Conor Ryan is a Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a Science Foundation Ireland-funded Investigator since 2002 and a member of multiple research centres including Lero – the Irish Software Research Centre and the Limerick Digital Cancer Research Centre. His research focuses on Genetic Programming, Grammatical Evolution, and their applications in domains like healthcare analytics, digital circuit design, and financial modeling. He has authored over 250 publications, with recent work emphasizing automated feature selection in medical diagnostics, neural architecture search, and blockchain ecosystems. Teaching includes courses on Foundations of Computer Science and Computer Games Programming. Research interests span evolutionary computation, machine learning, and interdisciplinary applications. Collaborations involve global institutions, reflecting his work's impact across computer science, engineering, and healthcare. His research has addressed challenges in breast cancer diagnosis via genetic algorithms, cryptocurrency volatility prediction using random forests, and automated generation of digital circuits. Ongoing projects explore interpretability in AI, energy-efficient computing, and sustainable transport systems through predictive analytics. Professional memberships include roles in the Centre for Research Training in Foundations of Data Science and the Data-Driven Computer Engineering Research Centre, underscoring his commitment to interdisciplinary innovation.
Daniel Varro is a Professor affiliated with McGill University (Faculty of Engineering, School of Computer Science), with strong ties to Budapest University of Technology and Economics and Linköping University. He is a leading researcher in model-driven engineering, cyber-physical systems, and software engineering, actively contributing to top-tier conferences such as MODELS, ICSE, and ASE. His research focuses on model-based systems engineering (MBSE) , automated model generation , model transformations , and constraint-based consistency checking . Recently, his work has expanded into integrating large language models (LLMs) and machine learning into modeling workflows, including model querying, domain modeling, and bug detection. The recent publications reveal a strong trend toward AI-augmented modeling, logic-based solvers (e.g., Refinery), and safety assurance of autonomous systems (e.g., COLREGs compliance). His work bridges formal methods with practical software engineering challenges in industrial and safety-critical domains. Scientific Awards: No specific awards mentioned in the text. Advising and Grants: While no explicit list of students or grants is provided, his mentorship in the Doctoral Symposium and repeated leadership roles suggest active supervision and likely grant funding. He has led projects on automated model generation, model quality, and AI integration in modeling. Labs and Teams: Daniel Varro is associated with research groups focused on model-driven engineering and software evolution, likely leading or co-leading teams working on the VIATRA and Refinery frameworks for model transformation and solving.
Jelle Hellings is an Assistant Professor in the Department of Computing and Software at McMaster University , Canada. His research focuses on high-performance large-scale data management systems with a strong theoretical and algorithmic component, including resilient systems (blockchains) , graph databases , and external-memory algorithms . He previously worked as a Postdoc Scholar at the University of California, Davis and earned his PhD from Hasselt University in Belgium. Education: Doctor of Sciences in Computer Science (2018), Hasselt University Master of Science in Computer Science and Engineering (2011), Eindhoven University of Technology His research interests include scalable resilient systems with Byzantine fault tolerance, database theory, graph query languages, constraints on graph data, and external-memory algorithms for large graph datasets. He has authored numerous high-impact publications on blockchain-based resilient systems, query optimization in graph databases, and theoretical advancements in relation algebra expressiveness. Hellings actively contributes to academic service through program committee memberships and tutorial organization, and he currently teaches courses on future resilient databases and foundational computer science topics.
Professor Brian Leeds DeMarco is a leading academic in the Department of Physics at the University of Illinois Urbana-Champaign (UIUC), serving as the IQUIST Director and Bardeen Faculty Scholar. He specializes in Atomic, Molecular, and Optical Physics, with a focus on quantum simulation using ultracold atoms in optical lattices to address problems in condensed matter physics. His research has led to groundbreaking discoveries, including the first quantum-degenerate Fermi gas and three-dimensional Anderson localization of matter, earning him prestigious awards like the NSF CAREER Award and Sloan Fellowship. DeMarco holds a B.A. in Physics from SUNY Geneseo (1996) and a Ph.D. from the University of Colorado Boulder (2001). His postdoctoral work at NIST with David Wineland advanced quantum computing with trapped ions. At UIUC since 2003, he has held roles such as Associate Head for Undergraduate Programs and currently chairs NASA's Fundamental Physical Sciences Standing Review Board. His research explores quantum simulation, quantum computing architectures, and ultracold matter. Notable achievements include identifying phase-slip crossover in optical lattices and pioneering 3D Anderson localization. He has published over 30 high-impact articles, with recent work focusing on quantum simulators and hybrid quantum systems. Awards: APS Fellow, University of Illinois Scholar, NASA Review Board Chair, Defense Sciences Study Group member. Grants: NSF, ONR, Sloan Foundation support for quantum simulation and ultracold atom research. DeMarco leads the DeMarco Group, advancing quantum technologies and training students in experimental physics. His lab’s work on quantum testbeds and trapped ion processors positions him at the forefront of quantum information science.
Wolfgang Kunz is a Full Professor (C4, W3) and Chair of Electronic Design Automation at the Technische Universität Kaiserslautern since 2001. His academic career spans multiple prestigious institutions, including Goethe-University Frankfurt/Main and the University of Massachusetts, Amherst. He has held leadership roles such as Dean (2005-2007) and Vice-Dean (2007-2009) at TU Kaiserslautern. Habilitation (Dr. rer. nat. habil.), Computer Science, University of Potsdam (1996) Doctoral degree (Dr.-Ing.), Electrical Engineering, University of Hannover (1992) Dipl.-Ing. degree, Karlsruhe Institute of Technology (1989) His research focuses on hardware verification, security, and optimization, particularly in embedded systems and processors. His work on formal verification methods has been commercialized by companies like Synopsys, Mentor Graphics, and Siemens EDA. His 2016-2021 publications address critical security issues such as Spectre/Meltdown and introduce innovative verification frameworks adopted by industry leaders like Infineon and OneSpin Solutions. Scientific awards include the IEEE Fellow (2006), German IT Society Award (2005), and TU Kaiserslautern Distinguished Teaching Award (2016). He has served on editorial boards of major journals and coordinated the Erasmus Mundus European Master Program in Embedded Computing Systems since 2010. Key students: Jörg Bormann, Raik Brinkmann, Tobias Ludwig Collaborations: Siemens EDA, Infineon, AbsInt, Intel SCAP Spin-offs: LUBIS EDA, OneSpin Solutions
Dr. Chunyan Mu serves as a Senior Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen, actively contributing to both academic instruction and cutting-edge research in computing science while currently accepting new PhD students. Her research program centers on Trustworthy AI and Safe Autonomy, with specialized expertise in formal verification of responsibility, accountability, and privacy mechanisms within multi-agent systems. She investigates resilience frameworks for autonomous intelligent systems and develops advanced methodologies for information flow security analysis, bridging theoretical computer science with practical security implementations. Analysis of her publication trajectory (2014-2025) reveals consistent innovation in applying formal methods to security-critical systems. Key thematic developments include probabilistic strategy logic for observability analysis, quantitative verification of opacity properties, and game-theoretic approaches to security verification, demonstrating increasing sophistication in handling multi-agent accountability and system resilience challenges. Dr. Mu currently supervises PhD candidates and offers a fully funded doctoral position focused on formal verification of safety properties in autonomous systems, providing comprehensive financial support including tuition coverage, £20,780 annual stipend, and dedicated research funding for candidates with strong backgrounds in formal methods and artificial intelligence.
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Frederick Eberhardt is a Professor of Philosophy at the California Institute of Technology (Caltech) since 2013. He holds a B.S. from the London School of Economics (2002), an M.S. from Carnegie Mellon University (2005), and a Ph.D. from Carnegie Mellon (2007). His research focuses on the intersection of philosophy of science, machine learning, and cognitive science, emphasizing causal discovery from data, experimental methods in causality, and foundational issues in probability and causality. He also explores computational models in psychology and historical work on Hans Reichenbach's philosophy. Recent publications span topics like causal emergence, Reichenbachian probability coordination, and causal mapping in neuroscience. His work bridges formal philosophy with empirical applications in cognitive science and computational methods. Education: B.S., London School of Economics, 2002 M.S., Carnegie Mellon University, 2005 Ph.D., Carnegie Mellon University, 2007 Research interests include formal philosophy of science, causal inference techniques, machine learning applications to causal discovery, and the philosophical underpinnings of probability. His work on causal abstraction and computational models in cognitive science highlights interdisciplinary approaches to understanding causal mechanisms. Recent publications emphasize integrating experimental and observational data for causal discovery, with applications in neuroscience and psychology. Publications reflect a focus on advancing causal reasoning methods, from theoretical frameworks to empirical validation. No scientific awards are explicitly listed in the provided text. Advising and grants details are not available here. Eberhardt is affiliated with Caltech’s Philosophy Department and contributes to theoretical and applied research in causality and its implications across disciplines.
Dr. Vasant Honavar is a Professor of Computer Science and Informatics at Pennsylvania State University, holding the Edward Frymoyer Endowed Chair. He serves as Director of the Center for Artificial Intelligence Foundations and Scientific Applications and Associate Director of the Institute for Computational and Data Sciences. His expertise spans artificial intelligence, machine learning, causal inference, and bioinformatics. Honavar has led over $60M in research grants and mentored 36 PhD students, 30 MS students, and numerous undergraduates. He is a Fellow of the AAAS and recipient of NSF Director’s Awards. Education: Ph.D., Computer Science and Cognitive Science, University of Wisconsin–Madison (1990) M.S., Computer Science, University of Wisconsin–Madison (1989) M.S., Electrical and Computer Engineering, Drexel University (1984) B.E., Electronics Engineering, Bangalore University (1982) Research Interests: His work focuses on machine learning, causal inference, knowledge representation, health informatics, and algorithmic fairness. Notable contributions include scalable algorithms for big data analytics and predictive modeling, as well as computational infrastructure for interdisciplinary science. Awards: Fellow, AAAS (2018) ACM Distinguished Member NSF Director’s Award for Superior Accomplishment (2013) Edward Frymoyer Endowed Chair (2013) Leadership: Honavar co-founded the Penn State Center for Artificial Intelligence and led the NIH-funded Biomedical Data Sciences Ph.D. program. He is a Co-PI of the North East Big Data Innovation Hub and serves on editorial boards of journals like IEEE/ACM Transactions on Computational Biology and Bioinformatics. Lab & Teams: Directs the Artificial Intelligence Research Laboratory and the Center for Big Data Analytics and Discovery Informatics, fostering collaborations across computer science, life sciences, and health sciences.
Matthew Lakin is an Associate Professor with tenure in the Department of Computer Science at the University of New Mexico, with a courtesy appointment in the Department of Chemical & Biological Engineering. He is affiliated with the UNM Center for Biomedical Engineering and the School of Engineering, and collaborates extensively with the UNM Health Sciences Center and external institutions. Education: Ph.D., Computer Science, University of Cambridge, 2010 M.A. (Cantab), University of Cambridge, 2009 B.A. (Hons), Computer Science, University of Cambridge, 2005 Dr. Lakin's research focuses on molecular computing, DNA nanotechnology, synthetic biology, and formal verification of biomolecular circuits. He develops computational models and experimental systems for programmable biological devices, especially using heterochiral DNA to enhance stability in living cells. His work spans software tools for biodesign and experimental validation in mammalian systems, with applications in nanomedicine and biosensing. The recent publications highlight a strong trend in engineering robust, intelligent biomolecular systems. His work integrates machine learning concepts into chemical reaction networks, advances geometric modeling of DNA systems, and pioneers L-DNA-based circuits for intracellular applications. The research spans theoretical foundations, software tools, and wet-lab experimentation, emphasizing interdisciplinary innovation. Scientific Awards: Presidential Early Career Award for Scientists and Engineers (PECASE), 2025 NSF CAREER Award, 2021 UNM School of Engineering Junior Faculty Research Excellence Award, 2021 Multiple student awards under his mentorship, including the Outstanding Graduate Student Award and DNA28 Best Student Presentation recognition Dr. Lakin has advised numerous graduate and undergraduate students, including Ph.D. graduates in Biomedical Engineering and Computer Science. He leads major funded projects such as the NSF CAREER grant on heterochiral molecular computing, an EPSCoR Research Fellowship, and a $3M NSF grant on heavy metal biosensing in collaboration with Native American communities. He is also PI on multiple NSF grants related to synthetic cells and nucleic acid technologies. He directs the Lakin Lab for Programmable Biology, which operates within the Department of Computer Science and collaborates with Chemical & Biological Engineering and the Center for Biomedical Engineering. The lab emphasizes both computational modeling and experimental molecular biology, and runs an NSF-funded biotechnology summer camp in partnership with ¡Explora! science museum to strengthen STEM education in New Mexico.
Alex Lombardi is an Assistant Professor of Computer Science at Princeton University, specializing in cryptography and theoretical computer science. His work explores cryptographic proof systems, post-quantum security, and quantum cryptography. Princeton University (Current) Simons-Berkeley Postdoctoral Fellow (Former) MIT (Graduate Training) Visiting Scientist, Cryptography 10 Years Later Program (2025) Education: PhD in Computer Science from MIT (advised by Vinod Vaikuntanathan) Master's Thesis on Provable Instantiations of Correlation Intractability and the Fiat-Shamir Heuristic Dr. Lombardi's research spans foundational cryptography, with a focus on indistinguishability obfuscation, worst-case assumptions, and quantum cryptographic protocols. His work on SNARGs and PPAD hardness has advanced cryptographic proof systems, while his recent projects address quantum verification and post-quantum security. He encourages prospective cryptography students to apply to Princeton's PhD program. His publications highlight advancements in LWE-based cryptography, quantum protocols, and complexity-theoretic foundations. Key themes include secure computation, hash function design, and cryptographic reductions under quantum assumptions. Scientific Awards: Simons-Berkeley Postdoctoral Fellowship Dr. Lombardi serves on program committees for STOC 2025, EUROCRYPT 2025, and other conferences. He has taught courses like COS 433/533 (Cryptography) and COS 533 (Advanced Cryptography) at Princeton.
Tianyi Zhang is a Tenure-Track Assistant Professor in the Department of Computer Science at Purdue University, part of the College of Science. He leads the Human-Centered Software Systems Lab, focusing on AI-driven systems that synergize human expertise with machine intelligence to enhance programming productivity and software reliability. Prior to Purdue, he was a Postdoctoral Fellow at Harvard University under Dr. Elena Glassman and earned his Ph.D. from UCLA (2019) and B.Sc. from Huazhong University of Science and Technology (2013). Education: Ph.D. in Computer Science, University of California, Los Angeles (2019) Bachelor's in Computer Science, Huazhong University of Science and Technology (2013) Research Interests: His work spans Software Engineering, Human-Computer Interaction, and AI. Key areas include program synthesis, interactive debugging tools, autonomous driving system testing, and mitigating biases in AI models. He develops systems like Interpretable Program Synthesis and SQLucid to bridge human and machine intelligence. Recent Trends in Publications: Recent work emphasizes human-in-the-loop AI, including mixed-initiative systems for data wrangling (Dango), interactive program repair, and bias analysis in text representations (STILE). He also explores challenges in autonomous driving testing and LLM-based code generation errors. Awards & Grants: NSF Career Award (2024) Amazon Research Award Showalter Trust Research Award for pre-diabetes research $1.5M NSF grant for software supply chain security Best Paper Honorable Mentions at CHI and VAHC Advising & Teams: Supervises 12+ PhD/Master's students and 30+ research interns. Notable advisees include Bonan Kou (API misuse studies) and Yuan Tian (text-to-SQL systems). Collaborates with Harvard Medical School on healthcare data analysis. Labs & Initiatives: Directs Purdue's Human-Centered Software Systems Lab. Co-founded the Societal Impact Fellows program. Active in open-source projects like Examplore for API usage visualization and JShrink for Java debloating.
Andrew Rice is a Professor of Computer Science at the University of Cambridge's Department of Computer Science and Technology, and holds the Hassabis Fellowship in Computer Science. He is also the Director of Studies in Computer Science at Queens' College. His research focuses on programming languages, software engineering, and machine learning applications in software development. He leads projects like Isaac Computer Science and ALTA (Automated Language Teaching and Assessment), advancing adaptive learning technologies. His work includes static analysis tools such as Error Prone at Google, energy efficiency studies in computing infrastructure, and contributions to the Computing for the Future of the Planet initiative. His teaching emphasizes practical skill development through flipped classrooms and video lectures, earning him the 2014 Pilkington Prize for teaching excellence. He has held visiting roles at Google and collaborated on energy consumption research for mobile devices and data centers. His research spans systems, networking, and natural language processing, with a strong focus on applying computational methods to real-world challenges. Key Projects: Isaac Physics/Computer Science, ALTA, Error Prone Static Analysis Research Themes: Programming Languages, Machine Learning, Energy Efficiency Awards: Pilkington Prize (2014)
Rui Soares Barbosa is a Staff Researcher at the Quantum and Linear-Optical Computation group at the International Iberian Nanotechnology Laboratory (INL). He holds a BSc in Computer Science from Universidade do Minho (2009), an MSc in Mathematics and Foundations of Computer Science from the University of Oxford (2010), and a DPhil in Computer Science from Oxford (2015) with a thesis on Contextuality in quantum mechanics and beyond. Prior to joining INL, he held post-doctoral positions at Oxford (2015–2019) and the University of Edinburgh (2019–2020), and a Research Fellowship at the Simons Institute for the Theory of Computing, UC Berkeley (2017). Barbosa's research lies at the intersection of Computer Science, Physics, and Mathematics, focusing on quantum foundations, quantum computer science, and the mathematics of quantum theory. His work emphasizes logical, structural, and compositional aspects, particularly investigating non-locality and contextuality - phenomena that distinguish quantum theory from classical physics and have been linked to quantum informatic advantage. He employs mathematical tools from category theory, logic, probability, algebraic topology, and operator algebras to achieve a structural understanding of quantum systems' non-classical features. His recent publications demonstrate a strong focus on contextuality as a quantum resource, exploring its connections to causality, computational advantage, and logical structures. The research spans theoretical foundations to practical quantum computing applications, with particular attention to mathematical frameworks like sheaf theory that elegantly express quantum contextuality. His work often involves collaborations with leading researchers in quantum foundations and theoretical computer science. Barbosa actively advises multiple PhD and MSc students including Angelos Bampounis, Rafael Wagner, Nico Witrock, and others. His research group at INL, the Quantum and Linear-Optical Computation group, conducts cutting-edge research at the intersection of quantum theory and computer science, regularly presenting findings at major conferences like the International Conference on Quantum Physics and Logic (QPL).
Associate Professor Bambang Trigunarsyah holds a position at RMIT University since 2018, affiliated with the School of Property, Construction and Project Management (PCPM). He earned his BSc in Civil Engineering from Colorado School of Mines, a Master's in Civil Engineering (Construction Management) from the University of Indonesia, and a PhD in Engineering Project Management from the University of Melbourne. His career spans industry roles, including project engineering in Indonesia's oil and gas sector, academic leadership at the University of Indonesia, and international teaching/research appointments at Queensland University of Technology (QUT) and King Fahd University of Petroleum and Minerals in Saudi Arabia. Research interests focus on post-disaster reconstruction, infrastructure project governance, constructability of infrastructure, and project management in developing countries. Notable projects include risk management frameworks using blockchain, circular economy in Saudi construction, and BIPV energy solutions. He actively supervises HDR students in areas like public-sector project success determinants and thermal environment studies. Bambang serves as Higher Degree Research Delegate Authority (HDR DA) for the School of PCPM and teaches postgraduate project management programs. Professional memberships include AIPM, PMI, and ASCE. His industry advisory roles include Indonesia's National Construction Service Development Board (2002-2007) and Australia-Indonesia Centre's Infrastructure Cluster (2016-2018). Recent research emphasizes BIM applications in infrastructure preservation, risk assessment methodologies, and climate-resilient construction. His work bridges theory and practice, addressing challenges in emerging economies and complex project environments.
