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Markus BuehlerView profile

Professor

• Bioinspired materials
• Generative AI
• Multiscale materials modeling
• +7 more

Markus Buehler is a Professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT). His research focuses on bioinspired materials, generative AI, and multiscale materials modeling. He integrates principles from biology, engineering, and computation to address sustainability and material innovation challenges. Research interests include generative AI applications in materials design, protein mechanics, and nanotechnology. He develops frameworks like MechGPT and MechAgents to solve mechanics problems and accelerate discovery through multi-agent systems. His work also explores bio-inspired composites, such as spider silk and diatom-inspired structures, alongside sustainable solutions for energy storage and construction materials. Dr. Buehler has received the Foresight Institute Feynman Prize for nanotechnology advancements. His collaborative projects span industry and educational outreach, including a study on basalt fibers with MIT and JEOL USA. He emphasizes computationally driven approaches to bridge molecular and macroscopic scales, enhancing the design of adaptive and functional materials. His research teams, including SciAgents and ProtAgents , combine AI with physics-based models to advance protein discovery and materials informatics. These efforts aim to create robust, hybrid-living materials and improve fracture mechanics predictions through deep learning.

Nikos NtarmosView profile

Senior Lecturer

• Distributed Systems
• Big Data Management
• NoSQL Databases
• +3 more

Nikos Ntarmos is a Senior Lecturer at the University of Glasgow's School of Computing Science, with affiliations to the IDEAS research lab. He holds a PhD in Computing Engineering & Informatics from the University of Patras and specializes in distributed systems and large-scale data management. Research focuses on: Distributed data storage and indexing for NoSQL systems Efficient query processing for graph databases Scalable algorithms for big data analytics Recommendation systems and collaborative filtering Publications demonstrate evolving interests from distributed hash tables to contemporary graph processing and recommendation systems. Recent work increasingly addresses practical applications in education technology alongside core database research.

Guang WangView profile

Assistant Professor

• Cyber-physical systems
• Spatiotemporal data mining
• Human-centered computing
• +5 more

Dr. Guang Wang is an Assistant Professor at the Department of Computer Science at Florida State University (FSU). He holds a Ph.D. in Computer Science from Rutgers University and was a Postdoctoral Research Associate at MIT. His research focuses on cyber-physical systems, spatiotemporal data mining, and human-centered AI, addressing societal challenges in mobility, energy, disaster resilience, and healthcare. He leads projects on fairness-aware ride-hailing systems, equitable post-disaster power restoration, and AI-enhanced logistics systems. Education: Ph.D. in Computer Science, Rutgers University Postdoctoral Research, MIT (with Prof. Sandy Pentland) Research Interests: Generative AI for trajectory prediction and logistics Trustworthy machine learning in urban systems Fairness-aware optimization for mobility and energy systems Data-driven decision-making in disaster response Recent Article Trends: Focus on fairness-aware algorithms, spatiotemporal AI, and large-scale graph mining. Recent work includes optimizing post-disaster power restoration, improving ride-hailing fairness, and developing generative models for asynchronous trajectories. Scientific Awards: FSU CS Department Faculty Research Award (2025) National AI Research Resource Pilot Award (2024) CPS Rising Star (2022) Outstanding Paper Award at IEEE RTSS 2021 Advising & Grants: Advising 4 PhD, 2 MS, and 3 undergraduate students in AI and data science Lead PI on NSF-funded projects on sustainable mobility and disaster resilience Recipient of FSU Sustainability & Climate Solutions Grant ($150K) Labs & Collaborations: Directs the EV Simulation Lab and collaborates on projects like the National AI Research Resource. Leads the Data Science for Smart Cities initiative at FSU.

• Information Retrieval
• Machine Learning
• Big Data Stream Processing
• +6 more

Dr. Richard McCreadie is a Senior Lecturer in Computing Science at the University of Glasgow, specializing in Information Retrieval, Machine Learning, and Digital Finance. His research focuses on real-time data processing, financial asset recommendation systems, and crisis informatics. He leads the Information Retrieval Group and co-chairs the TREC Incident Streams initiative, advancing methods for actionable information extraction from social media during emergencies. Education: PhD in News Vertical Search using User-Generated Content (2012). Active collaborations include the FAR-AI platform for investment recommendations and the Event Tracker system for disaster response. Research Interests: His work bridges academia and industry, emphasizing practical applications in fintech, social media analysis, and event detection. Recent trends include leveraging large language models (LLMs) for personalized financial advising and robust vision transformers for crisis image classification. Grants & Projects: Key contributions include the TREC-IS track evaluations, Agile IR experimentation frameworks with Terrier, and the SUPER project for emergency management using social sensors. He has led over 75 publications in top venues like SIGIR and ECIR. Labs/Teams: Part of the Information, Data and Analytics section and collaborates internationally on AI-driven financial systems and disaster informatics platforms.

• Information Retrieval
• Recommender Systems
• Machine Learning
• +6 more

Professor Iadh Ounis is a leading academic in the field of Information Retrieval at the University of Glasgow's School of Computing Science. He heads the 60-strong Information, Data and Analysis (IDA) Section and leads the Terrier Team, known for developing advanced text retrieval techniques. His work focuses on machine learning applications in IR, recommender systems, and big data architectures. Roles: Professor of Information Retrieval, Head of IDA Section, Principal Investigator of Terrier search engine. Education: PhD from University Joseph Fourier (Grenoble, France), MSc from ENSIMAG (Grenoble). Awards: 2022 Tony Kent Strix Award. Research interests include fairness in AI, multi-modal recommendation systems, and large-scale IR architectures. He has published over 200 papers and led major initiatives like TREC Blog/Microblog tracks and NTCIR Math IR task. Active in conference program committees and journal editorial boards. Labs/Teams: Terrier Team, IDA Section. Collaborations include Scottish Informatics & Computer Science Alliance (SICSA) and The Data Lab Innovation Centre.

LI YuchenView profile

Associate Professor

• Artificial Intelligence
• Data Mining
• Graph Mining
• +6 more

Yuchen Li is an Associate Professor at the School of Computing and Information System (SCIS) at Singapore Management University (SMU), where he also holds the Lee Kong Chian Fellowship for Research Excellence. He earned his Ph.D. in Computer Science from the National University of Singapore (NUS) in 2017. His research focuses on social analytics, high-performance graph mining, and fintech applications leveraging large language models (LLMs). He has led significant projects funded by MoE, including 'Next-Gen Competitive Intelligence' and 'CONQUEROR' for concurrent graph query processing. His academic contributions span influential publications in top-tier conferences like SIGMOD, KDD, and ICML, with notable works on graph algorithms, fraud detection, and knowledge graph robustness. He advises Ph.D. students and has supervised over a dozen researchers, including current students Xiao Hanhua, Wang Sha, and Ye Chang. His awards include the Lee Kong Chian Fellowship and the Best Demo Paper Award at CIKM 2021. Li’s research bridges theoretical foundations and practical applications, addressing challenges in graph processing, social media analysis, and fintech. His team develops systems like 'Dupin' for fraud detection and 'ThunderRW' for in-memory graph processing, showcasing expertise in GPU acceleration and scalable algorithms.

• Probabilistic Machine Learning
• Causal AI
• Imprecise Probabilities
• +2 more

Marco Zaffalon is Professor and Scientific Director at IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale), affiliated with the Università della Svizzera italiana's Faculty of Informatics. He leads a 30-member research group on probabilistic machine learning and has published over 150 papers. Education: M.Sc. in Computer Science (Università degli Studi di Milano) Ph.D. in Applied Mathematics (Università degli Studi di Milano) Research spans probabilistic machine learning, causal AI, imprecise probabilities, and quantum computation. His work develops theoretical foundations for uncertainty reasoning and applies them to AI systems. Recent publications focus on causal inference with LLMs, counterfactual computation, and quantum decision models. Articles consistently explore intersections of probability theory, computational methods, and real-world applications like healthcare. Trends include advancing tractability in causal queries and bridging logical frameworks with machine learning. Administrative roles include co-founding Artificialy (as Chief Scientist) and directing IDSIA since 2019. He teaches courses in Causal AI, Uncertain Reasoning, and Probability.

• Internet of Things (IoT)
• Sensor Networks
• Automated Learning
• +6 more

Marimuthu Palaniswami is a Professor in the Department of Electrical and Electronic Engineering at the University of Melbourne, specializing in IoT, sensor networks, and computational intelligence. He leads large-scale research initiatives, including the ARC Research Network ISSNIP and the Centre of Excellence for Networked Decision Systems (CENDS). His work spans infrastructure, smart cities, healthcare, and environmental monitoring, with over 400 publications in top-tier journals and conferences. Education Ph.D., University of Newcastle, Australia Masters (Research), University of Melbourne Masters (Coursework), Indian Institute of Science Bachelor of Engineering (Honours), University of Madras Research Focus His research translates into deployable technologies for societal impact, including smart city projects (e.g., European Union's FP7 Smart Santander), healthcare diagnostics (e.g., CSIRO/Microsoft fetal monitoring tools), and environmental monitoring (Great Barrier Reef initiatives). He emphasizes interdisciplinary collaboration, leveraging IoT and AI for healthcare, transport, and governance. Recent Projects Developing Non-Invasive Diagnostics to Prevent Stillbirth (2019–2025) Hydrogen Storage Using Concrete Composites (2022–2025) Academic Centres of Cyber Security Excellence (2024–2026) Awards & Recognition Fellow of IEEE (2013–2015 Distinguished Lecturer) Knowledge Transfer Excellence Awards (2008, 2008, 2010) Recipient of multiple government and industry grants totaling millions in funding Professional Contributions Co-founder of the European IoT Forum, former co-director of CENDS, and advisor to EU and U.S. projects. Has organized over 10 IEEE conferences and served on NSF and EU panels. Labs & Teams Leads interdisciplinary teams in IoT, healthcare technology, and sensor networks, fostering collaborations with industry partners like Arup and Microsoft Research.

Mark van den BrandView profile

Professor

• Software Engineering
• Digital Twin Engineering
• Model-Driven Engineering
• +4 more

Mark van den Brand is a Full Professor of Software Engineering and Technology at Eindhoven University of Technology (TU/e), where he holds the chair in Software Engineering and serves as Scientific Director of the Digital Twin Lab (part of EAISI). He concurrently holds a Visiting Professorship at Royal Holloway University of London. His academic journey includes a PhD in Computer Science from Radboud University (1992), prior roles as part-time Associate Professor at Vrije Universiteit Amsterdam, and senior research positions at CWI. He also serves as Editor-in-Chief of the Journal on Software Engineering of Autonomous Systems (JSEAS) and editorial board member of Computer Languages, Systems and Structures . His research focuses on software engineering fundamentals, digital twins, model-driven engineering, safety-critical systems, and automotive software. Key contributions include work on domain-specific languages, model consistency, and safety assessment frameworks. He leads the Software Engineering and Technology cluster at TU/e and directs educational initiatives in the Department of Mathematics and Computer Science. Recent publications emphasize digital twin engineering, model management, and automotive systems safety. He collaborates internationally on projects like the C-ITS reference architecture and functional safety for autonomous driving. His work bridges academic research with industrial challenges through tools like SAMOS and COGENT, addressing scalability and consistency in large-scale systems. Current educational responsibilities include courses on programming, digital systems, and data science. He actively engages with industry through the TU/e innovation ecosystem, focusing on impactful research solutions for modern software engineering problems.

Barry DevereuxView profile

Senior Lecturer

• Machine Learning
• Natural Language Processing
• Cognitive Science
• +5 more

Dr. Barry Devereux is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, part of the Faculty of Engineering and Physical Sciences. His research focuses on interdisciplinary applications of machine learning, natural language processing (NLP), cognitive science, and healthcare analytics. He leads projects like the AIDE_NICYBER2025 initiative, exploring cybersecurity and AI integration. Dr. Devereux teaches the CSC2062 Artificial Intelligence and Machine Learning course and advises four PhD students, including those researching semantic vectors, deep learning in healthcare, and neurosciences ontology. His research interests include model interpretation in NLP, lexical semantics, and applying AI to neuroimaging and bioinformatics. Notable achievements include the British Council/IAESTE Employer Award (2018) and contributions to datasets like SynFinTabs and LAB-KG. He collaborates on projects funded by NIO New Deal, PWC, and Liberty IT, emphasizing AI's role in solving complex societal challenges. Dr. Devereux's work bridges computational models with human cognition, leveraging large-scale data analytics to address issues in healthcare, cybersecurity, and environmental science. His lab, affiliated with the Institute of Electronics, Communications & Information Technology (ECIT), fosters innovation in AI-driven solutions.

Sourav ChakrabortyView profile

Professor

• Theoretical Computer Science
• Boolean Functions
• Quantum Complexity
• +4 more

Sourav Chakraborty is a Professor in the Advanced Computing and Microelectronics Unit (ACMU) of the Computer and Communication Sciences Division at the Indian Statistical Institute (ISI), Kolkata, India. He joined ISI in July 2018 after serving as faculty at Chennai Mathematical Institute from 2010-2018. Previously, he held postdoctoral positions at Centrum Wiskunde & Informatica (CWI) in Amsterdam and Technion in Israel. Education: Ph.D. in Computer Science, University of Chicago (2008) M.S. in Computer Science, University of Chicago (2005) B.Sc. in Mathematics, Chennai Mathematical Institute (2003) Research Focus: Professor Chakraborty specializes in Theoretical Computer Science with emphasis on classical and quantum complexity of Boolean functions, including sensitivity analysis, property testing, and quantum database search. His work extends to graph algorithms, electronic commerce mechanisms, and coding theory. His research explores fundamental questions in computational complexity through innovative mathematical frameworks. Publication Trends: Recent work demonstrates a strong focus on property testing, sampling algorithms, and complexity theory, with significant contributions to streaming algorithms, Boolean function analysis, and quantum query complexity. His publications frequently appear in top theoretical computer science venues and exhibit consistent innovation in algorithm design and complexity boundaries. Awards & Honors: Praise from Donald E. Knuth for streaming algorithms research Inclusion in Oded Goldreich's 'my choices' list for Conditional Sampling and Huge-Object Model work Chakraborty's function named in his honor for Sensitivity Conjecture contributions Academic Service: Teaches courses in discrete mathematics and theoretical computer science, with detailed course materials available through institutional pages. Organized workshops including the 2020 Workshop on Sensitivity and Query Complexity at ISI.

• Knowledge Representation
• Mathematical Knowledge Management
• Natural Language Processing
• +5 more

Prof. Dr. Michael Kohlhase is a Professor in the Computer Science department at FAU Erlangen-Nürnberg and an Adjunct Associate Professor at Carnegie Mellon University . His work spans Computer Science and Formal Methods , focusing on Mathematical Knowledge Management (MKM), Knowledge Representation , and Natural Language Processing (NLP) in STEM domains. He leads the KWARC (Knowledge Adaptation and Reasoning for Content) research group, driving projects like OMDoc , sTeX , and MathHub . His research emphasizes modular logical foundations for mathematical knowledge and large-scale document corpora analysis, with applications in computer-supported education and semantic document systems . Collaborations include extended visits to institutions such as SRI International , University of Amsterdam , and University of Edinburgh . Key projects include developing scalable module systems for formal mathematics, theory graph models for knowledge integration, and mathematical search engines like MathWebSearch . His work bridges formal logic with practical software systems , ensuring interoperability across proof assistants , computer algebra systems , and educational tools .

Dirk FahlandView profile

Associate Professor

• Process Mining
• Data Engineering
• Complex Systems Analysis
• +3 more

Dirk Fahland is an Associate Professor in Process Analytics on Multi-Dimensional Event Data at the Analytics for Information Systems group, Eindhoven University of Technology (TU/e), School of Mathematics and Computer Science. He combines formal methods with data-driven approaches to analyze complex distributed systems through event data. His current research focuses on process mining, data engineering, and multi-dimensional analysis of business processes. Academic Background: PhD from Humboldt-Universität zu Berlin and TU/e under Profs Wolfgang Reisig and Wil van der Aalst Post-Doc at TU/e on EU-funded ACSI project Research stays at Weizmann Institute, HPI Potsdam, and National University of Singapore Appointments: Assistant Professor (2013), Tenure (2016), Associate Professor (2019) Research Interests: Dirk's work centers on analyzing complex systems through event data by developing techniques for large-scale preprocessing, model synthesis, and multi-angle behavioral analysis. He explores object-centric process mining, anomaly detection, and knowledge graph applications for auditing. His research emphasizes balancing model accuracy with simplicity and integrating domain knowledge for explainable process analysis. Scientific Awards: Best Paper Award BIS 2011 Best Paper Award BPM 2011 Best Paper Award ICPM 2020 Best Paper Award ICPM 2021 Best Reviewer Award ICPM 2019 Educational Contributions: He manages the "Data Science in Engineering" Master's program, leads the "Data Challenge" course series at JADS, and teaches advanced process mining courses. He also contributes to BPMN visualization and performance monitoring education.

• History of Chinese Medicine
• Daoism and Religion
• Digital Humanities
• +5 more

Michael Stanley-Baker (徐源) is an Associate Professor in the History Department at Nanyang Technological University, specializing in the history of Chinese medicine and religion, particularly Daoism. He also serves as an associate of the UCL China Centre for Health and Humanities and as treasurer on the council of the International Association for the Study of Traditional Asian Medicines. Stanley-Baker received his PhD from University College London and holds a clinical degree in Chinese medicine from Ruseto College, Boulder CO. His academic journey includes work and study at Indiana University, Bloomington; Academia Sinica, Taipei; and the Needham Institute, Cambridge. He has conducted extensive fieldwork with healers in Taiwan and China, bridging scholarly and clinical perspectives. His research focuses on the intersection of religion, medicine, and science in Chinese history, with particular emphasis on Daoist healing practices and the circulation of medical knowledge. He employs innovative digital humanities methods to analyze historical medical texts, creating tools like the Polyglot Asian Medicine Knowledge Graph System and the DAOBUDMED6D corpus for studying Six Dynasties medical, Daoist, and Buddhist texts. His recent publications reveal a growing emphasis on transnational medical exchanges, the historical geography of drugs, and the political dimensions of medical knowledge. Stanley-Baker's work challenges conventional boundaries between 'medicine' and 'religion,' arguing for more nuanced understandings of how healing knowledge was organized and transmitted in pre-modern contexts. International Association for the Study of Traditional Asian Medicines Council Member Lead developer of Polyglot Asian Medicine digital project Co-editor of the Routledge Handbook of Chinese Medicine Stanley-Baker directs the 'Drugs across Asia' digital humanities project, which creates new tools for analyzing pharmacological knowledge in Asian historical texts. His work combines traditional philological approaches with cutting-edge digital methods, facilitating large-scale analysis of drug references across religious and medical canons. His research has important implications for understanding how medical knowledge circulated across cultural and political boundaries in pre-modern Asia.

Tingting MuView profile

Senior Lecturer

• Machine Learning
• Computer Vision
• Text Mining
• +3 more

Tingting Mu is a Senior Lecturer in Machine Learning at the University of Manchester's Department of Computer Science. She holds a B.Eng. from the University of Science and Technology of China (2004) and a Ph.D. from the University of Liverpool (2008). Prior to joining Manchester in 2016, she was a lecturer at Liverpool’s Department of Electrical Engineering and Electronics. Her research focuses on advanced mathematical modeling and large-scale optimization in machine learning and data analytics. Key areas include developing algorithms for human intelligence simulation, analyzing complex data (e.g., text, images, signals), and applications in text mining and scientific data analytics. Teaching includes courses like COMP24112 Machine Learning and COMP34111 Natural Language Systems. Her work contributes to UN Sustainable Development Goals related to Digital Futures and Sustainable Futures. Notable projects include MCAIF (Centre for AI Fundamentals) as a researcher and a Knowledge Exchange Partnership with Fusion21 Ltd. Over 60 research outputs span topics like neural networks, adversarial learning, and ontology-based systems. Current projects emphasize AI fundamentals, surrogate models for engineering systems, and interdisciplinary applications. Her lab explores techniques to enhance model interpretability, scalability, and robustness across vision, NLP, and multimodal domains.