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Thanh NgoView profile

Professor

• Finance
• Mergers and Acquisitions
• Corporate Governance
• +4 more

Dr. Thanh Ngo is a Professor in the Department of Finance and Insurance at the College of Business, East Carolina University. She has been a faculty member at ECU since 2014, bringing extensive academic and professional experience from her prior role as an assistant professor at the University of Texas Rio Grande Valley. Her research spans critical domains in finance and accounting, with a strong emphasis on mergers and acquisitions , corporate governance , and international finance . These interests are reflected in her prolific publication record across leading academic journals. The body of her scholarly work, evidenced by over 100 peer-reviewed articles, demonstrates consistent engagement with empirical and theoretical challenges in financial economics. Her publications appear in top-tier outlets such as the Journal of International Business Studies , Journal of Corporate Finance , and International Review of Financial Analysis , highlighting trends in global finance, governance, and market behavior. Chartered Financial Analyst (CFA), since 2016 Certified Financial Planner™ (CFP®), since 2019 Certified Public Accountant (CPA), since 2023 Certified Management Accountant (CMA®), since 2024 Dr. Ngo has advised numerous graduate and undergraduate students in finance and accounting, contributing significantly to student development through mentorship and curriculum leadership. While specific grant information is not detailed, her sustained research output suggests active involvement in funded scholarly projects. She plays a key role in integrating professional standards into academic training through her multi-certified expertise. She is affiliated with the Department of Finance and Insurance, which supports research and teaching in financial markets, risk management, and accounting practices. The department fosters collaboration through seminars, industry partnerships, and interdisciplinary initiatives within the College of Business.

Anna-Carolina HaenschView profile

Assistant Professor

• Missing Data Analysis
• Synthetic Data Generation
• Big Data in Social Sciences
• +6 more

Anna-Carolina Haensch is a Lecturer at the University of Munich in the Chair of Statistics and Data Science in Social Sciences and the Humanities and an Assistant Professor at the University of Maryland in the International Program in Survey and Data Science. Her interdisciplinary work bridges statistics, computational social science, and natural language processing, with a focus on methodological innovation in social research. Education: PhD in Sociology, University of Mannheim (2017-2021) M.Sc. in Survey Statistics, University of Bamberg (2014-2017) B.A. in Political Science/Sociology, Ludwig-Maximilians-Universität München (2011-2014) Dr. Haensch's research centers on missing data, synthetic data, and big data applications in social sciences. She develops advanced statistical methods for survey data harmonization, multiple imputation techniques, and leverages natural language processing to analyze complex social phenomena. Her work consistently addresses methodological challenges in social research with practical applications for data collection and analysis. Her recent publications reveal a significant trend toward integrating large language models with traditional survey methodology, exploring how AI can enhance data collection, analysis, and interpretation in social science research. She has made substantial contributions to understanding missing data patterns, developing synthetic data approaches, and examining the societal implications of AI tools across multiple domains including mental health, political science, and housing policy. Scientific Awards: 2022 AAPOR Burns "Bud" Roper Fellow Award 2022 AAPOR Warren J. Mitofsky Innovators Award (as part of CTIS team) 2022 AAPOR Policy Impact Award (as part of CTIS team) 2011-2017 Max-Weber-Programm (undergraduate and graduate stipend) Dr. Haensch actively mentors the next generation of researchers, currently supervising 3 PhD theses on statistical education, machine learning applications in social sciences, and synthetic data generation with LLMs. She has guided approximately 6 master's theses and 15 bachelor's theses at the University of Munich since 2022, with topics primarily related to synthetic data, multiple imputation, and LLM applications. Her teaching spans statistical methods, data science techniques for survey researchers, and specialized topics in big data analysis. She has secured teaching grants including a €20,000 promotion for the RAINER project (R Assistant IN Error Resolution) for 2024-2025 and a €10,000 LMU-NYU Scholarship in 2023. She serves on several important boards including the Eurostat EMOS Board (2024-2026), the Ethics Commission of Faculty 16 at LMU (2023-2025), and as Women's Representative at the Institute for Statistics, LMU (2024-2026). Her collaborative work with the University of Maryland Social Data Science Center and involvement in the Global COVID-19 Trends and Impact Survey demonstrates her commitment to large-scale data collection initiatives and real-time social research.

Garofalakis MinosView profile

Professor

• Database Systems
• Data Streams
• Big Data Analytics
• +7 more

Minos Garofalakis is a Professor of Computer Science at the School of Electrical and Computer Engineering (ECE) of the Technical University of Crete (TUC), where he directs the Software Technology and Network Applications Laboratory (SoftNet). He is also the Director of the Information Management Systems Institute (IMSI) at the Athena Research and Innovation Centre in Athens. Previously, he held roles at Yahoo! Research, Intel Research Berkeley, and Bell Laboratories, and was an Adjunct Associate Professor at UC Berkeley. Education: He earned a BSc in Computer Engineering from the University of Patras (1992), followed by MSc (1994) and PhD (1998) in Computer Science from the University of Wisconsin-Madison. Research Interests: His work focuses on Big Data analytics , including database systems, data streams, approximate query processing, probabilistic databases, and secure/private data analytics. Key areas include distributed stream processing, data synopses, and machine learning applications. He has authored over 150 papers and holds 29 patents, with an h-index of 63 and 13,500+ citations. Recent Work Trends: His recent articles emphasize scalable stream analytics (e.g., OmniSketch), privacy-preserving techniques, and distributed event processing. He explores challenges in handling high-velocity data streams, uncertainty in databases, and real-world applications like healthcare analytics. Scientific Awards: ACM Fellow (2018), IEEE Fellow (2017), TUC Excellence Award (2015), and multiple patents from Bell Labs/Yahoo/AT&T. Advising & Grants: He led EU projects such as FERARI, LEADS, and The Human Brain Project. His lab, SoftNet, develops tools for extreme-scale analytics and declarative networking. Current work includes interactive cross-platform analytics (Infore) and AI-driven medical data systems. Labs/Teams: Director of SoftNet Lab and IMSI. Collaborates with industry partners on distributed systems and privacy-preserving technologies.

Natalie PengView profile

Lecturer

• Superannuation
• Financial Markets
• Disclosure Regulation
• +3 more

Dr Natalie Peng is a Lecturer in Accounting at the University of Queensland's School of Business. Her research focuses on superannuation systems, financial market information dynamics, disclosure regulation, and cognitive biases in financial decision-making. She holds a PhD in Accounting from UQ (2017), exploring 'Information Cost and Member Choice in the Australian Superannuation Industry'. Dr Peng teaches both financial and management accounting courses, emphasizing practical applications of theoretical concepts. Her work frequently appears in prominent journals like Pacific Basin Finance Journal and Journal of Financial Markets, alongside high-impact media outlets including The Conversation, Sydney Morning Herald, and Crikey. Recent publications address superannuation gender gaps, ASIC regulatory actions, and death benefit policy reforms. Her media commentary spans ABC News, SBS Mandarin Radio, and AusSMC platforms, discussing topics like early super withdrawals, pension fund transparency, and retirement savings equity. No formal awards are listed, but her active engagement in policy debates reflects significant industry influence.

Ester EinhornView profile

Professor

• Information Economics
• Information in Financial Markets
• Financial Accounting and Reporting
• +3 more

Prof. Ester Einhorn is a faculty member at the Coller School of Management, Tel Aviv University, specializing in financial accounting and information economics. She focuses on the dynamics of corporate disclosures in financial markets, analyzing how firms balance mandatory and voluntary reporting strategies. Her research investigates information asymmetry in financial markets, with a particular emphasis on voluntary disclosure mechanisms, earnings management detection through disaggregated accounting data, and the impact of biased disclosures on market efficiency. Her work often examines inter-temporal patterns in corporate communication and the strategic use of financial transparency. Prof. Einhorn's recent publications explore topics like cross-firm earnings management, analyst information processing, and the role of accounting data in regulatory compliance. Her research methodology combines theoretical modeling with empirical analysis of financial reporting practices. She can be contacted via email at einhorn@tauex.tau.ac.il. No scientific awards or student advising details were found in the provided text.

• Demography
• Population Geography
• Census Methodology
• +9 more

Oliver Duke-Williams is Professor of Population Information in the Department of Information Studies at University College London (UCL), where he also serves as Programme Director for the MA/MSc Digital Humanities programmes and Graduate Tutor. His academic appointments include Professor of Population Information (2023-present), Senior Lecturer/Associate Professor in Digital Information Studies (2015-2023), and Lecturer in Digital Information Studies (2011-2015). His educational background includes a Doctorate, Masters, and First Degree in Geography from the University of Leeds, with specialization in Geographic Information Systems. Duke-Williams' research focuses on census methodology and population data, with particular expertise in UK census systems, disclosure control issues, migration patterns, and geospatial data analysis. His work bridges demographic research with digital humanities, examining how population data can be made accessible while maintaining privacy standards. He has made significant contributions to understanding internal migration within the UK, census data accessibility, and the application of longitudinal studies for population research. Analysis of his recent publications reveals strong thematic continuity in census methodology, population geography, and data accessibility issues. His work increasingly addresses contemporary challenges including pandemic impacts on population mobility, LGBTQ+ data collection, and the development of new deprivation indices for urban health assessment. His research demonstrates a consistent focus on practical applications of demographic data for policy and academic research. Duke-Williams serves as Census Service Director at the UK Data Service and is a senior member of the Centre for Longitudinal Study Information and User Support (CeLSIUS), which facilitates access to the ONS Longitudinal Study. Since 2018, he has been a member of the National Statistician's Methodological Assurance Review Panel (MARP), providing guidance on the implementation of the 2021 Census and future population statistics. He has also served on the ESRC Peer Review College since 2010 and as an editor for the International Journal of Population Research since 2010. His teaching activities include leading the MA/MSc Digital Humanities programme and teaching modules on Data Visualisation and GIS, and Introduction to Programming within UCL's Department of Information Studies.

Sridhar KrishnanView profile

Professor

• Adaptive Signal Representation and Analysis
• Biomedical Signal/Image Analysis
• Multimedia Information Security
• +4 more

Sridhar Krishnan is a Professor and Associate Dean (Research, Development and External Partnerships) in the Faculty of Engineering and Architectural Science at Ryerson University, holding the Canada Research Chair in Biomedical Signal Analysis. He serves as Co-Director of the Institute for Biomedical Engineering, Science and Technology (iBEST) and Affiliate Scientist at St. Michael's Hospital's Keenan Research Centre. He earned his B.E. in Electronics and Communication Engineering from Anna University (1993), and M.Sc./Ph.D. in Electrical and Computer Engineering from the University of Calgary (1996/1999). His research specializes in adaptive signal processing with applications across biomedicine, multimedia security, and biometrics. Key focus areas include gait analysis for neurological disorders, cardiac signal interpretation, assistive technologies, and secure biometric authentication systems. His work bridges engineering principles with clinical and security applications through innovative algorithm development. Publication analysis reveals consistent contributions to biomedical signal processing, particularly in movement disorders and cardiac diagnostics, while extending methodologies to multimedia security and behavioral biometrics. This interdisciplinary approach demonstrates strong translational impact from theoretical signal analysis to real-world healthcare and security solutions. Major recognitions include: Fellow of the Canadian Academy of Engineering (2014) IEEE Exemplary Service Award (2014) Innovate Calgary Achievement Award (2013) Sarwan Sahota Distinguished Scholar Award (2011) Engineers Canada Young Engineer Achievement (2007) Multiple best paper awards and chapter leadership honors He has mentored 10 postdocs, 10 PhD, and 39 Master's students while securing significant research funding. His 310+ publications, 12 invention disclosures, and US patent reflect substantial scholarly output. He actively serves on advisory boards for research institutes and innovation centers, driving translational research initiatives. Leading the Signal Analysis Research Laboratory, he directs teams developing assistive technologies and affective computing systems through collaborations with hospitals and industry partners, focusing on practical implementations of signal processing breakthroughs.

Evan T.R. RosenmanView profile

Assistant Professor

• Causal Inference
• Political Science
• Public Health
• +5 more

Evan T.R. Rosenman is an Assistant Professor of Statistics at Claremont McKenna College’s Mathematical Sciences Department. Previously, he was a Postdoctoral Fellow at Harvard’s Data Science Initiative, affiliated with the Department of Statistics and the Institute for Quantitative Social Sciences. He earned his Ph.D. in Statistics from Stanford University in 2020, advised by Art Owen and Mike Baiocchi. His research focuses on causal inference, particularly hybridizing observational and experimental data to estimate causal effects, with applications in political science, public health, and gender-based violence prevention. His academic journey includes teaching roles as an instructor for STATS/CME 195 (R programming) at Stanford and as a teaching assistant for courses in statistical learning, linear models, and machine learning. He has contributed to open-source projects like the wru package for Bayesian racial category prediction using surnames and geolocation. Rosenman’s work spans methodological advancements in combining datasets, robust experimental design, and addressing census data challenges. His publications address topics such as recalibrating predictive probabilities, race imputation via Bayesian methods, and mitigating bias in sensitive topic trials. Collaborations include studies on sexual assault prevention in Kenyan informal settlements and policy evaluations related to the U.S. Census’s differential privacy system. Though no specific grants or awards are listed, his research demonstrates significant contributions to causal inference and applied statistics, with a focus on societal impact in health and policy domains. His work often bridges theoretical statistics and real-world applications, emphasizing interdisciplinary relevance.

Tasos ChristofidesView profile

Professor

• Probability Inequalities
• Demimartingales
• Stochastic Orders
• +2 more

Tasos Christofides is a Professor in the Department of Mathematics and Statistics at the University of Cyprus, School of Natural and Applied Sciences. He has been serving in this position since 2004 after previously holding the rank of Associate Professor from 1991 to 2004. Prior to his appointment at the University of Cyprus, he was an Assistant Professor at the State University of New York at Binghamton from 1987 to 1991. His educational background includes a Ph.D. (1987) and MSE (1985) in Mathematical Sciences from The Johns Hopkins University, USA, and a Mathematics degree (1983) from the University of Athens, Greece. Professor Christofides specializes in Probability Inequalities, Demimartingales, Stochastic Orders, Survey Methodology, and Indirect Questioning Techniques. His research primarily focuses on theoretical statistics with applications to survey methodology, particularly in developing techniques for handling sensitive questions while preserving respondent privacy. His work bridges probability theory with practical survey design, making significant contributions to both theoretical foundations and applied methodologies. His publication record demonstrates consistent contributions to statistical theory, with a particular emphasis on U-statistics, demimartingales, and randomized response techniques. The trajectory of his research shows progression from foundational work on U-statistics and probability inequalities in the early career to more applied survey methodology techniques in later years, while maintaining strong theoretical underpinnings. Professor Christofides serves as Associate Editor for several prestigious statistical journals including Communications in Statistics-Theory and Methods, Communications in Statistics-Computation and Simulation, Journal of Statistical Theory and Practice, and Statistics and Probability Letters. His editorial service reflects his standing in the statistical community and his expertise across multiple domains of statistical theory and methodology. While specific grant information is not provided in the available text, his sustained research output and editorial positions indicate active engagement with the research community.

Richard L. SweeneyView profile

Associate Professor

• Environmental & Energy Economics
• Energy Policy
• Industrial Organization
• +5 more

Richard L. Sweeney is an Associate Professor of Economics at Boston College, affiliated with the Morrissey College of Arts and Sciences and the Schiller Institute. He holds a B.S. from Boston College (2004) and a Ph.D. in Public Policy from Harvard University (2015). His research focuses on environmental and energy economics, including topics like climate policy, renewable energy markets, and industrial organization. Dr. Sweeney has held visiting scholar positions at MIT and the University of Chicago. Education: B.S., Boston College; Ph.D., Harvard University Key Roles: Tenured faculty since 2023, Co-PI on NSF and BC grants His research interests span energy policy design, regulatory impact analysis, and the economics of natural resource extraction. Recent work examines pipeline risk capitalization, wind energy incentives, and shale boom dynamics. He has published in top journals like the Journal of the Association of Environmental and Resource Economists and Review of Economics and Statistics . Grants & Awards: Includes NSF funding for shale research, BC Schiller grants for energy transition studies, and a Joseph L. Fisher Dissertation Fellowship.

Michael A. PalleyView profile

Professor

• Accounting Information Systems
• Management Information Systems
• IT and Organizational Strategy
• +5 more

Michael A. Palley is a Professor in the Paul H. Chook Department of Information Systems and Statistics at Zicklin School of Business, Baruch College (CUNY) . His expertise spans Accounting Information Systems , IT and Organizational Strategy , and Healthcare Information Systems . Education: PhD, MS, and MPhil in Information Systems from NYU Stern School of Business; BS in Finance & Multinational Enterprise from the Wharton School. His research focuses on information system security , statistical database confidentiality , and the intersection of IT with financial markets . He has contributed to journals such as Health Care Management Review and Journal of Official Statistics , with notable work on regression-based disclosure risks and IT diffusion in human services. Recent publications explore portfolio risk/return models for security and consequence frameworks for IT adoption . His work bridges theoretical rigor with practical applications, including user interface design and healthcare reimbursement systems . Scientific Awards: Zicklin Teaching Excellence Award (2001) Beta Gamma Sigma Honors (1986) IBM Fellowships (1983-1984) Invited Participant to ICIS Doctoral Consortium (1984) Paley has held leadership roles in academic governance, including Faculty Committee on Educational Technology and CUNY Baccalaureate Program Mentor . He has also served as a consultant for organizations like Citigroup and IBM.

Jose Manuel Feria DominguezView profile

Professor

• Financial Risk Management
• Banking
• Corporate Finance
• +5 more

Jose Manuel Feria Dominguez is a Professor in the Department of Financial Economics and Accounting at the Faculty of Business and Economics, Pablo de Olavide University, Spain. His academic career centers on financial risk management with emphases on banking regulation, operational risk modeling, and Latin American financial systems, contributing significantly to both theoretical frameworks and practical applications in volatile economic environments. He earned his Doctorate from the University of Seville in 2004 with the thesis "Market risk: Methodologies for its measurement and control" under the supervision of Dr. José Luis Martín Marín and Dr. María Dolores Oliver Alfonso. His educational background established the foundation for his expertise in quantitative risk assessment methodologies. Professor Dominguez's research spans eight core domains: Financial Risk Management (specializing in Value at Risk and stress-testing), Banking Sector Analysis (particularly Latin American dollarization regimes), Corporate Finance (with focus on SME resilience), Operational Risk Modeling (Basel regulatory frameworks), ESG Finance (investor reactions to environmental disasters), Reputational Risk (corporate corruption impacts), SME Finance in developing economies, and Latin American Financial Markets. His methodological approach frequently employs structural equation modeling and comparative empirical analysis. Analysis of his 2022-2025 publications reveals three dominant research trajectories: (1) Organizational-financial resilience interconnections in Ecuadorian SMEs using advanced statistical modeling, (2) Banking sector efficiency studies examining net interest margins across dollarized/non-dollarized Latin American economies, and (3) ESG-reputational risk investigations measuring market reactions to environmental disasters and corruption scandals. These works consistently apply quantitative methodologies to real-world financial instability scenarios. As an active academic supervisor, he mentors graduate students in financial risk management and corporate finance through the Doctoral Program in Management Control and Finance. His institutional contributions include membership in the PAIDI Areas for Social and Legal Sciences and leadership in the GIFEM (Corporate and Market Finance) research group, where he explores innovative applications of open government principles to university strategic planning and develops simulation-based pedagogical approaches in financial economics education.

• Public Administration Accounting
• Budgeting
• Management Control
• +7 more

Michela Soverchia is an Associate Professor of Business Economics at the Department of Economics and Law, University of Macerata. She obtained her PhD in Business and Public Administration Economics from the University of Siena in 2003 and earned the national scientific qualification for full professor in 2018. Her academic career spans teaching and research in public sector accounting, management control, and sustainability reporting. She earned her PhD in Business and Public Administration Economics from the University of Siena in 2003 (15th cycle). In 2018, she obtained the national scientific qualification to hold the position of full professor in the ECON-06/A Business Economics program. Professor Soverchia's research focuses on public administration accounting and budgeting, planning and management control systems for public entities, sustainability reporting practices, and the historical development of accounting systems. Her work bridges traditional accounting principles with modern sustainability frameworks, particularly examining how public institutions can improve performance measurement and reporting. She has made significant contributions to understanding waste management economics and the integration of ESG factors in community banking. Her recent publications demonstrate a strong trend toward sustainability and digital transformation in public sector accounting. She has published extensively on sustainability reporting frameworks, ESG integration in financial institutions, and the application of data analytics in public service performance measurement. Her work connects historical accounting practices with contemporary challenges in public financial management. Member of the Italian Academy of Business Economics (AIDEA) Member of the Italian Society of Accounting and Business Economics Teachers (SIDREA) Member of the Italian Society of the History of Accounting (SISR), serving on the Board of Auditors for 2024-2026 At the University of Macerata, Professor Soverchia serves on the Area Research Committee (CAR) for Economic and Statistical Sciences, the university spin-off technical commission, and the patent commission. She is the department delegate for placement and internships, SUA-CdS contact person, and member of the Master's in Marketing and Business Management Board of Directors. Her administrative roles reflect her commitment to connecting academic research with practical business applications and student career development. Professor Soverchia participates in multiple editorial boards including journals like 'Sustainability,' 'Azienda Pubblica,' and 'Management Control,' contributing to the scholarly discourse in her fields of expertise. She also serves as a referee for national and international research projects for organizations including MIUR, ANVUR, and various universities.

• Data Science
• Machine Learning
• Artificial Intelligence
• +7 more

Xiaobai Li is a Professor in the Department of Operations and Information Systems at the University of Massachusetts Lowell's Manning School of Business. His research spans data science, machine learning, business analytics, data privacy, and information economics with significant funding from NIH and NSF. Previously, he taught at the University of Texas at Dallas and accumulated 10 years of industry experience as an engineer, analyst, and manager. His research interests focus on privacy-preserving data analytics , data valuation economics , and machine learning optimization . Key contributions include developing novel methods for differential privacy, medical data anonymization, and cost-effective data acquisition frameworks that balance privacy protection with analytical utility. His work bridges theoretical computer science with practical business applications. Analysis of his recent publications reveals strong trends in privacy-aware business analytics (70% of articles), health informatics security (20%), and data economics (10%). The research consistently addresses the tension between data utility and privacy protection, with increasing focus on real-world implementation challenges in healthcare and consumer data markets. Manning Research Excellence Award (2023) Best Associate Editor Award (2021) Multiple Best Paper Awards (2004-2021) Graduate Teaching Award (2019) Dr. Li has secured significant research funding including a $706,669 NIH grant for patient privacy preservation (2011-2015) and a $100,000 NSF grant for consumer electronic privacy systems (2008). He serves as Associate Editor for Information Systems Research , Decision Support Systems , and other leading journals. His teaching portfolio includes database management, machine learning, and data analytics courses at undergraduate and graduate levels.

Susan CockrellView profile

Professor

• Accounting Education
• Financial Accounting
• Corporate Ethics
• +3 more

Susan Cockrell is a Professor in the Department of Accounting, Finance, & Economics at Austin Peay State University, where she has served for 18 years. She teaches courses such as Principles of Accounting, Auditing, and Regression Analysis. Her research focuses on educational innovations in accounting, corporate ethics, and supply chain technology integration. She actively contributes to the CAFE Advisory Board, emphasizing teaching excellence and student engagement. Dr. Cockrell’s research spans topics including the impact of technology in accounting education, whistleblower protections, and CSR in business education. Her work also explores IT offshoring, RFID systems in supply chains, and municipal financial reporting. She is passionate about enhancing student learning through creative methods, such as using Monopoly® simulations in accounting labs. In her personal life, she enjoys reading and cites Dead Poets Society as a favorite film, inspired by its message of challenging conventional perspectives. She values APSU’s student-centric environment and aims to inspire students through dynamic teaching.