جستجوی استاد راهنمای دانشگاهی

Iain Moffatt is a full-time Professor in the Department of Mathematics at Royal Holloway, University of London, and currently serves as the Head of Department. His research lies at the interface of combinatorics and topology, focusing on graph polynomials, delta-matroid theory, and their applications to knot theory and statistical mechanics. Education: MSci in Mathematics, University of St Andrews PhD in Mathematics, University of Warwick His research interests include algebraic combinatorics, graph theory, topology, and the application of graph theory to biology. He has contributed extensively to the study of topological extensions of graph polynomials, such as the Tutte and Penrose polynomials, and their connections to quantum invariants and knot theory. His recent work explores the Surface Tutte polynomial , delta-matroid theory , and hypermap polynomials , with a focus on deletion-contraction operations, partial duality, and algebraic structures. These studies often intersect with topological graph theory and combinatorial topology . Notable external roles include serving as Editor-in-Chief of the London Mathematical Society Newsletter (2016-2019) and organizing international conferences such as the SiMEGG: Structure in Matroids, Embedded Graphs and Graphs (2026). Grants & Projects: Hypermaps: Polynomials, Dualities, and Minors (2024-2025, EPSRC) Investigations of algebraic graphs of large girth (2023-2024, British Academy) Collaborative research with Prof. Sergei Lando (2017-2018, LMS)

• Algebraic combinatorics
• Graph theory
• Topology
• +۵ مورد دیگر

Angelina Wang is an incoming Assistant Professor at Cornell Tech and the Department of Information Science at Cornell University, starting Fall 2025. Her research focuses on responsible AI, particularly machine learning fairness and algorithmic bias. She holds a Ph.D. in Computer Science from Princeton University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley. Current postdoctoral work at Stanford’s HAI and RegLab explores sociotechnical challenges in AI deployment. Her research addresses fairness evaluation in generative AI, societal impacts of AI systems, and ethical trade-offs in algorithm design. Notable awards include the NSF GRFP, Siebel Scholarship, and Microsoft AI & Society Fellowship. Her work bridges technical and social dimensions of AI, emphasizing human-centered evaluation and interdisciplinary collaboration. Recent publications span medical AI applications (e.g., Alzheimer’s subphenotypes, corticosteroid treatment efficacy) and foundational fairness research. She advocates for proactive ethical considerations in technical work, citing examples like surveillance risks in facial recognition and dataset biases in computer vision. Angelina advises prospective PhD students in Cornell’s Information Science program and collaborates on projects like SciDaSynth for scientific knowledge synthesis. Her advocacy includes challenging fairness impossibility theorems and promoting algorithmic pluralism in auditing practices.

• Machine Learning Fairness
• Algorithmic Bias
• Responsible AI
• +۳ مورد دیگر

Rekha R. Thomas is a Professor of Mathematics and Undergraduate Program Director at the University of Washington. She holds a Ph.D. in Operations Research from Cornell University (1994), with postdoctoral experience at Yale University and the Konrad-Zuse-Zentrum in Berlin. Her research focuses on optimization, applied algebraic geometry, and computer vision, with contributions to semidefinite programming, graphical designs, and geometric algorithms. She has held distinguished positions such as the Robert R. and Elaine F. Phelps Professorship (2008–2012) and the Robert B. Warfield Jr. Faculty Fellowship (2017–2020). Her work bridges theory and application, addressing challenges in computer vision, combinatorial optimization, and algebraic geometry. Notable contributions include advancements in multiview geometry, kernel learning, and the geometric analysis of rank-deficient matrices. She actively collaborates across disciplines, publishing extensively and supervising numerous graduate students and postdocs. Rekha also engages in academic leadership, mentoring students, and participating in international conferences. Her research has been recognized through invited talks at major events like the International Congress of Mathematicians (2018) and SIAM Annual Meetings. She continues to explore the intersections of algebraic geometry, optimization, and computational methods.

• Optimization
• Applied Algebraic Geometry
• Computer Vision
• +۳ مورد دیگر

Prof Ghassan Beydoun is a Professor and Head of Discipline (Information Systems) at the School of Computer Science, University of Technology Sydney (UTS). He leads the Information Systems discipline and is affiliated with the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). His research focuses on AI-driven systems, agent-based modelling, ontologies, and disaster management, with notable contributions to knowledge graphs, enterprise architecture, and IoT applications. Beydoun actively supervises Masters and PhD students in these domains. His research interests span metamodelling, agent systems, and AI applications in disaster management (e.g., flood, landslide, and earthquake risk assessment), health systems, and smart infrastructure. He has pioneered frameworks for reproducible machine learning solutions, digital identity systems, and cloud migration strategies. Beydoun’s work integrates interdisciplinary methods, such as bibliometric analysis for journal evolution and XAI for spatial hazard prediction. Recent publications highlight his expertise in AI for climate-induced hazard modelling, agent-based knowledge transfer mechanisms, and metaverse applications in education. His funded projects include AI-powered circular economy initiatives, smart beach safety systems, and health data querying frameworks. Beydoun collaborates with industry partners like CSIRO, Capsicum Business Architects, and Data Zoo, translating research into practical solutions for enterprise architecture, cybersecurity, and public health.

• Information Systems Methodologies
• Artificial Intelligence
• Agent Systems
• +۸ مورد دیگر

Stefan Riezler is a full professor of Statistical Natural Language Processing at Heidelberg University's Department of Computational Linguistics (since 2010), affiliated with the Faculty of Mathematics and Computer Science. Prior to this, he worked in Silicon Valley at Xerox PARC and Google Research. He holds a PhD in Computational Linguistics from the University of Tübingen (1998) and conducted postdoctoral research at Brown University (1999). His research spans machine learning, NLP, and medical informatics, focusing on interactive statistical learning. He co-leads the Interdisciplinary Center for Scientific Computing (IWR) and serves on the editorial boards of Computational Linguistics and Transactions of the Association for Computational Linguistics . Key research areas include neural machine translation, healthcare AI (e.g., sepsis prediction), data augmentation, and reproducibility in ML. He develops tools like JoeyNMT and explores ethical challenges in clinical machine learning. Notable recent work includes advancements in time series analysis, multimodal interfaces (e.g., NLMaps for OpenStreetMap), and ethical frameworks addressing validity in healthcare ML. His publications emphasize practical applications of NLP in healthcare, speech translation, and cross-lingual systems. Grants and collaborations include interdisciplinary projects on medical data science and training next-gen NLP researchers. He actively contributes to open-source toolkits and reproducible research practices.

• Machine Learning
• Natural Language Processing
• Medical Informatics
• +۳ مورد دیگر

Chuchu Fan is the Leonardo Career Development Professor and Director of the REALM Lab (REliable Autonomous system Lab) at MIT's School of Engineering , with a primary appointment in the Department of Aeronautics and Astronautics and Laboratory for Information and Decision Systems . Her work bridges formal methods , control theory , and machine learning to ensure safety in autonomous systems. Ph.D., University of Illinois at Urbana-Champaign (2019) B.E., Tsinghua University (2013) Her research focuses on rigorous safety verification of autonomous systems through neural Lyapunov-barrier functions , control contraction metrics , and formal logic specifications . Recent work emphasizes LLM integration for symbolic planning, multi-robot collaboration , and robustness against model uncertainties . The 15 most recent articles highlight trends in safety-critical control using graph neural networks , reinforcement learning , and temporal logic . Key themes include collision avoidance , multi-agent coordination , and runtime safety filters applied to drones, self-driving cars, and microgrids. Scientific Awards & Honors : 2025 ONR YIP Award 2023 NSF CAREER & AFOSR YIP Awards 2020 ACM Doctoral Dissertation Award 2016 Rising Stars in EECS As head of the REALM Lab, she leads projects on autonomous air taxis , safety verification , and neural certificates for robotic systems. Her teaching includes courses on feedback control and formal methods for autonomous systems.

• Formal Methods
• Control Theory
• Machine Learning
• +۳ مورد دیگر

Abhishek Santra is a Senior Lecturer in the Department of Computer Science and Engineering at The University of Texas at Arlington. He holds a PhD in Computer Science from UT Arlington (2020), and earlier degrees from the University of Delhi (BS 2011, MS 2013). His research focuses on multilayer networks, graph mining, and data analysis, with contributions to complex data modeling and visualization tools like MLN-geeWhiz and ModViz. He is also a Post-Doctoral Research Scholar in the Information Technology Lab (ITLab), led by Dr. Sharma Chakravarthy. Teaching interests include Discrete Structures, Database Systems, and DBMS Models. He has advised numerous students on research projects and thesis work, such as substructure discovery in multilayer networks and video content analysis. Recent grants include REU-funded projects for dashboard development (2022–2025). His service roles include committee memberships in the CSE department and organizing technical workshops like QVC and MLN-DIVE. Key research areas involve analyzing multi-source data through multilayer network frameworks, with applications in healthcare monitoring, big data analytics, and visualization systems. Publications span conferences like BDA, IC3K, and IEEE BigDataService, emphasizing algorithmic innovation for network-centric data problems.

• Multilayer Networks
• Data Analysis
• Graph Mining
• +۳ مورد دیگر

Stavros Nikolopoulos is a Professor in the Department of Computer Science & Engineering at the University of Ioannina, Greece. He serves as Director of the Algorithms Engineering Lab and holds a PhD in Computer Science (1991, University of Ioannina). His research focuses on Algorithmic Graph Theory, Parallel Algorithms, Malware Detection, and Software Watermarking. He has published extensively in top-tier journals and conferences, including Discrete Applied Mathematics and Theoretical Computer Science. Education: B.Sc. in Mathematics, University of Ioannina (1982) M.Sc. in Computer Science, University of Dundee (1985) Ph.D. in Computer Science, University of Ioannina (1991) Research Interests: Design and Analysis of Algorithms Graph Algorithms (e.g., permutation graphs, cographs) Malware Detection via System-call Group Analysis Graph-based Watermarking Systems Discrete Event Simulation Awards & Recognition: Best Student Paper Award (WEBIST'13) Best Paper Award (CompSysTech'13) Labs & Projects: Director, Algorithms Engineering Lab Principal Investigator in EU-funded projects (e.g., HRAKLITOS, PENED-05)

• Algorithmic Graph Theory
• Parallel Algorithms
• Graph Theory
• +۴ مورد دیگر

J. Haadi Jafarian is an Assistant Professor in the Department of Computer Science and Engineering at the University of Colorado Denver, where he leads the Active Cyber and Infrastructure Defense (ACID) Lab. He earned his Ph.D. from the University of North Carolina Charlotte in 2017. Research Interests: Active Cyber Defense (Moving Target Defense, Cyber Deception) Big Data Analytics for Cyber Threat Intelligence Security for Cyber-Physical Systems & Critical Infrastructures Cyber Resilience and Automation Recent Publications highlight innovations in traffic obfuscation, adversarial machine learning, and deception-based threat detection. His work spans network security, cybersecurity analytics, and scalable defense frameworks. Teaching includes: CSCI 4743/5743: Cyber and Infrastructure Defense (Fall 2023) CSCI 4742/5742: Cyber Programming and Analysis (Spring 2023) CSCI 4741: Cybersecurity Principles (Spring 2022) CSCI 4800: Web Application Development (Spring 2021) CSCI 3761: Computer Networks (Spring 2020) Labs & Teams: The ACID Lab focuses on developing proactive cyber defense strategies, including moving target defense, deception techniques, and security analytics for critical infrastructure.

• Security and Privacy
• Active Cyber Defense
• Security Analytics
• +۶ مورد دیگر

Waldemar Karwowski is a Pegasus Professor and Chairman of the Department of Industrial Engineering and Management Systems at the University of Central Florida, USA. He also serves as Executive Director of the Institute for Advanced Systems Engineering. His affiliations include the College of Engineering and Computer Science and multiple international academic roles. He holds a Doctor of Science (dr hab.) in Management Science from Poland, a Ph.D. in Industrial Engineering from Texas Tech University, and three honorary doctorates from universities in Ukraine, Slovakia, and Russia. His research focuses on neuroergonomics, human systems integration, safety engineering, nonlinear dynamics in human-machine systems, and applications of soft computing. He co-edits leading journals including Human Factors and Ergonomics in Manufacturing and Theoretical Issues in Ergonomics Science . Key achievements include Fellowships from HFES, IEA, and the UK's Institute of Ergonomics, and past presidencies of HFES (2007) and the International Ergonomics Association (2000-2003). His work addresses grand challenges in human factors, AI ethics, and healthcare safety culture. Recent research explores EEG-based neuroergonomic metrics, graph neural networks for brain connectivity analysis, and pandemic modeling with nonlinear dynamics. Awards include the Handbook of Human Factors and Ergonomics (5th ed., 2022) co-edited with Gavriel Salvendy. His advisory roles span industry and government, emphasizing systems engineering and human-centered AI integration.

• Human Factors and Ergonomics
• Neuroergonomics
• Human Systems Integration
• +۷ مورد دیگر

Dr. Esam Abdel-Raheem is a Professor in the Department of Electrical and Computer Engineering at the University of Windsor, Faculty of Engineering. His research focuses on digital signal processing, biomedical engineering, cognitive radio networks, and VLSI design. He holds a Ph.D. from the University of Victoria (1995) and is a Professional Engineer (P.Eng.) in Ontario and a Senior Member of IEEE. Education: B.Sc. Electrical Engineering, Ain Shams University (1984) M.Sc. Electrical Engineering, Ain Shams University (1989) Ph.D. Electrical Engineering, University of Victoria (1995) Research Interests: Dr. Abdel-Raheem’s work spans signal processing for communications, biomedical signal processing, and VLSI implementations. He has pioneered algorithms for cognitive radio networks and adaptive filtering. His recent studies leverage deep learning for medical diagnostics (e.g., lung nodule detection, Parkinson’s disease voice analysis) and cognitive radio spectrum sensing. Publications Trends: Recent work emphasizes biomedical applications (e.g., CT scan analysis, diabetic retinopathy detection) and machine learning integration in communications (e.g., federated learning for traffic crowdsourcing). His articles often bridge theoretical signal processing with practical implementations in hardware (e.g., FPGA-based filters). Awards/Grants: Not explicitly listed in the text, though his senior IEEE membership and prolific publications suggest sustained professional recognition. Lab/Teams: While not detailed, his research themes imply involvement in interdisciplinary teams focusing on biomedical engineering, telecommunications, and VLSI design.

• Digital Signal Processing
• Biomedical Engineering
• Cognitive Radio Networks
• +۳ مورد دیگر

Ben Li is an Associate Professor in the Department of Computer Science at the University of Manitoba, Faculty of Science. His research focuses on combinatorics and theoretical computer science, including combinatorial design theory (e.g., Steiner triple systems, BIBDs, difference sets), graph theory, and algorithm design for combinatorial optimization problems. He also explores approximation algorithms for NP-Hard problems and subclasses of such problems with polynomial solutions. Dr. Li teaches a wide range of courses, including COMP 1010 (Introduction to Computer Science), COMP 2080 (Analysis of Algorithms), and advanced graduate courses like COMP 7720 on approximation algorithms and combinatorial optimization. His academic contributions span both theoretical computer science and interdisciplinary archaeological studies, as reflected in his publications. His recent work includes studies on ostrich eggshell beads' role in prehistoric social networks, archaeological site analysis in southern Africa, and medical education reforms in Canada. While his primary research aligns with computer science theory, his published articles also highlight interdisciplinary interests in anthropology, archaeology, and healthcare policy.

• Combinatorics
• Theoretical Computer Science
• Combinatorial Design Theory
• +۸ مورد دیگر

Yue Zhao is an Associate Professor in the Department of Electrical and Computer Engineering at Stony Brook University, with an affiliated appointment in Applied Mathematics and Statistics. Prior to this, she held postdoctoral positions at Stanford University and Princeton University. She earned her Ph.D. from UCLA in 2011 and B.E. from Tsinghua University in 2006. Her research focuses on smart grid systems, renewable energy integration, machine learning applications in power systems, and game-theoretic approaches to electricity markets. Key areas include transportation electrification, demand response mechanisms, and cyber-physical security of grid infrastructure. She teaches courses on digital signal processing, convex optimization, and communication systems. Her work spans over 50 publications in top journals and conferences like IEEE Transactions on Power Systems and ACM e-Energy. Notable contributions include dynamic state estimation frameworks for inverter-based resources, incentive-compatible market mechanisms for renewable aggregation, and cyber attack detection methodologies. Dr. Zhao advises a research group focused on interdisciplinary challenges in energy systems. Current openings exist for Ph.D. students with strong analytical backgrounds. Sponsors include NSF, DOE, and industry collaborators.

• Smart Grid
• Renewable Energy Integration
• Electricity Market Design
• +۶ مورد دیگر

Kamal Al Haddad is a Lecturer in the Department of Electrical Engineering at École de technologie supérieure (ÉTS). He holds a Doctorate from INTP, Toulouse, and advanced degrees from UQTR. His research focuses on power electronics, renewable energy integration, and smart grid technologies. He leads the GREPCI research group, specializing in Power Electronics and Industrial Control. Education: B.Eng., M.Sc.A. (UQTR), Doctorate (INTP, Toulouse). Research interests span energy conversion, industrial electronics, power quality, and electromagnetic interference. He emphasizes sustainable energy solutions, electric traction systems, and high-efficiency power sources. His work includes developing advanced power electronic converters and grid stability solutions. Recent articles highlight advancements in modular converters for STATCOM, AI-driven fault detection in hydrogenerators, and renewable energy policy frameworks. He has received notable awards, including the 2014 IEEE Eugene Mittelmann Prize and Fellowships from IEEE and other institutions. Supervised over 60 students, including doctoral theses on topics like hydrogenerator diagnostics, EV charging systems, and renewable energy integration. His research also involves real-time simulation of power systems and FPGA-based implementations. Labs/Teams: GREPCI – Power Electronics and Industrial Control Research Group, leading projects on smart grids and energy efficiency.

• Power Electronics
• Electrical Engineering
• Energy Conversion
• +۷ مورد دیگر

Asu Ozdaglar is the EECS Department Head and MathWorks Professor at MIT, serving as Deputy Dean of Academics in the MIT Schwarzman College of Computing. Her research bridges optimization theory, machine learning, and network science with societal implications, focusing on AI ethics, data-driven decision systems, and strategic interactions in networked environments. Her technical contributions include foundational work on large-scale optimization algorithms (e.g., distributed methods, first-order methods), game-theoretic models for network systems, and federated learning frameworks. Recent work addresses critical societal challenges like misinformation dynamics, data market inefficiencies, and algorithmic fairness in AI systems. Publications from 2023-2025 highlight advancements in graphon-based network game analysis, privacy-preserving data mechanisms, and multi-agent learning dynamics. She co-leads initiatives in MIT's AI+D program, emphasizing interdisciplinary education and ethical AI development. Notable institutional roles include oversight of MIT's computing education strategy and contributions to pandemic-related research on infection control through testing optimization. Her work integrates technical rigor with policy-relevant insights, influencing both academic and real-world systems.

• Optimization
• Machine Learning
• Game Theory
• +۴ مورد دیگر