جستجوی استاد راهنمای دانشگاهی

Eun-Hee Kimمشاهده پروفایل

دانشیار

• Corporate Sustainability
• Environmental Strategy
• ESG Disclosure
• +۱ مورد دیگر

Eun-Hee Kim is an Associate Professor and Area Chair of Strategy and Statistics at Fordham University's Gabelli School of Business. She joined the university in 2017. Her office is located at 140 W. 62nd Street, Room 353, New York, NY 10023, and she can be reached at ehkim@fordham.edu. Kim's research examines corporate environmental strategy, ESG transparency, and regulatory responses in sustainability contexts. Her work critically analyzes corporate carbon disclosure practices, environmental reporting ethics, and how regulatory uncertainty shapes business strategies. Research employs diverse methodologies including social media analytics and longitudinal target assessment.

Yilu Zhouمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Text Mining
• Business Analytics
• +۲ مورد دیگر

Yilu Zhou is an Associate Professor and Area Chair in Information, Technology, and Operations at Fordham University's Gabelli School of Business. She joined Fordham in 2013 and serves as Program Director for the MS in Artificial Intelligence in Business. Her research focuses on artificial intelligence applications in business, including text mining, social media analytics, and financial technology. Research interests include: AI-driven business intelligence systems Text mining for financial market analysis Social media trend forecasting Knowledge graph applications in finance

• Machine Learning
• Business Analytics
• Natural Language Processing
• +۴ مورد دیگر

Subhra Patra is Clinical Assistant Professor in Information Systems at UT Arlington. PhD in Computer Science from Homi Bhabha National Institute (2013) with thesis on computational intelligence for nuclear reactors. Applies machine learning to diverse domains including sentiment analysis, economic forecasting, and environmental modeling. Educational background: Ph.D. Computer Science, Homi Bhabha National Institute (2013) M.S. Electronic Science, Berhampur University (2006) M.S. Physics, Khallikote Unitary University (2004) Research spans business analytics applications and computational methods, including NLP for fake news detection, time series forecasting, and neural network architectures. Recent work focuses on operationalizing analytics in business contexts. Teaching responsibilities include Python programming, database systems, and data visualization. Previously held positions at VIT Chennai and KL University.

Swapna Gottipatiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Swapna Gottipati is a Full-time Associate Professor of Information Systems (Education) and Associate Dean (Undergraduate Education) at the School of Computing and Information Systems , Singapore Management University . Holding a PhD from SMU (2014) , she specializes in text analytics, education technology, and digital business. Current focus areas: Artificial Intelligence, Data Science, Technology-Enhanced Learning Interdisciplinary applications: Health Informatics, Social Media Analytics Research Interests span text analytics, opinion mining, curriculum analytics, and digital transformation. She applies machine learning and data mining to educational systems, business processes, and health domains. Scientific Contributions: 2016 Best Paper Award (AIS SIG-ED) 2016 MOE TRF Grant 2017-2020 Conference Best Paper Nominations 2013 Best Paper Finalist (CIKM) Academic Leadership includes: Director of Undergraduate Education Co-developer of SMU-X pedagogy Founder of MyCompetencies mobile platform Organizer of curriculum analytics workshops Grant Projects include the Learning Analytics on Qualitative Student Feedback system (MOE TRF 2016) and ICDL-funded digital literacy studies. She has delivered 12+ invited talks on digital transformation in education and industry.

• Energy Efficiency in Maritime Systems
• Renewable Energy Modeling
• Solar Power Forecasting
• +۳ مورد دیگر

Mohamed Abuella is a Research Fellow at Northumbria University's School of Engineering Physics and Mathematics, specializing in energy efficiency and renewable energy systems. His work bridges maritime engineering with data analytics and machine learning. Educational Background: PhD (expected 2099) His research focuses on Time-Series Analysis for maritime energy optimization, Renewable Energy Modeling (wind and solar), and Machine Learning Applications in power forecasting. Recent publications highlight advancements in sustainable shipping and solar/wind energy systems. Key trends in his recent articles include Explainable AI for maritime efficiency, Hybrid Forecasting Models for renewable energy, and Geospatial Analysis for vessel navigation. Notable collaborations span energy analytics and maritime technology domains.

Oshani Seneviratneمشاهده پروفایل

استادیار

• Decentralized Systems
• Blockchain Technology
• Health Informatics
• +۵ مورد دیگر

Oshani Seneviratne is an Assistant Professor of Computer Science at Rensselaer Polytechnic Institute (RPI), leading the BRAINS Lab. She holds a Ph.D. and S.M. from MIT (Computer Science) and a B.Sc. (Hons) from the University of Moratuwa, Sri Lanka. Her research focuses on decentralized systems, blockchain, health informatics, and federated learning. She previously directed RPI's Health Data Research division. Education Ph.D. in Computer Science, MIT S.M. in Computer Science, MIT B.Sc. (Hons) in Computer Science and Engineering, University of Moratuwa Research Interests Her work bridges decentralized technologies with clinical and financial applications. Key areas include blockchain-based frameworks for federated learning, explainable AI in healthcare, and semantic web technologies for data interoperability. She emphasizes ethical AI, data provenance, and scalable systems for real-world challenges. Recent Work Trends Recent publications highlight advancements in smart contract auditing, LLM applications in financial systems, and blockchain-enhanced data provenance. Her work often combines technical innovation with societal impact, such as improving health data sharing and combating misinformation. Awards & Grants No awards explicitly listed, but her research has been supported by grants related to blockchain, health informatics, and decentralized systems. Advising & Labs Leads the BRAINS Lab, focusing on resilient, intelligent networked systems. Collaborates on projects like the Punya platform for clinical apps and BlockIoT for health data integration. Future Work Expanding into AI explainability for clinical decision support, decentralized mental health monitoring, and blockchain interoperability solutions.

Kylie L. Anglinمشاهده پروفایل

استادیار

• Educational Policy
• Data Science
• Natural Language Processing
• +۳ مورد دیگر

Kylie L. Anglin is an Assistant Professor in the Department of Educational Psychology at the University of Connecticut, specializing in Research Methods, Measurement, and Evaluation (RMME). She holds a Ph.D. in Education Policy from the University of Virginia (2021), an MPP in Leadership and Public Policy (2018), and a BA in Political Science (2013) from Southwestern University. Her research focuses on developing natural language processing (NLP) techniques to monitor program implementation in impact evaluations, enhancing causal validity and replicability in education research. Dr. Anglin teaches graduate courses in research methods, data science, and text analytics. Her work has been published in journals such as the Journal of Research on Educational Effectiveness , Prevention Science , and AERA Open . She has received the NAEd/Spencer Dissertation Fellowship and participated in the IES Pre-doctoral Training Program. Her research emphasizes scalable methods for analyzing policy variation, improving intervention adherence, and leveraging large language models for education evaluation. She actively mentors students interested in education policy or data science, and her contact details are available at kylie.anglin@uconn.edu .

Chuan Yueمشاهده پروفایل

استاد

• Cybersecurity
• Web Security
• Mobile Security
• +۷ مورد دیگر

Chuan Yue is a Professor of Computer Science at the Colorado School of Mines (Mines), leading research in Cybersecurity with a focus on Web, Mobile, Cloud, CPS/IoT, and AI systems security. He founded the Center for Cyber Security and Privacy (CCSP) at Mines and serves as its point of contact. His research emphasizes usable security, privacy, vulnerability analysis, and cybersecurity education. Education: B.E. and M.E. in Computer Science from Xidian University (China, 1996/1999), Ph.D. in Computer Science from College of William and Mary (2010). Pre-Mines career: Bell Labs (1999-2003) and University of Colorado Colorado Springs (UCCS, 2003-2013). Awards include the Daniels Fund Faculty Fellowship (2016-2019) and an Outstanding Paper Award at CODASPY 2018. Research interests span security/privacy in web-based systems, human-computer interaction, and distributed computing. His work is funded by NSF, DOD, and Meta. Key contributions include PolicyChecker for GDPR compliance analysis, WtaGraph for web tracking detection, and studies on smart home privacy negotiation. Over 50 peer-reviewed publications in top venues like IEEE S&P, CCS, and CHI. Advised numerous PhD, Master’s, and undergraduate researchers, emphasizing interdisciplinary collaboration with industry/government. Developed security tools like LoginInspector (best paper at LISA 2012) and SafeSky cloud middleware.

Karthik Srinivasanمشاهده پروفایل

استادیار

• Machine Learning Interpretability
• Explanatory Modeling
• Text Mining for Business Applications
• +۳ مورد دیگر

Karthik Srinivasan is an Assistant Professor in the Analytics, Information, and Operations Academic Area at the University of Kansas School of Business. He holds a Ph.D. in Management Information Systems from the University of Arizona, an M.Mgt. in Business Analytics from the Indian Institute of Science, and a B.E. from Mumbai University. His research focuses on interpretable machine learning, explanatory modeling, text mining for business applications, and healthcare information systems. He develops methods to enhance transparency in AI systems and applies data science to healthcare, finance, and retail contexts. Recent work includes predictive modeling for incomplete data, graph-based retail analytics, and analyzing pandemic impacts on stock markets and public health. His publications span journals like MIS Quarterly, Decision Support Systems, and Nature Digital Medicine. He also contributes open-source tools like TextRegress and MoreThanSentiments for advanced text analysis. Teaching responsibilities include undergraduate and graduate-level data management courses. His research emphasizes practical applications in business and public health, leveraging interdisciplinary approaches to address real-world challenges.

Bill Chuمشاهده پروفایل

استاد

• Cybersecurity
• Cyber Threat Intelligence
• AI in Cybersecurity
• +۲ مورد دیگر

Bill Chu is a Professor and Managing Director of CyberDNA at the University of North Carolina at Charlotte's College of Computing and Informatics. His research focuses on cybersecurity automation, cyber threat intelligence, application security, and AI-driven cybersecurity solutions. He has contributed to numerous publications on threat detection, vulnerability analysis, and secure software development practices. Chu's work emphasizes practical applications of cybersecurity, including static analysis tools, interactive code annotation for vulnerability detection, and educational tools for secure programming. His research spans collaborations with institutions and industry to bridge gaps in cybersecurity education and practice. Notable contributions include developing frameworks for extracting actionable threat intelligence from social media, automated unit testing for XSS vulnerabilities, and leveraging large language models for vulnerability detection. Chu actively participates in workshops and conferences on cybersecurity and software engineering, contributing to advancements in both theory and application.

Niloufer Selvaduraiمشاهده پروفایل

استاد

• Artificial Intelligence Regulation
• Digital Finance Law
• Intellectual Property Law
• +۳ مورد دیگر

Niloufer Selvadurai is Professor and Director of Research & Innovation at Macquarie Law School, where she also co-directs the Data Horizons Research Centre and co-chairs the Australian Law Associate Deans Research Network. A technology law scholar focusing on AI regulation, she examines how laws can calibrate technological innovation benefits and risks. Her interdisciplinary research collaborates with computing, engineering, finance, and health fields. Key leadership roles and recognitions include: Co-Director, Data Horizons Research Centre Program Leader for 'AI and Law' at Centre for Applied AI 2022 Australian Legal Education Award for Excellence in Graduate Research Supervision 2023 Higher Degree Research Supervisor of the Year Her publications extensively cover AI governance, digital finance, copyright, and smart contracts law, with recent work examining AI inventorship and regulatory frameworks for central bank digital currencies.

Mark Baillieمشاهده پروفایل

استادیار

• Chemistry Education Research
• STEM Education
• Institutional Change Research
• +۳ مورد دیگر

Dr. Mark Baillie is an Assistant Professor in the Department of Chemistry at the University of Arkansas at Little Rock. He also serves as Director of the STRIVE Program and the Mobile Institute on Scientific Teaching (MoSI) Workshop. His research focuses on Chemistry Education Research and Institutional Change Research, exploring how faculty training, teaching strategies, and student backgrounds impact STEM success. He holds a PhD in Organic/Medicinal Chemistry from Emory University and a BS from Bucknell University. Education highlights include a Post-Doctoral Fellowship in Chemical Biology at EPFL (Switzerland), an Emory University NSF Fellowship, and a Six Sigma Green Belt from General Electric. His work bridges educational innovation with organic chemistry expertise, addressing barriers to equitable STEM participation through programs like Learning Assistant initiatives and evidence-based teaching workshops. Research trends show a transition from medicinal chemistry (e.g., Enigmol anti-cancer agents) to STEM education analytics, using machine learning for classroom monitoring. Awards include multiple Student Faculty Appreciation Awards and recognition as a Rising Star in STEM education. His Baillie Research Group collaborates on institutional reforms and active learning frameworks, emphasizing student mindset and belongingness in STEM retention. Key Awards: Student Faculty Appreciation Award 2023 NSF Graduate Fellowship (2007–2008) Outstanding Teaching Assistant Award (2006) Advising/Grants: Leads workshops and programs without explicit grant mentions in provided texts. Labs/Teams: Directs the Baillie Research Group, focusing on education research and institutional change.

Maribeth Priceمشاهده پروفایل

استاد

• Geographic Information Systems
• Remote Sensing
• Spatial Analysis
• +۳ مورد دیگر

Maribeth Price serves as Dean of Graduate Education and Professor at South Dakota Mines, with faculty tenure since 1995. Previously chaired Geology and Geological Engineering (2006-2011). Holds M.S./Ph.D. from Kharkov State University (Ukraine) and D.Sc. from Donetsk Physics & Technology Institute. Research expertise spans geospatial technologies , planetary remote sensing, and spatial database design. Authored instructional texts on GIS applications and taught professional workshops for over 1,300 participants. Planetary science contributions include Magellan Mission analysis of Venusian tectonics. Curates Maps at Museum of Geology and promotes online pedagogy as Quality Matters certified reviewer. Interdisciplinary collaborations include hydrogeology, forestry, carbon sequestration, and lightning pattern analysis.

Mark Stampمشاهده پروفایل

استاد

• Information Security
• Machine Learning
• Malware Analysis
• +۵ مورد دیگر

Mark Stamp is a Professor in the Department of Computer Science at San Jose State University (SJSU), part of the College of Engineering. His research focuses on information security, machine learning, malware analysis, and cryptography, with a strong emphasis on applying these fields to cybersecurity challenges. He has authored multiple textbooks, including Information Security: Principles and Practice and Introduction to Machine Learning with Applications in Information Security . Education: PhD in Mathematics (Texas Tech University, 1992), MS in Mathematics (Texas Tech, 1988), BS in Computer Science (Morningside College, 1983). Research interests include malware detection using machine learning, cryptographic algorithms, and adversarial attack analysis. His work spans theoretical research and practical applications, such as developing tools for malware clustering, steganography analysis, and secure communication protocols. Recent projects include AI-driven precision agriculture and detecting AI-generated content. Teaching includes courses on information security (CS 166), machine learning (CS 171), and cryptography. He advises numerous graduate and undergraduate students, many of whom contribute to his research in cybersecurity and AI. Publications span over 200 papers in journals like Journal of Computer Virology and Hacking Techniques and IEEE Transactions . He has also contributed to conferences and edited several Springer volumes on AI and cybersecurity. His work often bridges academic research with real-world security challenges.

Victoria Chiuمشاهده پروفایل

دانشیار

• Accounting Bibliometrics
• Fraud Detection
• Data Analytics
• +۴ مورد دیگر

Dr. Victoria Chiu is an Associate Professor of Accounting at the School of Business, SUNY Oswego, specializing in accounting bibliometrics, fraud detection, and data analytics. She joined SUNY Oswego in 2016 after serving as an Assistant Professor at SUNY New Paltz and a doctoral fellow at the AAA/Deloitte Foundation/J.M. Cook Consortium. Dr. Chiu holds a Ph.D. in Accounting from Rutgers University (2013) and a BS from National Changhua University of Education (2008). Her research focuses on applying bibliometric methods to analyze accounting subfields, with a particular emphasis on fraud detection, continuous auditing, and emerging technologies in accounting education and practice. Dr. Chiu has published over 25 peer-reviewed articles in journals like International Journal of Accounting Information Systems, Accounting & Finance, and Journal of Forensic Accounting Research. She has received two Best Faculty Paper Awards from the AAA Strategic and Emerging Technologies Workshop (2017, 2018) and has served on editorial boards for major journals, including the International Journal of Accounting Information Systems and Journal of Emerging Technologies in Accounting. Her teaching spans undergraduate and graduate courses in accounting, including Principles of Accounting and Cost Accounting. She has been recognized for her teaching excellence through multiple Professor Recognition Awards (student-nominated) and actively contributes to curriculum development integrating emerging technologies like process mining and machine learning. Dr. Chiu’s research also emphasizes pedagogical innovation, such as hands-on learning approaches for transaction cycles and adapting AACSB’s Standard A5 into accounting curricula. Professional service includes roles on the AAA Research and Publications Committee (2023–2024) and the Innovation in Accounting Education Awards Committee (2019–2020). Her work bridges academic research and practical applications, particularly in leveraging technology to enhance auditing, fraud detection, and educational outcomes.