Tina Zhu, MD is an Adjunct Assistant Professor at Queen’s University and a Full-Time Cardiologist at APEX Heart Centre. She holds dual degrees from the University of Toronto: a Bachelor of Science in Immunology and a Medical Degree. Her clinical training includes Internal Medicine at Western University and Adult Cardiology at Queen’s University, alongside specialized certifications in Level III Echocardiography and Nuclear Cardiology. Her research interests focus on heart failure , women’s heart health , and digital health innovation . She actively explores applications of AI and technology in cardiology, particularly in improving diagnostic workflows and patient care. Dr. Zhu’s academic contributions span AI-driven healthcare solutions, with recent work emphasizing automated planning models, text anonymization, and robust dialogue systems. Her publications reflect interdisciplinary expertise at the intersection of cardiology and computational science. Professional affiliations include Queen’s University and APEX Heart Centre, where she balances clinical practice with academic engagement. No formal awards or grants are explicitly listed in the provided materials.
Liang Zhan is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh. He holds a Ph.D. from the University of California, Los Angeles (2011). His research focuses on applying artificial intelligence and machine learning to neuroimaging analysis, particularly in Alzheimer’s disease and brain connectomics. Key areas include deep learning frameworks for structural and functional brain imaging, graph neural networks for multimodal data fusion, and explainable AI for disease diagnosis. His work integrates advanced computational methods with clinical neuroscience, addressing challenges in biomarker discovery, dynamic brain network analysis, and uncertainty quantification. Recent studies explore excitation-inhibition balance alterations linked to neurodegenerative risks and APOE-ε4 genotype effects. He has pioneered methods like Brain Posterior Evidential Networks (BPEN) and SIN-Seg for robust medical image analysis. Zhan’s publications emphasize cross-modal data integration, with contributions to frameworks like Zeus (zero-shot LLM instruction for segmentation) and NeuroCave (web-based visualization platform). His research bridges theoretical advances in graph representation learning and practical applications in neuroimaging diagnostics.
Vignesh Subbian is an Associate Professor at the University of Arizona with joint appointments in the Department of Biomedical Engineering, Systems and Industrial Engineering, Statistics, Applied Mathematics, Clinical Translational Sciences, and Medicine. He is affiliated with the College of Engineering, the BIO5 Institute, and the Center for Biomedical Informatics & Biostatistics (CB2), where he serves as Associate Director. He is also a Distinguished Fellow of the Center for University Education Scholarship (CUES) and a member of the Graduate Faculty. His research lies at the nexus of systems engineering, medicine, and informatics, focusing on clinical decision-making, digital phenotyping, explainability in AI, and health equity. His educational initiatives emphasize asset-based practices, ethics in engineering, and professional identity formation. He leads two major training programs: the NLM-funded PHIRE program and the NSF-supported eCAMINOS initiative. The recent publications highlight a strong trend in applying machine learning to critical care, particularly in acute respiratory failure and traumatic brain injury. His work emphasizes model interpretability (e.g., WindowSHAP, SLAC-Time), temporal modeling of EHR data, and ethical considerations in informatics and AI. There is a growing focus on large language models for phenotyping and inclusive language in scientific communication. Excellence at the Student Interface Award (2023, 2021, 2019) Arizona Champion, Office of the Provost (2022) Dr. Subbian leads the Health Systems Engineering & Informatics Laboratory, which is supported by the NIH, NSF, AHRQ, and BIO5 Institute. He advises students and mentors trainees through federally funded programs. His lab collaborates widely across medicine, pharmacy, and public health, and he plays a key role in national initiatives like All of Us and RECOVER.
Sarah Hasan is a Research Fellow in the Department of Computer Science at Aalborg University, Denmark, operating within The Technical Faculty of IT and Design. She is affiliated with the Section for Distributed, Embedded and Intelligent Systems, specifically the Data Engineering, Science and Systems group, focusing on cutting-edge artificial intelligence research at Selma Lagerløfs Vej 300, 9220 Aalborg Øst. Her research expertise spans Artificial Intelligence with concentrated efforts in Natural Language Processing, Knowledge Representation, and Machine Learning. She investigates logical consistency mechanisms in Large Language Models for fact-checking applications, addressing critical challenges like inconsistent responses through knowledge graph integration and retrieval-augmented techniques to enhance model reliability and reasoning capabilities. Her current research trajectory is highlighted by a forthcoming publication at the International Conference on Learning Representations (ICLR 2025), which examines how Large Language Models maintain logical coherence during fact verification tasks. This work demonstrates significant advancements in mitigating response inconsistencies through structured knowledge integration, reflecting broader trends in trustworthy AI development. She actively contributes to the Data Engineering, Science and Systems research ecosystem at Aalborg University, collaborating on intelligent systems projects that bridge theoretical AI research with practical data science applications in distributed computing environments.
Andreas Haeberlen is a Professor in the Department of Computer and Information Science at the University of Pennsylvania, where he is a member of the Distributed Systems Lab (DSL) and co-director of the NETS program. He is currently on a leave of absence from Penn to lead the new systems group at Roblox Research, focusing on large-scale distributed systems in cloud and metaverse environments. University: University of Pennsylvania School: School of Engineering and Applied Science Department: Department of Computer and Information Science Academic Rank: Professor Email: ahae@cis.upenn.edu His research centers on distributed systems, networking, security, and privacy, with key interests in differential privacy, fault tolerance, secure network provenance, accountability in federated systems, and synchronous data center architectures. He aims to build practical systems that provide strong, provable privacy and security guarantees for real-world applications. The recent publications reflect a strong trend toward privacy-preserving distributed analytics, resilient cyber-physical systems, and secure, accountable federated infrastructures. His work combines techniques from programming languages, operating systems, and distributed computing to address fundamental challenges in scalability, security, and timing predictability. Key themes include bounded-time recovery, differential privacy in federated settings, and secure provenance for network diagnostics. Scientific Awards: Recipient of the Otto Hahn Medal from the Max Planck Society Recipient of the Ford Motor Company Award for Faculty Advising Recipient of the Lindback Award for Distinguished Teaching He has advised graduate students such as Karan Newatia and Robert Gifford, often in collaboration with Linh Thi Xuan Phan. His research is supported by active projects in differential privacy, synchronous data centers, resilient cyber-physical systems, secure network provenance, and accountability. He leads or co-leads major research initiatives that bridge academic innovation with industrial-scale deployment, particularly in cloud and metaverse platforms.
Wenke Lee is the USG Regents Professor and John P. Imlay Jr. Chair in Software at Georgia Tech's College of Computing, where he has been a professor since 2001. He currently serves as Executive Director of the Institute for Information Security & Privacy (IISP), leading Georgia Tech's global cybersecurity initiatives. His academic roles also include leadership in the Georgia Tech Information Security Center (GTISC) from 2012 to 2015. Dr. Lee holds a Ph.D. in Computer Science from Columbia University (1999). His research focuses on systems and network security, including botnet detection, malware analysis, and secure software development. Notable contributions include co-founding Damballa, Inc. (2006), a cybersecurity firm targeting botnet mitigation. His research interests span anomaly detection, adversarial machine learning, and privacy-preserving technologies. Recent work emphasizes AI-driven security solutions, IoT protection, and cyber-physical system resilience. Lee’s projects are funded by NSF, DHS, DoD, and industry partners. Key awards include the USG Regents Professorship and the John P. Imlay Jr. Chair. He has authored over 100 publications, with many highly cited in cybersecurity domains. His affiliations include the School of Cybersecurity and Privacy, CERCS, and the Online Master of Science in Computer Science (OMSCS) program.
Dr. Amir-Hossein Karimi is an Assistant Professor in the Department of Electrical and Computer Engineering and Cheriton School of Computer Science at the University of Waterloo, with a cross-appointment and Vector Institute affiliation. He leads the CHARM Lab, focusing on safe human-AI collaboration through causal inference, explainable AI, and neuro-symbolic systems. Prior to academia, he held research roles at DeepMind, Google Brain, and Meta, and industry positions at BlackBerry and Meta (Facebook). Education: Ph.D. in Computer Science, Max Planck Institute & ETH Zürich (2018-2023) M.Math in Computer Science, University of Waterloo (2016-2018) B.A.Sc. in Engineering Science, University of Toronto (2010-2015) Research Interests: The CHARM Lab develops AI systems that integrate metacognitive strategies and causal reasoning to enhance safety, reliability, and human alignment. Key areas include algorithmic recourse, causal explainability, and applications in healthcare, finance, and transportation. Collaborations span social sciences, cognitive science, and reinforcement learning. Recent Trends in Publications: His work emphasizes causal foundations of AI explanations, fairness in recourse mechanisms, and robust system design. Notable contributions include defining algorithmic recourse frameworks and influencing Canada’s automated decision-making policies. Awards: 2024 Igor Ivkovic Teaching Excellence Award 2021 Google PhD Fellowship 2024 ETH Zurich Medal Advising & Grants: Supervises PhD/Master’s/postdoctoral researchers across disciplines. Secured funding from NSERC, CIFAR, Google, and Waterloo.AI. Alumni placements include OpenAI, Microsoft, and Google. CHARM Lab Activities: Hosts interdisciplinary collaborations with experts in human-computer interaction, game theory, and behavioral economics. Develops open-source tools and benchmarks for reproducible research.
Sreekanth Mallikarjun is a Lecturer at the School of Data Science and holds a joint appointment with the McIntire School of Commerce as a Visiting Scholar at the University of Virginia. He also serves as Chief Data Scientist at Reorg, a global provider of credit intelligence, data, and analytics, bridging academia and industry in data science and business applications. Education: Ph.D. in Engineering Technology and Policy and Innovation, Stony Brook University M.S. in Operations Research, Stony Brook University B.S. in Mechanical and Industrial Engineering, Osmania University His research focuses on leveraging data science to solve complex business problems, particularly in finance and operations. Key areas include machine learning, natural language processing, data mining, statistics, and operations research, with an emphasis on extracting insights from both structured and unstructured data. He explores innovative methodologies to improve model execution, scalability, and reliability in enterprise environments. The recent articles highlight a strong trend in applying data science to financial domains, emphasizing model simplicity, data quality, and organizational scalability. His work consistently addresses practical challenges in deploying and maintaining data science models in real-world settings, particularly within financial institutions and large organizations. Scientific Awards and Recognitions: Official Member, Forbes Technology Council Mallikarjun advises on data science strategy and model deployment, though no formal advisees are listed. He has not disclosed specific grants, but his industry role at Reorg and academic position suggest engagement in applied research and innovation. His contributions span thought leadership through Forbes, academic teaching, and high-impact industry applications. He is actively involved in promoting best practices in data science through publications and professional networks, contributing to the broader discourse on effective data science implementation in business contexts.
Jedidiah McClurg is an Assistant Professor in the Department of Computer Science at Colorado State University, with prior faculty appointments at Colorado School of Mines and the University of New Mexico. He received his Ph.D. in Computer Science from the University of Colorado Boulder in 2018, where he was a member of the CUPLV research group under the supervision of Pavol Cerny. His research focuses on programming languages, program synthesis, verification, and their applications in networking, compilers, and distributed systems. His educational background includes an M.S. in Computer Science from Northwestern University (2013) and a B.S. in Electrical Engineering from the University of Iowa (2009). He has completed internships at Microsoft Research (RiSE Group, 2014) and Rockwell Collins (2011, 2013, 2004). McClurg’s research interests include programming languages, formal verification, software synthesis, software-defined networking, compilers, and system security. His work aims to develop tools and techniques that help programmers write more secure, reliable, and efficient code, especially in safety-critical domains. He has led multiple NSF-funded projects, including FMitF and CRII grants, totaling over $1 million in funding. His recent publications span high-impact venues such as PLDI, CAV, DISC, and SOSR, with topics ranging from neural network optimization and regular expression synthesis to network program verification and FEC code generation. These works reflect a consistent trend toward automating correctness, improving performance, and enabling scalable solutions in systems and networking. NSF CRII: SHF: Foundations for Stateful Network Programming ($175,000) NSF FMitF: Game Theoretic Updates for Network & Cloud Functions ($355,000 for him) NSF FMitF: Robust Enforcement of Customizable Resource Constraints ($250,000 for him) NSF GRFP (awarded to student Lauren Baker) He has advised multiple graduate and undergraduate students, many of whom have secured positions at leading tech companies such as Google, Apple, and Amazon. He is actively involved in academic service, having served on program committees for PLDI, SOSR, CAV, and others, and as a reviewer for journals like IEEE/ACM Transactions on Networking (ToN) and ACM Transactions on Software Engineering (TSE). He also contributes to open-source research via GitHub and maintains a strong online academic presence.
Junho Hong is an Associate Professor at the University of Michigan–Dearborn's Department of Electrical and Computer Engineering, College of Engineering and Computer Science. He holds a PhD in Electrical Engineering from Washington State University (2014), with prior roles at ABB (2014–2019) and Ford Motor Company (2021 sabbatical). His research spans cybersecurity of energy delivery systems, AI applications in power systems, and cyber-physical systems. Doctor of Philosophy in Electrical Engineering, Washington State University, USA (2014) Master of Science in Electrical Engineering, Myongji University, South Korea (2010) Bachelor of Science in Electrical Engineering, Myongji University, South Korea (2008) Junho's research focuses on securing critical energy infrastructure through machine learning, anomaly detection, and advanced cybersecurity frameworks. His work includes projects on substation automation, HVDC systems, smart grids, and high-power EV chargers. He has secured grants from the U.S. Department of Energy, NSF, Ford, and South Korean institutions. Recent publications highlight his contributions to generative AI for anomaly detection, SDN-based cyber restoration, and physics-informed models for secure grid operations. His students have received notable awards, including the Rackham Predoctoral Fellowship and IEEE Best Paper Session. Senior Member, IEEE (2022–present) Associate Editor, IEEE ACCESS (2022–present) 13 US patents in energy cybersecurity He leads the Cyber-Physical System Lab for Energy Delivery Systems, which focuses on grid resilience, renewable integration, and advanced diagnostics.
Andreas Holzinger is a University Professor for Digital Transformation for Smart Agriculture and Forestry at the Institute of Forest Engineering, Department of Agricultural Sciences, University of Natural Resources and Life Sciences Vienna (BOKU). He pioneered interactive machine learning with human-in-the-loop frameworks to advance trustworthy AI, focusing on explainability, robustness, and ethical alignment with human values. His work bridges artificial intelligence with forestry technology, ensuring safety, privacy, and sustainability. 2022: University Professor at BOKU 2021: IFIP Fellow 2020: ELLIS Member 2019: Visiting Professor at University of Alberta 2019: Academia Europaea Full Member His research interests span Human-Centered AI , Explainable AI , Digital Transformation in Forestry , and Trustworthy AI . He develops frameworks where humans collaborate with AI systems to enhance decision-making in forest operations, focusing on transparency and controllability. His recent publications emphasize Interactive Machine Learning and Explainable AI in forestry contexts, including cable corridor planning, danger zone detection, and sensor-based data interpretation. Articles explore 3D point cloud analysis , human-robot collaboration , and contextual counterfactuals to improve AI systems' reliability. Scientific Awards: Most influential Paper Award (2025) Fellow of IFIP (2021) Member of ELLIS (2020) Full Member, Academia Europaea (2019) He supervises theses on Forestry Technology , Plant Development , and Stress Resilience , mentoring students working on smart sensors, genetic analysis, and AI applications in agricultural systems. His collaborations span institutions in Austria, Canada, Finland, Italy, New Zealand, and the USA.
Zhenyang Xu is a researcher affiliated with the University of Waterloo, specializing in Software Testing and Debugging . He has contributed to advancements in program reduction techniques, including projects like PPR (Pairwise Program Reduction), WDD (Weighted Delta Debugging), and LPR (Large Language Models-Aided Program Reduction). His work focuses on improving the efficiency and effectiveness of debugging through empirical studies and novel algorithms. Research Interests : Software Testing, Debugging, Program Reduction, Software Engineering Key Publications : Kitten (2025, ISSTA) for LLM-based compiler testing T-Rec (2025, ICSE) on lexical syntax-guided reduction Pushing the Limit of 1-Minimality (2023, SPLASH) Conferences : Active in ISSTA, ICSE, SPLASH, ESEC/FSE
Henry Hoffmann is Professor and Liew Family Chair of the Department of Computer Science at the University of Chicago. He serves as Chair of the department and leads research in self-aware and adaptive computing systems. His work bridges traditional computer systems areas with control theory and machine learning to create systems that automatically adapt to meet high-level goals. Hoffmann received his Ph.D. from MIT in 2013 under advisors Anant Agarwal and Srinivas Devadas, with his dissertation titled "SEEC: a framework for self-aware management of goals and constraints in computing systems." He earned an S.M. from MIT in 2003 and a B.S. with highest honors and distinction from UNC-Chapel Hill in 1999. Hoffmann's research focuses on developing self-aware computing systems that understand high-level goals and automatically adapt their behavior to meet those goals optimally. His recent work has shifted toward applying these techniques to control machine learning and AI systems, building learning systems that dynamically adapt their internal structure and resource usage to meet accuracy, energy, performance, and security goals at inference time. His interdisciplinary approach combines operating systems, computer architecture, control theory, and machine learning. Analysis of Hoffmann's recent publications reveals a clear trajectory toward increasingly sophisticated applications of self-aware computing principles. His work has evolved from foundational resource management to cutting-edge applications in AI/ML systems, quantum computing, and security. The publications demonstrate a consistent theme of using control theory and machine learning to create adaptive systems that optimize multiple competing objectives like performance, energy efficiency, and reliability. Recent papers show expanding applications into large language models, quantum algorithms, and privacy-preserving techniques. Presidential Early Career Award for Scientists and Engineers (PECASE) 2019 DOE Early Career Award 2015 Samsung Security Hall of Fame recognition IEEE Micro Top Picks Honorable Mention awards FSE Test of Time Honorable Mention ASPLOS Hall of Fame recognition Hoffmann has mentored numerous PhD and Master's students who have gone on to successful careers in academia and industry. His research has secured over $19 million in funding for the University of Chicago. He co-founded Config Dynamics in 2019 to commercialize aspects of his self-aware computing research. His work has practical applications across data centers, edge computing, AI systems, and quantum computing. Hoffmann leads the SEEC (Self-aware, Energy-Efficient Computing) research group at the University of Chicago. The group focuses on developing frameworks and techniques for building self-aware computing systems that can dynamically adapt to changing conditions and requirements. The group maintains strong collaborations with industry partners and other academic institutions, particularly in the areas of quantum computing, AI systems, and energy-efficient computing.
Lyne DA SYLVA is Full Professor and Head of the School of Library and Information Sciences (École de bibliothéconomie et des sciences de l’information) within the Faculty of Arts and Sciences at Université de Montréal. She also leads several large-scale interdisciplinary research projects funded by SSHRC and FRQSC, sits on the governing bodies of the Observatory of Sense-Text Linguistics (OLST) and the Interuniversity Research Centre on Digital Humanities (CRIHN), and supervises graduate students in information science. Education B.Sc. in Applied Mathematics, Université d’Ottawa (1987) Certificate in Linguistics, Université de Genève (1991) M.A. in Linguistics, Université de Montréal (1990) Ph.D. in Linguistics, Université de Montréal (1999) Research Focus Da Sylva’s work lies at the intersection of computational linguistics and information science . She employs symbolic, rule-based models to develop tools for automatic indexing, summarisation, and semantic enrichment of digital documents. A second stream examines digital libraries as complex sociotechnical systems, investigating how language technologies can improve discovery and use of multilingual scholarly content. Thirdly, she explores documentary semiotics , analysing the sign systems that underlie information architectures in libraries, archives and museums. Recent projects centre on: automatic back-of-the-book indexing for digital monographs; research-data management ecosystems in Canadian universities; semiotic aspects of entities and identity in semantic-web datasets; algorithmic law and the migration of legal norms into technical devices. Funding & Partnerships Since 2005 she has been principal investigator or co-investigator on more than CAD 5 million in grants from SSHRC, FRQSC and NSERC. She currently co-leads the strategic partnership “Autonomisation des acteurs judiciaires par la cyberjustice” (2018-2026) and heads the FRQSC team grant “Le lexique entre humains et machines” (2025-2030). Scientific Awards & Distinctions While no named awards are explicitly listed, her continuous success in highly competitive tri-council funding programmes and her invitations to keynote or guest-edit leading journals (Document numérique, Documentation et bibliothèques) attest to national and international recognition. Supervision & Teaching At the master’s and doctoral levels she teaches courses on research-data management, indexing methodology, thesaurus construction, digital libraries and NLP tools for information professionals. She has supervised or co-supervised four recent theses covering legal recognition of smart contracts, archival vocabulary for thematic access, linked-data initiatives at Bibliothèque et Archives nationales du Québec, and terminological variation in thesauri. Laboratories & Teams Her research is anchored in the OLST (Observatoire de linguistique Sens-Texte) and the CRIHN (Centre de recherche interuniversitaire sur les humanités numériques), both of which provide computational infrastructure and interdisciplinary collaboration networks for projects in language technology, digital humanities and data curation.
Gordon Fraser is a Professor and holds the Chair of Software Engineering II at the Faculty of Computer Science and Mathematics, University of Passau, since 2017. His academic journey includes a doctorate from Graz University of Technology and research experience at Saarland University and the University of Sheffield. Research Interests: His work centers on software analysis, software development, and programming education. Key themes include preventing software errors through automated testing, improving developer productivity using AI-based tools, and enhancing programming training—especially for beginners and children. He actively explores the role of gamification, large language models, and search-based techniques in software testing and education. Recent Research Trends: His recent publications (2023–2024) show a strong focus on flaky tests in Python, automated test generation for Android and block-based environments (Scratch), gamification of testing in IDEs, and educational studies on pair programming, gender dynamics, and feedback mechanisms in primary computing education. The integration of AI and machine learning in software testing is a recurring theme, especially in the context of large language models and neuroevolution. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: He leads several research projects, including DFG-funded TYPES4STRINGS and DeepMBT , and the DeepCode project which explores AI for code quality. He mentors a large number of students and researchers, particularly in the areas of automated testing and programming education. His advising spans PhD and Master’s students, many of whom are co-authors on his publications. Labs and Teams: He leads a research group at the University of Passau focused on software testing, analysis, and education. The team develops tools like Pynguin for automated Python testing and Code Defenders for gamified software testing education. They also work on LitterBox , a linter for Scratch programs, and various gamification plugins for IDEs and educational platforms.
