recent publications address pressing issues in patent ethics, algorithmic pricing fairness, and tax compliance. She has pioneered business ethics curricula approved for CPA candidates. Scientific awards and recognitions include: Outstanding Faculty Award (Texas Alpha Xi Chapter of Alpha Chi) Provost’s Excellence in Teaching Award Outstanding Student Organization Advisor Dr. Cowart has held administrative roles as Director of Undergraduate and Graduate Programs at Soules College of Business. She actively contributes to community organizations and legal associations, maintaining memberships in the State Bar of Texas, Academy of Legal Studies in Business, and Association for Practical and Professional Ethics.

• Migration Studies
• Race and Ethnic Relations
• Gender and Sexuality Studies
• +۷ مورد دیگر

Dr. Irene Fubara-Manuel is an Associate Professor in Digital Media Practice at the University of Sussex's School of Media, Arts and Humanities. Their work bridges media art, animation, game design, and critical theory, focusing on race, migration, surveillance, and queer futurities. They hold a PhD from Sussex (2016-2019) and a PgCert in Higher Education (2020-2022). Their research interrogates biometric surveillance systems, digital colonialism, and intersectional feminist praxis in technology. Key research areas include: Algorithmic governance of migration Data colonialism and global south perspectives Feminist digital infrastructures Queer media representation Intersectional methodologies in digital humanities Recent projects include the AHRC-funded Full Stack Feminism initiative (2021-2024), exploring anti-racist feminist tech ecosystems. Their creative practice spans installations addressing border control algorithms and queer digital futurisms. Notable works include the Full Stack Feminism Archive and Manifestations exhibition. Awards include the JUST AI Fellowship (2020). Teaching focuses on critical media production and digital culture. Their publications span peer-reviewed journals, edited collections, and creative-critical exhibitions, demonstrating a commitment to interdisciplinary praxis.

David Castañónمشاهده پروفایل

استاد

• Stochastic Control
• Estimation Theory
• Game Theory
• +۶ مورد دیگر

David Castañón is a Professor of Electrical and Computer Engineering (ECE) and Systems Engineering (SE) at Boston University. He holds a PhD from MIT (1976) and has held leadership roles including Department Chair of BU ECE (2010-2014) and President of the IEEE Control Systems Society (2008). His research focuses on stochastic control, optimization, game theory, and distributed computing, with applications in sensor management, inverse problems, and autonomous systems. Education: PhD, Massachusetts Institute of Technology (1976). Key affiliations include the Center for Information and Systems Engineering, the Rafik B. Hariri Institute for Computing, and the ALERT Department of Homeland Security Center of Excellence. He teaches courses such as EC702 Recursive Estimation and EC719 Statistical Learning Theory. Research interests span stochastic control, estimation theory, optimization algorithms, and multi-agent systems. Notable contributions include work on sensor management, cooperative operations, and inverse problem solutions for medical and security imaging. His work often integrates theoretical frameworks with practical applications in autonomous systems and distributed computing. Scientific achievements include IEEE Fellow status (2006), CSS Distinguished Member Award, and leadership roles in major conferences like the IEEE Conference on Decision and Control (2007 as General Chair). He has also served on the Air Force Advisory Board and the IEEE Society Review Committee. Grants and lab affiliations include the NSF Engineering Research Center for Subsurface Sensing (2001-2013) and the SENTRY DHS Center of Excellence (2021-present). His interdisciplinary collaborations bridge robotics, medical imaging, and security systems.

• socially responsible operations
• sustainable operations
• not-for-profit operations
• +۲ مورد دیگر

Can Zhang is an Associate Professor in the Operations Management area at Duke University's Fuqua School of Business. His research focuses on socially responsible and sustainable operations, particularly in emerging economies and underserved populations. Sustainable Operations : Healthcare supply chains Not-for-Profit Operations : Medical surplus recovery, blood collection analytics Agricultural Supply Chains : Smallholder farmer support systems Zhang's work integrates operational policy design with societal impact, addressing challenges in healthcare, nonprofit management, and sustainable agriculture. His publications emphasize data-driven solutions for real-world problems, such as optimizing platelet inventory systems and improving hepatitis C treatment prioritization in prisons. Scientific recognitions include: 2021 MSOM Best Paper Award winner 2019 MSOM Award for Responsible Research recipient Multiple INFORMS and POMS competition awards Meritorious Service Awards (Management Science, 2020-2024) He has taught courses at Duke's Fuqua School of Business and Georgia Tech's Scheller College of Business, covering operations management, sustainable practices, and management science. Zhang collaborates with industry partners to ensure practical relevance in his research, aiming to deliver actionable insights for societal benefit.

• Econometrics
• Agricultural Economics
• Resource Economics
• +۳ مورد دیگر

Zheng Li is an Assistant Professor in the Department of Agricultural and Resource Economics at North Carolina State University. His research focuses on econometric methodologies with applications in agricultural economics, resource management, and policy analysis. He holds expertise in nonparametric estimation, quantile regression, and structural econometric modeling. Key research interests include analyzing agricultural production risks, evaluating policy impacts on housing markets, and developing advanced statistical techniques for mixed data types. His work bridges econometric theory with practical applications in environmental, urban, and transportation sectors. Recent publications explore topics such as lung cancer detection via biomedical sensing technologies, ridesharing platform incentives, and pandemic effects on real estate markets. Methodologically, his contributions span kernel-based specification tests, bootstrap methods for heavy-tailed data, and monotonicity-constrained estimation techniques. No scientific awards or formal advisees are listed. His research often intersects with interdisciplinary challenges, reflecting a commitment to innovative solutions in applied economics and data science.

Damian Nale Dailisanمشاهده پروفایل

مدرس

• Computational Social Science
• Machine Learning
• Multi-Agent Systems
• +۳ مورد دیگر

Dr. Damian Nale Dailisan is a Lecturer in the Department of Humanities, Social and Political Sciences at ETH Zürich, affiliated with the Computational Social Science group. He holds a Ph.D. in Physics from the University of the Philippines, specializing in traffic modeling and machine learning applications. His research focuses on multi-agent systems, particularly in transportation and urban systems. He has held postdoctoral roles and contributed to projects like the ACCeSs@AIM lab. His work bridges computational methods with real-world challenges, including traffic control optimization, AI-driven decision-making, and smart city infrastructure. Notable projects include FAIRLANE for priority lane management and studies on democratizing traffic control systems. Dailisan’s publications span journals like Transportation Research Part C and IEEE Access, addressing topics such as reinforcement learning in traffic signals and ethical AI frameworks. He has presented at workshops like 'Back to the Future' at ETH Zurich and collaborates with interdisciplinary teams to enhance urban mobility solutions. His technical expertise includes Python, network analysis, and agent-based modeling, with contributions to open-source tools for earthquake networks and social systems analysis.

Michael Choiمشاهده پروفایل

استادیار

• Markov Chains
• Stochastic Algorithms
• Statistical Physics
• +۶ مورد دیگر

Michael Choi is an Assistant Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA). Previously, he held a joint appointment with Yale-NUS College (2022–2025) and worked at the School of Data Science (SDS) at the Chinese University of Hong Kong, Shenzhen. He earned his PhD from Cornell University’s School of Operations Research and Information Engineering (ORIE), advised by Prof. Pierre Patie, and holds an undergraduate degree in Actuarial Science from the University of Hong Kong. His research focuses on Markov chains and processes, stochastic algorithms (e.g., MCMC, simulated annealing, Langevin dynamics), and their applications in statistical physics, optimization, Bayesian statistics, information theory, game theory, and theoretical computer science. He explores interdisciplinary connections with quantum computation, control theory, and computational chemistry. Choi actively contributes to the academic community, serving as an Associate Editor of Statistics and Computing since August 2023. He has presented at numerous international conferences and workshops, including the IMS APRM 2026 (Hong Kong), INFORMS International Meeting 2025 (Singapore), and BayesComp 2025 (Singapore). His work bridges theoretical foundations with practical computational methods in stochastic systems.

Stephen Lee-Urbanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Game AI
• +۳ مورد دیگر

Stephen Lee-Urban is a Teaching Associate Professor in the Department of Computer Science & Engineering at Lehigh University, affiliated with the Rossin College of Engineering. He holds a Ph.D., M.S., and B.S. in Computer Science and Engineering from Lehigh University, all completed with summa cum laude distinction. His research focuses on fundamental and applied artificial intelligence, machine learning, game AI, cognitive systems, and automated planning. He has contributed to innovative projects such as HuManIC (human-machine interpretive control), CORA (cognitive systems framework), and crowdsourced narrative generation systems. His academic career includes significant work in cybersecurity through intelligent agent modeling of malware, as well as contributions to game AI for strategy games and military training simulations. Notable awards include summa cum laude honors for all three of his university degrees. Lee-Urban's scholarly output spans over 20 publications since 2000, with recent emphasis on AI applications in collaborative storytelling, adaptive planning systems, and human-computer interaction. His research integrates machine learning techniques with sociocultural analysis, crowd-powered content creation, and hierarchical task networks. Current work explores autonomous systems capable of leveraging crowd intelligence for generating interactive narratives and optimizing military training scenarios. While no specific grants or advising roles are listed, his interdisciplinary approach bridges computer science with game design, cybersecurity, and cognitive modeling.

Tingting Dingمشاهده پروفایل

استادیار

• Behavioral Economics
• Experimental Economics
• Game Theory
• +۳ مورد دیگر

Tingting Ding is an Assistant Professor of Economics at the College of Business, James Madison University (JMU). She holds a Ph.D. in Economics from New York University. Her research focuses on Behavioral Economics and Experimental Economics, with a particular emphasis on mechanism design, bargaining dynamics, and school-matching algorithms. Ding’s work explores how communication networks, information asymmetry, and intergenerational advice influence decision-making processes in economic systems. She has published in top journals such as Games and Economic Behavior and Economic Journal . Her research interests include experimental methods to analyze market mechanisms, particularly in educational and labor contexts. Recent studies examine reproducibility in management science, the role of news in bargaining scenarios, and the impact of network communication on school-matching outcomes. Ding’s contributions bridge theoretical models with empirical evidence, offering insights into practical policy design. While her profile does not explicitly list awards or grants, her academic positions and publications reflect a strong focus on advancing experimental economics methodologies. No advising or lab affiliations are detailed in the provided texts.

Danny Dolevمشاهده پروفایل

استاد

• Distributed Computing
• Fault-Tolerant Systems
• Algorithms
• +۴ مورد دیگر

Prof. Danny Dolev is a distinguished academic holding the Berthold Badler Chair in Computer Science at The Hebrew University of Jerusalem's Rachel and Selim Benin School of Computer Science and Engineering. He is an ACM Fellow and IEEE Fellow. His research focuses on distributed computing, fault-tolerant systems, algorithms, and secure protocols. He has held leadership roles, including Director of his school (1999–2002) and Chair of the Israeli National Committee for Information Technology (1994–1998). Education: B.Sc., The Hebrew University of Jerusalem, 1971 M.Sc., Weizmann Institute of Science, 1973 PhD., Weizmann Institute of Science, 1979 Research Interests: Danny Dolev's work spans distributed algorithms, Byzantine fault tolerance, consensus protocols, and hardware algorithms. His contributions include groundbreaking research on self-stabilizing systems, secure communication, and fault-tolerant clock synchronization. His HEX and Chronos protocols exemplify innovations in scalable synchronization and network security. Publications: His recent work emphasizes Byzantine agreement, asynchronous fault tolerance, and game-theoretic distributed systems. Key papers address optimal resilience in consensus algorithms and secure multi-party computation. Awards: ACM Fellow (2010) IEEE Fellow (2004) Grants & Leadership: Member of the Scientific Council, European Research Council (2010–2014) Chair of Israel's National Committee for Information Technology (1994–1998) Leadership roles at IBM Almaden Research Center (1987–1993) and Stanford University (1979–1981) Labs & Teams: His research group at Hebrew University focuses on distributed systems, with collaborations on projects like Steward (wide-area Byzantine replication) and Self-Stabilizing Circuits .

• Computer Science
• Machine Learning
• Data Science
• +۴ مورد دیگر

Yong Zhang is affiliated with Tsinghua University's Research Institute of Information Technology in Beijing, China. His research focuses on machine learning, optimization algorithms, edge computing, and their applications in areas like time series analysis, federated learning, and sensor networks. He has collaborated on projects involving neural networks, scheduling problems, and privacy-preserving techniques. Education: Yong Zhang earned a PhD in Computer Science and Engineering from Fudan University in 2007. His academic career includes roles at institutions like the Chinese Academy of Sciences and the University of Hong Kong, reflecting a strong interdisciplinary background. Research Contributions: His work spans theoretical computer science, algorithm design, and applied machine learning. Notable areas include developing efficient scheduling algorithms for energy systems, creating robust federated learning frameworks for industrial demand forecasting, and advancing methods for sentiment analysis using multimodal data. He has also contributed to biomedical engineering through smartphone-based health monitoring systems. Collaborations: He frequently collaborates with researchers at institutions like the University of Electronic Science and Technology of China, Nanyang Technological University, and The Hong Kong Polytechnic University. Key projects involve data caching optimization in edge computing, distributed algorithms for dynamic networks, and combinatorial optimization problems. Labs & Future Work: His team explores cutting-edge topics in AI-driven systems, including trust-aware machine learning, distributed resource allocation, and real-time data processing for IoT applications. Current research emphasizes scalable solutions for complex optimization challenges in both academic and industrial settings.

• Natural Computing
• Financial Modelling
• Operations Research
• +۳ مورد دیگر

Professor Anthony Brabazon is the College Principal and Dean of the UCD College of Business at University College Dublin. He holds a prominent administrative role alongside his academic contributions in natural computing and business analytics. His research focuses on natural computing theory, financial modeling using evolutionary algorithms, and complex adaptive systems in business contexts. Brabazon co-founded the Natural Computing Research and Applications Group (NCRA) at UCD, which explores theoretical and practical applications of bio-inspired algorithms. Education: B Comm and Dip. Prof Accounting – University College Dublin DBA – Kingston University, London DPA and Dip Stat – University College Dublin and Trinity College Dublin MBA – Heriot-Watt University, Edinburgh MS – Stanford University Research Interests: Anthony's work bridges computational intelligence and finance, with contributions to genetic programming, foraging-inspired optimization, and business analytics. His research highlights the application of evolutionary algorithms to real-world problems in finance, operations research, and organizational strategy. Grants and Collaborations: HEA Support for COVID-19 Related Research (2020–2025) Financial Mathematics and Computation Cluster (FMC²) (2009–2016) Teaching: Coordinated MAcc Management Accounting courses at the postgraduate level (2018–2019). Professional Activities: Member of CIMA Ethics Board Editorial board roles in multiple journals Founder of EvoBAFin workshop on computational finance

Lawrence M. Ausubelمشاهده پروفایل

استاد

• Microeconomic Theory
• Game Theory
• Economics of Asymmetric Information
• +۴ مورد دیگر

Lawrence M. Ausubel is a Professor of Economics in the Department of Economics at the University of Maryland. He received his PhD in Economics (1984), Master of Legal Studies, and MS in Mathematics from Stanford University. His research focuses on microeconomic theory, game theory, and the economics of asymmetric information, with applications to auction theory and market design. PhD in Economics (Stanford University, 1984) Master of Legal Studies (Stanford Law School) MS in Mathematics (Stanford University) AB in Mathematics (Princeton University) A leading expert in auction theory, Ausubel has made significant contributions to dynamic auctions, combinatorial bidding, and market mechanisms. His work includes developing the efficient ascending-bid auction format (known as the 'Ausubel auction'), analyzing durable goods monopoly, and studying bargaining under incomplete information. He has also explored credit card market dynamics, financial rescue strategies, and electricity market design. Recent research involves cryptographic choice mechanisms, core-selecting auctions with incomplete information, and iterative pricing models. His publications span journals like Review of Economic Studies , American Economic Review , and Econometrica , with patents in auction technology and applications in telecommunications, energy, and finance. 23 U.S. patents related to auction technology Testified before U.S. Congress on credit card markets Founded Power Auctions LLC (2003) Co-inventor of auction formats used by FCC, ISED Canada, and ACMA

Vera Tilsonمشاهده پروفایل

دانشیار

• Healthcare Operations Management
• Operations Management
• Supply Chain Management
• +۳ مورد دیگر

Dr. Vera Tilson is an Associate Professor at the Simon Business School, University of Rochester, specializing in Healthcare Operations Management and Operations Management. Her research focuses on healthcare operations optimization, stochastic scheduling, and supply chain management. Prior to academia, she held roles as a software engineer and project manager in telecommunications, medical instrumentation, and finance. Her teaching interests include quantitative decision-making and healthcare operations, for which she received the MBA Superior Teaching Award in 2009. Her work bridges academic research with practical applications, addressing challenges such as patient no-show management, medication waste reduction, and healthcare GPO strategies. She has published extensively in journals like Management Science, MSOM, and Operations Research. Recent research explores emergency department workflow optimization, humanitarian supply chains, and real-time capacity management in healthcare settings. Her contributions highlight interdisciplinary approaches combining operations research, data science, and healthcare analytics.

• Machine Learning
• Theoretical Computer Science
• Data Science
• +۳ مورد دیگر

Lev Reyzin is a Professor of Mathematics, Statistics, and Computer Science at the University of Illinois Chicago (UIC) and Director of the IDEAL Institute. He specializes in the theory of machine learning, data science, and artificial intelligence, with affiliations to theoretical computer science and mathematical foundations. Prior roles include a Simons Postdoctoral Fellowship at Georgia Tech and an NSF Computing Innovation Fellowship at Yahoo! Research. He earned his Ph.D. from Yale University (NSF doctoral fellowship) and a bachelor’s degree from Princeton University. Research interests focus on algorithmic learning theory, computational complexity, and applications of machine learning to real-world problems. Notable work includes contributions to statistical learning algorithms, adversarial bandits, and graph theory. His research has been funded by NSF grants (e.g., ECCS-2217023, CCF-2307106), DOD programs, and others since 2015. He has received awards at leading conferences (ICML, COLT, AISTATS). Reyzin holds editorial roles, including Editor-in-Chief of Mathematics of Data, Learning, and Intelligence and Chair positions in major conferences like FOCS 2024 and ALT 2017. His leadership in academic organizations highlights his influence in shaping theoretical computer science and machine learning research.