Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Edge Computing
• +7 more

Huan Liu is a Professor at Arizona State University's School of Computing, Informatics, and Decision Systems Engineering, with research spanning artificial intelligence, edge computing, and interdisciplinary applications. His work bridges theoretical advances with real-world implementations across healthcare, transportation, and environmental systems. His primary research interests include: Artificial Intelligence and Machine Learning for complex system optimization Edge-Cloud Computing architectures for IoT and vehicular networks Medical image analysis and clinical decision support systems Graph neural networks and multimodal learning Privacy-preserving federated learning frameworks Recent publications demonstrate strong emphasis on applied AI, with 15+ 2025 papers addressing UAV swarm intelligence, medical diagnostics, and sustainable transportation through novel algorithmic approaches. Publication trends reveal consistent leadership in: Developing efficient edge computing solutions for dynamic environments Creating medical AI tools with clinical validation Innovating in graph-based and multimodal learning Applying optimization techniques to logistics and environmental challenges His work frequently appears in top venues including IEEE Transactions, Bioinformatics, and AAAI.

• Statistical Physics
• Network Theory
• Decision Modeling
• +3 more

Luca Gamberi Gamberi is a researcher specializing in interdisciplinary studies at the intersection of statistical physics, network theory, and decision modeling. His work focuses on applying physical principles to social and economic systems, such as analyzing election discrepancies, legal frameworks, and information valuation. He completed his Doctor of Philosophy (PhD) with a thesis titled Decision making models in social and econophysics , supervised by Prof. P. Vivo and Dr. A. Annibale. His research outputs include peer-reviewed articles in journals like Physical Review Research and Journal of Physics A , as well as contributions to legal informatics through projects like Graphie, a network-based legal visualization tool. Key research interests include complex systems analysis, structural complexity in legal documents, and the application of statistical methods to electoral and polling data. His work bridges theoretical physics with practical applications in social sciences and policy analysis. Notable collaborations include projects on first-passage time dynamics and the statistical modeling of election discrepancies. His doctoral thesis explores decision models in econophysics, reflecting his expertise in agent-based modeling and social dynamics.

• Artificial Intelligence
• Computer Science
• Data Science
• +3 more

Florian Marwitz is a doctoral researcher at the University of Hamburg affiliated with the Cluster of Excellence 'Understanding Written Artefacts' (UWA) and the Artificial Intelligence in Humanities (Prof. Dr. Möller) department. His work focuses on advancing computational methods for humanities research, including graph algorithms, probabilistic models, and AI applications in cultural heritage preservation. He contributes to projects like the Data Linking Infrastructure and ChatHA chatbot development, emphasizing sustainable information systems and efficient prediction techniques. His research bridges computer science and humanities, addressing challenges in data representation and decision-making systems. Marwitz holds an MA and is based at the Centre for the Study of Manuscript Cultures. His activities include organizing workshops on data linking and AI ethics, reflecting commitments to interdisciplinary collaboration. He is part of the Institut für Humanities-Centered Artificial Intelligence (CHAI), actively involved in training programs and public engagement initiatives.

• Theoretical Computer Science
• Discrete Mathematics
• Probability Theory
• +5 more

Nick Gravin is a Professor at the School of Information Management and Engineering (SIME) of Shanghai University of Finance and Economics (SHUFE), affiliated with the Institute for Theoretical Computer Science (ITCS). His research focuses on Theoretical Computer Science, Discrete Mathematics, and Probability Theory, particularly in algorithmic auction design, equilibria computation in Algorithmic Game Theory, and online algorithms. Before joining SHUFE in 2017, he held postdoctoral positions at MIT-CSAIL (2016) and Microsoft Research New England (2013–2015). He holds dual PhDs: one in Mathematics from the Steklov Institute (Russia) under Dmitri Karpov, and another in Computer Science from Nanyang Technological University (Singapore), advised by Dmitrii Pasechnik, Edith Elkind, and others. His work bridges fundamental algorithmic challenges and practical market design, emphasizing mechanisms for online resource allocation, budget constraints, and auction optimization. Over 15 recent publications span prophet inequalities, secretary problems, and competitive analysis in dynamic environments. Gravin’s contributions include novel pricing strategies, nonadaptive algorithms for matching problems, and frameworks for Bayesian online settings. His research also explores the interplay between cardinal and ordinal data in algorithmic decision-making. No awards or grants are explicitly listed in the provided text. His advising role is not detailed, though his postdoctoral mentorship suggests involvement in training researchers. Current research continues to address theoretical and applied aspects of computational economics and combinatorial optimization.

• Computer Vision
• Medical Informatics
• 3D Scene Understanding
• +5 more

Overview Pedro Hermosilla Casajus is an Assistant Professor at the Department of Computer Vision within the Faculty of Informatics at Technische Universität Wien (TU Wien). His research focuses on advanced computer vision techniques with applications in medical imaging, 3D scene understanding, and deep learning methodologies. He is actively involved in teaching multiple courses including Fundamentals of Computer Vision, Deep Learning for Visual Computing, and Scientific Research and Writing. Projects & Collaborations MyeFLOW (2020–2025) : Developing automated analysis systems for flow cytometry data. EVOCATION (2018–2022) : Real-time shape acquisition technologies. Modeling the World at Scale (2020–2026) : Large-scale 3D modeling techniques. Research Highlights His work bridges computer vision with medical diagnostics, particularly in measurable residual disease detection using flow cytometry. He explores geometric deep learning for 3D point cloud analysis and scene graph generation, advancing unsupervised semantic segmentation methods. Recent contributions include stylized Gaussian splatting for neural rendering and open-vocabulary 3D scene understanding frameworks. Teaching He teaches core courses like Fundamentals of Computer Vision and supervises student projects in medical informatics, visual computing, and human-centered computing.

• Machine Learning
• Healthcare Technology
• Educational Technology
• +3 more

Fatema Nafa is an Associate Teaching Professor in the Department of Electrical and Computer Engineering at Northeastern University. Her research focuses on machine learning applications in healthcare, educational technology, and cognitive science. She joined Northeastern in January 2024, contributing expertise in developing user-friendly systems for medical diagnostics and educational assessment tools like ProGrader for programming courses. Her work spans interdisciplinary areas such as breast cancer detection using CNNs, Alzheimer’s diagnosis through ML GUIs, and improving HPV awareness via BERT-based text summarization. She explores dependency structures in knowledge units using Markov networks and semantic graph analysis. Recent projects include computational methods for prerequisite analysis in computer science curricula and social network efficiency studies. Notable trends in her publications include healthcare technology advancements, educational system automation, and cognitive skill modeling. She actively chairs conferences and publishes in venues addressing intersections of AI, education, and health. No awards or grants are explicitly mentioned in the provided texts. Her advising record is not detailed here, but her research emphasizes collaborative efforts in labs focused on AI-driven solutions for societal challenges.

Regino CriadoView profile

Professor

• Complex Networks
• Discrete Mathematics
• Cybersecurity
• +4 more

Dr. Regino Criado is Full Professor of Applied Mathematics at Rey Juan Carlos University (URJC), where he has held academic positions since June 2000. He currently serves as Academic Director of the Data, Complex Networks and Cybersecurity Sciences Institute (since March 2017) and was elected to the Academia Europaea in May 2023. His career spans European research projects (ESPRIT II/III) and interdisciplinary collaborations at the intersection of mathematics, computer science, and cybersecurity. His research focuses on Complex Networks , including hyper-networks, multiplex networks, and mesoscale structures, with applications to cybersecurity, intentional risk management, and synchronization phenomena. Key innovations include: A game theory-based intentional risk model integrating accessibility, anonymity, and value metrics Hybrid network mining algorithms for credit card fraud detection PageRank extensions to multiplex networks Framework for discrete resilience analysis in technological systems His work on the 2014 Physics Reports monograph The structure and dynamics of multilayer networks (3,500+ citations) established foundational concepts in multilayer network theory. Current research explores linguistic pattern analysis through multilayer hypergraphs for automatic text summarization. Member of Academia Europaea (2023) Chaos Journal Best Paper Award (2011) Academic Entrepeneurs Prize (2002) EUROPA-1992 Prize (1992) As director of the DCNC Institute (2017-present) and BBVA-URJC Chair (2017-2018), he has led major academic-industry collaborations. His 180+ publications (6,500+ citations) demonstrate sustained impact across network science, applied mathematics, and cybersecurity.

Mihiran Galagedarage DonView profile

Researcher

• Digital Twins
• Materials Science
• Mechanical Engineering
• +5 more

Mihiran Galagedarage Don is a Postdoctoral Researcher at Memorial University of Newfoundland, affiliated with the Department of Energy and Mechanical Engineering and the Materials to Products department. He holds a Ph.D. in Mechanical Engineering (2023) and a Master of Engineering (2019) from Memorial University, alongside multiple degrees from the University of Moratuwa, including a B.Sc. (Hons) in Materials Engineering (2009). His research focuses on digital twin applications in oil and mining industries, fatigue analysis of drilling systems, finite element modeling, and predictive maintenance. Key contributions include frameworks for ESP systems, vibration prediction in drill strings, and hybrid models for process fault prognosis. Publications emphasize digital twin development, fatigue life prognosis, and Industry 4.0 analytics. No scientific awards are explicitly mentioned. Advising students or grants are not listed. No specific labs or teams are highlighted in the provided texts.

Yinghui Catherine YangView profile

Professor

• Business Analytics
• Big Data Analytics
• Predictive Modeling
• +9 more

Professor Yinghui Catherine Yang is a faculty member at the University of California, Davis, affiliated with the Graduate School of Management. Her research spans business analytics, artificial intelligence, machine learning, and data mining, with a focus on business intelligence and online shopping behaviors. Ph.D. in Operations and Information Management, The Wharton School, University of Pennsylvania (2004) M.A. in Operations and Information Management, The Wharton School, University of Pennsylvania (2001) B.E. in Management Information Systems, Tsinghua University (1998) Her research centers on leveraging big data analytics for business intelligence, including user profiling, online shopping pattern analysis, and data mining methods. She has contributed to computational advertising, customer segmentation, and personalized systems. Catherine Yang's publications since 2019 highlight trends in sponsored search advertising, relational topic modeling, and behavioral analytics. Her work integrates machine learning and optimization frameworks for marketing and healthcare applications. Contact: yiyang@ucdavis.edu | Office: 540 Alumni Lane, 3418 Gallagher Hall, Davis, CA 95616 | Phone: (530) 754-5967

Tarique AnwarView profile

Lecturer

• Data Science
• Machine Learning
• Artificial Intelligence
• +3 more

Tarique Anwar is a Lecturer in the Department of Computer Science at the University of York, UK, since 2021. He holds a PhD in Data Science from Swinburne University of Technology, Australia. As Programme Leader for both MSc and BSc Data Science programs, he oversees admissions and curriculum development. His academic roles include Data Science Admissions Tutor and membership in the Artificial Intelligence Group. Research interests span Data Science , Machine Learning , Artificial Intelligence , and Social Media Analysis , with a focus on solving real-world problems using structured/unstructured data. Notable areas include mental health detection via social media, network analysis, and explainable AI. His work addresses challenges in: Depression severity assessment using attention mechanisms Rumor source detection in social networks Eating disorder classification through multimodal learning Geographic-social-temporal pattern mining He supervises MS/PhD students in Data Science and Analytics, emphasizing interdisciplinary research. His publications (2020–2025) demonstrate expertise in AI applications for health, social networks, and optimization. Labs/Teams: Artificial Intelligence Group at the University of York, collaborating with industry and academic partners globally.

• Data Science
• Computer Graphics
• Data Visualization
• +3 more

Professor Mark Jones is a faculty member at the School of Mathematics and Computer Science , Swansea University , specializing in Computer Science . He serves as a full-time professor and is available for postgraduate supervision. His research interests span Data Science , Computer Graphics , Data Visualization , Time-Series Analysis , Machine Learning , and 3D Rendering . He has contributed to advancements in photon mapping , ray tracing , global illumination , and volume rendering . His recent publications focus on point cloud completion , text-based person re-identification , graph neural networks , and deep learning for time-series data . These works highlight trends in 3D reconstruction , neural network optimization , and medical imaging compression . Professor Jones has secured significant research grants, including EPSRC Grant EP/N028139/1 (2016–2019) and Leverhulme Grant RPG-2013-190 (2013–2015), and has held roles such as Member of the Editorial Board for Computer Graphics Forum (2014–2017). He supervises PhD students in areas like 3D medical image compression , face reenactment , and urban modeling visualization , and has contributed to Computational Foundry , a research facility at Swansea University.

Yunshi LanView profile

Associate Professor

• Knowledge Bases
• Question Answering
• Information Extraction
• +6 more

Yunshi Lan is an Associate Professor at the School of Data Science and Engineering, East China Normal University (ECNU). He holds a Ph.D. from Singapore Management University (SMU) and a Bachelor's degree from Southwest University (SWU). His research focuses on knowledge bases, question answering systems, deep learning, and large language models. He has led multiple grants, including projects from the National Science Foundation of China and the Shanghai Pujiang Talent Program. Lan has published extensively in top venues like ACL, EMNLP, and AAAI, with notable contributions to visual question answering, grammatical error correction, and educational NLP. He serves on program committees for ACL, EMNLP, and AAAI, and has given invited talks on large language models and knowledge-based systems. His teaching includes undergraduate and graduate courses on deep learning, emphasizing practical applications and LLM integration. Lan mentors a vibrant research group, with students contributing to impactful projects like MWPToolkit and FlaCGEC datasets. Education: Ph.D., SMU (201X); B.Sc., SWU (201X). Key grants include 'Knowledge Base Question Answering System in Interactive Environments' (NSFC, 2023-2025) and 'Intelligent Low Carbon Oriented Theories for Cross-Border Electric Power Trading' (NSFC, 2024-2028). Awards include the First Prize in the Financial GraphRAG Competition (2024) and the Shanghai Pujiang Talent Program (2022). His research highlights include benchmarking Visual Chinese GEC, developing TreeEval for LLM evaluation, and creating educational NLP tools like 中介语智能批改 for CFL learning.

André SchlichtingView profile

Professor

• Stochastic Processes
• Partial Differential Equations
• Optimal Transport
• +4 more

André Schlichting is a full professor at the University of Ulm, leading the Institute of Applied Analysis. His research focuses on stochastic processes, partial differential equations (PDEs), and optimal transport, with a particular emphasis on gradient flows, nonlocal dynamics, and mathematical physics. He investigates systems such as stochastic particle models, McKean-Vlasov equations, and metric graphs, often exploring their long-time behavior and discretization techniques. Collaborations include work on diffusive transport distances, porous medium equations, and manifold analysis. His recent contributions address topics like convergence of particle systems to continuous models, gradient flows on graphs with reservoirs, and stationary solutions on high-dimensional spheres. His work bridges theoretical analysis with applications in statistical mechanics and numerical methods. Education and affiliations are not explicitly detailed in the provided texts, but his current role highlights academic leadership in applied analysis. Key collaborations involve researchers such as Daniel Matthes, Eva-Maria Rott, and Jan-Frederik Pietschmann. His research group at the University of Ulm is expanding, focusing on advanced analytical techniques and their implications in interdisciplinary fields.

Seong-Gyu YangView profile

Researcher

• Statistical Physics
• Complex Systems
• Network Theory
• +3 more

Seong-Gyu Yang is a researcher in the Department of Physics at Umeå universitet, Sweden, holding a postdoctoral position within the IceLab research group. His work focuses on applying statistical physics to interdisciplinary problems in biology, ecology, and network theory. He completed his PhD at Sungkyunkwan University (South Korea), followed by postdoctoral research at the Asia Pacific Center for Theoretical Physics and the Korea Institute for Advanced Study. Yang’s current research investigates aging mechanisms through epigenetic clocks, leveraging stochastic models of DNA methylation dynamics in CpG clusters. This work aims to link methylation patterns to biological age prediction. His broader interests include network structures, ecological modeling, and complex systems analysis. He collaborates with Ludvig Lizana on projects like the 2022–2025 initiative studying epigenetic clocks as biomarkers for aging. Notable research trends include interdisciplinary applications of statistical physics to biological systems (e.g., protein-DNA interactions), network theory, and social dynamics. His publications span topics from phase transitions in coupled oscillators to machine learning for power-grid stability. Yang participates in the Ludvig Lizana Lab and IceLab (Interactive Control and Learning Lab), focusing on theoretical and computational approaches to complex systems.

• Engineering Design Education
• Additive Manufacturing
• Systems Engineering
• +4 more

Joshua Summers is a Professor of Mechanical Engineering at the University of Texas at Dallas (UTDallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. His research focuses on engineering design education, additive manufacturing, and systems engineering. Summers has contributed extensively to understanding requirement generation processes, design methodology, and the integration of AI into engineering workflows. He also explores leadership dynamics in engineering teams and the impact of automation on manufacturing strategies. His work bridges theoretical frameworks (e.g., function structure modeling, morphological charts) with practical applications in industry and academia. Key themes include optimizing design processes through data-driven tools, improving educational outcomes in capstone projects, and addressing challenges in adopting agile methodologies for hardware development. Summers has collaborated on case studies involving automotive engineering, automation implementation, and cost estimation for additive manufacturing. Though no formal awards are listed, his prolific publication record (over 50 articles since 2018) reflects sustained academic impact. His articles often employ experimental methods, case studies, and data analysis to advance design theory and pedagogy. Grants and lab affiliations are not explicitly mentioned in the provided texts.