جستجوی استاد راهنمای دانشگاهی

Xiaowen Zhangمشاهده پروفایل

استاد

• Cryptography
• Information Security
• Cybersecurity
• +۵ مورد دیگر

Xiaowen Zhang is a Professor of Computer Science at the College of Staten Island (CSI), City University of New York (CUNY), and a Doctoral Faculty Member at the CUNY Graduate Center. His academic work bridges theoretical and applied research in cybersecurity, information systems, and network technologies. Dr. Zhang holds a Ph.D. in Computer Science from the CUNY Graduate Center (2007) and a Ph.D. in Electrical Engineering from Northern Jiaotong University (1999), along with an M.A. from CUNY Queens College, an M.S. from Northern Jiaotong University, and a B.S. from Shanxi University. His research focuses on Cryptography, Information Security, Cybersecurity, Secure Biometrics, RFID Security & Privacy, Information Retrieval, and Wireless Sensor Networks . He explores both foundational cryptographic methods—such as secret sharing schemes and hash functions—and their practical implementations in secure systems, including RFID authentication protocols and data visualization platforms for sensor networks. The analysis of his recent publications reveals a consistent focus on security mechanisms in distributed and wireless environments . His work frequently combines cryptographic theory with system-level implementations, particularly in RFID and sensor networks. There is a strong trend toward privacy-preserving protocols, efficient data retrieval, and secure information sharing , often leveraging mathematical structures like Latin squares and Bloom filters. Dr. Zhang has been actively involved in mentoring students, as evidenced by numerous co-authored publications with graduate and undergraduate researchers. His contributions span journals such as Security and Communication Networks , Journal of Applied Security Research , and International Journal of Security and Networks , as well as major conferences including IEEE LISAT, ACM CODASPY, and IEEE Sarnoff Symposium.

Sanjeev Baskiyarمشاهده پروفایل

استاد

• Scheduling and Cloud Computing
• Computer Architecture
• Real-time and Embedded Computing
• +۱۱ مورد دیگر

Sanjeev Baskiyar is a Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. He has been actively involved in research, teaching, and academic leadership, with a strong focus on computer systems, real-time and embedded computing, scheduling, cloud and fog computing, and energy-aware architectures. Education: Ph.D., Electrical and Computer Engineering, University of Minnesota M.S., Electrical and Computer Engineering, University of Minnesota B.S., Electronics and Communications, Indian Institute of Science, Bangalore B.S., Physics (with honors), and distinction in Mathematics Dr. Baskiyar’s research interests span scheduling, real-time and embedded systems, computer architecture, fog/cloud computing, thermal/energy-aware computing, and STEM education. His recent work explores machine learning applications in scheduling and quantum computing for fake news detection. He has supervised over 25 graduate students, many of whom now hold academic and industry positions. His recent publications emphasize fog computing simulation, service placement, quantum-inspired fake news detection, and adaptive scheduling using machine learning. These works reflect a trend towards intelligent, scalable, and energy-efficient computing systems, particularly in distributed and edge environments. Scientific Awards and Honors: Walker Teaching Excellence Award, Auburn University, 2020 Summer Faculty Fellow, Air Force Research Labs, 2020 Nominated Best Teaching Assistant, University of Minnesota, 1992 Multiples Merit and State-merit Scholarships Honors in Physics and Distinction in Mathematics Dr. Baskiyar has successfully advised numerous MS and PhD students and secured over $2 million in research funding as Principal Investigator from the National Science Foundation, DARPA, NASA, and industry partners like Wind River Systems and Mentor Graphics. His grants focus on parallel computing education, real-time micro-architectures, and embedded systems. He has also served on editorial boards, program committees, and as a reviewer for NSF and IEEE journals. He has held leadership roles including Senator in the University Faculty Senate and Chair of the E-day Committee. Labs and Research Groups: While not explicitly named, Dr. Baskiyar leads a research group focused on computer systems, scheduling, and embedded computing, as evidenced by his long list of graduate student supervision and funded projects in fog, cloud, and real-time systems.

Sanjeev Dewanمشاهده پروفایل

استاد

• Information Systems
• Economics of Information Technology
• Digital Platforms
• +۷ مورد دیگر

Sanjeev Dewan is a Professor of Information Systems and Associate Dean of Masters Programs at the Paul Merage School of Business, University of California, Irvine. He also serves as Faculty Director of the Master of Science in Business Analytics program. Prior to joining UCI in 2001, he held faculty positions at the University of Washington and George Mason University. PhD, University of Rochester MS, University of Rochester Bachelor of Technology, Indian Institute of Technology, Delhi His research focuses on the economics of digital platforms , social and mobile analytics , and the valuation of technology investments . He investigates how information technology creates business value, impacts consumer behavior, and influences firm performance. His work spans electronic markets, Web 2.0 technologies, IT productivity, and the digital divide. The most recent publications reveal a strong emphasis on empirical analysis of digital platforms , including studies on gender bias in open source communities, quality certification in the sharing economy (e.g., Airbnb), personalized ranking in app stores, and mobile health applications. His research frequently uses large-scale datasets to examine behavioral patterns, market dynamics, and the economic implications of IT innovations. Faculty Service Award for 2023-24, UCI Paul Merage School of Business Best Paper Award, INFORMS 2019 e-Business Cluster Faculty Service Award for 2016-17, UCI Paul Merage School of Business Best Paper Award, INFORMS Conference on Information Systems and Technology (2009) INFORMS Service Award (2009) INFORMS Certificate of Appreciation (2008) Beta Gamma Sigma Honor Society (1991) University of Rochester Fellowship (1985–1990) Sanjeev Dewan has advised numerous PhD students who have secured faculty positions at leading institutions such as the University of Wisconsin-Madison, HKUST, Penn State, and the University of Hong Kong. His editorial service includes senior editor roles at Information Systems Research and associate editor at Management Science . He has also chaired tracks at ICIS and served as program co-chair for PACIS. There is no indication of external grant funding in the provided text, but his sustained publication record and leadership roles suggest significant research activity and institutional support. He is actively involved in academic leadership and research dissemination, contributing to major conferences and editorial boards. His work bridges theory and practice, particularly in digital platform ecosystems and analytics-driven decision-making.

Nir Orenمشاهده پروفایل

استاد

• Multi-Agent Systems
• Formal Argumentation
• Computational Trust Theory
• +۱ مورد دیگر

Professor Nir Oren is a faculty member at the School of Natural and Computing Sciences , University of Aberdeen. His research focuses on multi-agent systems , formal argumentation , computational trust theory , and norm-based reasoning . He currently supervises PhD students in Computing Science and serves as Dean for Research Performance. Research Specialisms: Artificial Intelligence, Operational Research Contact: n.oren@abdn.ac.uk Research Trends (2022–2025): Nir Oren's publications span argumentation theory , BDI agent modeling , resilience in autonomous systems , and human-machine collaboration . His recent work addresses responsibility-aware AI , medical explainability , and environmental sensor networks . Key methods include probabilistic reasoning , game theory , and logical formalisms .

• Database Management Systems
• Transaction Processing
• In-Memory Databases
• +۶ مورد دیگر

Andrew Pavlo is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on database management systems, particularly in the areas of transaction processing, in-memory databases, and self-driving database systems. He leads a productive research group that has published extensively in top database venues including VLDB, SIGMOD, and CIDR. Pavlo's research interests span database management systems, transaction processing, in-memory databases, non-volatile memory databases, and self-driving database systems. His work often bridges theoretical database concepts with practical system implementation, focusing on performance optimization, query processing, and system architecture. Recent work has explored machine learning applications for database tuning, novel storage techniques, and innovative approaches to transaction processing. An analysis of his recent publications reveals a strong focus on self-driving database systems, with significant work on the Database Gym framework for training machine learning models to optimize database performance. His research also examines columnar storage formats, transaction scheduling, and novel approaches to user-defined function optimization. The work demonstrates a consistent trajectory toward making database systems more autonomous and efficient through a combination of systems techniques and machine learning. Pavlo has been instrumental in mentoring numerous PhD students who have become active contributors to the database research community. His research has been supported by significant grants that have enabled the development of innovative database technologies and frameworks. His research group operates within CMU's vibrant database ecosystem, collaborating with other researchers on projects related to database systems, storage engines, and query processing frameworks. The group maintains close connections with industry partners to ensure practical relevance of their research contributions.

Ningyuan Caoمشاهده پروفایل

استادیار

• Machine Learning Acceleration
• Real-Time Systems
• Low-Power Circuits
• +۷ مورد دیگر

Ningyuan Cao is an Assistant Professor in the Department of Electrical Engineering at the University of Notre Dame, College of Engineering. He leads the Circuit and System Intelligence Research Lab , focusing on the intersection of advanced hardware design and real-time/low-power machine learning applications. Education : Ph.D., Electrical and Electronics Engineering, Georgia Institute of Technology (2020) M.S., Electrical Engineering, Columbia University (2015) B.S., Electrical and Electronics Engineering, Shanghai Jiao Tong University (2013) His research investigates custom analog/mixed-signal circuits , digital architecture , and micro-system design for machine learning acceleration, distributed intelligence, and data-driven IC design automation. Key application domains include Internet-of-Everything, tactile internet, and mixed reality systems. Recent publications highlight work on Bayesian neural networks , privacy-preserving bio-signal encoders , transformer-based surrogate models , and compute-in-memory architectures . Technical themes span neuromorphic computing, uncertainty quantification, and hardware security.

Cleotilde Gonzalezمشاهده پروفایل

استاد پژوهش

• Decision Science
• Cognitive Psychology
• Human-Computer Interaction
• +۵ مورد دیگر

Cleotilde (Coty) Gonzalez is a Research Professor of Decision Sciences at Carnegie Mellon University, with primary affiliation in the Department of Social and Decision Sciences (SDS). She serves as the Founding Director of the Dynamic Decision Making Laboratory (DDMLab) and Research Co-Director of the NSF National Institute for AI for Societal Decision Making (AI-SDM). Her extensive academic affiliations include the Security and Privacy Institute (CyLab), the Societal Computing program in the Software and Societal Systems Department (S3D), the Human-Computer Interaction Institute (HCII) in the School of Computer Science, and the Center for Behavioral Decision Research (CBDR) and Center for Neural Basis of Cognition (CNBC). Dr. Gonzalez holds a Ph.D. in Management Information Systems and has developed Instance-Based Learning Theory (IBLT), a significant contribution to cognitive science that explains how people make decisions based on past experiences. Her research spans experimental studies and computational modeling of cognitive processes in dynamic decision environments, with applications in cybersecurity, human-machine teaming, and societal decision making. Her recent publications reveal a strong focus on human-AI collaboration, collective intelligence, cybersecurity, and cognitive modeling. The research trends show increasing integration of AI systems with human decision processes, particularly examining how humans and AI can complement each other in complex decision environments. Her work increasingly addresses cybersecurity challenges through behavioral science perspectives, exploring how cognitive models can improve defense mechanisms against social engineering attacks. Lifetime Fellow of the Cognitive Science Society Lifetime Fellow of the Human Factors and Ergonomics Society Member of the Governing Board of the Cognitive Science Society Member at Large of the Policy Council of the System Dynamics Society Committee member of the National Academies Division Committee for the Behavioral and Social Sciences and Education Dr. Gonzalez has mentored over 50 post-doctoral fellows and doctoral students, with many going on to successful careers in academia, government, and industry. Her research has been supported by major collaborative efforts including Collaborative Research Alliances (CRA) and Multi-University Research Initiative grants from the Army Research Laboratories (ARL) and Army Research Office (ARO), as well as projects with the Defense Advanced Research Projects Agency (DARPA). She directs the Dynamic Decision Making Laboratory, which conducts research involving laboratory experiments and cognitive computational models to derive theoretical conclusions about dynamic decision making and develop applications for societal problems.

Jan A. Van Mieghemمشاهده پروفایل

دانشگاهی

• Operations Management
• Supply Chain Operations
• Operations Strategy
• +۱۱ مورد دیگر

Jan A. Van Mieghem is the A. C. Buehler Professor and Professor of Operations at the Kellogg School of Management, Northwestern University. He also serves as a Professor by Courtesy at the McCormick School of Engineering and Applied Science. His research spans product, service, and supply chain operations, combining strategic and tactical perspectives with mathematical modeling, stochastic analysis, and empirical verification. Ph.D. in Business, Graduate School of Business, Stanford University (1995) MS in Electrical Engineering, Stanford University (1990) MS in Applied Sciences, KU Leuven, Belgium (1989) His research interests focus on operations strategy, supply chain optimization, and digital operations. He explores topics such as operational hedging, capacity investment, network flexibility, and the application of AI in inventory management. His work addresses healthcare operations, dual sourcing, and the interplay between collaboration and productivity in service networks. Recent research trends center on AI-driven inventory systems, dual sourcing under uncertainty, and healthcare resource allocation. He has authored over 60 academic articles and two textbooks: Operations Strategy: Principles and Practice and Managing Business Process Flows: Principles of Operations Management . 2023: First place in POMS College of Supply Chain Management Student Paper Competition 2020: Flexibility Excellence Award 2014: Wickham Skinner Award 2007: First MSOM Best Paper Award Van Mieghem has held leadership roles at Kellogg, including Deputy Dean (2023–present), Faculty Director of the Executive MBA program (2012–2017), and Chairman of the Department of Managerial Economics and Decision Sciences (2006–2009). He advises firms on operations strategy and management and has served on editorial boards of Management Science , Manufacturing & Service Operations Management , and Operations Research . He has mentored numerous Ph.D. students, including Audrey Bazerghi, Simrita Singh, Yangzi Jiang, and Dennis J. Zhang, who have secured academic and industry positions. His teaching spans operations management and strategy across MBA, Ph.D., and executive programs. Van Mieghem's work examines the intersection of operations strategy and execution, emphasizing financial value as a guiding metric. His empirical studies leverage real-world data from healthcare, manufacturing, and e-commerce to validate theoretical models and develop practical frameworks.

Berit Greinkeمشاهده پروفایل

استادیار

• Wearable Computing
• Electronic Textiles (E-Textiles)
• Smart Materials
• +۴ مورد دیگر

Berit Greinke is an Assistant Professor of Wearable Computing at the Berlin University of the Arts (UdK) and the Einstein Center Digital Future (ECDF) since 2018, previously serving as a researcher at UdK's Design Research Lab and DFKI (2016-2018). Her academic foundation includes a PhD from Queen Mary University of London (2017), an MA from Central St Martins (2009), and a Diploma from Weissensee Academy of Art Berlin (2007). Her educational trajectory: PhD in Media and Arts Technology, Queen Mary University of London (2017) MA in Design for Textile Futures, Central St Martins College of Art and Design (2009) Diploma in Textile and Surface Design, Berlin Weissensee School of Art (2007) Greinke's research pioneers the convergence of craft, textile design, and digital technology, with core expertise in electronic textiles and smart materials. She investigates metamaterial-based 'metatextiles' for electromagnetic applications and explores transdisciplinary collaboration between designers and scientists, particularly regarding 'negative data' in creative and scientific workflows. Her current UdK work focuses on four interconnected domains: performing materials for expressive textile/fashion design; multi-modal sensing converting visual processes into haptic/audible experiences; micro-to-macro material design spanning nanostructures to final products; and transdisciplinary processes for technology-art-science collaboration. Analysis of her 2020-2025 publications reveals dominant trends in sustainable textile electronics, with emphasis on knitted/folded sensor structures, origami-inspired capacitive shape estimation, and social sustainability in e-textile communities. Her work uniquely bridges fundamental material science (e.g., textile metamaterials) with artistic applications (e.g., interactive orchestra garments) and industrial production challenges. Berit Greinke supervises PhD students including Giorgia Petri. Her junior professorship is co-financed by SAP under a public-private partnership model, supporting projects like WEAR (Wearable technologists engage with artists for responsible innovation) and STELEC (Sustainable Textile Electronics), which emphasize ethical co-design and industry-academia collaboration. She leads research within UdK's Institute for Product and Process Design and collaborates with the Design Research Lab (formerly part of Connected Textiles group), focusing on sustainable industrial production of electronic clothing and transdisciplinary innovation frameworks.

• Causal Analysis
• Econometrics
• Machine Learning
• +۷ مورد دیگر

Martin Huber is Professor of Applied Econometrics and Policy Evaluation at the University of Fribourg, Switzerland, within the Faculty of Management, Economics and Social Sciences, Department of Economics. He leads the Chair of Applied Econometrics and maintains an active research profile with numerous publications in top economics and statistics journals. His work bridges theoretical econometrics with practical policy applications across multiple domains including labor, health, and education economics. Professor Huber earned his Ph.D. in Economics and Finance in 2010 and served as Assistant Professor at the University of St. Gallen until 2014. He has conducted research stays at Harvard University (2011/2012) and the University of Sydney (2014 and 2019), establishing an international research network. His academic affiliations include the Committee for Econometrics of the Verein für Socialpolitik, Global Labor Organization, Soda Labs (Monash Business School), and Centre for European Economic Research (ZEW) Mannheim. Huber's research focuses on data-based causal analysis , machine learning applications in economics , and policy evaluation methods . He specializes in developing and applying statistical and econometric methods for measuring causal effects, with particular emphasis on semi- and nonparametric microeconometrics. His work spans labor economics (gender occupational segregation, maternal labor supply), health economics, education policy, and competition policy (bid-rigging cartels detection). His recent publications (2023-2025) demonstrate a clear trajectory toward integrating machine learning techniques with traditional econometric methods for causal inference. This includes developing frameworks for causal discovery, improving difference-in-differences methods with machine learning, and creating novel approaches for detecting collusion in markets. His 2023 book "Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R" (MIT Press) has become a key reference in the field. As an active researcher, Professor Huber directs several research projects including experimental evaluations of gender occupational segregation in the Swiss apprenticeship market. His work combines theoretical rigor with practical policy relevance, often employing experimental and quasi-experimental methods to address questions of causal mechanisms in social and economic phenomena. Through his Chair of Applied Econometrics, Huber supervises Ph.D. students and maintains an active research group focused on advancing causal inference methodologies. His work has significant implications for evidence-based policymaking across multiple sectors, particularly in evaluating the effectiveness of social programs and economic policies.

Valentyn Panchenkoمشاهده پروفایل

استاد

• Financial Econometrics
• Time Series Analysis
• Dependence Measures and Copulae
• +۱۱ مورد دیگر

Professor Valentyn Panchenko is a leading academic in Economics at the UNSW Business School, specializing in advanced econometric methodologies and financial modeling. Holding a PhD from the University of Amsterdam and an MPhil from the Tinbergen Institute, his research bridges theoretical econometrics with real-world financial applications, emphasizing big data analysis, network structures, and dependence modeling in economic systems. His expertise spans financial econometrics, time series analysis, non-parametric statistics, and agent-based economic simulations. He focuses on Granger causality, model evaluation, structural economic modeling, and bounded rationality with heterogeneous agents. His work has secured significant grants including ARC Discovery Projects and DECRA fellowships, enabling cutting-edge research on market dynamics and economic interactions. Professor Panchenko's publications appear in top-tier journals like the Journal of Econometric Theory, AEJ: Micro, Journal of Economic Dynamics & Control, and Journal of Banking & Finance. His methodological contributions include novel approaches to copula-based forecasting, nonlinear causality testing, and evolutionary learning models in strategic economic environments. While specific student advising details aren't provided, his research leadership demonstrates sustained impact across econometric theory, financial markets, and experimental economics.

• Network Tomography
• Software-Defined Networking
• Cyber-physical Systems
• +۶ مورد دیگر

Ting He is a Professor in the Department of Computer Science and Engineering, specializing in interdisciplinary research at the intersection of network sciences, energy systems, and cybersecurity. Their work addresses critical challenges in network tomography, software-defined networking, and cyber-physical systems, with a strong emphasis on advancing edge computing and decentralized learning paradigms. NSF-funded research on Distributed Edge Intelligence (2024–2025) Collaborative projects on Overlay Networks and Adversarial Reconnaissance in SDN Recent publications analyze network topology inference, energy-efficient decentralized learning, and secure cloud file systems. Their research aligns with UN SDGs through contributions to sustainable energy systems and secure IT infrastructure. Key collaborations with Silvestri, La Porta, and Chaudhuri Active in Smart Grid resilience and cascading failure mitigation

Eduardo Mercado IIIمشاهده پروفایل

استاد

• Bioacoustics
• Cognitive Psychology
• Marine Ecology
• +۵ مورد دیگر

Eduardo Mercado III is a Professor in the Department of Psychology at the University at Buffalo, College of Arts and Sciences. His research focuses on bioacoustics, cognitive psychology, and marine ecology, particularly the vocal behavior of humpback whales and its implications for understanding human impact on marine ecosystems. He is also known for his work in perceptual learning, autism spectrum disorder, and comparative cognition. Scientific Awards Guggenheim Fellowship Harvard Radcliffe Institute Fellowship Research Trends His recent publications emphasize bioacoustic analysis of humpback whale songs, including their spectral entropy, cyclical variations, and adaptive adjustments to anthropogenic noise. Additional work explores perceptual learning mechanisms in autism, neural network modeling for acoustic classification, and cognitive processes in canines and rodents. Projects Mercado’s “Singers as Sentinels” project combines acoustic analysis of humpback whale songs with public awareness initiatives about ocean noise pollution. The project will produce a book, Why Whales Sing and Dolphins Don’t , and a web-based interface for public engagement.

Jacob Gardnerمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Optimization
• Computational Biology
• +۳ مورد دیگر

Jacob Gardner is an Assistant Professor in the Department of Computer & Information Science at the School of Engineering and Applied Science, University of Pennsylvania. His research bridges machine learning and scientific discovery with emphasis on computational biology and molecular design. His primary research interests include: Machine Learning Bayesian Optimization Computational Biology Molecular Design Artificial Intelligence Gaussian Processes Analysis of his 2024-2025 publications reveals a dominant focus on Bayesian optimization techniques integrated with large language models for biological applications. Key trends include therapeutic design using knowledge distillation from scientific literature, RNA splicing prediction, antibiotic development, and scalable Gaussian process methods. His work consistently addresses dimensionality challenges in molecular modeling while improving computational efficiency for high-dimensional biological data. No scientific awards were mentioned in the provided text. No information regarding student advising or research grants was provided in the source material. His research appears supported by institutional initiatives including Penn AI, Innovation in Data Engineering and Science (IDEAS), and the Data Driven Discovery Initiative (DDDI).

• Clinical exercise physiology
• Medical and health aspects of master athletes
• Prevention and rehabilitation of chronic disease
• +۶ مورد دیگر

Mike Climstein serves as a Lecturer in Human Sciences within the Faculty of Health at Southern Cross University (SCU), where he coordinates the Master of Clinical Exercise Physiology program and acts as Deputy Academic Integrity Officer. He concurrently holds an adjunct Associate Professor position in the Physical Activity, Lifestyle, Ageing and Well-being Research Group at the University of Sydney and directs SCU's Aquatic Based Research initiative. His academic credentials include a PhD in Exercise Science & Human Performance from Oregon State University (1990), an MSc in Exercise Science from Utah State University (1986), and a BSc in Biology from Utah State University (1982). Climstein's research spans clinical exercise physiology with emphases on master athletes' health, chronic disease rehabilitation, sports injury surveillance (particularly surfing), cardiac rehabilitation, smart textile monitoring, and osteoporosis. His work integrates technology-driven health solutions with population-specific exercise interventions, notably for aging populations and athletes. Analysis of his recent publications reveals three dominant trends: 1) AI applications in dermatological diagnostics (e.g., melanoma detection systems), 2) physiological adaptations in aging populations through martial arts and aquatic exercise, and 3) epidemiological studies of chronic conditions in master athletes and occupational groups. These reflect his dual focus on technological innovation and practical health interventions. His scientific recognition includes: Fellowship by Sports Medicine Australia (FASMF) Fellowship by American College of Sports Medicine (FACSM) Fellowship by Exercise and Sports Science Australia (FAAESS) Climstein actively supervises 4 PhD and 2 Master's students while co-supervising 6 Doctor of Physiotherapy candidates. His research program is supported by 38 grants totaling over $7.8 million AUD, including studies on aquatic rehabilitation and smart textile monitoring. As Director of Aquatic Based Research, he leads interdisciplinary teams investigating water-based exercise interventions for chronic disease management, with ongoing projects in cardiac rehabilitation and osteoporosis prevention.