Prof. Dr. Lena Wessel is a Mathematics Education faculty member at the Department of Mathematics Didactics, Faculty of Electrical Engineering, Computer Science and Mathematics, University of Paderborn. She leads teacher training programs and participates in university governance as elected Faculty Council member. Research Focus: Her work centers on comprehension-oriented mathematics instruction at secondary/vocational levels, emphasizing language development, continuity in curriculum design (spiral principle), and digital mathematics tools. As DZLM network partner, she develops professional development programs for secondary educators. LiaNMU project (scalar products, plane reflections) VioLa project (digital discussion formats) LLV.HD project (cross-university learning networks) Key Contributions: She has coordinated major projects like MESUT (language-supported fraction understanding) and LaMaVoC (language in vocational mathematics). Her 2024 publications analyze language-integrated pedagogy and algebraic concept unification in teacher training.
James Massey is a Senior Research Fellow at the University of Cambridge, affiliated with the Department of Engineering under the Energy Group. His research focuses on computational fluid dynamics (CFD), turbulent reacting flows, and hydrogen combustion, supported by funding from Mitsubishi Heavy Industries (MHI). He holds a PhD in Engineering (2015-2019) and an MEng in Mechanical Engineering (2011-2015) from The University of Manchester. PhD in Engineering, University of Cambridge (2015-2019) MEng in Mechanical Engineering, The University of Manchester (2011-2015) His work spans hydrogen combustion , thermo-acoustics , and large eddy simulation (LES) , targeting emissions prediction, flame stabilization, and combustion instability. Key themes include mitigating CO/NOx emissions, analyzing swirl-stabilized flames, and developing skeletal mechanisms for hydrogen-hydrocarbon blends. Recent publications emphasize multi-regime combustion modeling , thermo-acoustic instability analysis , and machine learning applications in LES. His research often involves cross-institutional collaboration with MHI and contributions to combustion physics through DNS and LES frameworks. Sugden Award (2024) for best paper in The Combustion Institute British Section James contributes to teaching as a lecturer for courses like 4A13 Combustion and Engines (2023-2025) and ETB-1 Clean Fossil Fuels (2022-2023). He is a fellow of Robinson College and an active member of the Institute of Physics Combustion Physics Group and The Combustion Institute British Section.
Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.
Dr. Reine van der Wal is an Associate Professor at the Faculty of Social and Behavioural Sciences , Utrecht University, specializing in Social, Health and Organisational Psychology . Her work focuses on the intersection of social relationships, forgiveness, and organizational dynamics. Research Themes: Forgiveness, interpersonal relationships, workplace psychosocial risks, and interdisciplinary teaching. Collaborations: Graduate School of Life Sciences, UWV (Dutch Employee Insurance Agency), Netherlands Labour Authority. Her research explores relationship imbalances in organizations and families, including restoring social safety after trust breakdowns, forgiveness in post-divorce co-parenting, and workplace aggression. She integrates findings into policy development on social safety at Utrecht University. Key Publications (2024-2017) address forgiveness in late childhood, romantic relationships, work-family guilt during the pandemic, and dyadic coping in chronic illness. These works span psychology, organizational ethics, and developmental studies, emphasizing emotional regulation and social cohesion. Scientific Awards: SPSP Service Award (In-Mind Magazine, 2016) Advising: Supervises PhD research on social safety in academia (Julia Houben) and institutional trust (Michiel Kuik). Involved in workplace psychosocial risk research with Netherlands Labour Authority.
Qi Long is a Professor at the University of Pennsylvania, holding joint appointments in the Department of Biostatistics, Epidemiology and Informatics (Perelman School of Medicine), Department of Computer and Information Science (School of Engineering and Applied Science), and Department of Statistics and Data Science (The Wharton School). He serves as Founding Director of the Center for Cancer Data Science, Associate Director of the Penn Institute for Biomedical Informatics, and Associate Director for Quantitative Data Science at the Abramson Cancer Center. His research bridges statistical and machine learning (ML/AI) method development with biomedical applications, focusing on precision medicine and population health. Education : Ph.D. (2005) and M.S. (2003) in Biostatistics from University of Michigan; B.S. (1998) in Computer Science from University of Science and Technology of China. Research interests include: Robust statistical and ML/AI methods for big health data (-omics, EHRs, imaging, mHealth) Multimodal data integration and subgroup heterogeneity analysis Missing data, causal inference, Bayesian methods, and clinical trials Data privacy, algorithmic fairness, and responsible AI in healthcare Foundation models and agentic AI for biomedicine His publications focus on privacy-preserving AI, fairness-aware ML, and integrative models for multi-omics and EHRs. Recent work explores LLMs and watermark detection in hybrid human-AI settings. Scientific Awards : Elected fellow: AAAS, ASA, IMS, ISI, AMIA He leads large NIH- and ARPA-H-funded initiatives, directing statistical coordinating centers for national clinical trials. His lab trains numerous PhD/Master’s students and postdocs, many of whom hold prestigious academic or industry positions.
Omobolanle Ogunseiju is an Assistant Professor in the School of Building Construction at Georgia Institute of Technology . She holds a Ph.D. in Environmental Design and Planning from the Department of Building Construction at Virginia Tech. Education: Ph.D. in Environmental Design and Planning, Virginia Tech Current Role: Assistant Professor, Georgia Tech School of Building Construction Her research focuses on integrating wearable robotics and Artificial Intelligence (via digital twin , cyber-physical systems , and data sensing ) to improve construction workforce safety, health, and well-being . She explores ethical implications of automation in construction, particularly in human-technological dynamics. Key research trends include: Advancing smart communities through robotics and AI Exoskeleton evaluation for ergonomic risk reduction Mixed reality environments for construction education Data analytics for cognitive and physical risk assessment Professional identity development in construction engineering students Industry-academia alignment for sensing technology integration Scientific awards: Outstanding Doctoral Candidate, Myers-Lawson School of Construction Outstanding Doctoral Student, College of Architecture and Urban Studies at Virginia Tech Teaching philosophy emphasizes experiential learning , engagement techniques , and hierarchical assessments . She developed the Construction Cost Management course at Georgia Tech and will lead Construction Technology courses. Previously, she taught Smart Construction , Building Systems Technology , and Wireless Sensing in Construction Management at Virginia Tech.
Professor Ruurd Jaarsma serves as Clinical and Academic Director of Orthopaedics and Trauma Surgery for the Southern Adelaide Local Health Network, practicing at Flinders Medical Centre and Flinders Private Hospital. He holds full membership in Flinders University's College of Medicine and Public Health, Flinders Health and Medical Research Institute, and Medical Device Research Institute within the College of Science and Engineering, bridging clinical practice and academic research since relocating from the Netherlands in 2004. His educational credentials include: MD from University of Groningen, Netherlands (1994) Orthopaedic Surgeon certification from Dutch College of Orthopaedic Surgeons (2003) PhD from University of Nijmegen, Netherlands (2004) FRACS (Orth) from Royal Australasian College of Surgeons (2009) FA(Orth)A from Australian Orthopaedic Association (2012) Research centers on orthopaedic trauma, paediatric orthopaedics, and biomechanical engineering of implants, with specialized focus on rotational malalignment after long bone nailing. His work directly supports UN Sustainable Development Goal 3 (Good Health and Well-being) through trauma care innovation and surgical education. Current investigations integrate machine learning with fracture diagnostics to improve clinical decision-making. Recent publications demonstrate strong AI integration in orthopaedic trauma, featuring machine learning algorithms for scaphoid fracture probability estimation, deep learning classification of tibial plateau fractures, and open-source neural networks for distal radius fracture detection. Concurrent clinical trials address compartment syndrome diagnosis while biomechanical studies examine acetabular fracture outcomes, reflecting his dual focus on computational innovation and clinical validation. His scientific recognition includes: 2006 Burns Alpers Award for excellence in teaching from Flinders University As Director of Orthopaedic Training, he supervises Australian Orthopaedic Association accredited fellowships with registered interests in orthopaedic surgery, biomechanical engineering, and musculoskeletal medicine. His supervisory framework emphasizes translational research connecting biomechanical principles with surgical practice. External engagement includes active participation in SA State Trauma Committees, driving statewide improvements in trauma systems and surgical protocols. Professor Jaarsma maintains clinical-academic synergy through Flinders Medical Centre's orthopaedic department, where he leads trauma service development while directing research initiatives in the Medical Device Research Institute. His current projects focus on AI-driven fracture assessment tools and biomechanical optimization of implant systems, with ongoing collaborations across the Machine Learning Consortium and international orthopaedic networks.
Eldan Cohen serves as an Assistant Professor of Industrial Engineering within the Department of Mechanical & Industrial Engineering at the University of Toronto's Faculty of Applied Science and Engineering. His academic journey includes a PhD from the same department followed by a postdoctoral fellowship in Computer Science at the University of Toronto and the Vector Institute for Artificial Intelligence. His educational background is detailed as follows: PhD in Mechanical & Industrial Engineering, University of Toronto Postdoctoral Fellowship in Computer Science, University of Toronto and Vector Institute for Artificial Intelligence Dr. Cohen's research centers on machine learning, deep learning, heuristic search, and optimization with strong emphasis on interpretable and human-compatible AI systems. His work bridges theoretical advancements with practical applications in healthcare (e.g., patient-physician interaction analysis, surgical safety diagnostics), automated planning, natural language processing, and software engineering. Recent projects develop interpretable clustering methods for medical data and optimization techniques for constrained sequence generation. Analysis of his 2023-2025 publications reveals a concentrated focus on healthcare AI applications, particularly using large language models for clinical text analysis and diagnostic support systems. Significant work also addresses interpretable machine learning for medical imaging, diverse plan selection in optimization, and constrained sequence generation in domains like vehicle routing. No major scientific awards or fellowships are documented in the available information. As an academic advisor, Dr. Cohen mentors graduate students in mechanical and industrial engineering, guiding research in optimization and machine learning. His OptiMaL research group fosters collaboration between computer science and industrial engineering to solve real-world decision-making challenges through human-centered AI approaches. The Optimization and Machine Learning (OptiMaL) research group, led by Dr. Cohen, serves as the primary hub for developing scalable, interpretable AI solutions for complex healthcare, planning, and engineering problems, with active projects in medical diagnostics and automated planning systems.
Nicole Novielli, Ph.D., is Associate Professor at the University of Bari “A. Moro” , Italy, where she conducts research on affective computing applied to software engineering and human-computer interaction. She leads the Collaborative Development Group and coordinates national projects investigating emotions in software teams, AI quality and IoT ecosystems. Education: Ph.D. in Computer Science, University of Bari, 2010 – thesis on “Lexical Semantics of Dialogue Acts” M.Sc. in Computer Science (Knowledge & Software Engineering), University of Bari, 2006 – summa cum laude B.Sc. in Computer Science, University of Bari, 2004 – summa cum laude Visiting researcher at USC-ICT, University of Aberdeen, FBK-irst (Trento) Research interests revolve around recognizing and exploiting affective and cognitive states in computer-mediated cooperative work. She studies sentiment and emotion mining in developers’ textual communication, multimodal emotion recognition via low-cost biometric sensors, and natural-language dialogue simulation for intelligent interfaces. Her work couples software engineering with natural language processing , social media analytics and human-computer interaction . Recent articles (2021-2025) reveal a clear trend: integrating deep learning and large language models into software engineering tasks—automated issue labelling, sentiment classification, technical-debt detection—while validating these techniques through rigorous empirical studies and biometric experiments . A parallel stream explores developer experience , measuring how emotions and cognitive load influence productivity, code quality and collaboration. Scientific awards include the 2020 Apex Award for Publication Excellence , multiple Distinguished Reviewer Awards at flagship venues (ESEC/FSE, ICSME, MSR), the Best Paper Award SANER 2019 and the Best Student Paper Award ACII 2009 . She currently teaches “Sentiment Analysis” in the Data-Science MSc and “Computer Networks” in the ITPS programme. She has advised numerous B.Sc., M.Sc. and PhD projects and is PI or Co-PI of four ongoing grants: EmoQuest (SIR), EMPATHY (PRIN), FAIR-Spoke 6 (PnRR), and QualAI (PRIN 2022). Dr. Novielli serves on the editorial boards of Empirical Software Engineering and Journal of Systems and Software , has guest-edited special issues on affect awareness in SE, and has chaired tracks at ICSE, SANER, MSR, ICSME and SSBSE. She co-leads the Collaborative Development Group and actively releases datasets and open-source tools for the community.
Asunción Gómez Pérez is a Spanish computer scientist and Full Professor at the Technical University of Madrid (UPM) . She currently serves as Vice-Rector for Research, Innovation and Doctoral Studies at UPM and holds a seat at the Real Academia Española . She has authored over 300 publications and accumulated 20,000 citations. Education : PhD in Computer Science (UPM, 1993), MBA (Comillas Pontifical University) Leadership Roles : Director of the Department of Artificial Intelligence (2008–2016), Academic Director of AI Master’s/PhD programs (2009–2016), Executive Director of UPM’s Artificial Intelligence Lab (1995–1998) Her research focuses on Semantic Web and Ontology Engineering , with applications in knowledge representation, machine-machine communication, and multilingual data integration. She pioneered methods for ontology validation, metadata licensing, and AI-driven social inclusion. Key publication trends include: Ontology evaluation frameworks (e.g., OOPS!) Linked Data quality models and validation tools Multilingual and cross-lingual AI applications Interoperability solutions for smart cities and healthcare Machine Learning for social exclusion prediction Ontology-driven library and lexicography systems Scientific Awards Fellow of the European Academy of Sciences Ada Byron Prize She has led projects like the NeOn Methodology for ontology development and contributed to the European framework for linked data rights (LD Terms). Her work bridges theoretical research with practical implementations in AI and Semantic Technologies.
Florian Brandl is an Argelander Professor (associate professor with tenure) at the University of Bonn, holding positions in both the Department of Economics and the Hausdorff Center for Mathematics. Previously, he was a postdoctoral research scholar at Princeton University and Stanford University. His academic career demonstrates a strong foundation in mathematical economics and game theory, with affiliations spanning multiple prestigious institutions. Brandl earned his Doctoral degree in Mathematics (summa cum laude) from the Technical University of Munich in 2018, following a Master's degree (2013) and Bachelor's degree (2011) from the same institution. His doctoral work focused on "Zero-Sum Games in Social Choice and Game Theory" under the supervision of Felix Brandt, establishing the foundation for his research trajectory. Prof. Brandl's research spans microeconomic theory with a focus on social choice theory, decision theory, and game theory. He is particularly interested in decision-making under uncertainty, connections between social choice and game theory, and dynamic processes converging to equilibrium. His work employs mathematical tools to analyze interactions of multiple entities in economic contexts, often incorporating algorithmic approaches and methods from theoretical computer science. He has made significant contributions to fair division, mechanism design, and probabilistic social choice. His publication record shows consistent contributions across multiple subfields, with recent work focusing on patience effects in fair division, social learning barriers, and axiomatic characterizations of equilibrium concepts. Brandl's research demonstrates strong interdisciplinary connections between economics, mathematics, and computer science, with publications in top journals across all three disciplines. Best Student Paper Award at WINE 2021 for "Funding Public Projects: A Case for the Nash Product Rule" Associate Editor for Theoretical Economics Co-organizer of the COMSOC Video Seminar Prof. Brandl actively contributes to academic service and community building. He co-organizes the COMSOC Video Seminar and will host a Trimester Program on "Advances in Mechanism Design" in Bonn in summer 2026. He serves on program committees for major conferences including COMSOC 2023 and EC 2023. His research has been supported through his position as a Bonn Junior Fellow at the Hausdorff Center for Mathematics since 2021. Based at the Institute for Microeconomics and affiliated with the Hausdorff Center for Mathematics, Brandl collaborates with a broad network of researchers across economics and computer science. His work often involves interdisciplinary collaboration, as evidenced by his numerous co-authored publications with researchers from various institutions worldwide. He maintains strong connections with the University of Oxford's Global Priorities Institute as a Research Affiliate.
Prof. Dr. Thomas Koop is a Professor of Physical Chemistry at Bielefeld University, where he leads the Atmospheric and Physical Chemistry research group within the Faculty of Chemistry. He has served as Dean of the Faculty of Chemistry from 2022-2024 and currently serves as Vice Dean (2024-2025). His research focuses on phase transition phenomena, particularly ice nucleation and growth, supercooled liquids, and the formation of amorphous glassy materials. His work has significant implications for understanding atmospheric aerosols, cloud formation mechanisms, and cryobiological processes. The group employs experimental techniques such as differential scanning calorimetry and optical cryo-microscopy, developing specialized equipment for studying phase transitions at micro and nanoscales. Prof. Koop's publication record shows a consistent focus on atmospheric chemistry with increasing exploration of biological ice nucleators, planetary atmospheres (including Venus), and the physical properties of atmospheric aerosols. His most cited work includes 'Water activity as the determinant for homogeneous ice nucleation in aqueous solutions' (Nature, 2000), which established fundamental principles in the field. 2024-2025: Vice Dean of Faculty of Chemistry 2022-2024: Dean of Faculty of Chemistry 2001-2022: Co-founder and Executive Editor of Atmospheric Chemistry and Physics Since 2004: Coordinator of Graduate School of Chemistry and Biochemistry Prof. Koop has mentored numerous students and postdoctoral researchers, contributing significantly to the development of the next generation of atmospheric scientists. His research has been supported by various funding agencies and has led to collaborations with institutions worldwide, from MIT and UC Berkeley to research centers in Switzerland and Israel.
Steve Tanimoto is a Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, with an adjunct appointment in the Department of Electrical & Computer Engineering. His work focuses on human-centered computing, particularly in educational technology and collaborative problem-solving environments. He has made significant contributions to the understanding of liveness in programming environments and their application to education, including a keynote at the International Conference on Live Coding (2015) that traced historical influences leading to widespread use of liveness in modern software environments. Dr. Tanimoto's research spans several interconnected domains: Novice programming environments and educational technology Collaborative problem-solving environments and tools Technology for educational assessment, particularly using pattern-recognition methods for teaching written language on tablets Liveness in programming environments and its applications Image processing from interdisciplinary perspectives (as detailed in his MIT Press book "An Interdisciplinary Introduction to Image Processing: Pixels, Numbers, and Programs") His recent publications demonstrate a consistent focus on the intersection of computing education, human-computer interaction, and collaborative problem-solving. A notable trend is the exploration of "liveness" in programming environments and how this concept can enhance educational experiences. His work increasingly integrates AI technologies with educational applications, particularly in the areas of writing instruction and collaborative problem-solving, with significant NIH funding support (P50 HD071764 and U54 HD083091). His notable recognition includes: VL/HCC Best Showpiece Award in 2015 for "Solving Problems by Drawing Solution Paths" Dr. Tanimoto has advised several graduate students through to completion, including Robert Thompson (2019), Sandra Fan (2013), and Tyler Robison (2012). He currently advises Emilia Gan (co-advised with B. Mako Hill) and Edward Misback. His research has been supported by NIH grants for work on computerized writing and reading instruction for students with learning disabilities. His CoSolve research group has developed experimental facilities for collaborative problem-solving, exploring tools that support problem formulation, visualization of problem spaces, and team collaboration dynamics, with applications in education, design, and various problem-solving domains.
James Gray is an Associate Professor of Physics and Affiliate Professor of Mathematics at Virginia Tech, affiliated with the Department of Physics within the College of Science. His research focuses on string compactifications, particularly exploring the intersection of String Theory with particle physics phenomena. He holds a Ph.D. from the University of Sussex and has been supported by grants such as NSF PHY-2310588. Gray’s research interests include mathematical string theory, algebraic geometry applications to string phenomenology, and computational methods like the STRINGVACUA Mathematica package. His work involves classifying Calabi-Yau manifolds, studying fibrations, and analyzing heterotic and F-theory compactifications to derive realistic particle physics models. His recent articles emphasize geometric structures (e.g., Calabi-Yau fibrations, moduli spaces) and computational approaches for metric approximations using machine learning. He collaborates on datasets for Calabi-Yau fourfolds and line bundle cohomology, contributing to string phenomenology’s algorithmic tools. Gray leads efforts in theoretical particle physics at Virginia Tech and is involved with the Center for Neutrino Physics. His work bridges pure mathematics (e.g., algebraic geometry) with high-energy physics, aiming to connect abstract geometric constructions to observable particle physics parameters.
Dr. Kathryn Lester is an Associate Professor in Developmental Psychology at the University of Sussex's School of Psychology. She leads internationally recognized research on childhood anxiety, focusing on intergenerational transmission, cognitive biases, and school mental health interventions. Her work includes developing evidence-based programs for emotionally-based school avoidance and evaluating whole-school approaches to mental health. She holds leadership roles in Sussex’s senior management team, including Subject Group Lead for Developmental Psychology and Deputy Director for Postgraduate Research. She co-leads the Sussex Foundation Partnership Trust School Mental Health Research Team Clinic and has secured funding from the National Institute for Health Research, ESRC, and The National Lottery Community Fund. Her academic journey includes a D.Phil. in Psychiatry from the University of Oxford (2008) and postdoctoral research at the University of Sussex and King’s College London. Key research interests include anxiety prevention, school-based interventions, and the impact of parenting behaviors on child mental health. She has collaborated with organizations like the Sussex Wildlife Trust and provided consultancy for educational content development, such as children’s book series on emotions and ITV’s ‘Planet Child’ series. Her research spans mixed-methods approaches, including participatory methods with children and caregivers. Notable projects include NIHR-funded studies on digital mental health toolkits and Kavli Trust-funded interventions to reduce anxiety transmission from parents to children. She actively engages in knowledge exchange and mentoring early-career researchers.
