جستجوی استاد راهنمای دانشگاهی

Ali Shouridehمشاهده پروفایل

دانشیار

• Mechanism Design
• Taxation
• Market Regulations
• +۱۳ مورد دیگر

Ali Shourideh is an Associate Professor of Economics at Carnegie Mellon University's Tepper School of Business since 2020, previously serving as Assistant Professor at CMU (2016-2020) and the University of Pennsylvania's Wharton School (2011-2016). He holds a PhD in Economics from the University of Minnesota (2012), an MA from the University of British Columbia (2005), and a BS in Mechanical Engineering from Sharif University of Technology (2004). PhD: Economics, University of Minnesota (2012) MA: Economics, University of British Columbia (2005) BS: Mechanical Engineering, Sharif University of Technology (2004) His research focuses on Mechanism Design and its applications in Taxation , Market Regulations , and Information Systems . He explores how information structures affect market outcomes, particularly in price discrimination and monopolistic segmentation. His work on dynamic markets addresses financial frictions, sovereign debt cycles, and optimal policy design under uncertainty. Recent publications analyze market liquidity under dual frictions, optimal trade policy with dynamic imbalances, and reputational effects in loan markets. His 15 most recent articles span topics from pension reform to algorithmic market design, reflecting interdisciplinary approaches combining economic theory with computational methods. 2020 : Excellence in Refereeing Award, Review of Economic Dynamics 2010-2011 : Doctoral Dissertation Fellowship, University of Minnesota 2003 : Gold Medal, International Mathematics Competition 1999-2000 : Silver Medals, International Mathematical Olympiad Shourideh has supervised numerous PhD students in microeconomic theory, public finance, and market design while teaching graduate courses in Mechanism Design, Macroeconomics, and Public Economics. He collaborates extensively with researchers at CMU, University of Minnesota, and global institutions, maintaining active service roles in academic governance.

• Human-Computer Interaction
• Usability and User Experience Methodologies
• Technology-Enhanced Learning
• +۷ مورد دیگر

Professor Effie Lai-Chong Law is a leading academic in Human-Computer Interaction (HCI), currently serving as a Professor in the Department of Computer Science at Durham University. Previously, she held positions at the University of Leicester (Full Professor in HCI) and ETH Zurich (Senior Research Fellow). PhD in Psychology (minor: Computer Science) from Ludwig Maximilian University of Munich (summa cum laude, DAAD scholarship) BSc and MSc in Social Science from the University of Hong Kong Her research focuses on usability and UX methodologies applied to Technology-Enhanced Learning (TEL) , Affective Computing , and Trustworthy Autonomous Systems (TAS) . Recent projects examine multidimensional UX measurement , automatic emotion recognition , conversational AI , and mixed reality applications . She leads the H2020 ARETE project and ESA's P-STEP platform. Recent publications reveal trends in chatbot trust dynamics , emotion recognition frameworks , and mixed reality pedagogical tools . Her work bridges psychology and computer science for practical UX solutions. DAAD four-year full scholarship summa cum laude distinction for PhD She supervises four postgraduate students and has led major EU projects including UNIVERSAL , PROLEARN , iCamp , ROLE , 80Days , and Go-Lab/NextLab . Her editorial roles include positions at Interacting with Computers and International Journal of Human-Computer Studies .

• Applied Micro-theory
• Industrial Organization
• Political Economy
• +۳ مورد دیگر

Daniel Li is an Associate Professor at the Durham University Business School. He holds a DPhil from the University of Oxford (2012). His research focuses on applied micro-theory, industrial organization, and political economy, with particular emphasis on auction mechanisms, public policy analysis, and Chinese economic development. Education: DPhil in Economics, University of Oxford (2012). Research Interests: - Applied Micro-theory - Industrial Organization - Political Economy - Public Policy Analysis - Market Design - Development Economics Advising: Currently supervising Yan Wang. His research has explored topics such as auction default mechanisms, public-private partnerships in China, and the impact of factional politics on policy outcomes. Labs/Teams: While no specific lab is mentioned, his work is conducted within the Durham University Business School's research environment, focusing on interdisciplinary economic analysis.

Tony Wirjantoمشاهده پروفایل

استاد

• AI & Deep Learning in Cybersecurity
• Climate Change, Climate Finance & Climate Risk
• Computational Finance & Quantitative Finance
• +۲ مورد دیگر

Professor Tony Wirjanto holds dual appointments as a full professor at the School of Accounting & Finance (Faculty of Arts) and cross-appointed to the Cheriton School of Computer Science and Department of Statistics and Actuarial Science (Faculty of Mathematics) at the University of Waterloo. He specializes in econometrics and quantitative finance, with a focus on AI-driven cybersecurity, climate finance, and time series analysis. His research bridges finance, computer science, and environmental policy. Education: PhD in Econometrics from Queen’s University. He teaches courses on quantitative finance, financial econometrics, and sustainable finance. Notable grants include funding from the SOA Research Institute for climate change risk studies and SSHRC grants for systemic financial crises research. His work appears in top journals like Energy Economics and the Journal of Mathematical Finance. He leads professional initiatives at the World Economic Forum and contributes to institutes addressing climate, energy, and cybersecurity challenges.

Phong Truongمشاهده پروفایل

استادیار

• Financial Reporting
• Capital Markets Regulation
• Information Intermediaries
• +۱ مورد دیگر

Phong Truong is an Assistant Professor of Accounting at the Department of Accounting, Pennsylvania State University. His research focuses on financial reporting effects, capital markets regulation, information intermediaries, and behavioral finance. He holds a Ph.D. in Industrial Administration - Accounting from Carnegie Mellon University (2019), an M.S. from the same program (2016), and a B.S. in Mathematics (Computer Science) from the University of Massachusetts Boston (2014). His recent publications explore topics such as the real effects of earnings announcements, crypto fraud, peer effects in disclosure timing, and mutual fund portfolio disclosures. His work often bridges behavioral finance with empirical analysis of market reactions and investor decision-making. Notable themes include the interplay between information dissemination and market efficiency, and the role of linguistic imprecision in analyst forecasts. While no specific scientific awards are listed, his contributions to accounting and finance research are evident through his active publication record. No advising or grant information is provided in the text. Truong’s interdisciplinary background in mathematics and computer science complements his accounting research, particularly in methodological approaches to data analysis. His expertise also extends to audit quality and the impact of employee satisfaction in audit contexts.

Stuart Gemanمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Statistical Theory
• +۳ مورد دیگر

Stuart Geman is the James Manning Professor of Applied Mathematics at Brown University's Division of Applied Mathematics. He holds a Ph.D. from MIT (1977), focusing on stochastic differential equations. His research spans machine and natural vision, statistical theory, neuroscience, financial modeling, and computational linguistics. He has advised numerous students, including Asohan Amarasingham, Lo-Bin Chang, and Ya Jin. His work explores hierarchical models for visual recognition, neural spike train analysis, market dynamics, and generative image modeling. Key contributions include the 'compositionality' framework for efficient learning in biological systems, probabilistic image models with hierarchical structure, and statistical methods for neurophysiological data. He co-developed the Gibbs sampling method for image restoration and pioneered nonparametric statistical estimation via sieves. His recent work addresses scale invariance in natural images and transsaccadic neural coding in macaque V1. Publications span journals like Journal of Neuroscience , Neural Computation , and Proceedings of the National Academy of Sciences . His research integrates computational, statistical, and biological perspectives to address challenges in vision, neuroscience, and financial systems.

• Mathematical Statistics
• Asymptotic Statistics
• Nonparametric Statistics
• +۸ مورد دیگر

Prof. Dr. Arnold Janssen is a retired professor (emeritus) at the Department of Mathematics, Heinrich-Heine-Universität Düsseldorf, affiliated with the Faculty of Mathematics and Natural Sciences. He specializes in mathematical statistics, asymptotic theory, nonparametric methods, and survival analysis. His research spans statistical methodology, including bootstrap techniques, false discovery rate control, and applications in biostatistics and financial mathematics. Janssen has contributed extensively to statistical theory through numerous peer-reviewed publications and has collaborated with researchers across disciplines. Research interests include: Mathematical statistics and asymptotic methods Nonparametric and semiparametric models Survival analysis and clinical trial design Extreme value theory and high-dimensional data analysis Bootstrap methods and permutation tests Applications in biostatistics and financial risk modeling His recent work emphasizes methodological advancements in multiple testing procedures, with applications to genomics and medical research. Janssen has also explored statistical foundations in functional data analysis and stochastic processes. Key contributions include theoretical developments in false discovery rate (FDR) control, nonparametric hypothesis testing frameworks, and the application of Le Cam's theory to modern statistical challenges. His collaborations span interdisciplinary projects with biologists, clinicians, and financial researchers.

Chunmei Liuمشاهده پروفایل

استاد

• Artificial Intelligence
• Cybersecurity
• Blockchain Technology
• +۳ مورد دیگر

Dr. Chunmei Liu is Professor in the Department of Electrical Engineering and Computer Science at Howard University's College of Engineering and Architecture. Her research spans artificial intelligence, cybersecurity, and networked systems, with recent focus on blockchain-enhanced fraud detection (2024), federated learning privacy (2021-2024), and THz-enabled UAV communications (2022-2023). Her 2020-2024 publications demonstrate strong emphasis on resilient machine learning systems, adversarial defense mechanisms, and optimization techniques for cyber-physical systems. Technical applications include medical imaging, vehicular networks, military communications, and financial security.

Benjamin Segalمشاهده پروفایل

دانشیار

• Financial Reporting
• Corporate Governance
• Debt Covenants
• +۲ مورد دیگر

Benjamin Segal is an Associate Professor in the Accounting and Taxation department at Fordham University's Gabelli School of Business, where he has been affiliated since 2014. His office is located at 45 Columbus Avenue in New York. Segal's research examines financial reporting quality, corporate disclosure strategies, debt covenant enforcement, and governance mechanisms. His work frequently addresses regulatory compliance, managerial decision-making, and market reactions to accounting information. Recent publications investigate geographic influences on valuation, fiduciary duties in debt contracting, cyber-risk disclosure effectiveness, and audit committee governance. His studies combine empirical finance with regulatory analysis to assess reporting transparency and governance efficacy.

An Yanمشاهده پروفایل

استاد

• Financial Economics
• Economic Policy
• Corporate Finance

An Yan is The Robert Bendheim Chair Professor in Economics and Financial Policy and Professor of Finance at Fordham University's Gabelli School of Business. He serves as Area Chair for Finance and Business Economics and Co-Director of the Center for Research in Contemporary Finance. His research focuses on financial economics, economic policy, and corporate finance, with implications for market regulation and business strategy. His work addresses structural challenges in financial systems through quantitative analysis of market behaviors and policy impacts. Research spans risk assessment models, corporate governance, and emerging financial technologies. Recent publications demonstrate interdisciplinary work across computer science and medical genetics, though core focus remains financial systems. Articles show strong technical methodology with applications in cybersecurity, data integrity, and biomedical analytics.

Alex T Tabarrokمشاهده پروفایل

استاد

• Political Economy
• Empirical Law and Economics
• Health Economics
• +۴ مورد دیگر

Alex T Tabarrok is a Professor of Economics at George Mason University and holds the Bartley J. Madden Chair at the Mercatus Center. His research focuses on the theory of voting, political economy, and empirical law and economics. He has authored or edited books such as Entrepreneurial Economics and The Voluntary City , and co-founded FDAReview.org to analyze FDA policies. His work spans health economics, innovation, regulatory policy, and criminology. Tabarrok has supervised numerous PhD dissertations on topics including health economics, crime analysis, and regulatory impacts. He teaches courses and maintains an active research agenda addressing global challenges like pandemic preparedness, vaccine distribution, and institutional corruption. His affiliations include the Mercatus Center, a think tank focusing on market processes and public policy. Key research themes include applying economic principles to crises, testing fractional vaccine dosing during pandemics, and investigating political-economic relationships in urban development. His recent work emphasizes leveraging technology and market mechanisms for social and economic progress. Tabarrok’s advising record reflects diverse interests, from crime economics to entrepreneurship barriers. He advocates for evidence-based policy reforms and has contributed to debates on intellectual property, tort law, and education innovation.

• Neurolinguistics
• Language Development
• Educational Neuroscience
• +۵ مورد دیگر

Dr. Jodi M Tommerdahl is an Associate Professor at The University of Texas at Arlington, affiliated with the Department of Higher Education Adult Learning and Organizational Studies under the School of Educational Leadership and Policy Studies. Her research focuses on neurolinguistics, language development, reasoning, and the intersection of neuroscience with education. She holds a PhD in Neurolinguistics from the University of Paris-Sorbonne, an MA in European Languages and Literature from the University of London, and a BA in Scandinavian Languages and Literature from the University of Minnesota Twin Cities. Dr. Tommerdahl’s teaching spans courses like Neuroscience and Language, Educational Neuroscience, and Special Education. She has received awards including the Spotlight Award from Tech Fort Worth (2016) and a Faculty Development Leave (2016). Her research projects include grants funded by NSF and private partnerships, focusing on topics like neuro-feedback anger management, assistive technologies for disabilities, and mobile learning innovations. Her articles explore diverse topics such as cryptocurrency, language learning apps, and cognitive aging. She actively collaborates on interdisciplinary projects, including studies on emotion recognition via EEG and the efficacy of handwriting apps for children. Dr. Tommerdahl is a member of the International Clinical Phonetics and Linguistics Association and has presented at conferences globally, including the International Conference on Linguistics and Literature in Paris.

Daniel E Houserمشاهده پروفایل

استاد

• Experimental Statistics
• Emotion and Individual Differences
• Political Economy

Daniel E Houser is Professor and Chair of the Economics Department at George Mason University. His research examines experimental statistics, emotion and individual differences, and political economy using behavioral and neuroeconomic approaches. Research interests span neuroeconomics, trust dynamics, social preferences, and institutional impacts on behavior. His work integrates laboratory experiments with field data to explore how emotional processes and cognitive mechanisms influence economic decisions. Publications demonstrate consistent focus on behavioral game theory, with recent expansions into neuroimaging studies of betrayal aversion, cross-cultural trust experiments, and institutional impacts on cooperation. Article keywords frequently include experimental methodology, social norms, and decision neuroscience. Dissertation supervision highlights extensive mentorship: 27 doctoral graduates since 2005, with recent topics covering motivated beliefs, group identity dynamics, opioid policy economics, and alcohol-related decision making.

Mehdi Maadooliatمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Bioinformatics (Genomics & Proteomics)
• Functional Data Analysis
• +۳ مورد دیگر

Dr. Mehdi Maadooliat is an Associate Professor in the Department of Mathematical and Statistical Sciences at Marquette University. His research focuses on statistical machine learning, bioinformatics, functional data analysis, and skewed distribution modeling. He holds a Ph.D. in Statistics from Texas A&M University (2011) and has held postdoctoral positions at Texas A&M and KAUST. He leads the Machine Learning and Bioinformatics Lab (MLAB) and advises multiple Ph.D. students. His work has led to software packages like Rfssa and GWID, and he has been recognized with the Way Klingler Young Scholar Award (2016). Education: Ph.D., Statistics, Texas A&M University, 2011 M.Sc., Mathematics & Statistics, Marquette University, 2006 B.Sc., Applied Mathematics, Sharif University of Technology, 2003 Research Interests: Dr. Maadooliat specializes in developing statistical methods for bioinformatics, functional data analysis, and spectral density estimation. His work bridges theory and application, with contributions to protein structure modeling, genome-wide analysis, and medical data science. Recent efforts include functional singular spectrum analysis and regularized multivariate functional PCA. Grants & Awards: National Institute of Health (NIH) R01 grants (2020–2027) for fatigability studies Marshfield Clinic Research Institute grant (2018–2020) for genetic analysis NVIDIA GPU Grant (2017) Teaching & Service: Teaches advanced courses in statistical machine learning and computational probability. Served as Graduate Chair and co-director of Bioinformatics programs at Marquette. Editor for Journal of Statistical Theory and Applications and Journal of the Iranian Statistical Society . Labs & Collaborations: His MLAB develops statistical tools for bioinformatics and functional data analysis. Collaborates with biomedical researchers on projects like aortic coarctation modeling and protein loop prediction.

Alexandra Piryatinskaمشاهده پروفایل

استاد

• Time Series Analysis
• EEG Signal Processing
• Complexity Theory
• +۳ مورد دیگر

Professor Alexandra Piryatinska is a faculty member in the Department of Statistics at San Francisco State University, located at TH 942. Her research focuses on time series analysis, EEG signal processing, complexity theory, and applications in medical diagnostics and biomarker identification. She holds a Ph.D. in Mathematics and has published extensively in interdisciplinary areas combining statistics with neuroscience and oncology. Her work emphasizes novel methodologies for change-point detection in multidimensional time series, classification of EEG records using ϵ-complexity theory, and statistical modeling of cancer drug treatment success. Notable contributions include applications in Parkinson’s disease postural balance analysis, neonatal EEG sleep stage detection, and glycoprotein profiling in breast cancer subtypes. Her research trends span algorithmic complexity-based classification, biomedical signal processing, and data-driven medical decision-making. She collaborates across disciplines to address challenges in clinical diagnostics and pharmacogenomics. Her advising and grant activities are not explicitly detailed in the provided text, though her publications suggest involvement in collaborative research projects. She is affiliated with the SF State Faculty site and maintains an active research program in statistical methods for complex biomedical data.