Yongfeng Zhang is an Associate Professor in the Department of Computer Science at Rutgers University. He is also the Director of the AIOS Foundation . His research focuses on Machine Learning, Data Mining, Recommender Systems, and Explainable AI , with notable contributions to fair and personalized AI, AI for science, and social good. Education and Experience: PhD in CS (2011–2016) from Tsinghua University Postdoc at UMass Amherst (2016–2017) Joined Rutgers as Assistant Professor (2018), promoted to Tenured Associate Professor (2024) Research Interests: Machine Learning, Data Mining, Information Retrieval, Recommender Systems, Natural Language Processing, ML Systems, AI Agents, Explainable AI, Fairness, and AI for Science. Recent Achievements: Received the 2024 ACM SIGIR Test of Time Award, 2024 Presidential Teaching Excellence Award, and multiple NSF grants. He has advised over 15 PhD students and led projects on trustworthy AI, generative recommendation, and causal inference. Awards: 2024 ACM SIGIR Test of Time Award 2021 NSF CAREER Award 2015 Microsoft PhD Fellowship Grants & Labs: Led NSF grants on explainable AI, conversational recommendation, and neural-symbolic AI. Active in the AIOS platform development and multiple research labs.
Dr. Meredith Carroll is a Professor of Aviation Human Factors and Director of the ATLAS Lab at Florida Institute of Technology's College of Aeronautics. She holds a B.S. in Aerospace Engineering from the University of Virginia, an M.S. in Aviation Science from Florida Tech, and a Ph.D. in Applied Experimental Psychology and Human Factors from the University of Central Florida. Her research focuses on human performance in aviation systems, including decision-making under stress, human-autonomy teaming, advanced air mobility (AAM), and cybersecurity education. Key research initiatives include: Investigating trust dynamics in human-agent teams funded by AFOSR User-centered design of multi-UAV ground control stations for NASA eVTOL battery display design and pilot interface studies Cybersecurity curriculum development for underrepresented high school students Adaptive flight training strategies funded by the Naval Air Warfare Center Her lab facilities include Florida Tech's Center for Aeronautics and Innovation with advanced flight simulators and UAS testing environments. Current projects explore VR training immersion effects, multi-agent team repair strategies, and AAM operational challenges. She advises 12+ graduate and undergraduate researchers and collaborates with industry partners like P17 Solutions and Rockwell Collins. Recent publications emphasize trust calibration in autonomous systems, IoT-based cybersecurity education, and adaptive training efficacy. She has secured grants from NASA, FAA, AFRL, ONR, and NAWC totaling over $5M in external funding.
Dr. Yuanyuan Yuan is a Researcher at the Department of Computer Science, ETH Zurich, Switzerland, based at CNB H 104.1, Universitätstrasse 6, 8092 Zurich. Her work bridges computer security and machine learning with a focus on practical vulnerabilities in deployed AI systems. Her research centers on exposing and mitigating security flaws in deep learning deployments, particularly targeting trusted execution environments (TEEs) and on-device inference systems. Key contributions include pioneering side-channel attacks against TEE-shielded neural networks (CipherSteal, HyperTheft), bit-flip attack surfaces in DNN executables (BitShield), and novel testing methodologies for neural network robustness. She investigates cache/timing side channels, ciphertext analysis, privacy leakage in partitioned ML, and concept-based explainability. Analysis of her 2023-2025 publications reveals a cohesive focus on offensive security research for AI infrastructures, with consistent contributions to top venues in security and machine learning. Her work demonstrates expertise in low-level system interactions (memory, cryptography) applied to ML security, spanning attack vectors, defensive mechanisms, and validation frameworks. The research trajectory shows increasing sophistication in exploiting hardware-software interfaces while developing practical hardening techniques for real-world deployments.
Alfonso Emilio Gerevini is a prominent researcher in artificial intelligence with over 30 years of continuous academic contributions. His work spans theoretical foundations of automated planning to practical healthcare applications, with recent publications demonstrating significant impact in both traditional AI domains and emerging interdisciplinary areas. His research interests focus on automated planning systems , temporal reasoning , and multi-agent coordination , with recent expansion into healthcare applications using machine learning techniques. Gerevini has made fundamental contributions to planning algorithms, particularly in width-based search, case-based planning, and privacy-preserving multi-agent planning. His work on PDDL (Planning Domain Definition Language) has been influential in standardizing planning representations. Analysis of his 15 most recent publications reveals a strategic evolution from core planning research toward impactful healthcare applications, particularly during the COVID-19 pandemic. While maintaining his expertise in planning algorithms, he has successfully integrated machine learning techniques to address real-world medical challenges including radiology report analysis, prognosis prediction, and lab test interpretation. His work demonstrates exceptional versatility across both theoretical and applied domains of artificial intelligence. Gerevini maintains a robust collaborative network, primarily with Italian researchers including Ivan Serina, Alessandro Saetti, and Luca Putelli. His publications appear consistently in top-tier AI venues including Artificial Intelligence journal, Journal of Artificial Intelligence Research, and AAAI/ICAPS conferences. The collaborative patterns suggest he leads a significant research group focused on advancing planning systems while applying them to critical real-world problems.
Dr. Julian Hough is an Associate Professor of Human-Computer Interaction at Swansea University, affiliated with the School of Mathematics and Computer Science under the Faculty of Science and Engineering. His research focuses on improving human-agent interaction through Natural Language Processing (NLP) and AI, emphasizing ethical and quality outcomes in human-robot collaboration. His work spans Human-Robot Interaction (HRI), dialogue systems, and cognitive applications of speech technology. Notable projects include the FLUIDITY initiative exploring virtual reality platforms for HRI and the ARCIDUCA project annotating dialogue using conversational agents in games. He has secured significant grants, including a £587,000 EPSRC New Investigator Award for FLUIDITY and a £1.09M EPSRC grant for ARCIDUCA. Research interests include multimodal communication, disfluency analysis in dialogue, and applying LLMs to word sense disambiguation. His work often bridges computational linguistics with practical robotics and health technology, such as analyzing wearable sleep-tracker subjectivity and detecting Alzheimer’s through speech patterns. Collaborations span institutions globally, with contributions to workshops and conferences on HRI and dialogue systems. He actively supervises postgraduate research in areas like incremental intention recognition and computational law semantics.
Camelia D. Brumar is a PhD Candidate in Computer Science at Tufts University and a Visiting PhD Student at Harvard University's Visual Computing Group. She co-founded Boston Vis , a collaborative network for visualization researchers in the Greater Boston Area. Education: B.S. in Theoretical Mathematics from University of Maryland, College Park Research Focus: Systematic visualization design for decision-making processes, bridging gaps between problem spaces and design spaces through qualitative methods Her work intersects Visual Analytics , Human-Computer Interaction , and Machine Learning , with recent publications on decision-making taxonomies, dimensionality reduction explanations, and knowledge graph visualization. Key trends include: Interactive predicate logic for pattern explanation Domain expert challenges in automated data science Anomaly reasoning frameworks Medical AI applications for embryo grading Scientific Achievements: Organizer of Boston Vis (2024) Tutorial presenter on LLMs for research paper interaction (2024) IEEE Visualization 2024 Doctoral Colloquium participant Contributor to Dagstuhl Seminar on provenance in automated data science (2023) Industry experience includes roles at Tableau Research , Alife Health , and Bose Corporation , with collaborations spanning MIT Lincoln Laboratory, National Renewable Energy Laboratory, and Worcester Polytechnic Institute.
Dr. Yu (Chelsea) Jin is an Assistant Professor in the Department of Industrial Engineering at the University at Buffalo, specializing in quality inspection, predictive modeling, and data analytics for advanced manufacturing systems. She holds a PhD in Industrial Engineering from the University of Arkansas, an ME from the University of Michigan, and dual BS degrees in Network Engineering and Finance from Jinan University. Her research focuses on integrating machine learning and physics-based models to optimize manufacturing processes, such as additive manufacturing, PCB assembly, and pharmaceutical distribution systems. She has developed frameworks like ReflowNet for reflow oven optimization and physics-informed neural networks for thermal profile prediction. Her work emphasizes both theoretical advancements and practical applications in smart manufacturing and healthcare logistics. Dr. Jin's recent publications highlight contributions to generative AI for knowledge retrieval, AGV system optimization, and multi-source transfer learning for pandemic modeling. She actively collaborates with industry partners to bridge academic research and real-world manufacturing challenges.
Tushar Athawale is a Research Scientist at Oak Ridge National Laboratory (ORNL) and a Joint Faculty Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. His primary research focuses on uncertainty visualization, statistical data analysis, and high-performance computing for large-scale scientific data. He holds a PhD in Computer Science from the University of Florida (2015) and has held roles including Postdoctoral Fellow at the University of Utah's Scientific Computing & Imaging Institute and Application Support Engineer at MathWorks. His academic and professional affiliations include ORNL's Computer Science and Mathematics Division, the IEEE Visualization Conference program chair (2025), and associate editor for IEEE Transactions on Visualization and Computer Graphics. He has organized workshops, tutorials, and served on program committees for major visualization conferences. Key research interests span uncertainty quantification, topological methods, and visualization techniques for biomedical imaging, fusion simulations, and quantum computing. His work emphasizes trustworthy scientific data analysis through advanced visualization frameworks like VTK-m and implicit neural representations. Awards include ORNL's 2024 Special Award and Best Paper Honorable Mention at the IEEE Uncertainty Visualization Workshop 2024. His contributions bridge visualization theory with practical applications in exascale computing and AI-driven decision-making.
Brian Mitchell is a Teaching Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics (CCI). He brings over two decades of combined industry and academic experience, transitioning fully into academia in 2022 after serving as a Distinguished Engineer at a Fortune 15 company. His work bridges cutting-edge research and practical innovation in software systems. Drexel University, College of Computing & Informatics, Department of Computer Science Education: PhD in Computer Science, Drexel University MS in Computer Science, Drexel University BS in Computer Science, Drexel University ME in Computer & Telecommunication Engineering, Widener University Brian Mitchell's research centers on the intersection of Software Engineering, Software Architecture, Cloud Native Computing, and AI . His early foundational work helped establish the field of Search-Based Software Engineering (SBSE) , particularly in automated software clustering and architecture recovery. Recently, his focus has shifted to modern challenges in cloud-native environments , including misconfiguration detection, malware analysis, and resilient system design. He integrates security, scalability, and intelligent automation into software engineering practices. His recent publications reflect a clear trend toward AI-enhanced cloud-native systems , emphasizing automated analysis, security, and architectural robustness. These works appear in AI and cloud computing venues, showing interdisciplinary engagement. The evolution from source code clustering to cloud-native engineering illustrates his adaptability and leadership in emerging domains. Scientific Awards: Best Paper Award, GECCO'03 Best Paper Award, WCRE'01 Brian is actively involved in mentoring students and encourages research collaboration, particularly with those seeking deeper engagement beyond coursework. He emphasizes hands-on learning and uses modern tools like GitHub and Discord in his teaching. While no specific grants are listed, his industry leadership in digital innovation and open-source contributions suggests strong applied research support. He previously led large engineering teams and drove disruptive technological adoption in enterprise settings. Though no formal lab name is mentioned, his research group appears focused on software architecture, cloud systems, and AI-driven engineering , likely operating under informal or course-based research initiatives. His website and GitHub presence (@ArchitectingSoftware) suggest an active, open, and collaborative environment for student research.
Ammar Mian is an Associate Professor at Université Savoie Mont Blanc, affiliated with the LISTIC lab and Polytech Annecy-Chambéry. He holds a PhD from CentraleSupélec (2016-2019) and conducted postdoctoral research at Aalto University (2019-2020). His research focuses on statistical signal processing, machine learning, and Riemannian geometry with applications in remote sensing and frugal computations. He leads the Qanat project, an experiment tracking tool for reproducible research. Research interests include covariance-based methods for SAR image analysis, robust detection algorithms for sonar and GPR systems, and optimization on Riemannian manifolds. His work emphasizes reproducibility in ML and efficient computational techniques for resource-constrained environments. Key contributions include real-time SAR time-series change detection, robust classification using second-order deep learning models, and novel methods for handling missing data in EEG signals. His recent articles (2023-2025) explore reproducibility frameworks, GPR-based object classification, and Riemannian geometry applications. No awards listed, but maintains active collaborations through LISTIC and industry partnerships. Advises students via internship programs (e.g., Federated ML energy cost analysis). Lab work involves developing open-source tools like Qanat for experiment management and reproducibility.
Martin Diehl is a computational materials scientist affiliated with KU Leuven (Departments of Computer Science and Materials Engineering) and the Max-Planck-Institut für Eisenforschung GmbH in Germany. His work focuses on crystal plasticity simulations, computational materials engineering, and multi-physics modeling of metallic systems. Research interests include: Crystal plasticity finite element method (CPFEM) and spectral solvers Microstructure evolution and damage mechanics Machine learning applications in materials design Development of the DAMASK simulation toolkit Multi-phase steel alloys and heterogeneous deformation Integrated computational materials engineering (ICME) Key trends in his publications since 2021 highlight advancements in: Multi-physics DAMASK framework for coupled chemo-mechanical and thermal simulations AI-driven inverse design of steel microstructures Damage modeling in dual-phase steels Collaborative software development for materials science Experimental-simulation integration for stress-strain partitioning High-resolution spectral methods for finite strain analysis He actively collaborates with institutions like Harbin Institute of Technology, University of Oxford, and research groups across Europe and Asia.
Soyeon Ahn is a full-time Professor in the Department of Education and Psychological Studies at the University of Miami's School of Education and Human Development. Her academic profile demonstrates active research leadership in health communication, educational assessment, and psychometrics, with recent publications spanning 2024-2025. Contact details include email s.ahn@miami.edu, phone (305)284-2929, and ORCiD 0000-0003-2581-306X. Research interests focus on Health Belief Model applications in social media health campaigns, fairness evaluation of high-stakes educational exams, and eHealth interventions for obesity management. She investigates how content creator characteristics (e.g., race, occupation) affect user engagement with health messages, examines psychometric validation of assessment tools via Rasch modeling, and explores generative AI's pedagogical impact. Her work integrates intersectionality frameworks to address health disparities in clinical settings. Recent publications reveal strong interdisciplinary trends connecting public health, education, and technology. Key themes include methodological rigor in meta-analyses of occupational cancer studies, social media's role in vaccine behavior change, and AI literacy training for educators. Her findings consistently emphasize practical implications: optimizing health messaging through HBM constructs, addressing test bias in educational equity, and leveraging technology for behavior intervention. The 2024-2025 output shows increasing focus on real-world application of measurement theory.
Gemma Catolino is an Assistant Professor at the Department of Computer Science, University of Salerno, and affiliated with the Software Engineering (SeSa) Lab. She has also served as an Assistant Professor at Tilburg University and Eindhoven University of Technology through the Jheronimus Academy of Data Science from September 2022 to December 2023, and previously as a Postdoctoral Researcher at Delft University of Technology and Tilburg/Eindhoven institutions. PhD in Computer Science, University of Salerno (2020), supervised by Prof. Filomena Ferrucci MSc in Management and Information Technology, University of Salerno (2016, magna cum laude) BSc in Computer Science, University of Molise (2014) Her research centers on empirical software engineering, focusing on both technical and social aspects affecting software development. Key areas include code smells, defect prediction, testability, changeability, and the emerging concept of “Community Smells”—social dysfunctions in developer teams. She investigates how human factors, team diversity (especially gender), and developer experience influence software quality and maintenance effort, often using mining software repositories and machine learning techniques. Her recent publications span high-impact journals and conferences such as IEEE TSE, EMSE, JSS, ICSE, and ICSME, with a strong trend toward integrating social and technical metrics for just-in-time defect prediction in mobile applications, analyzing community dynamics, and applying software quality metrics to cybersecurity contexts like dark web analysis. She has also contributed to MLOps and serverless computing. She has received several honors including a DEI research grant (2020), Best Technical Paper at BENEVOL 2019, first and second place in ACM Student Research Competitions (2018, 2017), and the Best Master Thesis award from the Italian Software Metrics Association (2017). Gemma Catolino has been actively engaged in academic service as a referee for top journals like IEEE TSE, EMSE, JSS, and IST, guest editor for special issues, and program/organizing committee member for major conferences including ICSE, MSR, SANER, and MobileSoft, where she served as Program Co-Chair in 2022. She has also contributed as a teaching assistant, lecturer, and course coordinator in machine learning and software engineering courses. She leads and contributes to research projects involving international collaborations, particularly with researchers such as Prof. Filomena Ferrucci, Prof. Andy Zaidman, Prof. Willem-Jam van den Heuvel, and Prof. Alexander Serebrenik. Her work bridges empirical software engineering with practical tool development and socio-technical analysis, positioning her at the forefront of modern software engineering research.
Lauren Margulieux serves as Associate Professor in the Department of Learning Sciences within Georgia State University's College of Education & Human Development. She founded and directs the Snap Inc. Center for Computer & Teacher Education, where she coordinates statewide teacher preparation programs to integrate computing across all disciplines. Her academic credentials include a Ph.D. and M.S. in Engineering Psychology from Georgia Institute of Technology and a B.A. in Psychology from Southwestern University. Her research focuses on computer science education for programming novices, computational literacy development, and instructional design for teacher training. Key initiatives include creating Georgia's computer science endorsement program for in-service teachers and developing models for computational thinking integration in K-12 education. Her work bridges cognitive science principles with practical classroom applications, particularly examining spatial skills, failure resilience, and self-efficacy in programming contexts. Current scholarly trends reveal deep engagement with generative AI integration in teacher education, computational activity design across disciplines, and neurocognitive aspects of learning from failure. Her publications consistently address equity in computer science pathways while developing robust assessment instruments for programming self-efficacy and spatial ability impacts. Margulieux leads significant outreach through the Snap Inc. Center, establishing partnerships with school districts to broaden participation in computer science education. Her work emphasizes practical implementation frameworks for teacher educators and develops resources for urban school contexts, particularly focusing on marginalized student populations. The center serves as both research hub and professional development engine for Georgia's teaching workforce.
Massimo Poncino is a Full Professor at the Department of Control and Computer Science (DAUIN) within the Faculty of Engineering at Politecnico di Torino. He serves as Scientific Advisor for the STMicroelectronics partnership and coordinates basic engineering subjects. A Senior Member of IEEE since 2012 and Fellow since 2012, he has served on editorial boards for IEEE Transactions on Computer-Aided Design, IEEE Design & Test of Computers, and ACM Transactions on Design Automation. Education: Laurea in Electronic Engineering (1989) and PhD in Computer and Systems Engineering (1993) from Politecnico di Torino Academic Career: Visiting Scientist University of Colorado (1993-1994), Researcher at Politecnico di Torino (1995-2001), Associate Professor at University of Verona (2001-2004), Full Professor at Politecnico di Torino (2006-present) His research focuses on energy-efficient digital systems , including design automation of SoCs, hardware-aware AI, battery management, cyber-physical systems, and embedded systems. Recent publications highlight advancements in digital twins for batteries , low-power neural network deployment , and IoT privacy . Scientific Awards: Recognition of Service Award - ACM (2013) Certificate of Appreciation - IEEE Circuits and Systems Society (2006, 2008, 2009) IEEE Fellow (2012-) Research Involvement: EU H2020, VI/VII Framework Programs evaluator Scientific Director for projects: Approxim@ction, EMBAI, DISLO-MAN, DAMASCO Member of EDA research group Teaching: Course director for Energy Management for IoT (2019-2025) Lecturer for Computer Science courses (2003-2025)
