جست‌وجوی استاد و پژوهشگر

Pontus Ekbergمشاهده پروفایل

دانشیار

• Algorithms
• Real-Time Scheduling Theory
• Computational Complexity
• +۳ مورد دیگر

Pontus Ekberg is an Associate Professor at Uppsala University's Department of Information Technology. His email address is pontus.ekberg@it.uu.se , and he can be reached at +46 18 471 73 41. He is affiliated with the Division of Computer Systems and holds the academic merit 'Docent' in real-time scheduling theory. Ekberg's research focuses on algorithms and computational problems in real-time scheduling theory. His work addresses NP-hardness in scheduling, fixed-priority algorithms, and formal verification of task feasibility. Current projects include applying pseudo-polynomial time analysis, combating butterfly attacks, and integrating deep learning for schedulability verification in safety-critical systems. His recent publications span topics like pseudo-polynomial time analysis (2025), optimistic period predictions (2024), and explainability in real-time schedulability (2023). He collaborates frequently with Sanjoy Baruah and others on uniprocessor and multiprocessor scheduling models. Although no formal awards are listed, his contributions to real-time scheduling theory are evident through 15+ publications in top conferences like RTSS and ECRTS.

• Multi-objective optimisation
• Evolutionary computation
• Explainable artificial intelligence
• +۴ مورد دیگر

Dr David Walker is a Senior Lecturer in Computer Science at the University of Exeter and a member of the Institute for Data Science and Artificial Intelligence . He also contributes to the Environmental Intelligence @Exeter research network. Education: PhD in Computer Science, University of Exeter (2008–2013) BSc (Hons) in Computer Science, University of Exeter (2004–2007) Research Interests Dr Walker’s work sits at the intersection of multi-objective optimisation , evolutionary computation , explainable AI and hyper-heuristics . He develops algorithms and visual analytics that help engineers and scientists understand complex optimisation landscapes, with recent emphasis on renewable-energy planning (especially offshore wind farms) and trustworthy AI systems. Publication Trends Between 2022 and 2025 he produced a prolific stream of articles on explainable optimisation , many-objective wind-farm design and visual analytics for evolutionary algorithms . These works combine rigorous algorithmic innovation with real-world case studies, demonstrating a clear trajectory toward transparent, human-centred AI for engineering decision-making. Scientific Awards No specific awards or fellowships are mentioned in the provided material. Advising & Funding No explicit list of PhD students, post-docs or grant awards is supplied. Laboratory & Teams Dr Walker is affiliated with the Institute for Data Science and Artificial Intelligence and the Environmental Intelligence @Exeter network, indicating collaborative, interdisciplinary research environments.

Andrej Bogdanovمشاهده پروفایل

استاد

• Cryptography
• Computational Complexity
• Pseudorandomness
• +۶ مورد دیگر

Andrej Bogdanov is a Professor at the University of Ottawa in the School of Electrical Engineering and Computer Science . He earned his B.S. and M.Eng. from MIT and Ph.D. from UC Berkeley . Before joining Ottawa, he held positions at the Chinese University of Hong Kong , ITCS (Tsinghua) , DIMACS (Rutgers) , and the Institute for Advanced Study . He has served as a Visiting Professor at the Tokyo Institute of Technology (2013) and the Simons Institute (2017, 2021). Research Interests : Computational complexity, cryptography foundations, pseudorandomness, one-way functions, property testing, quantum algorithms, and sublinear-time algorithms. Teaching : Courses on Discrete Mathematics, Great Algorithms, Computational Complexity, and Cryptography at University of Ottawa, Chinese University of Hong Kong, and Rutgers University. Publications : 15+ recent works in TCC , CRYPTO , ICALP , RANDOM , and journals like Journal of Cryptology and Theory of Computing . Service : Program co-chair for SAC 2026 , and committee member for major conferences including CRYPTO , TCC , Eurocrypt , and FOCS . Advising : 12 current and former Ph.D./M.Phil. students, with postdoctoral advisees at institutions like IIT Palakkad and Academia Sinica . His work bridges theoretical computer science with applications in cryptography, quantum computing, and network security.

Debabrota Basuمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +۶ مورد دیگر

Debabrota Basu is a tenured faculty member (Inria Starting Faculty Position - ISFP) at the Scool team (previously called SequeL) of Inria Centre at University of Lille in France. He teaches postgraduate-level courses on privacy, responsible machine learning, and research methods in AI at École normale supérieure-PSL University, Université de Lille, and Centrale Lille. He is also a member of the ELLIS Society (European Laboratory for Learning and Intelligent Systems) and the Paris unit of ELLIS. Dr. Basu earned his PhD in Computer Science from the Department of Computer Science, School of Computing, National University of Singapore, advised by Stéphane Bressan and Pierre Senellart. Prior to that, he obtained a B.E. degree with Honours in Electronics and Telecommunication Engineering from Jadavpur University. Before joining Inria, he was a postdoctoral researcher at Chalmers University of Technology's Data Science and AI Division. Dr. Basu's research focuses on constructing algorithms for developing efficient, robust, private, and ethical learning machines that solve real-world problems. His methodological approach blends statistics, machine learning, and optimization. His application interests span sustainable agro-ecology, medical and pharmaceutical applications, energy-efficient autonomous systems, and algorithmic audits. Recent collaborations include the Inria-Indian Statistical Institute associate team SeRAI for developing Sequential Testing and Learning Algorithms for Verifiably Robust and Responsible AI, and the Inria-INRAE collaboration on Resilient Agricultural Decision Making under Environmental Risks. His publication record demonstrates expertise in bandit algorithms, reinforcement learning, and privacy-preserving machine learning. Recent work shows a strong theoretical foundation with practical applications, particularly in pure exploration bandits, differential privacy mechanisms, constrained optimization, and fairness verification. His research bridges the gap between theoretical guarantees and real-world deployment challenges across multiple domains. Dr. Basu has received notable recognition for his contributions: Best Student Paper Award at ACM EAAMO 2022 for 'On Meritocracy in Optimal Set Selection' Young researcher (JCJC) grant from the French National Research Agency (ANR) in 2022 in 'Artificial Intelligence and Data Science' He leads the project 'RL under Real-life Constraints: Regrets and Algorithms' and supervises PhD students and postdoctoral researchers. His research is supported by multiple projects including REPUBLIC ('Vers l'IA responsable avec l'apprentissage par renforcement sous contraintes') and 'Foundations of robustness and reliability in artificial intelligence.' Dr. Basu actively collaborates with institutions worldwide, including establishing the RELIANT associate team with Kyoto University for investigating structured multi-armed bandit problems.

Stefan Winterمشاهده پروفایل

پژوهشگر

• Flaky Tests
• Robustness Testing
• Fault Injection
• +۵ مورد دیگر

Stefan Winter is a postdoctoral researcher and software engineer at LMU Munich, Germany, with a Ph.D. (Dr.-Ing.) in computer science from TU Darmstadt. He focuses on software dependability, particularly addressing non-deterministic behavior in software systems, such as flaky tests and reproducibility challenges in experimental research. Education: Ph.D. (Dr.-Ing.) in Computer Science from TU Darmstadt under Prof. Neeraj Suri. Current Role: Researcher at LMU Munich in Dirk Beyer’s group. His research spans test automation, robustness testing, fault injection, and operating systems, with a recent emphasis on mitigating flaky tests and ensuring deterministic software behavior. His work has been published in venues like ASE, ESEC/FSE, and ICSE. Stefan actively contributes to the academic community as a committee member, artifact evaluation co-chair, and session chair across conferences such as ECOOP, ISSTA, and SPLASH. He maintains expertise in reproducibility, experimental validity, and software testing frameworks.

Xing Xinyuمشاهده پروفایل

دانشیار

• Kernel Security
• Reverse Engineering
• AI Security
• +۳ مورد دیگر

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 Jangمشاهده پروفایل

استادیار

• Cybersecurity
• Artificial Intelligence Security
• Network Systems
• +۳ مورد دیگر

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 Peeblesمشاهده پروفایل

استاد

• Cognitive Architectures
• Computational Modeling
• Diagrammatic Reasoning
• +۲ مورد دیگر

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 MacIsaacمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Software Engineering
• Knowledge Engineering
• +۱ مورد دیگر

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 Hubbiمشاهده پروفایل

دانشیار

• Power Systems
• Load Flow Analysis
• Control Systems
• +۱ مورد دیگر

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. Khalafمشاهده پروفایل

استاد

• Econometrics
• Energy Economics
• Financial Economics
• +۶ مورد دیگر

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 Leungمشاهده پروفایل

استاد

• Big Data Science
• Data Mining
• Machine Learning
• +۵ مورد دیگر

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 Rusheمشاهده پروفایل

استادیار

• Deep Learning
• Sign Language Recognition
• Domain Adaptation
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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 Deshpandeمشاهده پروفایل

دانشیار

• Audiology
• Hearing Health Services
• Social Justice in Healthcare
• +۵ مورد دیگر

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.