Professor Boris Konev is a faculty member at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He holds the academic rank of Professor in Computer Science. Description Logics Ontologies Automated Reasoning Temporal Logic Formal Verification Encrypted Database Applications His recent research focuses on temporal queries mediated by ontologies, knowledge evaluation agents using large language models, and semantic modularity in description logics. Key sub-fields include LLM applications, encrypted databases, and formal verification techniques. He has contributed to software development projects and industry partnerships, including design of equine simulators and online services with Racewood Limited. Current teaching includes the Foundations of Computer Science module (COMP109). Professional roles include guest editorships for AI Communications and program committee membership for the European Conference on Logics for Artificial Intelligence (JELIA).
Professor Saman Amarasinghe is a faculty member in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), where he leads the Commit compiler research group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on programming languages and compilers that maximize application performance on modern computing platforms, with a particular emphasis on high-performance domain-specific languages. Professor Amarasinghe received his bachelor's degree in electrical engineering and computer science from Cornell University in 1988, followed by master's and PhD degrees in electrical engineering from Stanford University in 1990 and 1997, respectively. He joined the MIT faculty as an assistant professor in 1997 and has since become a world leader in his field. Professor Amarasinghe's research interests span programming languages, compiler design, and high-performance computing, with a particular focus on domain-specific languages. His group has developed numerous influential languages and compilers including Halide, TACO, Simit, StreamIt, StreamJIT, PetaBricks, MILK, Cimple, and GraphIt, which deliver unprecedented performance for application domains such as image processing, stream computations, and graph analytics. He has also pioneered the application of machine learning for compiler optimizations, from Meta optimization in 2003 to the OpenTuner autotuner framework. Professor Amarasinghe's publication history reveals a consistent research trajectory toward creating specialized language and compiler solutions that address performance challenges in specific domains while hiding complexity from application developers. His recent work focuses heavily on sparse computing, tensor algebra, graph processing, and the integration of machine learning techniques into compiler technology, demonstrating his ability to identify and address emerging computational challenges. ACM Fellow (2019) As an educator, Professor Amarasinghe has developed the popular Performance Engineering of Software Systems (6.172) class with Professor Charles Leiserson and created innovative project-based courses including the Open Source Software Project Lab, the Open Source Entrepreneurship Lab, and the Bring Your Own Software Project Lab. He also serves as the faculty director of MIT Global Startup Labs, which has helped create more than 20 startups across 17 countries. His research has translated into practical applications through startups like Determina, Inc. (acquired by VMware), demonstrating the real-world impact of his academic work. Professor Amarasinghe co-led the Raw architecture project with Professor Anant Agarwal, which did pioneering work on scalable multicores. His entrepreneurial activities include founding Determina, Inc. based on computer security research from his MIT lab and co-founding Lanka Internet Services, Ltd., the first Internet Service Provider in Sri Lanka, showcasing his ability to bridge academic research with commercial applications.
Gaurav Rattan is an Assistant Professor in the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), where he joined in May 2024. His research focuses on the mathematical foundations of machine learning on graphs and discrete structures, with particular emphasis on theoretical aspects of graph neural networks. University of Twente, Department of Applied Mathematics (May 2024-present) TU Darmstadt, Postdoctoral Researcher in Pascal Schweitzer's group RWTH Aachen, DFG Eigene Stelle Researcher in Martin Grohe's group Dr. Rattan completed his PhD at IMSc Chennai under V. Arvind and earned his B. Tech. from IIT Bombay, establishing a strong foundation in theoretical computer science and mathematics. His research spans graph theory, algorithms, and machine learning on graphs, with specific expertise in graph isomorphism, graph homomorphisms, and the theoretical underpinnings of graph neural networks. He applies mathematical techniques from logic and algebra to develop theory-driven approaches for graph learning systems, with practical applications in optimization, bioinformatics, and databases. Dr. Rattan's publication record reveals a consistent focus on the intersection of theoretical computer science and machine learning. His recent work explores Weisfeiler-Leman algorithms, symmetry breaking techniques, and parameterized complexity of graph problems, demonstrating how classical graph algorithms connect with modern graph learning methodologies. His research provides crucial theoretical foundations for understanding the capabilities and limitations of graph neural networks. Active in the academic community, Dr. Rattan regularly presents at conferences including the Netherlands Mathematical Congress, SIGAlgo, LOGAMS, and specialized workshops on graph learning. Recent presentations include "From Graph Homomorphisms Densities to Graph Learning" at the Graph Learning Workshop at NITMB Chicago and "Color Refinement: One Algorithm, Many Facets" at SIGAlgo 2024.
Dr. He Xu is a Visiting Professor in the Department of Engineering Science at the University of Oxford, with a focus on Biomaterials , Tissue Engineering , and Biomechanics . She previously worked at Shanghai Normal University, rising from lecturer (2014) to associate professor (2018) and full professor (2024). Education: BEng in Materials Science and Engineering (China University of Geosciences), DPhil in Biomedical Engineering (Shanghai Jiao Tong University, 2014) Her research explores: Biomaterials : Smart hydrogels, piezoelectric systems, and nanogenerators for therapeutic applications. Tissue Engineering : Innovations in intervertebral disc and tendon regeneration. Drug Delivery : Targeted activation, nitric oxide therapy, and bioelectronic systems. Her publications span 2021–2025 , combining Biomaterials , Nanotechnology , and Medical Imaging to address challenges in Diabetes , Cancer , and Cardiovascular Disease . Key collaborations include the 3DMed Interreg 2 Seas Consortium and work on rapid Covid-19 testing .
Jan Majkutewicz serves as an Assistant lecturer at the Department of Computer Systems Architecture within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His academic position focuses on advancing computational methodologies in natural language processing and knowledge representation systems. His research spans Natural Language Processing, Machine Learning, and Knowledge Representation with specific expertise in Large Language Models and graph-based embeddings. Current work investigates human-AI alignment through historical text analysis and neural representations of semantic hierarchies. Methodological innovations include cost-effective preference dataset generation and category graph embedding techniques that preserve structural relationships in knowledge bases. Publication analysis reveals consistent specialization in NLP and knowledge representation. The 2025 EditPrefs framework leverages historical text edits to address dataset scarcity in preference learning, while the 2021 Wikipedia category embedding research demonstrates practical applications for taxonomy navigation and parent category prediction through neural graph representations.
Jürgen Pfeffer is a Professor of Computational Social Science & Big Data at the Technical University of Munich's School of Social Sciences and Technology, with an additional appointment as Adjunct Professor at Carnegie Mellon University's Institute for Software Research. His interdisciplinary work bridges computer science and social science with a focus on analyzing large-scale socio-technical systems. His research expertise spans computational social science, network analysis, and big data methodologies. Pfeffer's work examines methodological, algorithmic, and theoretical challenges in analyzing dynamic social systems, with current projects focusing on modeling and detecting negative dynamics from social media, particularly online firestorms and hate speech against politically active women. His research combines network science approaches with computational methods to understand complex social phenomena. Pfeffer's publication record demonstrates significant contributions to the field since his 2010 doctorate, with high-impact papers in journals like Science and EPJ Data Science. His work on social media analysis, particularly the influential 2014 Science paper 'Social Media for Large Studies of Behavior' co-authored with Derek Ruths, has shaped methodological approaches in the field. His research shows consistent evolution from foundational network analysis to contemporary applications in political discourse, hate speech detection, and multi-layer network analysis. Hennig, M., Brandes, U., Pfeffer, J., & Mergel, I. (2012). Studying Social Networks. A Guide to Empirical Research Ruths, D., & Pfeffer, J. (2014). Social Media for Large Studies of Behavior Pfeffer, J., Morstatter, F., & Mayer, K. (2018). Tampering with Twitter's Sample API As an advisor and collaborator, Pfeffer has worked extensively with researchers including Raji Ghawi, Mirco Schönfeld, Momin Malik, and Kathleen Carley. His work demonstrates strong connections between theoretical network science and practical applications in social media analysis. His current research continues to address pressing issues in online discourse, with recent work focusing on hate speech classification, lexical change in negative word-of-mouth, and polarization dynamics in social media environments. Pfeffer leads the Pfeffer Lab, which focuses on developing methodological approaches for analyzing complex social systems through computational methods. His work has implications for understanding political legitimacy, social influence, and community dynamics in both online and offline contexts.
Yuhong Nan is an Associate Professor in the School of Software Engineering at Sun Yat-sen University, China, specializing in software security and privacy leakage analysis for emerging platforms including IoT, mobile systems, and blockchain. Previously a Post-doctoral Research Associate at Purdue University under Prof. Dongyan Xu, she builds practical security tools to detect and mitigate vulnerabilities in real-world systems. Dr. Nan earned her PhD from Fudan University in 2018 supervised by Prof. Min Yang. Her academic journey spans rigorous research in security engineering with emphasis on empirical validation and tool development for complex platform ecosystems. Her research program focuses on uncovering systemic security flaws through innovative analysis techniques. Key contributions include vulnerability detection in smart contracts (e.g., state dependencies, reentrancy), privacy leakage analysis in mobile/IoT ecosystems, and countermeasures against deceptive UI patterns. She employs hybrid approaches combining static/dynamic analysis, machine learning, and large-scale empirical studies to develop deployable security solutions. Analysis of her 15 most recent publications (2023-2025) reveals dominant themes in blockchain security (60%), particularly smart contract/DApp vulnerabilities, with significant work in mobile privacy (30%) and cross-platform threats (10%). Her methodology consistently leverages fine-grained static analysis, semantic enrichment, and feedback-driven fuzzing, yielding tools like SmartAxe and Midas that have influenced industry practices. Dr. Nan actively mentors graduate researchers with 17 advisees including Tencent-employed graduates, and serves as a trusted reviewer for premier journals (IEEE TDSC, TMC, TOPS) and conference committees (ASIACCS, ICICS). Her leadership in security communities bridges academic research with practical defense mechanisms. At Sun Yat-sen University, she directs a high-output research group that collaborates with industry partners to address evolving threats in decentralized systems, maintaining her position among top publishing authors in USENIX Security, CCS, and NDSS venues through rigorous technical innovation.
Jingling Xue is a Scientia Professor at the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney, Australia. As an IEEE Fellow of the Computer Society, he leads the Programming Languages and Compilers research group, focusing on practical applications of compiler optimization and program analysis techniques. His work bridges theoretical foundations with real-world software systems, particularly in developing open-source tools for large-scale program analysis. Professor Xue received his B.Eng and M.Eng degrees from Tsinghua University in 1984 and 1987, respectively, followed by a PhD from the University of Edinburgh in 1992. His academic journey has established him as a leading figure in programming languages and compiler technology. Xue's research spans programming languages, compiler technology, and program analysis with emphasis on practical relevance. His current projects include compiler techniques for improving parallelism and locality, pointer/alias analysis for million-line-scale programs, and static/dynamic analysis for detecting bugs and security vulnerabilities in real-world applications like web browsers and Android apps. His group actively develops open-source tools to support scientific replicability and reproducibility in these areas. His recent publications demonstrate a strong focus on applying program analysis techniques to modern challenges including AI compilers, homomorphic encryption, security vulnerability detection, and graph processing systems. The work shows evolution from traditional compiler optimization to addressing emerging domains like privacy-preserving computation and deep learning systems while maintaining rigorous theoretical foundations. Scientific Awards: Best Paper Award at CGO'13 Best Paper Award at CGO'16 Distinguished Paper Award at ECOOP'16 Distinguished Paper Award at ICSE'18 Distinguished Paper Award at ISSTA'19 Distinguished Paper Award at ASE'19 Distinguished Artifact Award at ISSTA'23 Best Artifact Award at FSE'23 Distinguished Paper Award at ASE'23 Test-of-Time Award at CGO'21 Professor Xue has successfully supervised 30 PhD students to completion, many of whom now work as professors or researchers in academia and industry. He has served as Program Chair for major conferences including LCTES'13, CC'18, CGO'20, and General Chair for LCTES'20. His group currently focuses on memory safety in Rust, smart contract analysis, AI compilers, compilation for privacy-preserving computation, and adversarial attacks in deep learning. The Programming Languages and Compilers group maintains strong connections with industry partners, translating theoretical advances into practical tools for real-world software development challenges. Their work on pointer analysis, memory safety, and compiler optimizations continues to influence both academic research and industrial practice.
Sungsoo Ahn is an Assistant Professor at the Graduate School of AI, KAIST, where he leads the Structured and Probabilistic Machine Learning (SPML) Lab. His research focuses on developing machine learning algorithms for molecular science, particularly in drug discovery, material design, and generative modeling. He directs a team of 13 researchers (including 2 post-docs and 11 students) and maintains collaborations with institutions like Mila and industry partners. His core research integrates probabilistic machine learning , generative models , and AI for science , with applications spanning molecular dynamics simulation, language model reasoning, combinatorial optimization, and graph neural networks. Key methodologies include flow matching, diffusion models, GFlowNets, and equivariant neural networks applied to chemical and biological domains. Recent publications (2023–2025) demonstrate strong emphases on: (1) Molecular generation/optimization for drug design, (2) Enhancing reliability and reasoning in large language models, (3) Graph-based machine learning for scientific discovery, and (4) Efficient training paradigms for generative samplers. These appear predominantly in NeurIPS, ICML, ICLR, and ACL. He advises multiple PhD/master's students and post-doctoral researchers in the SPML Lab. Current research directions include torsion-aware molecular generation, causal AI safety, neural operators for quantum chemistry, and multi-agent systems for molecular optimization.
Giovanni Colavizza holds a dual academic appointment as Professor in the Department of Communication at the University of Copenhagen and Associate Professor at the University of Bologna (since November 2023). His research bridges information science, digital humanities, and open science practices. His primary research areas include: Quantitative analysis of open research practices (data/code/preprint sharing) and citation impact Wikipedia's role in scientific knowledge dissemination and news source reliability assessment Digital tools for humanities including knowledge graphs and NLP applications for historical archives Recent publications (2024-2025) reveal strong interdisciplinary trends, combining scientometrics with natural language processing to analyze scientific communication patterns and colonial archival materials. His work appears in leading venues including PLoS ONE, Scientometrics, and IEEE Journal of Biomedical and Health Informatics, demonstrating methodological innovation across information science and humanities domains.
Gagan Agrawal is the UGA Foundation Professorship in Computing and a Professor at the School of Computing, University of Georgia. He serves as Director of the School and is affiliated with the Franklin College of Arts & Sciences. Agrawal holds a PhD and MS in Computer Science from the University of Maryland (1994-1996). His research focuses on high-performance computing, parallel algorithms, compiler optimization for deep learning, GPU acceleration, and interdisciplinary applications in health informatics. Notable contributions include frameworks like ForensiBlock (blockchain for data forensics) and DELITE (tensorized instruction compilation). Agrawal has secured over $2.5M in NSF grants for projects addressing extreme-scale computing challenges. Recent grants include: SHF: Small: Memory Hierarchy Optimizations Meet Transformers (MITTEN) ($600K, 2024-2027) DELITE compilation system for deep learning models ($600K, 2023-2026) Publications span parallel computing methodologies, cybersecurity frameworks, and health outcomes analysis. His work on social determinants of health in cancer survival has been systematically reviewed in top-tier medical journals. Agrawal leads UGA's computing initiatives, emphasizing interdisciplinary research and student mentorship in HPC and AI domains.
Michalis Mountantonakis is a Postdoctoral Researcher at FORTH and Laboratory Teaching Staff in the Department of Computer Science at the University of Crete, Greece. He holds a PhD (2020), MSc (2016), and BSc (2014) in Computer Science from the University of Crete, all with top grades. His research focuses on Large-Scale Semantic Data Integration, Linked Open Data, and Semantic Web technologies, with over 45 publications in top venues like ACM VLDB, ISWC, and ECML. He has been awarded the prestigious SWSA Distinguished Dissertation Award (2020) and the Maria Michael Manasaki Fellowship (2020). His work includes tools like LODsyndesis and LODChain, addressing challenges in knowledge graph connectivity and validation of AI-generated content. Education: PhD in Computer Science (2016-2020), University of Crete (Excellent GPA 9.74/10) MSc in Computer Science (2014-2016), University of Crete (Excellent GPA 9.87/10) BSc in Computer Science (2010-2014), University of Crete (2nd in class with GPA 8.42/10) Research Interests: His work bridges semantic web technologies with modern AI challenges, emphasizing large-scale data integration, knowledge graph applications, and validation frameworks. He has contributed to cultural heritage informatics, machine learning-augmented semantic systems, and cross-lingual NLP solutions. Recent trends include leveraging LLMs for query generation and semantic enrichment while ensuring factual accuracy through knowledge graph-driven validation. Key Achievements: Developed LODsyndesis, a global-scale semantic integration service Pioneered real-time validation of ChatGPT responses using RDF knowledge graphs Won Best Paper Award (ISWC 2022) for entity enrichment techniques Recipient of Stelios Orphanoudakis Undergraduate Fellowship (2013-2014) Participated in Roche Continents 2019 (top 100 European science students) Grants & Labs: His research has been supported by GSRT/HFRI. He collaborates with FORTH-ICS and leads projects in EU-funded initiatives like iMarine and BlueBridge. Current work focuses on governance models for ontologies, interoperable thesaurus creation (e.g., FoodEx2), and semantic analytics for cultural heritage datasets.
Xiaoli Fern is an Associate Professor in the School of Electrical Engineering and Computer Science at Oregon State University. She holds a Ph.D. in Computer Engineering from Purdue University (2005) and dual degrees (B.S. and M.S.) in Automation and Computer Science from Shanghai Jiao Tong University (2000). Her research focuses on applied machine learning , graph learning , and explainability in AI systems , with applications in microbiome analysis , ecological monitoring , and human-computer interaction . Research Expertise: Unsupervised learning, clustering, correlation analysis, outlier detection, and scientific data mining. Collaborations: Active involvement in the IGERT Ecosystem Informatics program and interdisciplinary projects with ecologists, roboticists, and biologists. Awards: 2011 NSF CAREER Award for early-career excellence in research. Her recent work includes applying deep learning to microbiome data and developing interactive systems that bridge theory with real-world applications in biology and materials science. She mentors students across all academic levels and emphasizes the importance of collaborative, real-world problem-solving in her research lab.
Dr. Vladimir Vlassov is a full Professor in Computer Systems at the Division of Software and Computer Systems (SCS) , Department of Computer Science (CS) , School of Electrical Engineering and Computer Science (EECS) , KTH Royal Institute of Technology , Stockholm, Sweden. He leads the AVA project in ALEC2, an AI-powered system for mental health care. He is a member of the Distributed Computing research group (DC@KTH) . Education & Roles: Holds a PhD and is a member of ACM and IEEE. Previously visited MIT (1998) and UMass Amherst (2004). Teaches courses on Data Mining , Distributed Systems , and Concurrent Programming . Research Interests: Focus on scalable AI, Cloud computing, distributed systems, and NLP for mental health. Projects include ExtremeEarth (Copernicus data analytics) and EMJD-DC (distributed computing PhD program). Grants & Projects: Principal Investigator in ALEC2 (adaptive mental health care) and ExtremeEarth (EU H2020). Led EU projects like ENCORE (manycore systems) and PaPP (embedded systems). Labs & Teams: Directs the Distributed Computing group, contributing to Hopsworks (machine learning feature store) and Maggy (hyperparameter optimization).
Xiuyuan Cheng is an Associate Professor of Mathematics at Duke University, affiliated with the Trinity College of Arts & Sciences. Her expertise lies in applied analysis, focusing on developing theoretical and computational techniques for high-dimensional data analysis, signal processing, and machine learning. She holds a Ph.D. from Princeton University (2013). Her research emphasizes graph-based methods, kernel techniques, and deep learning applications in manifold data analysis. Notable contributions include work on graph Laplacian convergence, optimal transport for single-cell data, and rotation-equivariant neural networks. She has received grants from the National Science Foundation, including a CAREER award (2023–2028) for learning graph diffusion from high-dimensional data. Recent publications explore topics such as bi-stochastic graph normalization, neural tangent kernels, and adversarial defense using basis transformations. Teaching includes advanced courses like Measure and Integration and High-Dimensional Data Analysis. She collaborates extensively in interdisciplinary projects, integrating mathematical theory with computational tools for real-world applications.
