Professor Tommy Chan is Chair in Civil Engineering at Queensland University of Technology's School of Civil and Environmental Engineering. With over $10M in research funding, his work focuses on structural health monitoring of bridges and infrastructure systems. His research group develops cutting-edge methods for assessing structural integrity using vibration analysis, optical sensors, and machine learning. Professor Chan leads major projects including the ARC-funded 'Next Generation Bridge Monitoring' initiative developing real-time monitoring systems for prestressed concrete bridges. His team's innovations include GNSS-based settlement monitoring and synergic identification methods for prestress force evaluation. Current research explores vehicle-bridge interactions, damage detection algorithms, and novel materials for impact protection. He has received numerous honors including the Vice Chancellors' Leadership Award and Top Supervisor Award. Professor Chan founded the Australian Network of Structural Health Monitoring and serves on editorial boards for multiple journals in structural engineering.
Dietmar Maringer is Professor of Computational Economics and Finance at the University of Basel's Faculty of Business and Economics (WWZ), where he leads research at the intersection of finance, computational methods, and artificial intelligence. His work focuses on risk management, portfolio optimization, algorithmic trading, and financial simulations. His research interests span computational finance, artificial intelligence in finance, data analysis, risk management, portfolio optimization, algorithmic and high-frequency trading, financial networks, complex adaptive systems, and market simulations. He applies advanced computational and heuristic optimization techniques to solve real-world financial problems, contributing significantly to quantitative finance and financial engineering. His recent publications demonstrate a consistent focus on applying evolutionary algorithms, reinforcement learning, and numerical optimization to portfolio management, market impact modeling, and financial forecasting. The research integrates econometrics, machine learning, and financial theory, emphasizing practical implementation and robust risk-aware decision-making. Several best-paper awards Maringer has served as Chair of the Portfolio Optimization Section of the IEEE Computational Economics and Finance Technical Committee from 2008 to 2018 and is frequently involved in organizing and program committees of international conferences. He has advised or collaborated with numerous researchers, though specific student names are not listed. His research has been supported through academic affiliations and likely institutional or conference-based grants, though explicit funding sources are not detailed. He is affiliated with several research groups, including IEEE Computational Economics and Finance TC, COMISEF, ERCIM, Centre for Innovative Finance, and the European Financial Management Association, reflecting a broad collaborative network in computational finance and economics.
Ameya Jagtap is an Assistant Professor (Tenure-Track) in the Department of Aerospace Engineering at Worcester Polytechnic Institute (WPI), USA. Prior to this, he served as an Assistant Professor of Applied Mathematics (Research) at Brown University from 2021 to 2024. He holds a Ph.D. and M.E. in Aerospace Engineering from the Indian Institute of Science (IISc), and completed postdoctoral research at TIFR-CAM (India) and Brown University's Division of Applied Mathematics. His research bridges mechanical/aerospace engineering, applied mathematics, and computation, focusing on scientific machine learning algorithms that integrate data and physics. Key areas include physics-driven deep learning, uncertainty quantification, multi-scale simulations, and novel neural network architectures like quantum and graph networks. He serves on editorial boards for Neural Networks , Neurocomputing , and others. His work emphasizes interpretable neural operators for PDE solutions, domain decomposition methods, and adaptive activation functions to enhance PINN convergence. Notable contributions include XPINNs (extended physics-informed neural networks) and causal sweeping frameworks for PDEs. His research has been widely cited, particularly for PINN applications in supersonic flows and high-dimensional PDEs. Jagtap has delivered invited talks at institutions like Los Alamos National Laboratory, Tsinghua University, and the Alan Turing Institute. He is also recognized as a Top 2% World Scientist by Stanford University.
Marco Martino Rosso is a Research Fellow at the Department of Structural, Building and Geotechnical Engineering (DISEG) at the Polytechnic University of Turin, where he also serves as an external lecturer and teaching assistant in both DISEG and the Department of Mathematical Sciences (DISMA). He is affiliated with the Doctoral School (SCDOTT) and completed his PhD under the supervision of Professor Giuseppe Carlo Marano. His academic work bridges civil engineering with advanced computational methods, focusing on structural health monitoring, optimization, and machine learning applications. His research interests center on Structural Health Monitoring , Machine Learning in Civil Engineering , Earthquake Engineering , Structural Optimization , Operational Modal Analysis , and AI-driven diagnostics for infrastructure. He applies deep learning, neural networks, and hybrid modeling techniques to problems such as damage detection, post-earthquake assessment, tunnel and bridge monitoring, and dynamic analysis of timber and concrete structures. His recent publications, spanning from 2023 to 2025, demonstrate a strong trend toward integrating artificial intelligence with structural engineering, particularly in automating modal analysis, optimizing structural forms, and enhancing seismic resilience. These works appear in journals like Mechanical Systems and Signal Processing , Computers & Structures , and Bulletin of Earthquake Engineering , as well as in proceedings of international conferences such as IOMAC and EWSHM. Marco Rosso has not received any explicitly mentioned scientific awards in the provided text. However, his extensive publication record and active role in research projects indicate strong recognition in his field. He has contributed to teaching as a course collaborator in subjects including Dynamic Identification of Structures , Statistics , Construction Techniques , and Safety Assessment and Retrofitting of Structures . He has also been involved in the ARTISTE 2025 Summer School, indicating engagement in advanced training programs. While no formal lab or team name is specified, his frequent collaborations with researchers such as Angelo Aloisio, Giuseppe Carlo Marano, and Jonathan Melchiorre suggest he is part of a vibrant research group focused on intelligent structural systems and data-driven engineering at Politecnico di Torino.
Dr. Christopher S. Hlas is a Professor of Mathematics Education at the University of Wisconsin-Eau Claire within the College of Arts and Sciences Department of Mathematics. With a doctorate from the University of Iowa, he specializes in mathematics pedagogy, technology integration, and cross-disciplinary connections between math and foreign languages. Ph.D. in Mathematics Education (University of Iowa, 2005) B.S. in Mathematics and Computer Science (University of Iowa, 2001) His research focuses on teaching through problem solving , student motivation , and technology-enhanced mathematics instruction . He has developed innovative formative assessment probes and conducted extensive studies on homework design, flow theory, and creativity in K-12 education through grants like the ESEA Title II Mathematics and Science Partnerships. His work spans curriculum development, professional development for teachers, and mathematical modeling. Recent publications highlight his exploration of creativity assessment in language education and the application of game mechanics to classroom engagement. He serves as an AP Calculus Reader and Table Leader, while maintaining active roles in the Wisconsin Mathematics Council and National Council of Teachers of Mathematics. Bilingual education initiatives GeoGebra integration in geometry instruction Formative assessment frameworks Game-based learning strategies Scientific Awards CARE Award (2015) ACTFL Research Priority Grant (2010) Multiple teaching scholarships UWEC student-nominated award (2007) Outstanding teaching assistant recognition (2004) Mentoring over a dozen student research projects and securing more than $4 million in federal and state grant funding, Dr. Hlas has supervised numerous collaborative research initiatives. He maintains open-source educational tools like interactive Pascal's Triangle and rational functions resources at math.hlasnet.com while serving on multiple university committees and grant review panels.
Tanja Blascheck is a PostDoc Researcher and Margarete von Wrangell Fellow at the Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart. Her work focuses on visual analytics , eye tracking , and microvisualizations for smartwatches and other wearable devices.
Catherine Bovill serves as Personal Chair of Student Engagement in Higher Education at the University of Edinburgh's Institute for Academic Development (IAD). As a member of the Learning and Teaching Team, she leads the Programme Design and Teaching Enhancement Team, which provides comprehensive support for designing programs and courses, enhancing teaching approaches, and administering the Principal's Teaching Award Scheme. Professor Bovill actively contributes to the University's strategic educational initiatives as a member of the Learning and Teaching Strategy Implementation sub-stream, Senate Quality Assurance Committee, Student Lifecycle Management Group, and Bristol Case Working Group. Her leadership extends to organizing the University's annual Learning and Teaching Conference and managing the influential Teaching Matters blog. Professor Bovill's research program centers on co-created curriculum and student-staff partnership in learning and teaching, establishing her as a globally recognized authority with nearly 100 keynote presentations across 15 countries. Her scholarship examines how students and staff can collaboratively design curriculum, assessment, and teaching approaches to create more democratic, inclusive, and effective educational experiences. She has developed influential frameworks for understanding different types and levels of co-creation in higher education settings, with particular attention to power dynamics, identity, and equity considerations within partnership models. Her work bridges theoretical foundations with practical implementation strategies, making significant contributions to transformative educational practices. Analysis of Professor Bovill's recent publications reveals an evolving research trajectory with increasing focus on practical applications of student-staff partnership, particularly in assessment design and implementation. Her scholarship consistently demonstrates attention to identity, power dynamics, and equity considerations within partnership frameworks. The pandemic period expanded her research into online and hybrid learning environments, examining how co-creation principles can be maintained during remote education. Her work increasingly addresses intersectional aspects of partnership, recognizing diverse student experiences and backgrounds, while maintaining her signature emphasis on practical tools and frameworks that educators can implement in their teaching contexts. National Teaching Fellow Principal Fellow of the Higher Education Academy Fellow of the Staff and Educational Development Association Member of the Society for Research in Higher Education Member of UK National Teaching Excellence Awards Panel Visiting Fellow at the University of Bergen, Norway Professor Bovill plays a significant role in academic development through her teaching on the PGCAP programme and mentoring staff undertaking the Edinburgh Teaching Award, the University's CPD framework for Advance HE recognition. She assesses colleagues seeking Advance HE recognition as part of this scheme, directly influencing teaching excellence across the institution. Her strategic leadership in educational development extends to supporting implementation of the University's Learning and Teaching Strategy and enabling colleagues to develop and enhance their teaching practice and expertise through various workshops and networks. Within the Institute for Academic Development, Professor Bovill leads the Programme Design and Teaching Enhancement Team, which serves as a central hub for educational innovation at the University of Edinburgh. This team not only provides direct support to academic staff but also organizes major institutional initiatives like the annual Learning and Teaching Conference. Her work connects with broader educational communities through her editorial roles, including previously serving as Associate Editor (Europe) of the International Journal for Academic Development and currently as a Review Board member for Higher Education.
Dr. Leila Moslemi Naeni is a Senior Lecturer at the University of Technology Sydney (UTS), School of Built Environment, with a dual appointment in the Faculty of Design, Architecture and Building. She previously served as a Lecturer at UTS (2016-2018) and Sessional Lecturer at Curtin University (2015-2016). PhD in Computer Science (University of Newcastle, 2017) MSc in Industrial Engineering (Sharif University of Technology, 2007) BSc in Industrial Engineering (Iran University of Science and Technology, 2004) Her research focuses on project management under uncertainty, integrating fuzzy systems and mathematical modeling with applications in construction, ESG reporting, and disruptive technologies. She developed innovative methods for statistical control charts in project monitoring and leads research on leveraging blockchain and digital tools for sustainable infrastructure. Recent publications highlight her work on resource-constrained scheduling algorithms, ESG integration in megaprojects, and gamification in project management education. She serves as Review Editor for Frontiers in Environmental Science and on the Editorial Board of Smart and Sustainable Built Environment . 2016 PMI NSW Research Award 2022 Walt Lipke Award 2013 FEBE Postgraduate Research Prize As an active research mentor, she supervises PhD students in machine learning applications, disaster management technologies, and social infrastructure investments. Her teaching emphasizes simulation-based learning, collaborating with Oulo University (Finland) to quantify educational value.
Arrasy Rahman is a Postdoctoral Research Fellow at Professor Peter Stone’s Learning Agents Research Group (LARG) in the College of Natural Sciences at The University of Texas at Austin. His research focuses on creating adaptive autonomous agents for collaborative tasks, with expertise in game theory, reinforcement learning, and graph neural networks, particularly applied to the ad hoc teamwork (AHT) problem. Education: PhD and MSc from the University of Edinburgh, BSc from Universitas Indonesia His work explores methods to generate diverse teammate policies for training robust agents capable of collaborating with unseen teammates. Recent projects include partnerships with Lockheed Martin Corporation and organizing a workshop at AAAI-24. Arrasy’s research aims to build intelligent agents that assist humans in real-world collaborative decision-making challenges. Research trends in his publications include ad hoc teamwork, reinforcement learning, graph-based policy learning, and multi-agent systems. He has contributed to advancing techniques for best-response diversity and sub-task curriculum frameworks in autonomous agent collaboration. Labs and teams: Arrasy collaborates with the Autonomous Agents Research Group at the University of Edinburgh and the Learning Agents Research Group (LARG) at UT Austin. He is also involved in organizing the Ad-Hoc Teamwork Seminar Series and a AAAI-24 workshop.
Stefan Wildermann is a Professor at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he leads the Reconfigurable Computing Group within the Chair of Computer Science 12 (Hardware-Software Co-Design) in the Department of Computer Science. He has maintained continuous research activity at FAU since 2006, progressing from researcher to his current leadership position. Dr. Wildermann earned his Diploma degree in Computer Science from FAU in 2006 and completed his doctorate (Dr.-Ing.) in Computer Science at the same institution in July 2012. His academic career has been entirely rooted at FAU, demonstrating a strong institutional commitment and progression through the ranks. His research spans multiple cutting-edge areas in computer science and engineering, with particular emphasis on reconfigurable systems and hardware-software co-design. Wildermann's work in edge computing explores efficient processing at the network periphery, while his research in organic computing investigates self-organizing systems that can adapt to changing environments. His expertise extends to optimization techniques for embedded systems, applying game theory principles and convex optimization methods to solve complex resource allocation problems. More recently, he has integrated reinforcement learning approaches to enhance system adaptability and performance. His teaching portfolio includes courses on event-driven systems, computer engineering fundamentals, embedded systems, and hardware-software co-design. Analysis of Wildermann's publication record from 2021-2025 reveals a strong focus on hardware acceleration, security, and embedded systems. His work demonstrates consistent evolution from foundational research in reconfigurable architectures toward practical applications in IoT, robotics, and secure computing. A significant portion of his recent work addresses near-data processing using FPGAs for database acceleration, while maintaining parallel research streams in side-channel security analysis and energy-efficient embedded systems design. His publications frequently appear in top-tier conferences including DATE, FPL, ASP-DAC, and HOST, reflecting strong recognition within the computer architecture and embedded systems communities. Wildermann has held significant leadership roles including Head of the Reconfigurable Computing Group since 2015 and previously served as Head of the Self-organizing Systems Group (2012-2015) and Lab Leader of the Automotive Lab within the Embedded Systems Initiative (2016-2020). His research has been consistently funded through multiple projects investigating invasive computing, reconfigurable architectures, and embedded systems design methodologies. Currently based in Room 02.116 at Cauerstr. 11, 91058 Erlangen, Wildermann continues to lead active research in the Hardware-Software Co-Design group, supervising projects that bridge theoretical computer science with practical hardware implementation challenges.
François Goulette is a Professor and Deputy Director of the Computer Science and Systems Engineering Unit (U2IS) at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on 3D point cloud processing, LiDAR perception, and autonomous systems within the Robotics Center (CAOR). His primary research interests lie in 3D point cloud processing , LiDAR perception , and autonomous systems . His work spans fundamental algorithm development to practical applications in autonomous driving, cultural heritage digitization, and robotics. He has made significant contributions to domain generalization of LiDAR perception, semantic segmentation of 3D point clouds, and point cloud registration techniques. The analysis of his recent publications reveals a strong focus on domain generalization for LiDAR perception systems, with multiple papers addressing challenges in 3D semantic segmentation across different environments. His work combines multi-scale architectures , unsupervised learning , and dataset creation to advance the state-of-the-art in autonomous systems perception. The research spans both theoretical algorithm development and practical applications in urban environments. François Goulette leads research activities within the Robotics Center (CAOR) at ENSTA Paris. His team develops advanced techniques for 3D environment understanding, with applications in autonomous vehicles, cultural heritage preservation, and industrial robotics. The research combines computer vision, machine learning, and robotics to solve challenging problems in 3D perception and scene understanding.
Danny Caballero is a Professor at Michigan State University with joint appointments in the Department of Physics & Astronomy and the Department of Computational Mathematics, Science and Engineering . He holds the Lappan-Phillips Chair of Physics Education and co-directs the Physics Education Research Lab , while also serving as a principal investigator for the Learning Machines Lab and research faculty at the University of Oslo’s Center for Computing in Science Education. His research focuses on: How computational tools and scientific practices influence learning Cognitive and sociocultural theories in STEM education Departmental valuation of computational methods WCAG-compliant open educational resource development Recent publications highlight: Statistical modeling in physics education Computational literacy assessment Graduate admissions policy analysis Machine learning applications in educational assessment Scientific recognitions include: Lappan-Phillips Chair (2025) $300,000 NSF grant for computational education tools Additional roles: Founder of OER-Forge platform Union of Tenure System Faculty organizer Editor for upcoming Computing in Physics Education book
Yikun Ban is a tenure-track Associate Professor in the School of Computer Science and Engineering at Beihang University, where he is a member of the State Key Laboratory of Software Development Environment. He earned his PhD in Computer Science from the University of Illinois Urbana-Champaign (2023), MS in Computer Science from Peking University (2019), and BS in Software Engineering from Wuhan University (2016). PhD: University of Illinois Urbana-Champaign (2023) MS: Peking University (2019) BS: Wuhan University (2016) His research focuses on principled algorithms for reinforcement learning with human feedback, neural contextual bandits, and exploration-exploitation problems. He develops frameworks combining deep learning with bandit theory for applications in recommendation systems, disinformation detection, and dynamic graph learning. Recent publications address: Robust neural contextual bandits (NeurIPS 2024) Graph neural bandits (KDD 2023) Meta-learning for bandit scheduling (NeurIPS 2023) Clustering in contextual bandits (WWW 2021, AAAI 2021) Honors include the NeurIPS Scholar Award and recognition as ICML Outstanding Reviewer . His open-source LOCB repository provides Python implementations for contextual multi-armed bandit algorithms with local clustering, supporting applications in recommendation systems and online learning.
Emily Oh Navarro is a Continuing Lecturer in the Department of Informatics at the Donald Bren School of Information and Computer Sciences, University of California, Irvine. She has been actively involved in software engineering education, focusing on innovative teaching methods and simulation-based learning environments. Dr. Navarro earned her Ph.D. in Information and Computer Sciences from UC Irvine in 2006, with her dissertation titled "SimSE: A Software Engineering Simulation Environment for Software Process Education." She also holds an M.S. in Information and Computer Sciences and a B.S. in Biological Sciences, both from UC Irvine. Dr. Navarro's research primarily focuses on software engineering education, particularly using simulation and game-based approaches to teach software processes. Her work centers around SimSE, an educational software engineering simulation environment designed to help students learn and practice software engineering processes in an interactive, graphical setting. She has extensively explored how learning theories can be applied to improve software engineering education, investigating various educational approaches and their effectiveness. Her research demonstrates how simulation environments can overcome the limitations of traditional lectures and small-scale class projects by allowing students to experience complex software engineering processes that would be infeasible to practice in real-world academic settings. Dr. Navarro's publications reveal a consistent focus on simulation-based learning tools for software engineering education. Her work spans from theoretical explorations of learning theories to practical implementations of educational tools like SimSE and Problems and Programmers. She has conducted multi-site evaluations of her educational tools, demonstrating their effectiveness across different institutions and student populations. Her research shows progression from conceptual frameworks to practical implementations and rigorous evaluations, establishing her as a significant contributor to the field of software engineering education. Dr. Navarro teaches numerous courses in software engineering and design, including: Informatics 43: Introduction to Software Engineering Informatics 113: Requirements Analysis and Engineering Informatics 117: Project in Software System Design Informatics 121: Software Design I Informatics 122: Software Design II Informatics 191: Senior Design Project ICS 45J: Programming in Java ICS 139W: Critical Writing on Information Technology SWE 241P: Applied Data Structures and Algorithms SWE 245P: GUI Programming SWE 246P: Mobile Programming SWE 272P: Project Management Her teaching methodology reflects her research interests, emphasizing practical experience with software engineering processes through simulation-based learning. While specific information about her advising and grants is limited in available materials, her extensive research on educational methods suggests involvement in educational research projects and likely mentorship of students in software engineering projects. Dr. Navarro's primary research contribution is the SimSE project, which has evolved through multiple iterations and evaluations. This simulation environment represents a significant innovation in software engineering education, addressing the critical gap between theoretical knowledge and practical application in software process management. Her work continues to influence how software engineering is taught at UC Irvine and potentially at other institutions through her multi-site evaluations.
Miloš Racković serves as a full Professor in the Department of Mathematics and Informatics at the University of Novi Sad, Serbia. He maintains active academic engagement through the Laboratory for the development of information systems, with his office located in the Information technologies and systems office (DMI&DF) on the second floor, room 49. Contact is available via telephone (485)-2868 or email rackovic@dmi.uns.ac.rs, and his personal website (http://www.is.pmf.uns.ac.rs/rackovicm/) provides additional resources. His research spans foundational and applied computer science, with seminal contributions in fuzzy database systems including PFSQL query language development and prioritized fuzzy logic for relational databases and XML. He has pioneered deep learning methodologies through innovative classification techniques using negative and missing features in convolutional neural networks. Additional expertise includes high-performance computing implementations of Lattice Boltzmann methods using OpenCL, robotics (symbolic modeling and trajectory planning), and blockchain applications for Industry 4.0 production processes. His sports analytics work applies neural networks to basketball player and referee movement analysis. Analysis of his 2012-2025 publications reveals a strategic evolution toward interdisciplinary applications, particularly in industrial transformation (blockchain-enabled traceability) and sports analytics. His work consistently bridges theoretical computer science with practical implementations, demonstrating increasing focus on real-world problem solving while maintaining strong foundations in database theory and computational methods. Professor Racković leads the Laboratory for the development of information systems, which focuses on advancing information system methodologies through formal modeling extensions (including Petri net innovations) and practical implementations for uncertainty management. The laboratory's work spans from foundational research in fuzzy logic systems to applied projects in high-performance computing and blockchain integration, fostering innovation in information technology development.
