جست‌وجوی استاد و پژوهشگر

Shane G. Hendersonمشاهده پروفایل

استاد

• Simulation Optimization
• Applied Probability
• Health Systems
• +۵ مورد دیگر

Shane G. Henderson is the Charles W. Lake, Jr. Professor in Productivity at Cornell University's School of Operations Research and Information Engineering (ORIE). His research focuses on the intersection of optimization and simulation, with applications to complex systems such as bike-sharing networks, medical scheduling, and ambulance deployment. He emphasizes practical societal relevance, addressing challenges in healthcare, transportation, and parallel computing environments. Education: B.Sc. (Mathematics), University of Auckland (1992) M.S. (Statistics), Stanford University (1995) Ph.D. (Operations Research), Stanford University (1997) Research Interests: Structured simulation optimization, parallel computing applications, applied probability, and case-driven problem-solving. Recent work includes MRI scheduling, radiation treatment planning, and land/air ambulance systems. He advocates for methodologies that leverage convexity or quasi-convexity to enhance algorithmic efficiency and robustness. Awards and Recognition: 2016 Tau Beta Pi Professor of the Year 2014 Sonny Yau '72 Excellence in Teaching Award 2011 Hess Research & Education Award NSF CAREER Award (1999) Teaching & Service: Courses in probability, simulation, and mathematical modeling using case-based learning. Served on award committees for INFORMS Nicholson Prize, Pierskalla Prize, and Impact Prize. Organized major conferences including the 2009 INFORMS Applied Probability Society conference and multiple Winter Simulation Conferences. Currently co-edits simulation-optimization testbeds for experimental comparisons. Labs & Collaborations: Active in Cornell's collaborative research environment, focusing on interdisciplinary projects with industry partners like Motivate (bike-sharing systems). Engaged in advancing methodologies for complex systems through data-driven approaches.

Alexandre RUBESAMمشاهده پروفایل

دانشیار

• Finance
• Asset Pricing
• Financial Econometrics
• +۵ مورد دیگر

Alexandre RUBESAM is an Associate Professor at IÉSEG School of Management (France), specializing in Finance with a focus on asset pricing, financial econometrics, and quantitative trading. He holds a Ph.D. in Finance from Cass Business School (UK), an MSc in Statistics from the State University of Campinas (Brazil), and a Bachelor in Statistics from the same university. Education: Ph.D., Finance, Cass Business School, UK (2008) MSc., Statistics, State University of Campinas, Brazil (2004) Bachelor, Statistics, State University of Campinas, Brazil (2001) His research interests span behavioral finance, risk management, machine learning applications in finance, and portfolio optimization. Notably, he explores topics like market herding during crises, volatility forecasting, and the low-beta anomaly through behavioral lenses. Prof. Rubesam has authored influential papers on information transmission in financial markets, risk parity strategies, and the efficacy of linear models in volatility prediction. His work bridges theoretical finance with practical applications, such as developing machine learning-based portfolio construction methods for emerging markets. Awards: 2007 Dimitris N. Chorafas Foundation Prize 2006 Best Paper Award, Cass Business School His professional roles include Chief Risk Officer at Itaú-Unibanco (2013–2017) and Quantitative Researcher/Trader at Principia Capital Management (2009–2011). He is a member of LEM (Laboratory of Economics and Management) and teaches courses on financial programming, risk management, and portfolio analysis.

Matthew B. Blaschkoمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۵ مورد دیگر

Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.

Petter N. Kolmمشاهده پروفایل

استاد بالینی

• Quantitative Trading Strategies
• Delegated Portfolio Management
• Financial Econometrics
• +۵ مورد دیگر

Petter N. Kolm serves as a Clinical Professor of Mathematics and Program Director at New York University, with his office located in Warren Weaver Hall (520). He can be contacted at petter.kolm@nyu.edu or 212-998-4855, and holds an editorial board position at the Journal of Portfolio Management. His academic qualifications include: Doctorate in Mathematics from Yale University M.Phil. in Applied Mathematics from the Royal Institute of Technology in Stockholm M.S. in Mathematics from ETH Zurich Dr. Kolm's research centers on quantitative finance, with primary focus areas including quantitative trading strategies, delegated portfolio management, financial econometrics, risk management, and optimal portfolio strategies. His work integrates advanced mathematical modeling with practical investment applications, bridging theoretical frameworks and real-world market dynamics through rigorous empirical analysis. Analysis of his 15 most recent publications reveals consistent emphasis on portfolio optimization techniques—particularly Bayesian methods and the Black-Litterman model—alongside significant contributions to algorithmic trading systems, factor-based equity portfolio construction, and machine learning applications for financial sentiment analysis. His scholarly output demonstrates evolution from foundational portfolio theory toward contemporary computational finance challenges. As Program Director, Dr. Kolm oversees academic programming and likely mentors graduate students in quantitative finance, though specific advisee details are not documented. His prior industry role at Goldman Sachs Asset Management provided direct experience in developing hedge fund strategies, informing his applied research approach. Dr. Kolm's professional trajectory includes significant industry engagement through his tenure in Goldman Sachs' Quantitative Strategies Group, where he developed quantitative investment systems. His current academic leadership position leverages this practical experience to shape quantitative finance education and research at NYU.

• Conservation ecology
• Population ecology
• Landscape ecology
• +۳ مورد دیگر

Robert Fletcher is a Researcher at the University of Cambridge, affiliated with the Department of Zoology and the C-CLEAR Doctoral Training Partnership . His work focuses on applied ecology and conservation science, utilizing landscape and population ecology to address biodiversity challenges globally. Research Areas : Conservation biology, population ecology, landscape ecology, environmental informatics Collaborations : Partners in North America, Europe, Africa, and Southeast Asia Key Themes : Species extinction prevention, landscape conservation prioritization, and rapid biodiversity data delivery Email : rf497@cam.ac.uk Fletcher's interdisciplinary approach integrates fieldwork (e.g., Everglades endangered species, African elephants) with advanced modeling of habitat loss, fragmentation, invasive species, and climate change impacts. His recent work emphasizes: Drivers of species decline and recovery strategies Landscape management and restoration techniques Interdisciplinary collaborations with engineers, social scientists, and computer scientists His publications span topics like savanna ecosystem dynamics, community science applications, and conservation forecasting, reflecting a commitment to actionable science for global biodiversity preservation.

Anne Magurranمشاهده پروفایل

استاد

• Biological Diversity
• Ecology
• Community Ecology
• +۵ مورد دیگر

Prof. Anne Magurran is a Professor (Re-engaged) at the School of Biology, University of St Andrews. Her research focuses on biological diversity, quantifying ecological patterns, and understanding processes influencing biodiversity change over space and time to inform conservation efforts. She holds academic qualifications including a D.Phil. from the University of Ulster and an honorary doctorate from the University of Bergen. She is a Fellow of the Royal Society of Edinburgh (FRSE) and an honorary life member of the Fisheries Society of the British Isles. Her work spans global ecosystems and involves collaborations across five continents. Key projects include the ERC-funded BioTIME initiative, analyzing biodiversity time series, and NERC-supported studies integrating taxonomic, phylogenetic, and functional diversity metrics. She has supervised PhD students such as Cher Chow, Amber Cowans, and Volodymyr Hudgins. Prof. Magurran has received prestigious awards like the Beverton Medal (2006), ASAB Medal (2021), and the CBE (2022). Her research also addresses UN SDGs related to life on land and below water. She leads interdisciplinary teams at the Centre for Biological Diversity and contributes to institutions like the Marine Alliance for Science & Technology Scotland.

• Asset Management
• Hedge Funds
• Risk Management
• +۲ مورد دیگر

Robert Kosowski is Professor of Finance and Head of the Department of Finance at Imperial College Business School, Imperial College London. He holds a Ph.D. from London School of Economics, M.Sc. in Economics from London School of Economics, and B.A./M.A. in Economics from Trinity College, Cambridge University. His research examines asset management, risk management, machine learning applications in finance, hedge funds, and performance measurement. He has published in top finance journals including Journal of Finance, Journal of Financial Economics, and Review of Financial Studies. Awards include European Finance Association Best Paper Award (2007), four INQUIRE best paper awards, and British Academy Mid-Career Fellowship (2011-2012). Recent publications focus on machine learning in finance, regulatory impacts on funds, and innovative risk management approaches. Articles demonstrate consistent methodological rigor across quantitative finance topics with practical applications for investment management. Professor Kosowski is co-author of 'Principles of Financial Engineering' and directs executive education programs in Risk Management. He has industry experience as Head of Quantitative Research at Unigestion and previously worked at Goldman Sachs and Deutsche Bank.

Leland Bybeeمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Econometrics
• +۲ مورد دیگر

Leland Bybee is an Assistant Professor of Finance at the University of Chicago Booth School of Business . He leverages machine learning and natural language processing to address economic and financial questions, particularly focusing on belief measurement with applications to asset pricing and behavioral economics. Ph.D. in Financial Economics, Yale School of Management (2024) M.S. in Statistics, University of Michigan (2017) B.A. in Economics, University of Chicago (2013) His research integrates computational methods with economic theory to analyze: Textual analysis of business news for macroeconomic tracking Narrative-driven asset pricing models Memory-based belief formation using kernel methods Macroeconomic determinants of currency returns He has received multiple awards including: Dimension Fund Advisors Distinguished Paper Award BlackRock Applied Research Award HEC Top Finance Graduate Award The Brattle Group PhD Candidates Award EFA Engelbert Dockner Memorial Prize Bybee teaches Machine Learning in Finance and participates in finance seminars, contributing computational tools like regIPCA (Python) and changepointsHD (R) to the research community.

David Hartzellمشاهده پروفایل

استاد

• Real Estate Finance
• Private Equity
• Real Estate Investment Strategies
• +۳ مورد دیگر

David Hartzell is the Steven D. Bell and Leonard W. Wood Distinguished Professor of Finance at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill. He specializes in real estate finance, private equity, and institutional investment strategies. As director of the Leonard W. Wood Center for Real Estate Studies, he oversees academic and practical initiatives in real estate education and research. He advises the UNC Real Estate Investment Fund, a student-managed private equity fund, and holds board roles at Highwoods Properties and multiple academic and corporate entities. Educational Background: PhD from UNC-Chapel Hill; MA and BS from University of Delaware His research focuses on global real estate investment strategies, portfolio construction, and the performance of real estate private equity. He co-authored Global Property Investment: Strategies, Structures, Decisions , widely adopted in academic curricula. His work bridges academic research and industry practices, with extensive consulting experience for firms like Heitman Capital Management and Salomon Brothers Inc. Key contributions include analyzing real estate volatility in global markets (e.g., Hong Kong), financial flexibility in REITs, and the role of mezzanine finance in commercial real estate. His articles span topics from beta asymmetries to land-use regulations in metropolitan areas. Professional Roles: Board member at Highwoods Properties, AREUEA president (2000), former associate dean for MBA Programs He leads the Real Estate Program at Kenan-Flagler, emphasizing hands-on learning through student funds and industry collaborations.

Guofu Zhouمشاهده پروفایل

استاد

• Empirical Asset Pricing
• Machine Learning in Finance
• Financial Economics
• +۱۰ مورد دیگر

Guofu Zhou , the Frederick Bierman & James E. Spears Professor of Finance at Washington University's Olin Business School , has been a faculty member since 1990. His academic career includes multiple Reid Teaching Awards (2020, 2019, 2018, 2014, 2010) Best Paper Awards (Institute for Quantitative Investment Research 2019, Chinese Finance Association 2010, Inquire UK/Europe 2019 & 2024) and affiliations with journals like the Journal of Financial Economics and Management Science . Education: PhD, Duke University (1990) MA, Duke University (1987) MS, Academia Sinica (1985) BS, Chengdu University of Technology (1982) His research bridges empirical asset pricing and applied AI/machine learning , with a focus on market efficiency anomaly exploitation Bayesian inference option pricing Chinese financial markets behavioral finance He has contributed to understanding equity risk premium predictability, technical analysis, and portfolio optimization techniques. Key trends in his Journal of Financial Economics , Journal of Finance , and Review of Financial Studies publications include machine learning applications in asset pricing , anomaly-market linkages , and fear sentiment in Treasury markets . Recent work with ChatGPT explores textual analysis of earnings calls. Scientific Awards: Best Paper Award, Institute for Quantitative Investment Research (2019) Finalist for Crowell Memorial Prize (2024) Led multiple Best Paper Awards at conferences like FMA and SIF Contact: zhou@wustl.edu | Office: Simon Hall Room 207

Qiwei Yaoمشاهده پروفایل

استاد

• Statistical Inference
• Time Series Analysis
• High-Dimensional Statistics
• +۶ مورد دیگر

Qiwei Yao is a Professor of Statistics at the Department of Statistics, London School of Economics (LSE), where he maintains an active research program in statistical methodology and applications. His office is located in Columbia House, Room 7.16 at LSE's Houghton Street campus in London. Professor Yao's research focuses on statistical inference for complex time series, with particular expertise in high-dimensional time series, dynamic networks, spatio-temporal processes, functional time series, nonlinear time series, and high-frequency data. His work bridges theoretical statistics with practical applications, especially in financial econometrics. He has developed innovative methodologies for dimension reduction, factor modeling, and network analysis that have become influential in the field. His recent publications reveal a strong trend toward developing statistical methods for increasingly complex data structures, particularly focusing on high-dimensional and network-based time series. His work integrates machine learning techniques with traditional statistical approaches, as evidenced by papers on deep learning for Markov property testing and tensor decompositions for matrix time series. There's also a clear emphasis on privacy-preserving methods and differential privacy in network analysis. Professor Yao has secured substantial research funding through multiple EPSRC Programme Grants and Research Projects, including the EPSRC Programme Grant for 'Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS)' and 'Network Stochastic Processes and Time Series (NeST)'. He also leads the EPSRC Research Project on 'Statistical Network Analysis: Model Selection, Differential Privacy, and Dynamic Structures' and has collaborated with industry partners like Andurand Capital Management on projects such as 'Forecasting Oil Prices Based on Quantitative Methods'. His research has significant applications across various domains, particularly in energy forecasting (electricity load prediction), financial modeling (volatility modeling, oil price forecasting), and ecological modeling (spatio-temporal population dynamics). Professor Yao maintains strong collaborative relationships with researchers across multiple institutions and disciplines.

• Finance
• Corporate Governance
• Market Microstructure
• +۶ مورد دیگر

Prof. Dr. Erik Theissen is a full-time faculty member at the University of Mannheim, holding the Chair in Business Administration and Finance. His research focuses on market microstructure, corporate governance, and information-based trading in equity markets. University of Mannheim, Department of Business Administration Research areas: Finance, Market Microstructure, Corporate Governance His work compares auction and dealer markets, analyzes insider trading regulations, and evaluates the impact of trader anonymity on market liquidity. He has explored competition between exchanges (e.g., Euronext vs. Xetra) and the role of internalized trading under MiFID. His publications include Organizational Forms of Securities Trading (1998), with over 15 major peer-reviewed articles since 2001. Key journals include the Review of Financial Studies, European Financial Management, and Journal of Corporate Finance. 2001 Financial Innovation Award of Bethmann Bank 2001 Best Paper Award (Association of University Professors of Business Administration) 1998 Dissertation Award of the German Stock Institute His research spans experimental economics (e.g., market design, risk attitude estimation) and empirical analysis of German capital markets, including IPO pricing and mutual fund performance.

Matthias Breuerمشاهده پروفایل

استاد

• Corporate Reporting
• Sustainability Reporting
• Financial Regulation
• +۳ مورد دیگر

Matthias Breuer serves as Heisenberg-Professor for Corporate Reporting & Regulation at Goethe University Frankfurt's Faculty of Economics and Business, where he investigates corporate transparency mechanisms and regulatory solutions for sustainability reporting challenges. His interdisciplinary work bridges historical accounting contexts with modern ESG assurance frameworks. His academic foundation includes: PhD from the University of Chicago Master’s degree from the London School of Economics and Political Science Bachelor’s degree from WHU Breuer's research program centers on information verification dynamics in corporate reporting, with particular emphasis on how financial regulation shapes resource allocation, innovation incentives, and disclosure practices. He examines historical precedents (e.g., 1890s streetcar industry) to inform contemporary sustainability assurance systems, employing advanced econometric techniques to analyze regulatory impacts across public and private firms. His scholarship consistently addresses the tension between voluntary and mandatory disclosure regimes in capital markets. Analysis of his 15 most recent publications reveals escalating focus on sustainability regulation, with 60% directly addressing ESG-related disclosure challenges. Methodologically, he integrates Bayesian statistics, fixed effects modeling, and historical analysis to dissect reporting anomalies, while recent work increasingly explores spillover effects in peer disclosure networks and minority representation dynamics within corporate environments. Key recognitions include: DFG Heisenberg Fellowship TRR 266 Research Fellowship (Accounting for Transparency) Fellowship at the Centre for Advanced Studies on Law and Finance Affiliate Fellowship at Chicago Booth's Stigler Center As principal investigator of the DFG-funded Heisenberg project, Breuer directs substantial research resources toward transparency regulation while maintaining active collaboration with Columbia University and Chicago Booth through prior appointments. His grant portfolio reflects sustained interest in regulatory economics, with current projects examining auditing mandates and corporate innovation under disclosure requirements. Breuer contributes to Goethe University's Accounting Research Seminar series and is embedded in the TRR 266 collaborative research center, where he coordinates interdisciplinary teams analyzing transparency standards. His methodological laboratory combines archival financial data with historical industry records to model disclosure dynamics under varying regulatory regimes.

Travis B. Thompsonمشاهده پروفایل

استادیار

• Mathematical Modeling
• Scientific Computing
• Machine Learning in Medicine
• +۵ مورد دیگر

Travis B. Thompson, Ph.D. is an Assistant Professor in the Department of Mathematics and Statistics at Texas Tech University, leading the TM4 (Texas Tech Translational and Theoretical Mathematical Modeling and Machine Learning in Medicine) research group. His academic journey includes postdoctoral work at Rice University, Simula Research Laboratory, and the University of Oxford, focusing on mathematics applied to neurodegenerative diseases. Education: Ph.D. in Mathematics from Texas A&M University (2013) Dr. Thompson develops theoretical mathematical models and applies scientific computing and machine learning to study neurological pathologies, particularly Alzheimer’s disease. His work explores complex biological processes on networks, translational healthcare applications, and nutritional security implications. Current research trends integrate neuroimaging data with finite element simulations to model tau progression , amyloid beta dynamics , and glymphatic clearance in age-related diseases. Scientific awards and honors were not explicitly mentioned in the provided materials. Dr. Thompson’s interdisciplinary approach connects computational neuroscience with biomedical engineering , utilizing techniques like diffusion tensor imaging and level set methods to analyze pathological protein spread and brain tissue mechanics . The TM4 research group focuses on network neurodegeneration , personalized medicine , and machine learning diagnostics . Their work spans from microfluidic cancer detection to computational modeling of brain clearance mechanisms , addressing challenges in both neurodegenerative diseases and biomedical engineering through rigorous mathematical frameworks.

Christine Staatzمشاهده پروفایل

دانشیار

• Quality Use of Medicine
• Pharmacometrics
• Transplant Pharmacology
• +۳ مورد دیگر

Dr Christine Staatz is an Associate Professor at the University of Queensland 's School of Pharmacy and Pharmaceutical Sciences , specializing in Quality Use of Medicine and Pharmacometrics . Her career spans both clinical pharmacy practice and academic research, with international experience at the University of Glasgow and Uppsala University. B.Pharm (Hons I), University of Queensland (1996) PhD, University of Queensland (2002) Pharmacist registration: Australia (1997), UK (2003) Her research focuses on individualized drug therapy for transplant recipients and critically ill patients, particularly optimizing immunosuppressants (tacrolimus, mycophenolate) and antibiotics (beta-lactams, gentamicin). Recent publications examine age-related pharmacokinetics and microsampling techniques to reduce patient burden. Key contributions include 15+ peer-reviewed articles in 2024-2025 addressing: Bayesian forecasting of drug exposure Free vs total plasma drug concentrations Time-dependent clearance in pediatric chemotherapy Immune-related adverse effects in transplant recipients NHMRC Neil Hamilton Fairley Fellowship (2004) Lions Medical Research Fellowship National grants exceeding AUD$2 million With 14+ completed PhD/MPhil supervisees and ongoing projects in autoimmune diseases and hematopoietic transplantation , she actively mentors students in clinical pharmacology and drug monitoring research.