جستجوی استاد راهنمای دانشگاهی

Louis Duvivierمشاهده پروفایل

مدرس ارشد

• Network modeling
• Graph analysis
• Community detection
• +۹ مورد دیگر

Louis Duvivier is a Senior Lecturer at the University of Grenoble Alpes since October 2024, affiliated with the Science Department Drôme Ardèche. He is associated with the Coordination, Cooperation & Control of Complex Systems (CO4SYS) team within the Systems Design and Integration Laboratory (LCIS). Former ATER at École Centrale de Lyon (2023–2024) Postdoctoral Fellow at ENS Lyon (2022–2023) ATER at Institute of Financial and Insurance Science (2021–2022) His research focuses on network modeling , graph analysis , and statistical inference , particularly in stochastic block models , community detection , and temporal graph analysis . His recent work explores probabilistic validation techniques and geometric interpretations of network structures. Publications demonstrate expertise in Bayesian inference , model selection , and sensor network estimation . Earlier contributions to distributed estimation and link prediction date back to 2019. He teaches courses in complementary computing , algorithms , numerical analysis , and statistics at both bachelor's and master's levels.

James Stovoldمشاهده پروفایل

استادیار

• Emergent Behavior
• Cognitive Robotics
• Unconventional Computing
• +۴ مورد دیگر

Dr. James Stovold is an Assistant Professor at the Data Science Institute, Lancaster University. His research focuses on emergent behavior, cognitive robotics, and unconventional computing methods, particularly leveraging neural cellular automata and swarm robotics for innovative applications in AI and computational systems. His recent work explores topics such as human-AI symbiosis, reaction-diffusion chemistry for neural networks, and mixed-initiative design tools. Publications span disciplines including artificial intelligence, computational biology, and human-AI interaction. His research has been featured in numerous conference contributions and journal articles, emphasizing interdisciplinary approaches to solving complex computational and robotic challenges. He can be reached via email at j.stovold@lancaster.ac.uk.

• Logic and Philosophy of Logic
• Linguistic Communication
• 19th Century German Philosophy
• +۸ مورد دیگر

Jens Lemanski is an Associate Professor at the Philosophical Seminar of the University of Tübingen, with part-time roles as an Adjunct Professor at the University of Münster and a Researcher at Fernuniversität Hagen. His research spans Logic and Philosophy of Logic , Linguistic Communication , 19th Century German Philosophy , and Visualization in Mathematics . His work focuses on the historical and philosophical foundations of logic diagrams, including projects like Logic Diagrams in Kantianism (Fritz-Thyssen-Stiftung) and Gestures and Diagrams in Visual-Spatial Communications (DFG-priority programme). He has contributed extensively to understanding the evolution of logic from antiquity to modernity, emphasizing the role of diagrams in Euler-type systems , Byzantine logic , and Kantian thought . Lemanski co-edits Historia Logicae (College Publications) and serves as a Book Review Editor for History and Philosophy of Logic . His recent publications analyze the interplay between transcendental philosophy , formal logic , and AI-driven multimodal communication . He holds a PhD from Johannes Gutenberg University Mainz (2011) and contributes to conferences like Diagrammatic Representation and Inference (2024).

Kevin Limantaمشاهده پروفایل

پژوهشگر

• Combinatorics
• Number Theory
• Algebra
• +۳ مورد دیگر

Dr Kevin Limanta is a Researcher at the School of Mathematics and Statistics , UNSW Sydney. He teaches MATH1131 Mathematics 1A (Calculus) and MATH1231 Mathematics 1B (Algebra), serving as course convenor for both. His research spans advanced mathematical domains including: Combinatorics (super Catalan numbers, Dyck paths) Algebraic Structures (finite field Fourier summation) Harmonic Analysis (spherical harmonics on S³) Functional Analysis (Morrey space properties) Dr Limanta's publications focus on algebraic interpretations of Catalan numbers, permutation-generated Dyck path mappings, and harmonic function theory on mathematical spaces. His work intersects pure mathematics with computational applications.

• Information Retrieval
• Data Science
• Text Analytics
• +۵ مورد دیگر

Christina Lioma is a Full Professor at the Department of Computer Science (DIKU), University of Copenhagen . She has held academic positions including Associate Professor (2014-2018) and Freja Fellow/Assistant Professor (2012-2013) at the same institution. M.Hons (University of Glasgow, 2001) M.Sc. (University of Manchester, 2003) Ph.D (University of Glasgow, 2007) Her research focuses on Information Retrieval , Text Analytics , and Recommender Systems within Applied Machine Learning and Natural Language Processing . Recent work examines fairness-relevance tradeoffs in recommendation systems and neural mechanisms for knowledge conflict tracing. Recent publications in Nature Communications and top conference proceedings (WWW, SIGIR) explore hybrid computation architectures, brain-based language generation, and fairness evaluation metrics. She actively participates in academic conferences as organizer and speaker, including the European Conference on Information Retrieval.

Bo Huiمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Graph Neural Networks
• +۶ مورد دیگر

Bo Hui is an Assistant Professor of Computer Science at The University of Tulsa's College of Engineering & Computer Science. He received his Ph.D. in Computer Science and Software Engineering from Auburn University in 2023 and earned his B.S. in Computer Science from Xi'an Jiaotong University in 2013. Prior to his Ph.D., he worked as a senior software engineer in the industry. Education: Ph.D., Computer Science and Technology, Auburn University, 2023 B.S., Computer Science and Technology, Xi'an Jiaotong University, 2013 Dr. Hui's research focuses on data mining and machine learning, particularly in designing machine learning methods for complex real-world data while addressing social concerns such as privacy in AI. His work spans multiple application domains including traffic prediction, social recommendation systems, and biological data analysis. He has made significant contributions to graph neural networks, knowledge graph unlearning, and federated learning systems. Dr. Hui's publication record demonstrates a strong focus on machine unlearning (the 'right to be forgotten' in AI models), graph neural networks, and traffic prediction systems. His work bridges theoretical machine learning with practical applications, with a growing emphasis on privacy-preserving AI techniques. He frequently publishes in top-tier venues including ICCV, ICLR, KDD, and AAAI. Awards and Honors: NSF award #2348177 for machine unlearning research AAAI student scholarship award Dr. Hui currently advises two Ph.D. students (Ruimeng Ye and Yang Xiao) who began their studies in Fall 2024. His research is supported by an NSF grant (#2348177) focused on machine unlearning. He has served as a reviewer for major conferences including ICLR, AAAI, KDD, CVPR, ACL, and EMNLP. His teaching includes Data Mining (2024 Spring) and Interaction Design (2023 Fall). His research group appears to focus on data mining and machine learning with applications to real-world problems requiring privacy-aware solutions.

Giovanni Silenoمشاهده پروفایل

مدرس

• Artificial Intelligence
• Logic Programming
• Knowledge Representation
• +۵ مورد دیگر

Giovanni Sileno is an academic specializing in Artificial Intelligence, Logic Programming, and Knowledge Representation, with teaching appointments at the University of Amsterdam, EPITA Paris, and Université Pierre et Marie Curie. His primary affiliation is with the Informatics Institute at the University of Amsterdam where he serves as a Lecturer. His research interests span multiple domains within computer science, focusing particularly on formal methods for normative systems, agent-based programming, and logic-based knowledge representation. His work bridges theoretical computer science with practical applications in policy modeling, forensic science, and business information systems. Dr. Sileno has developed several notable software projects including AgentScriptCC for single-threaded intentional agents, DCPLschema for normative policy specifications, and libraries for normative primitives in Answer Set Programming. His GitHub activity shows consistent contributions from 2012 through 2025, indicating active engagement in both research and development. His publications reflect a strong focus on formal methods applied to practical problems, with particular emphasis on normative systems, agent architectures, and logic programming applications. The research trajectory shows evolution from foundational topics in logic and numeral systems toward increasingly sophisticated applications in policy modeling and agent-based systems. As an educator, he has taught across multiple institutions and disciplines, covering topics from basic programming paradigms to advanced knowledge representation and formal modeling techniques. His teaching spans undergraduate to master's level courses in information studies, cognitive science, data science, and forensic science.

Yi Panمشاهده پروفایل

استاد

• Parallel and Cloud Computing
• Wireless Networks
• Bioinformatics
• +۳ مورد دیگر

Dr. Yi Pan is a Distinguished University Professor and Chair of the Department of Computer Science at Georgia State University. He holds a B.Eng. and M.Eng. from Tsinghua University (China) and a Ph.D. from the University of Pittsburgh (USA). His research focuses on parallel computing, wireless networks, bioinformatics, and machine learning, with over 330 publications and 39 authored/edited books. Dr. Pan has organized international conferences and delivered over 40 keynote speeches globally. Education: B.Eng., Computer Engineering, Tsinghua University, 1982 M.Eng., Computer Engineering, Tsinghua University, 1984 Ph.D., Computer Science, University of Pittsburgh, 1991 Research Interests: Dr. Pan’s work spans parallel and cloud computing, wireless networks, and bioinformatics. His contributions include scalable algorithms for functional module discovery in protein networks, self-defense mechanisms for cyber threats, and efficient routing protocols for sensor networks. Recent efforts integrate AI into healthcare, such as lesion localization in OCT imaging and autism spectrum disorder diagnosis via EEG analysis. Awards & Recognition: IEEE Distinguished Speaker (2000–2002) Yamacraw Distinguished Speaker (2002) Listed in Who’s Who in America and Who’s Who in Computational Science Awards from NSF, AFOSR, IBM, and Mellon Foundation Advising & Leadership: As department chair, Dr. Pan oversees interdisciplinary collaborations between Computer Science and Computer Information Systems. He has advised numerous students and holds editorial roles in 15 journals, including several IEEE Transactions. His grants focus on advancing AI-driven healthcare, cybersecurity, and computational biology. Labs & Teams: Leads research initiatives in bioinformatics, medical AI, and parallel computing, collaborating with industry partners like IBM and Shell Oil. Active in organizing conferences such as the IEEE International Conference on Bioinformatics and Biomedicine.

Haitao Heمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Transport Engineering
• Urban Planning
• +۳ مورد دیگر

Haitao He is a Reader in Transport and AI at Loughborough University, UK, and leads the Transport AI Innovation Centre (TRAICE). He holds a BSc (First-Class Honours), MPhil, and PhD, and is a Fellow of the Higher Education Academy (FHEA). His research focuses on AI-driven solutions for smart cities and sustainable urban mobility, including traffic simulation, machine learning, and digital twinning. He develops methodologies in traffic flow theory, agent-based modeling, and deep learning to address challenges in automation, electrification, and shared mobility. Research Interests: AI in transportation, micro-mobility, sustainability, and data-driven urban planning. Projects: UKRI Future Leaders Fellowship on net-zero urban transport, TraffEase (Manchester Prize), and BusMONITOR (KTP with Vectare). His expertise spans predictive traffic analytics, micro-mobility patterns, and infrastructure optimization. He has been awarded the UKRI Future Leaders Fellowship and has contributed to projects like SignBus (Swiss National Science Foundation) and WEAVE (highway weaving section design). Key Achievements: Developed TRAICE to advance transport AI, published over 30 articles on topics like traffic flow modeling and pandemic mobility impacts. Haitao advises on PhD and postdoctoral research, focusing on AI and transport intersections. His work integrates academic and industry collaborations, driving innovation in smart mobility and policy-making.

Konstantinos Kyriakopoulosمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Dr. Konstantinos Kyriakopoulos is a Senior Lecturer in Cyber Intelligence, specializing in cybersecurity challenges and AI-driven solutions for converged Information and Operational Technology infrastructures. His work integrates machine learning with cyber-physical systems to enhance decision-making under uncertainty, with a focus on resilient AI algorithms and autonomous defense mechanisms. He has contributed to projects funded by Dstl, Innovate UK, and industry partners, addressing Industry 4.0 security, smart vehicles, electromagnetic attacks, and smart buildings. His research interests include adversarial machine learning, evidence theory for uncertainty modeling in ML, and autonomous cyber defense systems. He has developed novel approaches using reinforcement learning, hidden Markov models, and Bayesian optimization. His work emphasizes creating ML algorithms resistant to adversarial attacks while improving cybersecurity resilience in dynamic environments. Dr. Kyriakopoulos has also explored applications of AI in educational technology, such as automated assessment of spoken English proficiency. His recent publications focus on federated learning security, causal reinforcement learning, and anomaly detection techniques. He has experience in translating research into practical solutions, including business model pitches to investors in Canary Wharf for threat modeling tools. His contributions span over 40 peer-reviewed articles, with notable work in network intrusion detection, attack prediction via HMMs, and wavelet-based compression for network measurements. His expertise bridges academia and industry, addressing both theoretical advancements and real-world implementation challenges.

• Linear and Multilinear Singular Integrals
• Real Harmonic/Fourier Analysis
• Differential Operators
• +۶ مورد دیگر

Professor Vjekoslav Kovac is a full professor at the Department of Mathematics, Faculty of Science, University of Zagreb. He holds a PhD from UCLA (2011) under Christoph Thiele and was a Fulbright Scholar at Georgia Tech (2019-20). His research spans harmonic analysis, singular integrals, combinatorics, and number theory, with a focus on Fourier restriction problems and Erdős-type conjectures. Kovac leads the HRZZ-funded 'Fourier Analysis and Applications' grant (2023-2027) and has previously directed projects like 'Multilinear Harmonic Analysis' (2018-2023). Education: PhD, UCLA (2011); MSc, University of Zagreb (2006) Research Interests: Singular integrals, real harmonic analysis, geometric measure theory, additive combinatorics, ergodic theory He is renowned for solving longstanding open problems, including Erdős conjectures on unit fractions and irrationality. Kovac has authored/co-authored over 40 publications and mentored multiple PhD and diploma students. His awards include the Croatian National Science Award (2023) and the Brdo Award (2025 nomination). Teaching roles include courses on mathematical analysis, Fourier analysis, and competition mathematics. Grants: Current FANAP (HRZZ), Previous MUNHANAP (HRZZ), MZO/DAAD grants Media Highlights: Coverage in 'What's New' (Tao), 'TotalCroatia', and Croatian TV for resolving 40-year-old mathematical problems

Narada Dilp Warakagodaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Autonomous Systems
• Computer Vision
• +۳ مورد دیگر

Narada Dilp Warakagoda is an Associate Professor at the University of Oslo, affiliated with the Section for Autonomous Systems and Sensor Technologies. His research focuses on Artificial Intelligence, Autonomous Systems, Computer Vision, Robotics, Machine Learning, and Deep Learning, with notable contributions to underwater acoustics, renewable energy forecasting, and unmanned vehicle control systems. He collaborates extensively with researchers across disciplines to advance applications in environmental monitoring, energy systems, and autonomous technologies. His work integrates advanced machine learning techniques with domain-specific challenges, such as synthetic aperture sonar analysis, spatio-temporal wind forecasting, and reinforcement learning for autonomous navigation. He has published in leading journals and conferences, addressing topics like probabilistic solar irradiance prediction, graph-based wind energy modeling, and deep learning for seabed munitions detection. Warakagoda’s research bridges theoretical advancements with practical implementations, emphasizing applications in robotics, environmental science, and defense technologies. His projects often involve interdisciplinary teams, reflecting his commitment to solving real-world problems through innovative AI-driven solutions.

Torsten Braunمشاهده پروفایل

استاد

• Machine Learning
• Networking and Telecommunications
• Edge Computing
• +۳ مورد دیگر

Prof. Torsten Braun is a Professor and Head of the Communication and Distributed Systems (CDS) research group at the Institute of Computer Science, University of Bern. His research focuses on advanced networking technologies, edge computing, and machine learning applications in telecommunications. He leads projects addressing challenges in 5G/6G networks, federated learning optimization, and intelligent systems for smart cities. His work integrates theoretical frameworks with practical implementations, emphasizing distributed systems, service-oriented architectures, and IoT security. Key research areas include edge caching strategies for VR/AR applications, trajectory prediction using reinforcement learning, and resilient network design against jamming attacks. Braun has contributed to innovations in vehicular networks (V2X), RAN intelligence, and decentralized machine learning frameworks. He holds leadership roles in developing adaptive resource management systems for edge-cloud environments and has pioneered solutions for energy-efficient federated learning in heterogeneous IoT ecosystems. Publications span topics like spatial-temporal point cloud sensing, mobility-aware service orchestration, and secure positioning systems. His team explores cross-disciplinary applications such as LoRaWAN-based urban heat monitoring and blockchain-inspired public key infrastructures for IoT (Veritaa-IoT). Braun actively engages in standardization and industry collaborations to advance next-generation network architectures.

Mengchu Liمشاهده پروفایل

استادیار

• Change Point Analysis
• High-Dimensional Statistics
• Robust Statistics
• +۳ مورد دیگر

Roles & Affiliations: Dr. Mengchu Li is an Assistant Professor in the School of Mathematics at the University of Birmingham. Previously, he served as a Postdoctoral Research Fellow in the Department of Statistics at the University of Warwick (Feb 2023–Sept 2024), and completed his PhD in Statistics at Warwick under Prof. Yi Yu. Education: PhD in Statistics, University of Warwick (2023) MASt in Mathematical Statistics, University of Cambridge (2019) BSc in Mathematics and Economics, Durham University (2018) Research Interests: Focuses on statistical theory and methodology for heterogeneous data analysis, particularly in high-dimensional settings and under privacy constraints. Key areas include change point analysis, robust statistics, differential privacy, and transfer learning. His work emphasizes minimax optimality and explores fundamental trade-offs between data contamination, privacy, and statistical performance. Research Trends in Articles: Recent work spans federated learning with privacy guarantees, robust change point testing in high-dimensional settings, and privacy-preserving network analysis. His papers often bridge statistical theory with applications in machine learning and distributed systems. Awards & Honors: Harrison Award (2023) for highly commended PhD thesis NeurIPS 2022 Scholar Award Teaching & Service: Taught graduate courses on statistical inference and computation at Birmingham. Previously led tutorials in mathematical statistics and linear modeling at Warwick. Active reviewer for Annals of Statistics , Biometrika , NeurIPS , and other top venues. Contributed to the R package changepoints for change-point detection methods. Labs & Collaborations: Engaged in interdisciplinary research through workshops on heterogeneous data (2024) and change point analysis (2023). Collaborates with institutions like the Alan Turing Institute and Warwick’s Statistics department.

Sam Nallaperuma-Herzbergمشاهده پروفایل

دانشگاهی

• Machine Learning
• Artificial Intelligence
• Optical Communication Systems
• +۵ مورد دیگر

Dr. Sam Nallaperuma-Herzberg is an Assistant Research Professor at the University of Cambridge's Department of Computer Science and Technology. He holds prestigious fellowships including the Borysiewicz Interdisciplinary Fellowship, Bye Fellowship at Murray Edwards College, and the Schmidt/Accelerate Fellowship in AI for Science and Society. His research focuses on machine learning applications in optical communication systems, neural networks for medical diagnostics, and reinforcement learning for network optimization. Affiliations: Department of Computer Science and Technology, Murray Edwards College Key Research Themes: Machine Learning, AI for Healthcare, Optical Fiber Networks, Reinforcement Learning Algorithms His work bridges computational methods with real-world challenges in telecommunications and healthcare. Notable achievements include developing graph neural networks for insomnia modeling and optimizing ultra-wideband fiber-optic systems using deep learning. He has been recognized with multiple interdisciplinary research fellowships, reflecting his cross-cutting contributions. Research outputs span over 30 peer-reviewed articles since 2017, with recent emphasis on AI-driven solutions for optical network optimization and circRNA-RBP interaction prediction. His projects include the BrainTwin initiative applying AI to sleep disorder analysis. Awards: Borysiewicz Interdisciplinary Fellow Bye Fellow, Murray Edwards College Schmidt/Accelerate Fellow in AI for Science and Society Current research explores hybrid neural architectures for medical diagnostics and Gaussian process modeling for optical signal optimization. He collaborates extensively on EU-funded initiatives and industry partnerships in AI and telecommunications.