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Suresh JagannathanView profile

Professor

• Programming Language Semantics
• Formal Verification
• Type Theory
• +7 more

Professor Suresh Jagannathan is a leading researcher in programming languages and formal verification. His work focuses on semantics of high-level languages, type theory, program analysis, and compiler design, with a particular emphasis on concurrent and distributed systems. He explores formal methods for certified compilation, memory model consistency, and verification techniques enabling safe program optimizations. Core research areas: programming language semantics, formal verification, type theory, compiler design Key applications: concurrent/distributed systems, certified compilation, safe neural network verification Recent publications highlight his contributions to automated verification of concurrent data structures, type-guided repair of input generators, and differentiable logic specifications for AI planning systems. His work combines theoretical rigor with practical implementations like ECOOP best papers and tool developments. Professor Jagannathan has received multiple scientific awards (not specified in available data) and maintains active collaborations with researchers in formal methods and systems verification communities.

Jesper Bláfoss IngvardsonView profile

Associate Professor

• Transportation Systems Modelling
• Transportation Science
• Public Transport
• +9 more

Jesper Bláfoss Ingvardson is an Associate Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), specializing in Transportation Systems Modelling and Transportation Science. His work contributes to sustainable urban mobility and public transport planning. Education: M.Sc. in Transport & Logistics, DTU (2009–2012) B.Sc. in Civil Engineering, DTU (2006–2009) His research focuses on travel behavior, public transport route choice, transit-oriented development (TOD), and the use of smart card data for behavioral modeling. He investigates how urban design, perceived safety, and station characteristics influence mode choice and ridership. His work integrates psychology, data science, and transport engineering to improve public transport attractiveness. The 15 most recent publications reflect a strong trend in using big data—especially smart card data—to model passenger behavior, reduce estimation bias, and evaluate policy impacts in cities worldwide, including Santiago, Addis Ababa, and Copenhagen. The research spans behavioral modeling, sustainable transport adoption, and emotional responses in urban mobility. Scientific Awards: P.H. Bendtsens Traffic Research Award (2019) RA's Research Award (2018) Jesper actively supervises PhD students and leads research projects such as 'Network Optimisation for Connected, Cooperative and Automated Mobility' and 'Behavioural Modelling for Public Transport.' He has been the principal investigator of the 'BELMOPAN' project, focusing on big data sources for passenger behavior modeling. His grants and projects often involve interdisciplinary collaboration with institutions and urban planners. He is involved in multiple research networks and has presented at numerous international conferences, contributing to the academic discourse on sustainable urban transport and behavioral modeling.

• Computer Science
• Telecommunications Engineering
• Mobile and Wireless Networks
• +3 more

Dr Shahedur Rahman is a Senior Lecturer in Computer Science at Middlesex University , with extensive contributions to telecommunications engineering, image processing, and bioinformatics. His research focuses on wireless network optimization, perceptual distortion metrics for video coding, and molecular biology database integration. Research Interests : Interference management in LTE/5G networks Perceptual quality assessment in multimedia systems Computer vision for accessibility and mobility aids Integration of biological and medical databases Wavelet-based image compression Genomic data analysis Selected Scientific Publications highlight trends in: Dynamic channel allocation techniques for LTE networks SSIM/SATD hybrid distortion metrics Gene mutation data modeling Image processing for visual impairment assistance Database interoperability in medical systems Optimization algorithms for hardware systems

Willard RafnssonView profile

Associate Professor

• Language-Based Security
• Programming-Language Theory
• Formal Methods
• +9 more

Willard Rafnsson is an Associate Professor in the Department of Computer Science at the IT University of Copenhagen (ITU), where he conducts research in language-based security, programming-language theory, and formal methods. He is a member of the Center for Information Security and Trust (CISAT), the Programming, Logic and Semantics (PLS) group, and the Software Quality Research (SQUARE) group. He has held visiting and postdoctoral positions at Carnegie Mellon University and the Max Planck Institute for Software Systems. His research focuses on enabling developers to build trustworthy software through program analysis, transformation, and information-flow control. He has led and contributed to multiple funded projects in cybersecurity, including static analysis for JavaScript, privacy risk modeling, and secure software education. His recent publications span top venues in security and programming languages, demonstrating a consistent focus on formal techniques for enforcing security policies and quantifying privacy risks. Willard Rafnsson has served on program committees for IEEE SecDev, NordSec, FORTE, POST, and FCS, and regularly peer-reviews for CCS, S&P, CSF, NDSS, and other leading journals and conferences. He teaches courses in operating systems, program verification, and information security, and has developed educational materials for public cybersecurity awareness. PhD: Chalmers University, supervised by Andrei Sabelfeld Postdoc: Max Planck Institute for Software Systems (Deepak Garg), Carnegie Mellon University (Limin Jia, Lujo Bauer) Visiting Researcher: Carnegie Mellon University (Stephanie Balzer, 2022) He actively supervises student projects in areas such as binary analysis, vulnerability fixing, formal verification, and privacy risk visualization, many of which have led to publications. He is also involved in public outreach, having participated in media interviews and educational videos on cybersecurity topics.

Monica RiccardoView profile

Researcher

• Computer Graphics
• Robotics
• Artificial Intelligence
• +7 more

Monica Riccardo is a Researcher at the Department of Engineering and Architecture, University of Parma, where she actively contributes to research and teaching in computer graphics, robotics, and intelligent systems. She teaches core courses such as Computer Graphics and Object Oriented Programming in undergraduate programs including Computer, Electronic and Communications Engineering. Her research interests span Computer Graphics , Robotics , Artificial Intelligence , Machine Learning , Probabilistic Modeling , and Human-Computer Interaction . Her work integrates deep learning, sensor fusion, and motion planning for applications in virtual reality, soft robotics, and autonomous systems. The recent publications highlight a strong trend in intelligent robotic systems, particularly in probabilistic occupancy prediction, hybrid tracking, in-hand manipulation, and integrated task-motion planning. Her research bridges theoretical AI methods with real-world robotic applications, especially in perception, planning, and human-robot collaboration. Scientific Awards No awards mentioned in the provided text. Advising and Grants No formal students or advisees are listed. No grants or funding sources are mentioned. Labs and Teams While specific lab affiliations are not stated, her collaborative publications with researchers such as Aleotti J., Lodi Rizzini D., and Caselli Stefano suggest active participation in a robotics and intelligent systems research group at the University of Parma.

• Natural Language Generation
• Computational Linguistics
• Referring Expression Generation
• +4 more

Kees van Deemter is a Professor in the Department of Computing Science at the University of Aberdeen's School of Natural and Computing Sciences. With a research career spanning over three decades, he has established himself as a leading figure in Natural Language Generation and Computational Linguistics. His work bridges theoretical linguistics with practical NLP applications, with particular focus on referring expression generation, multilingual processing, and evaluation methodologies. Van Deemter's research interests center on Natural Language Generation, with special emphasis on Referring Expression Generation (REG), computational modeling of linguistic phenomena across languages, and the development of robust evaluation frameworks for NLP systems. His work spans theoretical foundations to practical applications, including logic-to-text generation, cross-linguistic studies comparing East Asian and European languages, and addressing challenges in hallucination and reproducibility in NLP. His recent publications demonstrate a consistent focus on improving evaluation methodologies in NLP, with particular attention to task-based assessments rather than purely ratings-based approaches. He has made significant contributions to understanding how linguistic features influence reference production across languages, with extensive work on Chinese and Mandarin processing. His research group has developed several important corpora including QTUNA for quantifier studies and Mtuna for Mandarin referring expressions. Co-authored the influential survey 'Computational Generation of Referring Expressions: A Survey' (2012) Developed the Mtuna corpus for Mandarin referring expressions Contributed foundational work on the incremental algorithm for referring expression generation Pioneered research on vague language generation from a game-theoretic perspective Investigated cross-linguistic differences in reference production between English and East Asian languages Van Deemter has supervised numerous PhD students who have become active researchers in NLP, including Guanyi Chen, Fahime Same, and Albert Gatt. His collaborative work extends across multiple institutions and has significantly shaped current approaches to referring expression generation and evaluation in NLP. His recent work critically examines the limitations of current evaluation practices in NLP and proposes more scientifically grounded assessment frameworks.

Gunnar SchmidtmannView profile

Associate Professor

• Visual Neuroscience
• Shape Perception
• Object Recognition
• +19 more

Gunnar Schmidtmann is an Associate Professor of Optometry and Vision Science at the School of Health Professions, University of Plymouth, where he has been employed since 2017, first as a Lecturer and promoted to Associate Professor in 2021. He holds a PhD in Vision Science from Glasgow Caledonian University and completed postdoctoral research at McGill University in Montreal, Canada. He is actively involved in teaching, research, and supervision, with a focus on visual perception and clinical vision science. Education: PhD in Vision Science, Glasgow Caledonian University, UK (2008–2013) BSc in Optometry, University of Applied Sciences, Lübeck, Germany (2005–2008) Medical School, University of Lübeck, Germany (2001–2005) Research Interests: His research spans visual neuroscience, shape perception, object recognition, visual psychophysics, and face perception. He investigates contour and curvature encoding, spatial and probability summation, peripheral vision, visual illusions, and the visual functions of patients with traumatic brain injury, stroke, and glaucoma. His work combines computational modeling with experimental psychophysics. Publication Trends: His recent articles (2023–2025) focus on sensorimotor aging, tablet-based vision testing, letter identification biases, and visual illusions such as the 'Pint Glass Illusion'. These reflect a strong trend toward developing accessible, quantitative tools for clinical and research use, while maintaining a deep theoretical interest in visual perception mechanisms. Scientific Engagement: Active member and elected committee member of the Applied Vision Association (AVA) Principal contributor to the McGill Face Database for complex mental state recognition Regular presenter at AVA and ECVP conferences Supervision and Grants: He supervises PhD students and is a Co-Investigator on an active NHS-funded project Portable Low-Cost Haptic Interfaces for Motor Injury Assessment (2025–2026). His teaching includes module leadership in Visual Perception, Human Anatomy, and Applied Quantitative Research Methods at both undergraduate and postgraduate levels. Labs and Teams: He leads research within the Eye and Vision Research Group at the University of Plymouth and collaborates with the Centre for Cognitive Neuroscience at Brunel University London. His lab focuses on psychophysical experiments and computational modeling of visual perception.

Warren A. Hunt, Jr.View profile

Professor

• Formal Methods
• Computer Architecture
• Low-Power Computing
• +7 more

Warren A. Hunt, Jr. is a Professor in the Department of Computer Sciences at The University of Texas at Austin. His research focuses on formal methods for hardware and software verification, computer architecture, low-power computing, and biological-inspired systems. He leads projects in specifying and validating computer memory models, extending the ACL2-based x86 specification, and exploring rapid single-flux quantum (RSFQ) computing. Research Interests Formal specification and verification of hardware/software systems Computer architecture and low-power design Biological-inspired computing ACL2 theorem prover applications X86 ISA modeling Notable Contributions Verification of complex microprocessor designs Development of formal tools for hardware/software co-verification Leadership in the FMCAD conference (chairman until 2020) Academic Collaborations Work with Centaur Technology on x86 processors Collaborations with industry partners (Intel, AMD, ARM) Keynote talks at international venues (Newton Institute, Royal Society) Publications (2017–2025) span formal verification of multipliers, self-timed circuits, RSFQ systems, and x86 machine-code analysis. His Google Scholar page lists all his work. PhD Students Current: Balasubraman Muthuvelu, Gavin Meil, Carl Kwan Graduates: Jun Sawada (1999), Sol Swords (2010), Shilpi Goel (2016), and others Leadership Chairman of FMCAD steering committee (1996–2020) Editorial roles in multiple FMCAD/CHARME conferences

• Software Engineering
• Formal Methods
• Software Testing
• +5 more

Milos Gligoric is an Associate Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin. His research focuses on software engineering and formal methods, particularly in software testing (test generation and regression testing), proof engineering, systems-supported software engineering, and software engineering for scientific computing. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2015) and M.Sc./B.Sc. degrees from the University of Belgrade. Research Interests: Improving software quality and developer productivity through automated testing techniques, compiler optimizations, and formal verification methods. Recent work explores applications of large language models in test generation and code evolution. Publication Trends: Recent articles (2023-2025) show strong emphasis on LLM applications for test generation, JIT compiler testing, Python/C++ performance optimization, parallel computing, and innovative testing tools. Work frequently appears at top venues like ICSE, FSE, ISSTA, and OOPSLA. Scientific Awards: ACM SIGSOFT Outstanding Doctoral Dissertation Award David J. Kuck Outstanding PhD Thesis Award Multiple ACM SIGSOFT Distinguished Paper Awards Best Paper Award nominations (ICST 2012, ICS 2021) New Ideas and Emerging Results Distinguished Paper Award Research Support & Advising: Funded by Army Futures Command, Cisco, DOE, Google, Huawei, NSF, Runtime Verification, and Samsung. Mentors 7 PhD students and has graduated 12 PhD/MS students. Maintains industry collaborations with DBT (part-time contractor), Katana Graph, and Samsung. Labs & Tools: Leads UT Austin's software engineering research group. Developed multiple open-source tools including Ekstazi (regression test selection), mCoq (mutation analysis for Coq), Roosterize (lemma suggestion for Coq), and JAttack (JIT compiler testing).

Peter WalshView profile

Professor

• Embedded Systems
• Design Verification
• Software Engineering
• +3 more

Dr. Peter Walsh is a Professor in the Department of Computing Science at Vancouver Island University. He holds a B.Sc. and M.Sc. from University College Cork (Republic of Ireland) and a Ph.D. from the University of Victoria (Canada). His teaching and research focus on Embedded Systems and Design Verification , particularly using VHDL for hardware description language applications. University College Cork - B.Sc. (Republic of Ireland) University College Cork - M.Sc. (Republic of Ireland) University of Victoria - Ph.D. (Canada) Research Focus Dr. Walsh's research explores the intersection of Software Engineering and Hardware Verification . He investigates how techniques from software testing can be applied to VHDL design verification , emphasizing automated testing tools and specification-driven development . His work on object-oriented software engineering focuses on creating behavioral test benches for class libraries. Selected Publications His publications from 1994-1999 include works on VHDL integration in education, automated behavioral testing , and document-driven software design methodologies. These works connect software testing with hardware verification practices.

• Intelligent Transport Systems
• Electromobility
• Connected and Automated Vehicles
• +4 more

Jiaming Wu serves as Assistant Professor at Chalmers University of Technology within the Department of Architecture and Civil Engineering. His work focuses on modeling and optimization of intelligent transport systems , with specialized attention to electromobility , connected and automated vehicles , and intersection control . Key research trends from his publications include: Advancements in electric vehicle charging optimization using decomposition techniques Development of deep learning architectures for autonomous vehicle trajectory prediction Exploration of multi-objective routing algorithms for mixed traffic environments Innovations in edge computing for transportation network problems Analysis of energy efficiency in shared micromobility systems He actively contributes to: Academic leadership as Associate Editor for IEEE Transactions on Intelligent Transportation Systems Editorial work for Multimodal Transportation Conference committee memberships in transportation technology domains

Michail MakridisView profile

Senior Lecturer

• Transportation Engineering
• Traffic Flow Modeling and Simulation
• Intelligent Transportation Systems
• +7 more

Dr. Michail Makridis is a Senior Lecturer at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering, where he leads the Road Traffic Technology Group. He serves as the main responsible lecturer for Microscopic Modelling and Simulation of Traffic Operations and has taught Traffic Engineering courses. His work focuses on developing innovative approaches to transportation challenges with a particular emphasis on system resilience and antifragility. Dr. Makridis's research interests span multiple dimensions of modern transportation systems. His primary focus areas include traffic flow modeling and simulation, intelligent transportation systems, and traffic estimation and control. He has made significant contributions to understanding vehicle dynamics and driver behavior simulation, with particular attention to vehicle energy demand and emissions. His work on antifragile transportation systems represents a cutting-edge approach to designing networks that improve performance under disruption. His recent publications demonstrate a strong trend toward integrating machine learning with traditional transportation engineering. The research shows increasing focus on antifragility concepts in traffic networks, congestion-aware prediction models, and multimodal transportation solutions that balance the needs of different road users. His work spans theoretical frameworks and practical applications, with several publications addressing real-world challenges in urban mobility. MFC and Battery Consumption Model (April 2022) Simsub Best Simulation Development Paper Award (TRB 2020) for Enhanced MFC: Introducing dynamics of electrified vehicles for free flow microsimulation modeling Dr. Makridis leads multiple significant research projects including AntifragiCity, which aims to revolutionize how urban mobility systems respond to disruptions, and ANTIGONES, focusing on designing anti-fragile large-scale traffic frameworks. His work on ASTRA BGT examines the impact of autonomous vehicles on tunnel safety and efficiency, while BikeZ develops models for mass cycling as a service. He also contributes to OptFlow, which focuses on travel-time estimation using FLIR camera sensors. His research has practical applications in improving urban transportation resilience and efficiency.

David AP WombleView profile

Assistant Professor

• Critical Theory
• Nineteenth-Century British Literature
• Restoration and Eighteenth-Century Literature
• +7 more

David AP Womble is an Assistant Professor in the Department of English at the University of Houston's College of Liberal Arts and Social Sciences. His research spans science, literature, and political theory across 18th–19th century Europe and the British Empire, with a focus on embodied mass behavior and thermodynamic thought. Ph.D., University of Chicago B.A., Duke University Research Interests : • Intersections of empire studies and ecocriticism • Biopolitics and crowd theory in Victorian literature • Thermodynamic energy in Enlightenment thought • Catholic/Protestant epistemological conflicts in science • Object-oriented ontology and literature's secular enchantment Publication Trends : His work examines how scientific discourses (nervous systems, thermodynamics) shaped literary and political conceptualizations of masses, migration, and materiality. Recent scholarship connects energy systems to colonial demographics and object vitality. Awards : William Lee Pryor College Professorship Multiple fellowships (Blair, Nicholson, Duke) Excellence in Writing Pedagogy award Teaching : Undergraduate courses on Romantic Movement, Victorian Age, and modern novel history. Graduate seminars explore 19th-century studies and literature's vital force.

Juho KimView profile

Associate Professor

• Human-Computer Interaction
• Human-AI Interaction
• Interactive Systems
• +5 more

Juho Kim is an Associate Professor in the School of Computing at KAIST, where he directs KIXLAB (the KAIST Interaction Lab). He is also affiliated faculty in the Kim Jaechul Graduate School of AI at KAIST. His academic journey includes a Ph.D. from MIT, M.S. from Stanford University, and B.S. from Seoul National University. From 2015-2016, he served as a Visiting Assistant Professor and Brown Fellow at Stanford University. His educational background includes: Ph.D. in EECS from MIT M.S. in Computer Science from Stanford University B.S. in Computer Science and Engineering from Seoul National University Professor Kim's research in human-computer interaction and human-AI interaction focuses on building interactive and intelligent systems that support interaction at scale, aiming to improve the ways people learn, collaborate, discuss, make decisions, and take action online. His work spans several key areas including educational technology, online collaboration platforms, AI-powered learning systems, and tools for human-AI collaboration. He is particularly interested in how interactive systems can scale to support large numbers of users while maintaining quality interactions. His recent publications show a strong trend toward human-AI interaction, with particular emphasis on educational applications, collaborative systems, and addressing challenges in AI reliability and user experience. His work often combines technical innovation with rigorous user studies to evaluate real-world impact. Professor Kim has received numerous scientific awards including a KIISE/IEEE-CS Young Computer Researcher Award, KAIST's Songam Distinguished Research Award, Grand Prize in Creative Teaching, Q-Day Creative Education Award, and Excellence in Teaching Award. He has also received nearly 20 paper awards from top conferences including ACM CHI, ACM CSCW, ACM Learning at Scale, ACM IUI, ACM DIS, and AAAI HCOMP. KIISE/IEEE-CS Young Computer Researcher Award KAIST's Songam Distinguished Research Award Grand Prize in Creative Teaching Q-Day Creative Education Award Excellence in Teaching Award Nearly 20 paper awards from ACM CHI, ACM CSCW, ACM Learning at Scale, ACM IUI, ACM DIS, and AAAI HCOMP KAIST Global Research Cooperation Award Outstanding Ethics Professor Award from KAIST Graduate Student Association Professor Kim actively mentors students at all levels, with a substantial roster of Ph.D. and M.S. students. His recent sabbatical year was spent as a chief scientist at Ringle Inc. to transfer his research on AI-powered analysis and diagnosis of English learners' proficiency into real products. He is also an Educational Innovation Advisor at KAIST, with the mission to promote AI-applied teaching and learning in the university. Professor Kim directs KIXLAB (the KAIST Interaction Lab), which focuses on human-AI interaction research. The lab brings together students and researchers from diverse backgrounds to work on projects that bridge interactive systems and artificial intelligence. KIXLAB has established collaborations with industry partners and regularly contributes to top-tier conferences in HCI and AI.

Xiang SongView profile

Senior Lecturer

• Logistics and Transportation
• Supply Chain Management
• Cutting and Packing Problems
• +3 more

Dr. Xiang Song is a Senior Lecturer at the Department of Mathematics, University of Portsmouth, within the Faculty of Technology and School of Mathematics and Physics. He is affiliated with the Centre for Operational Research & Logistics and the Portsmouth AI and Data Science Centre. His research focuses on cutting and packing problems, operational research, logistics optimization, and artificial intelligence applications in supply chain management. He has held EPSRC-funded projects, including the LANCS Initiative in Foundational Operational Research (2008-2011) and contributed to projects like automated algorithm selection for cutting/packing problems (2004-2008). His research interests include unmanned vehicle routing for offshore inspections, maintenance planning for renewable energy infrastructure, stochastic supply chain modeling, and heuristic algorithms for complex optimization problems. He has published extensively in journals like European Journal of Operational Research and Expert Systems with Applications. His work bridges theoretical advancements with real-world applications in logistics, healthcare, and manufacturing. PhD: Jointly awarded by CIMS, Shenyang Institute of Automation (Chinese Academy of Sciences) and Université de Technologie de Troyes (2004) EPSRC Projects: Including strategic maintenance planning for offshore wind farms and blood supply network optimization in disaster scenarios His research outputs span 27 peer-reviewed articles, with recent work addressing carbon cost impacts on supply chains, multi-modal logistics in medical supply networks, and UAV/USV coordination for offshore inspections. He supervises PhD students and collaborates with industry partners on practical operational research challenges.