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Xing XinyuView profile

Associate Professor

• Kernel Security
• Reverse Engineering
• AI Security
• +3 more

Xing Xinyu is an Associate Professor of Computer Science at Northwestern University's McCormick School of Engineering. Their research focuses on kernel security, reverse engineering, and AI security, with a strong emphasis on fuzzing techniques, adversarial machine learning, and vulnerability discovery. They hold a PhD from Georgia Institute of Technology, an MS from the University of Colorado Boulder, and a BASc from Beihang University. Research interests include advanced cybersecurity methodologies such as heap memory protection, automated exploit generation, and defense mechanisms against adversarial attacks on large language models. Their work bridges theoretical computer science with practical applications in system security and AI ethics. Publications span topics like LLM jailbreak assessments, reinforcement learning optimization, and blockchain anomaly detection. Notable contributions include frameworks like BandFuzz for collaborative fuzzing and SeaK for secure kernel allocators.

Rhongho JangView profile

Assistant Professor

• Cybersecurity
• Artificial Intelligence Security
• Network Systems
• +3 more

Rhongho Jang is an Assistant Professor in the Department of Computer Science at Wayne State University's College of Engineering. He holds a PhD from the University of Central Florida and directs the Networked Intelligence and Distributed Security (NIDS) Lab. His research advances security and privacy in distributed systems, networks, and AI. Research spans cybersecurity, AI security, network algorithms, and distributed systems, with applications in intrusion detection, malware analysis, and in-network computing. Recent publications focus on data plane security, adversarial ML, and IoT security. Awards include NSF grants (2022-2023), Best Paper Award at DSN 2021, and KISA's Outstanding Young Researcher (2018). Current PhD advisees include Seyed Mohammad Mehdi Mirnajafizadeh, Seungsam Yang, Fatima Kamal Khaja, and Sian Kim. Manages NIDS Lab with projects on cyber-physical security and in-network computing.

David PeeblesView profile

Professor

• Cognitive Architectures
• Computational Modeling
• Diagrammatic Reasoning
• +2 more

David Peebles is Professor and Director of the Centre for Cognition and Neuroscience at the University of Huddersfield's School of Human and Health Sciences. His research develops computational cognitive models to understand human interaction with external information representations, using the ACT-R cognitive architecture to investigate diagrammatic reasoning and problem-solving. Peebles' work examines how people interpret complex visualizations like graphs, maps, and interfaces, with applications in healthcare training, legal decision-making, and autonomous systems. His recent research explores dementia assessment methodologies, lie detection paradigms, and immersive technologies for medical education. The work integrates experimental psychology with computational modeling to understand high-level cognitive processes.

Dawn MacIsaacView profile

Associate Professor

• Biomedical Engineering
• Software Engineering
• Knowledge Engineering
• +1 more

Dr. Dawn MacIsaac is an Associate Professor in the Department of Computer Science at the University of New Brunswick’s Faculty of Computer Science, where she has served for over 14 years. She holds a PhD and Master of Science in Engineering from the same institution. Her research focuses on Biomedical Engineering, Software Engineering, Knowledge Engineering, and Signal Processing, particularly in the context of myoelectric control systems and biomedical signal analysis. Dr. MacIsaac has authored 49 peer-reviewed publications and supervised 25 graduate students. Her work emphasizes improving the performance and robustness of myoelectric prosthetics through advanced machine learning techniques, signal processing algorithms, and adaptive control strategies. Notable contributions include developing the Myosim 2.0 EMG simulation tool and pioneering self-supervised learning approaches for pattern recognition in unclear-label environments. She currently serves on the Editorial Board of the Journal of Electromyography and Kinesiology and is a Professional Engineer (PEng) registered with the Association of Professional Engineers and Geologists of New Brunswick (APEGNB). Her research bridges engineering and healthcare, addressing challenges in signal quality assessment, fatigue monitoring, and human-machine interface design. Key themes in her publications include enhancing EMG signal analysis for medical applications, optimizing control systems for prosthetic devices, and advancing methodologies for automated biosignal evaluation. Her interdisciplinary work impacts rehabilitation technology, clinical diagnostics, and biomedical instrumentation.

Walid HubbiView profile

Associate Professor

• Power Systems
• Load Flow Analysis
• Control Systems
• +1 more

Walid Hubbi is Associate Professor in Electrical and Computer Engineering at NJIT. He holds a PhD from Queen's University Belfast and degrees from the University of London and Aleppo University. His research focuses on power system analysis and control, particularly optimization techniques for reactive power compensation, load flow methodologies, and stability enhancement. Key contributions include fuzzy logic controllers for static VAR compensators, neural network applications for load modeling, and optimal placement strategies for grid control devices. His publications consistently address practical challenges in transmission efficiency, voltage stability, and measurement accuracy using computational intelligence and optimization frameworks.

Lynda A. KhalafView profile

Professor

• Econometrics
• Energy Economics
• Financial Economics
• +6 more

Lynda A. Khalaf is a Full Professor and Co-Director of the CMFE at Carleton University's Department of Economics, part of the Faculty of Public and Global Affairs. She holds a B.A. and M.B.A. from Beirut and a Ph.D. from Université de Montréal. Her research focuses on econometrics, energy econometrics, and financial econometrics, with expertise in simulation-based inference, identification-robust methods, and multivariate models. Khalaf has over 40 publications in top journals like the Journal of Econometrics and Review of Economic Studies, and her work has been cited over 1,300 times. She served as Canada Research Chair (Tier II, 2004–2008), and her contributions include groundbreaking work on Monte Carlo tests and exact simulation-based inference. She has advised numerous students, many of whom have won awards for their thesis work. Khalaf has held editorial roles, organized major conferences, and chaired committees such as the SSHRC Adjudication Committee for Economics. Her research addresses challenges in financial, macroeconomic, and environmental models, emphasizing methodological innovations and practical applicability.

Carson LeungView profile

Professor

• Big Data Science
• Data Mining
• Machine Learning
• +5 more

Dr. Carson Kai-Sang Leung is a Full Professor in Computer Science at the University of Manitoba's Faculty of Science. He founded and directs the Database & Data Mining Lab. His research focuses on big data science, data mining, machine learning, health informatics, and visual analytics. He holds SMIEEE and SMACM fellowships, reflecting his contributions to the field. Education: B.Sc., M.Sc., and Ph.D. from the University of British Columbia (UBC). Research interests include human-centered exploratory data mining, image databases, and scalable algorithms. He emphasizes user-driven constraints in mining processes and has developed techniques like the segment support map and OSSM for optimized frequency counting. His work on subimage queries in large image databases addresses real-world challenges in visual data retrieval. Publications span data mining, healthcare analytics, and transportation systems. His lab collaborates on projects like visual analytics for motor vehicle accidents and environmental data science for smart cities. He is affiliated with institutions such as the Institute of Industrial Mathematical Sciences (IIMS) and TRLabs. Key awards: Senior Member of IEEE (SMIEEE) and Senior Member of ACM (SMACM).

Ellen RusheView profile

Assistant Professor

• Deep Learning
• Sign Language Recognition
• Domain Adaptation
• +3 more

Dr. Ellen Rushe is an Assistant Professor at Dublin City University's School of Computing specializing in deep learning with limited supervision. Her research develops solutions for audio-visual data challenges including sign language recognition (SignOn Project), novelty detection, and domain adaptation for sports analytics. Previously a Research Fellow at Trinity College Dublin and Postdoctoral Fellow at UCD, she holds an MSc in Computer Science from UCD and BA in Music Technology from Maynooth University. Research focuses on: Limited-label learning paradigms Sign language recognition for low-resource languages Domain adaptation in sports video analysis Novelty detection in data streams

• Econometrics
• Time Series Analysis
• Cointegration
• +3 more

Anastasios Magdalinos is a Professor of Economics at the University of Southampton, specializing in econometric theory and time series analysis. His research focuses on cointegrated systems, predictive regressions, and econometric inference near unit roots. Current research projects include: ESRC-funded work on robust econometric inference in cointegrated systems British Academy grants for analyzing stochastic economic systems and hypothesis testing in mixed identification regression models His publications span journals like Econometric Theory , Journal of Econometrics , and Review of Financial Studies , addressing topics such as explosive autoregression, GARCH modeling, and financial predictability. Scientific contributions are recognized through grants from prestigious institutions. Key scientific awards: ESRC Grant for Robust Econometric Inference British Academy Grants for Stochastic Systems Analysis and Mixed Identification Hypothesis Testing Magdalinos supervises PhD economics students and maintains a personal webpage with further research details.

Shruti DeshpandeView profile

Associate Professor

• Audiology
• Hearing Health Services
• Social Justice in Healthcare
• +5 more

Dr. Shruti Deshpande serves as an Associate Professor and Vincentian Research Fellow in the Department of Communication Sciences and Disorders at St. John's College of Liberal Arts and Sciences, St. John's University. She also holds the leadership position of Director of the Doctor of Audiology Program and was named an ASHA Innovator in 2024. Dr. Deshpande is actively involved in the American Speech-Language-Hearing Association as a board member of the Multicultural Issues Board (2023-2025) and serves as a subject matter expert for the ASHA Practice Portal in Audiology. Her research focuses at the intersection of audiology and social justice, particularly on accessible and affordable hearing health services for diverse and vulnerable populations. Dr. Deshpande has pioneered community-based research approaches, developing innovative service-learning models that address hearing health disparities. Her work emphasizes cultural competence and aims to increase awareness of audiology as a profession among ethnically diverse adolescents through educational interventions. Analysis of Dr. Deshpande's publication record reveals consistent focus on health equity in audiology, with significant attention to representation in clinical research, social media's role in health information dissemination, and innovative approaches to hearing healthcare delivery. Her work spans multiple subfields including tinnitus research, central auditory processing disorders, electrophysiology, pediatric audiology, and cross-cultural assessment tools. ASHA Innovator (2024) Vincentian Research Fellow Faculty Excellence Award in Community-Based Outcomes and Poverty Research (2022) Multiple Faculty Recognition Awards for Excellence in Research & Scholarship (2017, 2019, 2020, 2021) Values and Inclusion Program Honoree (2020-21) Excellence in Teaching Award from National Society of Leadership and Success (2018) Dr. Deshpande has successfully mentored numerous undergraduate and graduate students, with many serving as co-authors on peer-reviewed publications and presenting at conferences. Her grant portfolio demonstrates significant external funding support, including awards from the Faculty Research Consortium, ASHA's Advancing Academic-Research Careers Award, HearX Group, and multiple internal university grants totaling over $25,000. Her current projects focus on hearing health access for diverse communities, community-based hearing assessment approaches, and innovative teaching methods that integrate technology with service learning. As Director of the Doctor of Audiology Program, Dr. Deshpande oversees a robust curriculum that includes specialized courses in Central Auditory Processing Disorder, Electrophysiology, Pediatric Audiology, and Hearing Science. Her community engagement extends beyond the classroom through partnerships with Liberty Partnership Program and other initiatives aimed at increasing awareness of hearing health and audiology careers among underserved populations.

Yingyan ZengView profile

Assistant Professor

• Data Quality
• Data Valuation
• Data Sharing
• +4 more

Dr. Yingyan Zeng is an Assistant Professor at the Department of Mechanical and Materials Engineering, University of Cincinnati, leading the Data-Centric Intelligence Lab. Her research focuses on creating data quality assurance paradigms to enhance AI modeling performance in Manufacturing Industrial Internet and Healthcare Systems. Ph.D. in Industrial and Systems Engineering, Virginia Tech (2024) M.S. in Computer Engineering, Virginia Tech (2024) B.Eng. in Industrial and Systems Engineering, Shanghai Jiao Tong University (2019) Her work spans data generation, acquisition, valuation, and sharing, with applications in additive manufacturing, bio-manufacturing, semiconducting, and IoT infrastructure design. She employs statistical and machine learning models, optimization methods, and interpretable AI techniques. Key trends in her publications include data-centric AI, privacy-preserving frameworks, synthetic data generation, and reinforcement learning in manufacturing. Her research often integrates domain knowledge and addresses challenges in industrial cyber-physical systems. 2022 Finalist, QSR Best Paper Competition (INFORMS Annual Meeting) She has secured industry grants, such as the MME Industry 4.0/5.0 Institute project on AI-driven reinforcement learning for factory operations. Her service roles include VP Marketing and Finance at the INFORMS VT Student Chapter and membership in IEEE Technical Committees.

• Computer Science
• Software Engineering
• Formal Methods
• +6 more

Thomas Henzinger is a Professor at the Institute of Science and Technology Austria (IST Austria) since 2009, serving as its President from 2009 to 2022. He holds honorary doctorates from Fourier University and Masaryk University. His research focuses on formal methods for improving software reliability in concurrent and embedded systems, with contributions to quantitative verification, quantum computing, and algorithmic fairness. He leads the Henzinger Group , which addresses challenges in software correctness, safety-critical systems, and AI ethics. Education: PhD from Stanford University (1991), followed by postdoctoral work at Université Joseph Fourier (1991). Research Interests: Concurrent and embedded systems, formal methods, quantum algorithms, fairness in AI, and runtime verification. His team develops mathematical foundations and tools to ensure software robustness in complex systems. Recent Trends in Publications: Focus on advancing formal verification techniques for neural networks, quantum computing, and ethical AI. Key contributions include fairness shields for decision systems and middleware for third-party monitoring (VAMOS). Awards: Royal Society Fellow (2024), US National Academy of Sciences Member (2020), Wittgenstein Award (2012), and numerous test-of-time awards. Advising & Grants: Advised over 20 PhD students. Secured ERC Advanced Grants (VAMOS, QUAREM) and FWF grants (Wittgenstein Award). Labs/Teams: Leads the Henzinger Group , collaborating on projects like SPyCoDe (semantic security) and VAMOS (third-party monitoring middleware).

Tselil SchrammView profile

Assistant Professor

• Algorithms
• Optimization
• Computational Complexity
• +3 more

Tselil Schramm is an Assistant Professor at Stanford University in the Department of Statistics, with courtesy appointments in Computer Science and Mathematics. Her work bridges theoretical computer science and statistics, focusing on algorithm design and information-computation tradeoffs. Research interests include: Theoretical power of sum-of-squares semidefinite programs Fast spectral methods for high-dimensional estimation Characterizing computational complexity in statistical problems Interplay between different computation models Teaching: She has taught courses like Intro to Statistics , Machine Learning Theory , and The Sum-of-Squares Algorithmic Paradigm in Statistics since joining Stanford in 2022. Selected publications highlight: Robust algorithmic paradigms for high-dimensional data, computational barriers in low-degree polynomial estimation, and theoretical foundations of statistical query models. Scientific honors: NSF CAREER Award (2020s) COLT 2021 Best Paper Runner-up (Statistical query algorithms paper)

Gennady SamorodnitskyView profile

Professor

• Stochastic Modeling
• Heavy Tailed Distributions
• Long-Range Dependence
• +6 more

Gennady Samorodnitsky is a Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University. He holds a B.S. from the Moscow Steel and Alloys Institute (1978), M.S. from Technion – Israel Institute of Technology (1983), and a D.Sc. from Technion (1986). He joined Cornell in 1988 and has held visiting positions at the University of North Carolina at Chapel Hill and Boston University. His research focuses on stochastic processes, particularly heavy-tailed distributions, long-range dependence, and extreme value theory, with applications in finance, teletraffic, and climate modeling. Education: B.S., Moscow Steel and Alloys Institute, USSR, 1978 M.S., Technion – Israel Institute of Technology, 1983 D.Sc., Technion – Israel Institute of Technology, 1986 Samorodnitsky’s research interests span stochastic modeling, including heavy-tailed processes, self-similar processes, and extreme value analysis. He examines the behavior of financial and telecommunication systems under long memory and non-Gaussian conditions. Key areas include the statistical analysis of extremes in climate data and the theoretical foundations of stable and infinitely divisible processes. His work bridges probability theory with applications in risk management, network traffic analysis, and climate science. His publications explore topics such as high-level excursion sets in random fields, tail inference, and the interplay between ergodic theory and stochastic processes. He has contributed to books like Stochastic Processes and Long Range Dependence and authored numerous technical reports on topics like ruin probabilities and multivariate extremes. Samorodnitsky teaches advanced courses, including ORIE 7590: Martingales in Discrete and Continuous Time , and maintains an active role in academic conferences and collaborations. His research group investigates cutting-edge problems in high-dimensional extremes, privacy-aware learning, and topological data analysis.

• Renewable Energy
• Fluid Dynamics
• Turbine Design
• +3 more

Christopher Vogel is a Research Fellow at New College and a Senior Research Associate in the Department of Engineering Science at the University of Oxford. He holds a first-class BE(Hons) in Engineering Science from the University of Auckland and completed his DPhil at Oxford under the Oxford Martin School’s Programme on Globalising Tidal Power Generation. His work focuses on advancing renewable energy technologies, particularly in tidal and wind energy systems. His research interests include fluid dynamics of tidal turbines, aerodynamic performance optimization, blade design under erosion, and large-scale renewable energy array modeling. He has contributed to projects like the Tidal Energy Research Group and the FastBlade facility for full-scale tidal blade testing. Vogel’s work integrates computational fluid dynamics (CFD), experimental testing, and multi-scale analytical models to address challenges in energy extraction efficiency, structural durability, and system reliability. Recent publications highlight advancements in actuator line methods for turbine wake prediction, uncertainty quantification in blade-element momentum theory, and dynamic loading analysis of tidal arrays. His studies often bridge theory and application, emphasizing practical solutions for marine and wind energy deployments. Collaborations include the Tidal Benchmarking Project and investigations into hybrid systems combining wave energy converters with breakwaters. Vogel’s interdisciplinary approach addresses both technical and environmental dimensions of sustainable energy systems.