جستجوی استاد راهنمای دانشگاهی

Jin Maمشاهده پروفایل

استاد

• Stochastic Analysis
• Stochastic Differential Equations
• Stochastic Control Theory
• +۳ مورد دیگر

Jin Ma is a Professor in the Department of Mathematics at the University of Southern California (USC), where he has served since 2007. He previously held professorships at Purdue University (1994–2008). His research focuses on stochastic analysis, stochastic differential equations, mathematical finance, and control theory. He directs USC's Mathematical Finance Program and serves on editorial boards for journals like Probability, Uncertainty and Quantitative Risk and SIAM Journal on Control and Optimization . Ma received his Ph.D. in Mathematics from the University of Minnesota (1992) and M.S./B.S. in Applied Mathematics from Fudan University (1985/1982). His work bridges theoretical stochastic analysis and applied domains like finance and insurance, with notable contributions to forward-backward SDEs and mean-field games. Research Highlights: Developed frameworks for stochastic control and backward SDEs in financial and insurance contexts. Advanced mean-field game models for limit order book dynamics and equilibrium analysis. Explored set-valued stochastic differential equations and their applications in risk management. Grants & Advising: Advised numerous graduate students in stochastic processes and mathematical finance. Research supported by NSF grants and industry collaborations.

Gary Kingمشاهده پروفایل

استاد

• Political Methodology
• Statistical Inference
• Quantitative Social Science
• +۷ مورد دیگر

Gary King is the Albert J. Weatherhead III University Professor at Harvard University and Director of the Institute for Quantitative Social Science. He is based in the Department of Government within Harvard's Faculty of Arts and Sciences. One of only 22 University Professors at Harvard, this represents the institution's most distinguished faculty position. King received his B.A. from SUNY New Paltz in 1980 and his Ph.D. from the University of Wisconsin-Madison in 1984. His academic journey has led him to become one of the most influential scholars in political methodology and quantitative social science. Professor King's research spans numerous areas of methodological innovation in the social sciences. His work focuses on developing and applying empirical methods across various domains. Key research interests include: Ecological Inference - developing methods to infer individual behavior from group-level data Automated Text Analysis - creating techniques for extracting knowledge from massive text collections Causal Inference - methods for detecting and reducing model dependence in causal effect estimation Missing Data and Measurement Error - statistical approaches to handle incomplete or imperfect data Survey Research - developing methods for more accurate cross-cultural survey comparisons Unifying Statistical Analysis - integrating diverse methodological approaches into coherent frameworks King's recent publications demonstrate a continued focus on methodological innovation with practical applications. His work spans political science, public health, and data science, with particular emphasis on privacy-preserving data analysis, maternal health metrics, survey methodology, and media effects. A notable trend is the increasing interdisciplinary nature of his research, bridging political methodology with public health, computer science, and demography. His work on census data privacy, maternal mortality disparities, and media influence represents cutting-edge applications of social science methodology to critical societal issues. His scientific achievements have been recognized with numerous prestigious awards: Fellow of the National Academy of Sciences (2010) Fellow of the American Statistical Association (2009) Fellow of the American Academy of Arts and Sciences (1998) Guggenheim Foundation Fellow (1994-1995) Career Achievement Award (2010) Warren Miller Prize (2008) Multiple awards for research software and methodology King has mentored numerous students and postdocs, many of whom now hold faculty positions at leading universities. His research has been supported by major funding agencies including the National Science Foundation, Centers for Disease Control and Prevention, World Health Organization, and National Institute of Aging. He has collaborated with over seventy scholars on research publications and served on numerous editorial boards and professional organization councils. His work on the Mexican universal health insurance program represents one of the largest randomized health policy experiments to date, demonstrating his commitment to rigorous evaluation of real-world policy interventions. As Director of the Institute for Quantitative Social Science, King leads a vibrant research community focused on methodological innovation. His work has practical applications in diverse areas including legislative redistricting (used by the U.S. Supreme Court), health policy evaluation (including the largest randomized health policy experiment to date in Mexico), Chinese censorship analysis (revealing government fabrication of 450 million social media comments annually), and automated text analysis (through Crimson Hexagon, a company he co-founded).

Xiaodong Linمشاهده پروفایل

استاد

• Information Security
• Privacy-Enhancing Technologies
• Digital Forensics
• +۴ مورد دیگر

Dr. Xiaodong Lin is a Professor at the University of Guelph's School of Computer Science and an IEEE Fellow (2017) for contributions to secure vehicular communications. He leads the Blockchain Technology and Cybersecurity (BTC) lab, focusing on privacy-enhancing technologies, digital forensics, wireless network security, blockchain applications, and DeFi security. PhD, Beijing University of Posts and Telecommunications, China PhD, University of Waterloo, Canada His research bridges theoretical and applied domains in cybersecurity, particularly vehicular networks, smart grids, and decentralized systems. Recent work examines blockchain security, privacy-preserving protocols for IoT, and secure data aggregation in wireless networks. Key publication trends include secure fog computing for vehicular crowdsensing, privacy-preserving authentication in 5G, and cryptographic solutions for smart grids. Awards highlight multiple Best Paper recognitions at IEEE conferences. IEEE Fellow (2017) Best Paper Awards (IEEE INFOCOM 2018, GLOBECOM 2017, SECURECOMM 2016) Dr. Lin supervises graduate students in blockchain security, AI security, and digital forensics. His lab provides financial support to qualified students.

Dong Louمشاهده پروفایل

استاد

• Asset Pricing
• Investment Management
• Behavioural Finance
• +۲ مورد دیگر

Dong Lou is a Professor of Finance at the Hong Kong University of Science and Technology (HKUST), where he serves as Associate Dean of Business and Management (Strategic Planning and Research) and Acting Head and Chair Professor of the Department of Finance. He is also a CEPR Research Fellow at the London School of Economics (LSE). His research focuses on asset pricing, investment management, and behavioral finance, with a particular emphasis on market inefficiencies, trading dynamics, and macrofinancial linkages. PhD in Finance, Yale University, 2009 His scholarly work explores liquidity provision, arbitrage activity, investor behavior, and the impact of information dissemination on financial markets. Recent studies include the effects of dollar asset holdings on global yields, retail margin trading dynamics, and the relationship between communication rates and security prices. His publications appear in top journals like Management Science , Journal of Financial Economics , and Review of Financial Studies . His research has been recognized with numerous awards, including the Crowell Memorial Award, NASDAQ OMX Award, and multiple INQUIRE and CRSP Forum grants. He mentors PhD students in finance and co-directs the HKUST-DXM AI for Finance Joint Laboratory, advancing AI applications in financial research and practice.

Chee-Wooi Tenمشاهده پروفایل

استاد

• Power systems engineering
• Power grid cybersecurity
• Cyber-informed security engineering
• +۴ مورد دیگر

Chee-Wooi Ten is a tenured Professor in the Department of Electrical and Computer Engineering at Michigan Technological University, where he has served since 2010 and achieved tenure in 2016. He concurrently holds an Affiliated Professor appointment in Applied Computing and directs both the PSERC Site and ICC CPS Center. His institutional roles emphasize cyber-physical security integration within power infrastructure. His educational background includes: PhD in Electrical Engineering from University College Dublin (2009) MSc in Electrical Engineering from Iowa State University (2001) BSc in Electrical Engineering from Iowa State University (1999) Ten's research pioneers cyber-informed security engineering strategies for bulk power systems, focusing on quantifying rare events through system risk models and data science. His work bridges power grid interactions with robotics and transportation systems to advance decarbonization and electrification. Key methodologies include validating cyber-physical security frameworks against steady-state and dynamic grid approaches, with emphasis on attack/defense combinatorics and smart home technologies. This transdisciplinary approach supports the fourth industrial revolution's resilience requirements. His publication trends reveal strong focus on risk-aggregated substation testbeds using generative adversarial networks, cyber insurance models for power systems, and cascading failure analysis from switching attacks. Recent works increasingly integrate machine learning with physics-based modeling to address cybersecurity threats in inverter-based resource integration and distribution emergency operations. Ten has secured over $6.5M in active funding including: $2M DOE grant (MTU portion $105,000) for CyDERMS Center on DERs/Microgrids cybersecurity $704,409 CyManII award for secure digitalization in smart manufacturing $1.05M DOE ARPA-E grant for decarbonized freight transportation modeling NSF CyberCorps Scholarship for Service program ($3.38M) His grants consistently address risk management through data-driven and physics-based modeling, with industry partnerships through PSERC and utility collaborations. As ICC CPS Center Director, he leads research on cyber-physical security testbeds and coordinates the PSERC Summer Transformation School. His team develops validation frameworks for NERC CIP compliance while addressing practical pain points in OT cybersecurity for grid operators.

Yuguo Chenمشاهده پروفایل

استاد

• Network Analysis
• Monte Carlo Methods
• Statistical Inference
• +۷ مورد دیگر

Yuguo Chen is a Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign (UIUC), serving as Interim Department Chair and Director of the Illinois Statistics Office. He holds affiliations with the Department of Computer Science, Information Trust Institute, Coordinated Science Lab, and Illinois Informatics Institute. Chen earned his PhD in Statistics from Stanford University (2001) and a B.S. in Mathematics from the University of Science and Technology of China (1997). His research focuses on Monte Carlo methods, network data analysis, state space models, bioinformatics, and Bayesian inference. Key interests include scalable network estimation, community detection, and applications in public health, education, and computational biology. Recent work highlights include advancements in dynamic network modeling, Bayesian latent class models for cognitive diagnosis, and statistical methods for analyzing multi-layer networks. His contributions have been recognized through awards such as the American Statistical Association Fellowship (2018) and the Charles Edison Lectureship (2018). Editorial Roles: Associate Editor of Journal of the American Statistical Association , Journal of Computational and Graphical Statistics , and Journal of Algebraic Statistics . Grants & Consulting: Directs the Illinois Statistics Office, providing interdisciplinary research support. Active in collaborative projects involving healthcare, education, and computational infrastructure. Labs & Teams: Leads initiatives at the Coordinated Science Lab and Information Trust Institute, integrating statistical methods with cybersecurity and data-driven decision-making.

Andreas Gulyasمشاهده پروفایل

استادیار

• Macroeconomics
• Labor Economics
• Search Theory
• +۱ مورد دیگر

Prof. Andreas Gulyas is an Assistant Professor of Macroeconomics at the Department of Economics, University of Mannheim, and a research affiliate at IZA. He holds a Ph.D. from UCLA and a M.Sc. from the University of Vienna. His research focuses on Macroeconomics, Labor Economics, Search Theory, and Machine Learning applications in economics. Education: Ph.D. in Economics, UCLA, Los Angeles, USA M.Sc. in Economics, University of Vienna, Austria Research Interests: Gulyas examines labor market dynamics, monetary policy impacts, wage transparency, and the consequences of job displacement. His work combines theoretical models with empirical analysis, often employing field experiments and machine learning techniques to explore labor market inefficiencies and policy implications. Notable areas include cross-country comparisons of economic inequality and pandemic-induced unemployment effects. Articles Trends: His recent work emphasizes labor market policies, wage structures, and macroeconomic shocks. Key themes include the role of fringe benefits in job search behavior, monetary policy's heterogeneous effects, and pandemic-related unemployment disparities. Machine learning is increasingly used to analyze heterogeneous outcomes in displacement scenarios. Awards: No scientific awards listed. Advising/Grants: No advising records or grant information provided in available texts. His current research is likely supported through institutional funding and affiliations like IZA. Labs/Teams: Affiliated with the AEE Seminar at Mannheim, contributing to collaborative economics research initiatives.

Hau-Tieng Wuمشاهده پروفایل

استاد

• Biomedical Signal Processing
• Applied Mathematics
• Machine Learning
• +۵ مورد دیگر

Hau-Tieng Wu is a Professor in the Department of Mathematics at the Courant Institute of Mathematical Sciences, New York University. Originally from Kaohsiung, Taiwan, he holds an MD from National Yang-Ming University (2003) and a PhD in Mathematics from Princeton University (2011). His research focuses on developing mathematical foundations for biomedical signal analysis, particularly in high-frequency and heterogeneous physiological signals such as ECG, EEG, and PPG. He leads the MISTA Lab, which bridges theoretical advancements with clinical applications in areas like sleep dynamics, surgical monitoring, and wearable device data analysis. Key academic roles include tenured positions at Duke University (2017–2023) and the University of Toronto (2014–2017). Notable awards include the Sloan Research Fellowship (2015) and PIMS Early Career Award (2017). His lab actively collaborates with physicians and engineers to advance interpretable medical AI systems. Research interests span nonlinear time-frequency analysis, manifold learning, and spatiotemporal data processing. Over 100+ journal publications and 10 conference proceedings highlight contributions to signal processing theory and clinical applications. The lab is recruiting PhD students/postdocs with backgrounds in applied math, statistics, or biomedical engineering.

• German and European Capital Markets law
• German and European Company Law
• Legal methods
• +۳ مورد دیگر

Professor Dr. Prof. h.c. Thomas M.J. Möllers holds the Chair for Civil Law, Commercial Law, European Law, International Private Law and Comparative Law at the Faculty of Law, University of Augsburg. As a full professor, he leads research and teaching in multiple legal disciplines with a particular focus on German and European financial regulation, corporate law, and legal methodology. His work bridges traditional legal scholarship with contemporary challenges posed by digitalization and globalization. Professor Möllers' research interests span German and European Capital Markets law, German and European Company Law, Legal methods, Comparative Law, European Union Law, and Competition Law. His scholarly work demonstrates exceptional breadth, connecting doctrinal legal analysis with practical applications in evolving regulatory environments. He has made significant contributions to understanding the interface between national legal systems and EU regulation, particularly in financial markets. His recent publications reveal a strong focus on capital markets regulation, particularly regarding the Wirecard scandal and auditor liability, consumer protection in e-commerce, data protection across different legal systems, and the impact of AI on legal methodology. The trajectory of his work shows increasing engagement with comparative legal analysis between European, American, and Chinese legal systems, reflecting his growing international influence. TOP 1% Most Cited Books (2019-2023) - Legal Translations in China for his 'Legal Methodology' Honorary Professor at Central South University, Changsha, Hunan (2024) Professor Möllers has mentored numerous successful legal scholars who have gone on to academic and professional careers, including Dr. Liuhua Shen (now a professor at Jiangsu University), Dr. Maximilian Merwald, and Dr. Lisa Wolf. His research is supported by various grants, including DAAD funding for US exchange programs and participation in the Jean Monnet Centre of Excellence - INspiRE on consumer protection, capital markets, and competition law enforcement. He leads the Research Center of Innovation and Legal Studies between China and Europe (RICE) and is actively involved in the Caplaw initiative, which provides resources on European and national business law. His work connects legal scholarship with practical applications through podcasts, student research showcases, and collaborative projects across multiple legal systems.

Rudi Zagstمشاهده پروفایل

استاد

• Financial Engineering
• Risk Management
• Asset Management
• +۳ مورد دیگر

Prof. Dr. Rudi Zagst is a Professor of Mathematical Finance at the Technical University of Munich (TUM), where he serves as Head of the Department of Mathematical Finance within the TUM School of Computation, Information and Technology. He has held this position since 2001 and is actively involved in teaching, research, and academic leadership. In 2003, he was appointed as a second member of the Faculty of Economics, and since 2004, he has served as Deputy Chairman of the joint elite degree program 'Finance & Information Management' of the University of Augsburg and TUM. Prof. Zagst earned his doctorate in business mathematics from the University of Ulm, where he later completed his habilitation in 2000. His academic journey began with a professional career at HypoVereinsbank AG, where he served as Head of Product Development in Institutional Investment Management before becoming Managing Director of RiskLab GmbH in 1997. His research focuses primarily on financial engineering, risk management, and asset management, with particular emphasis on portfolio optimization, mathematical finance, and quantitative risk management. His work bridges theoretical finance with practical applications, often incorporating advanced mathematical techniques to solve complex financial problems. Recent publications demonstrate his continued interest in GARCH models, portfolio optimization under various constraints, and the application of machine learning techniques to financial problems. Analysis of his recent publications (2024-2025) reveals a strong focus on portfolio optimization under complex market conditions, particularly using GARCH models to capture volatility dynamics. His work increasingly incorporates machine learning techniques (as seen in the credit spread analysis paper) while maintaining rigorous mathematical foundations. Many papers explore the intersection of theoretical finance with practical investment strategies, reflecting his commitment to bridging academic research with real-world financial applications. Professor of the Year 2007 (awarded by Unicum Profession magazine) Prof. Zagst has supervised numerous bachelor's, master's, and doctoral theses through TUM's Finance and Actuarial Science research group. His collaborative work with industry partners through the TUM CAIR Labs and RiskFactory demonstrates strong connections between academic research and practical financial applications. He has received research funding through various industry partnerships with major financial institutions including Allianz, Munich Re, and ERGO Group AG. Prof. Zagst leads the Research Group Finance and Actuarial Science at TUM, which includes Professors Matthias Scherer, Aleksey Min, and Christoph Knochenhauer. The group maintains strong industry connections through the TUM CAIR Labs initiative, collaborating with over 25 financial institutions including Allianz, Munich Re, Deloitte, PwC, and KPMG. Their RiskFactory laboratory serves as a bridge between academic research and practical financial risk management applications in the industry.

Brett Sandersمشاهده پروفایل

استاد

• Flood modeling and prediction
• Coastal and urban flooding
• Hydrological modeling
• +۷ مورد دیگر

Brett Sanders is a Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering, University of California, Irvine. His research focuses on developing innovative algorithms for flow and transport in river and coastal systems and integrating information technologies to create more accurate and efficient simulation tools for flood hazard assessment. His primary research interests include: Flooding and erosion hazards, particularly coastal flooding and urban flooding Surface water quality Low impact development impacts on hydrology Dam-break flooding Aerial and terrestrial lidar scanning Geographical information systems High performance computing for simulation tools Social dimensions of flood risk and adaptation behaviors Dr. Sanders' recent publications (2024-2025) reveal a comprehensive research program addressing both technical and social aspects of flood risk. His work spans computational hydrodynamics, flood hazard mapping, infrastructure vulnerability assessment, and the socioeconomic dimensions of flood risk. He has made significant contributions to understanding multi-grid modeling of urban flooding, post-fire flood hazards, satellite-based monitoring of land motion, and social inequalities in flood exposure. His research demonstrates how flood dynamics are more complex than simple bath-tub filling models suggest, with important implications for urban planning and climate adaptation. Dr. Sanders has received recognition as a Chancellor's Professor at UC Irvine, indicating distinguished scholarly achievement. His educational background includes: Ph.D. in Civil Engineering from the University of Michigan (1997) M.S. in Civil Engineering from the University of Michigan (1994) B.S. in Civil Engineering from the University of California, Berkeley (1993)

Elinor Benamiمشاهده پروفایل

استادیار

• Environmental Economics
• Development Economics
• Agricultural Economics
• +۳ مورد دیگر

Elinor Benami is an **Assistant Professor** in the Agricultural and Applied Economics Department at Virginia Tech. She holds affiliations with the VT Remote Sensing & Global Change Center, the Center for Advanced Innovation in Agriculture, and Stanford’s RegLab. Her research focuses on environmental and development economics, leveraging satellite imagery and machine learning to enhance disaster financing and environmental compliance. She earned a B.A. from UNC Chapel Hill, a Ph.D. from Stanford University, and a postdoc at UC Davis. **Research Interests**: Climate risk management, agricultural resilience, remote sensing applications, and policy design for sustainable agriculture. Current projects include NASA-funded work on Moroccan irrigation and drought financing, and evaluating U.S. environmental compliance using AI. **Awards/Grants**: Led a $650K NASA Harvest grant, part of a $80M climate-smart agriculture initiative, and received the VT Early Career Scholarly Impact Award Nominee. Active in policy, including advising the EPA on environmental compliance algorithms. **Teaching**: Courses include 'Remote Sensing in Social Sciences', 'Climate Risk Management', and 'Environmental and Sustainable Development Economics'. Mentors students at all levels, emphasizing computational skills and social impact. **Affiliations**: NASA Harvest, VT Remote Sensing IGEP, and the Alliance for Climate-Smart Agriculture. Collaborates with global institutions on drought financing and satellite data applications.

Marcel Dettlingمشاهده پروفایل

مدرس

• Predictive Analytics
• Applied Statistics
• Data Mining
• +۶ مورد دیگر

Dr. Marcel Dettling is a Group Lead in Data Analysis and Statistics at the ZHAW School of Engineering , focusing on predictive analytics, applied statistics, and complex data analysis. He also serves as a Lecturer at ETH Zurich , teaching advanced statistical methods. Education : PhD in Mathematics (2000-2004), ETH Zurich Postdoc in Applied Statistics (2004-2006), Johns Hopkins University His research spans predictive analytics (regression, classification, time series), data mining, and applications in health economics, transportation safety, social sciences , and business analytics . Recent work includes pharmaceutical cost group analysis for Swiss healthcare and predictive maintenance for marine vessels. Selected publications highlight his expertise in flight trajectory modeling , deep learning error mitigation , and statistical frameworks for rehabilitation finance . His projects address diverse fields like crowdworking in nursing, energy optimization for shipping, and customer behavior prediction.

• Data-Driven Organizations
• Artificial Intelligence
• Digital Transformation
• +۳ مورد دیگر

Javier Zamora is a Professor at IESE Business School , part of the University of Navarra. He holds a Ph.D. in Electrical Engineering from Columbia University and an M.Sc. in Telecommunications Engineering from the Polytechnic University of Catalonia. Education Ph.D., Electrical Engineering, Columbia University M.Sc., Telecommunications Engineering, Polytechnic University of Catalonia PDG, IESE Business School Research Interests : Zamora specializes in digital transformation, focusing on data-driven organizations and artificial intelligence. His work explores how new technologies reshape business models, organizational structures, and leadership strategies. Publications span topics from AI implementation in finance and sports management to cloud computing in crisis scenarios. His research emphasizes practical frameworks like the "Digital Density" concept and tools for assessing digital maturity. Professional Roles : He co-founded Inqbarna (AI-powered mobile solutions) and served as CEO of eNeo Labs (digital home products). Currently, he directs executive programs at IESE, including Digital Transformation: Senior Management Program and Navigating IT Architecture .

Theo Arentzeمشاهده پروفایل

استاد

• Spatial Choice Behavior
• Decision Support Systems
• Activity-Based Modeling
• +۹ مورد دیگر

Theo Arentze is a Full Professor at Eindhoven University of Technology (TU/e) in the Department of the Built Environment, leading the Real Estate Management and Development group. He is affiliated with EAISI Health and EAISI Mobility research institutes. Education: MSc in Psychology (Cognitive Psychology & AI) from Groningen University, PhD from TU/e Urban Planning Research: Spatial choice behavior, decision support systems, activity-based modeling, agent-based simulation His research integrates bounded rationality into spatial choice models to enhance behavioral realism, with applications in real-estate management , neighborhood development , healthy cities , and hybrid work environments . Recent work includes child-friendly urban planning and energy-efficient housing impacts . Prominent article themes include: Hybrid work location decisions Urban public space affective experiences Child friendliness in residential choices Sustainable energy preferences Spatial decision support systems Social network modeling Scientific recognition includes: Best Poster Award (2025) - Computational Urban Planning Conference Long Paper of Distinction (2021) - Healthy Buildings Europe EuroFM Best Paper Award (2017) Pyke Johnson Award (2016) As an educator, he teaches courses in Urban Planning , Housing Economics , and Quantitative Research Methods . His multidisciplinary team combines expertise from psychology, sociology, and urban economics to create high-quality built environments through behavioral research.