Palak Suri serves as an Assistant Professor in the Economics Department at West Virginia University, specializing in urban and labor economics with emphasis on public transit distributional impacts and gender employment dynamics. Education: Ph.D. in Economics, University of Maryland, 2022 Her research investigates socioeconomic effects of urban infrastructure in developing contexts, particularly analyzing Mumbai's metro rail system for travel time savings, employment accessibility, land value appreciation, and air quality improvements. She concurrently examines transportation's role in women's labor market participation through empirical studies on gender-transport-employment linkages. Recent publications (2021-2024) demonstrate methodological rigor through combined reduced-form and structural approaches, establishing connections between public transportation investments and multidimensional welfare outcomes including environmental health, gender equity, and household economic benefits across Indian urban settings.
Professor Chongmin Song is a faculty member at the University of New South Wales (UNSW), affiliated with the School of Civil and Environmental Engineering. His academic rank is Professor, and he specializes in computational mechanics with a focus on innovative numerical methods. He holds a BE and ME from Tsinghua University and a DEng from the University of Tokyo. His research explores computational mechanics, fracture analysis, wave propagation, and soil-structure interactions. Key methodologies include the Scaled Boundary Finite Element Method (SBFEM), image-based modeling, and dynamic simulations of infrastructure systems. He leads significant ARC-funded projects like 'A scaled boundary framework for nonlinear dynamic analysis of structures' (DP250100955) and 'Developing sustainable graded porous cementitious structures' (LP240100123), totaling over $1M in recent grants. Recent publications emphasize adaptive modeling techniques, multiphysics simulations, and high-performance computing applications. Trends include topology optimization for structural dynamics, phase-field fracture modeling for brittle materials, and GPU-accelerated elastodynamics. His work integrates computational efficiency with real-world engineering challenges, particularly in geomechanics and material failure analysis. Professor Song collaborates extensively on projects involving computational fracture mechanics and maintains laboratories focused on numerical simulation advancements. Future work targets scalable algorithms for 3D crack propagation and multiphysics coupling in infrastructure systems.
Charles Perin is an Assistant Professor of Computer Science at the University of Victoria, leading the UViz research group. He holds a PhD from Université Paris-Sud (2014) and has held roles including Post-doc at the University of Calgary and Lecturer at City, University of London. His research focuses on information visualization, personal visualization, human-computer interaction, and sports visualization. Education: PhD in Computer Science (2014), Université Paris-Sud; Post-doc at University of Calgary (InnoVis lab); MS and earlier studies in Computer Science and HCI in France. Research interests include designing interactive visualization tools for personal data reflection, health data communication, and sports analytics. He emphasizes authoring tools for non-experts and physical/tangible visualization systems. Recent work explores embedded data physicalizations and mobile visualization design. His articles span topics like data storytelling, patient-generated health visualizations, and soccer data analysis, often appearing in top venues like IEEE VIS, CHI, and Eurovis. He has advised over 20 students across PhD, MSc, and undergraduate levels. Teaching includes courses on Information Visualization and HCI at UVic, City, and other institutions. He co-organized workshops on topics like Personal Visualization and Sports Data at IEEE VIS. His UViz lab collaborates internationally with institutions like Monash University and the National Archives (UK).
Rocío Titiunik is a Professor of Politics at Princeton University and Director of the Data-Driven Social Science Initiative. She holds affiliations with the School of Public and International Affairs, the Department of Operations Research and Financial Engineering, the Center for Statistics and Machine Learning, the Program in Latin American Studies, the Center for the Study of Democratic Politics, and the Research Program in Political Economy. Her work bridges quantitative methodology, political economy, and statistical analysis, focusing on causal inference and program evaluation through regression discontinuity (RD) designs. She earned her undergraduate degree at the Universidad de Buenos Aires and a Ph.D. in Agricultural and Resource Economics from UC-Berkeley (2009). Before joining Princeton, she served as faculty at the University of Michigan’s Department of Political Science, where she was affiliated with the Center for Political Studies and the Michigan Institute for Data Science. Rocío’s research emphasizes the application of quasi-experimental methods to study political institutions, democratic accountability, and party systems in developing democracies. Her methodological contributions include advancements in RD designs, synthetic controls, and uncertainty quantification. Recent substantive work investigates charismatic leaders’ impact on democratic stability and the effects of voter registration reforms on public safety. Her scholarly achievements include the 2016 Emerging Scholar Award from the Society for Political Methodology and 2020 fellowship in the same society. She currently serves as an associate editor for Science Advances and a Board member for Science, while previously holding roles at the American Journal of Political Science and the NSF’s Social, Behavioral, and Economic Sciences Directorate. Rocío’s advising and grant activities include co-leading the EITM Summer Institute and securing federal research funding for projects on political methodology and democracy. She teaches advanced quantitative analysis courses and collaborates across disciplines to strengthen empirical research practices. Her work is anchored in collaborative initiatives like the Center for Statistics and Machine Learning, which integrates computational tools with social science inquiry, and the Program in Latin American Studies, reflecting her commitment to regional and methodological innovation.
Ivon Arroyo is a Professor in the Department of Teacher Education & Curriculum Studies (TECS) at the University of Massachusetts Amherst. Her research focuses on integrating novel technologies into math and computational thinking education, emphasizing affective and metacognitive states. She develops intelligent tutoring systems, such as COVES, which personalize learning in real-time and utilize facial expression recognition to enhance engagement. Her work on WearableLearning explores embodied, physically active multiplayer games for K-12 classrooms, leveraging mobile devices and wearable technologies to create immersive learning experiences. Dr. Arroyo holds an Ed.D. (2003) and M.S. (2000) from UMass Amherst and a B.S. from Universidad Blas Pascal in Argentina (1995). She has been recognized with multiple awards, including Best Paper Awards at the 2009 International Conference on Artificial Intelligence in Education and the 2010 Educational Data Mining Conference, a Fulbright Fellowship (1996), and a 1994 undergraduate prize for computer vision research. Her research interests span interdisciplinary areas such as Learning Sciences , Computer Science , Data Science , and Psychology . She prioritizes culturally responsive pedagogical agents and cross-cultural studies in educational technology, particularly in Argentina, India, and the U.S. Her projects often address challenges in developing countries, including localization of tutoring systems to Spanish. Advising and grants are central to her work, with grants like the NSF CAREER Award (2020) supporting embodied math classrooms. She collaborates on teacher dashboard frameworks and explores ethical AI integration in education. Her labs focus on creating tools that merge computational innovation with theoretical learning science principles, emphasizing real-world applications like the WearableLearning Cloud Platform.
Professor Bing Chu is an academic at the University of Southampton, actively contributing to research in control systems, robotics, and machine learning. They are a member of the Vision, Learning and Control Centre for Internet of Things and Pervasive Systems and the Centre for Robotics, focusing on interdisciplinary approaches that combine control theory with data-driven methodologies. Current research interests include: Iterative learning control Human-robot interaction Wind farm power optimization Robot behavior modeling Control system architectures Collaborative learning systems Recent publications highlight trends in data-driven control systems, human-robot interaction datasets, and optimization techniques for both continuous-time systems and wind energy applications. Professor Chu supervises multiple PhD students across robotics and electronic engineering, including Balint Gucsi, Haonan Shen, and Aleksander Wolski, while leading projects funded by Zhengzhou University and the Royal Society.
Dr. rer. nat. Thomas Hermann is a faculty member at Bielefeld University's Faculty of Engineering, leading the Ambient Intelligence Group and coordinating the Computer Science program. He specializes in sonification, auditory data science, and smart environments. Head of Ambient Intelligence Working Group Computer Science Program Coordinator Member of multiple academic advisory boards His research focuses on interactive sonification for biomedical applications, quantum systems, and smart environments. Key projects include ECG sonification for cardiac diagnosis, real-time auditory feedback in swimming, and sonic interfaces for AR cooperation. Recent publications span 2025 with Python-based sonification tools ( pya AGen ), quantum system sonification, and ST-elevation myocardial infarction monitoring. He contributes to open-access supplementary materials and interdisciplinary workshops. As a researcher , Hermann develops practical sonification frameworks like Panson for facial behavior analysis, CardioScope for portable ECG monitoring, and Base Cube One for smart environments. His work bridges academic research with industry applications.
Dr. Aykut Koç is an Associate Professor at the Department of Electrical and Electronics Engineering and a faculty member of the National Magnetic Resonance Research Center (UMRAM) at Bilkent University, Turkey. He leads the AykutKoc Lab, focusing on interdisciplinary research at the intersection of machine learning, signal processing, natural language processing, and graph signal processing. Education: B.S. in Electrical and Electronics Engineering (2005, Bilkent University); M.S. in Electrical Engineering (2007), M.S. in Management Science and Engineering (2009), and Ph.D. in Electrical Engineering (2011) under Professor Lambertus Hesselink at Stanford University; LL.B. in Law (Ankara University). His research integrates mathematical signal processing techniques (e.g., fractional Fourier and linear canonical transforms) with modern machine learning architectures like transformers and graph neural networks. Recent work explores semantic communication systems, bias mitigation in legal language models, and cross-modal applications in biomedical imaging and radar technology. Dr. Koç has published extensively in IEEE and Springer journals, with recent articles analyzing Fourier-enhanced transformers, graph-based NLP methods, and time-vertex signal analysis. His work addresses both theoretical innovations and practical applications, including schizophrenia diagnosis, legal outcome prediction, and maritime surveillance. Scientific Awards: Science Academy Young Scientists Award (BAGEP), 2023. He has supervised numerous graduate and undergraduate researchers, many of whom have transitioned to top-tier institutions such as MIT, UCLA, and TU Darmstadt. Dr. Koç actively serves as Associate Editor for multiple IEEE journals and participates in conference program committees, including EMNLP's Natural Legal Language Processing (NLLP) workshop.
Jian Zhao is an Associate Professor at the University of Waterloo's School of Computer Science, specializing in Information Visualization (InfoVis), Human-Computer Interaction (HCI), and Data Science. With a Ph.D. from the University of Toronto (2016), his research emphasizes interactive visualization techniques, AI integration in design processes, and socio-technical systems. He explores how human-AI collaboration can enhance data analysis, presentation, and user experience in complex systems. Key research areas include: 1) AI-Driven Design (e.g., code generation via sketching, infographic creation), 2) Health Informatics (therapeutic AI tools for autism support), 3) Immersive Technologies (VR/AR interfaces for presentations and education), and 4) Social Computing (remote family communication, multi-modal emoticons). His work bridges technical innovation with human-centered design principles. His publications (2021–2025) reflect a focus on interactive visualization frameworks (e.g., iTrace for cross-view data analysis), AI-human collaboration (CoLadder for hierarchical code editing), and specialized applications like TherAIssist for art therapy and EMooly for autism support. Zhao frequently explores novel interaction modalities , including gesture-based VR interfaces and sketch-based programming tools. He leads projects in computational notebooks (EDAssistant, Slide4N), visual analytics (MissBin for bipartite networks), and neurofeedback training games (Eggly). His work often emphasizes systematic design considerations for missing data, cross-view analysis, and contextual visualization in spatial AR environments.
Dr Emily Hewson is a Cancer Institute NSW Early Career Fellow and member of the Sydney School of Health Sciences at the University of Sydney's Faculty of Medicine and Health. Her research focuses on advancing real-time radiation therapy techniques, particularly in managing intrafraction motion for prostate and other cancers. She leads projects involving multileaf collimator (MLC) tracking, dose optimization, and deep learning integration in radiation oncology. Research interests include adaptive radiotherapy systems, kilovoltage intrafraction monitoring (KIM), and clinical trial implementation (e.g., TROG 15.01 SPARK trial). Her work emphasizes improving treatment accuracy through real-time dose-guided approaches and multitarget tracking for tumors with complex motion patterns. Developed experimental validations for MRI-linac integration and MLC tracking systems Authored a textbook chapter on Adaptive Radiation Therapy (ART) Recipient of Cancer Institute NSW Early Career Fellowship (2023) Recent grants include an AI platform for targeted radiotherapy (2024) and national critical infrastructure funding for lung cancer applications (2023). Her lab collaborates on real-time dose calculation algorithms and clinical trial implementation across multiple institutions.
Ruth Kanfer is a Professor of Psychology at the Georgia Institute of Technology's School of Psychology, specializing in adult learning, motivation, and career development. Her research addresses the impacts of technological advancements, demographic shifts, and global economic changes on work and career trajectories. She co-directs the PARK Lab, focusing on topics such as self-regulation in job search, motivational dynamics, and the psychology of workplace environments. Dr. Kanfer holds a Ph.D. in Psychology from Arizona State University and has contributed to seminal works on aging and workforce diversity. She is a Fellow of prominent organizations including the Academy of Management and the American Psychological Association, and has received prestigious awards such as the SIOP's William R. Owens Scholarly Achievement Award. Her research employs mixed-methods approaches, including experimental studies and large-scale field research. Key themes include adult learning efficacy, team-based motivation, and the design of workspaces to enhance employee well-being. Dr. Kanfer has led projects funded by the Sloan Foundation and the National Academy of Sciences, emphasizing interdisciplinary collaboration. Notable contributions include studies on the future of work, the role of future time perspective in career decisions, and the application of a 'whole-person' framework to adult learning. Her work has been published in journals like Journal of Applied Psychology and American Psychologist . She actively participates in professional committees, including the Sloan Research Network on Aging and Work and the National Academy of Sciences' How People Learn II initiative.
Ke Yang serves as Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), College of Sciences. He founded and leads the Cohort for AI REsponsibility (CAREAI) initiative, while also holding core faculty positions in UTSA's School of Data Science and MATRIX (AI Consortium for Human Well-being). Education: Ph.D. from New York University (supervised by Prof. Julia Stoyanovich) Research Focus: Dr. Yang's work centers on AI trustworthiness and responsibility , with specialized expertise in algorithmic fairness, data ethics, and human-centered data science. His research addresses critical challenges including Large Language Model hallucinations, explainable AI frameworks, and algorithmic accountability mechanisms. He actively develops open-source tools like Ranking Facts and FairDAGs to implement these principles in practical systems. Publication Trends: Recent work (2020-2025) demonstrates evolving focus from foundational fairness in ranking systems toward generative AI safety and medical applications. His publications show strong theoretical grounding combined with real-world implementation, particularly in privacy policy analysis and medical question-answering systems using causal inference techniques. Scientific Recognition: Pearl Brownstein Doctoral Research Award (NYU Tandon School of Engineering) CDS Postdoctoral Fellowship (University of Massachusetts) Professional Development: Dr. Yang has secured significant research funding including the CDS Postdoctoral Fellowship at UMass. His graduate work at NYU and Drexel University was fully supported by research assistantships, demonstrating consistent funding acquisition throughout his career. He actively contributes to academic community building through conference tutorials and educational initiatives. Research Ecosystem: He directs CAREAI at UTSA while collaborating across institutional boundaries through MATRIX and the School of Data Science. Previously, he contributed to the Data systems Research for Exploration, Analytics, and Modeling (DREAM) lab and Center for Data Science at UMass Amherst, maintaining continuity in his responsible AI research trajectory.
Prof. Dr. Christian Mayer is a Professor in Physical Chemistry at the Faculty of Chemistry, University of Duisburg-Essen. He serves as Head of the working group focusing on origin of life research, nanocapsules, and NMR spectroscopy techniques. His research group is located at Universitätsstraße 5, D-45141 Essen, Germany, with contact information including phone number +49 201 183-2570. Prof. Mayer's research interests primarily focus on the origin of life in deep tectonic fault zones of the first continental fragments, where he collaborates with Prof. Dr. Ulrich Schreiber from the Faculty of Biology and Prof. Dr. Oliver Schmitz from Applied Analytical Chemistry. His work investigates how vesicle formation occurs in tectonic fault systems through cyclic phase transitions of carbon dioxide, creating ideal conditions for molecular evolution. He specializes in pulsed field gradient NMR (PFG-NMR), high-resolution NMR, and solid-state NMR techniques to characterize nanoscale systems including nanocapsules, vesicles, and microemulsions. His recent publication trends reveal a strong interdisciplinary focus spanning physical chemistry, prebiotic chemistry, and astrobiology. The articles demonstrate increasing integration of computational methods with experimental approaches, particularly in analyzing molecular structures and dynamics. His research has evolved from fundamental studies of nanocapsule systems to broader investigations of protocell formation mechanisms under early Earth conditions, with recent work extending to astrobiological contexts including potential life formation on Titan. Prof. Mayer has established significant collaborations across multiple disciplines, particularly with geologists and biologists, to investigate the physical chemical processes that could have led to the emergence of life. His work bridges fundamental physical chemistry with practical applications in nanomedicine, particularly in developing artificial oxygen carriers based on nanocapsule technology. His laboratory utilizes high-pressure facilities to simulate early Earth crust conditions, with a particular focus on supercritical CO 2 environments. The working group combines experimental approaches with theoretical modeling to understand vesicle formation processes and their implications for the origin of cellular life.
Frank Chan is a Professor of Information Systems at ESSEC Business School in France, where he currently serves as Department Head of Information Systems, Decision Sciences and Statistics (2022-2025). He has been with ESSEC since 2013, progressing from Assistant Professor to Associate Professor and now Professor. His academic career focuses on the intersection of information systems, public administration, and organizational behavior. Dr. Chan earned his Ph.D. in Information Systems from Hong Kong University of Science and Technology (HKUST) in 2010 and completed his BBA in Information Systems and Finance from the same institution in 2003. His educational background provided the foundation for his research in technology implementation and electronic government. His research interests span electronic government, technology implementation, agile methodologies, and internet privacy. Dr. Chan's work examines how digital technologies transform public services, organizational processes, and citizen experiences. He investigates the human aspects of technology adoption, including leadership dynamics in agile teams, citizen satisfaction with e-government services, and privacy concerns in digital environments. His multidisciplinary approach combines insights from information systems, public administration, and organizational behavior. Analysis of Dr. Chan's publication record reveals a consistent focus on e-government systems and technology implementation, with increasing attention to agile development methodologies in recent years. His work demonstrates a progression from foundational technology adoption studies to more nuanced investigations of leadership dynamics, privacy concerns, and the societal impacts of digital initiatives. The interdisciplinary nature of his research bridges business, public administration, and technology domains. Pacific Asia Conference on Information Systems Best Associate Editor Award (2022) International Conference on Information Systems Outstanding Associate Editor Award (2019) MIS Quarterly Reviewer of the Year Award (2019) MIS Quarterly Reviewer of the Year Award (2018) Journal of Operations Management Ambassador Award (2017) Finalist for Journal of Operations Management Jack Meredith Best Paper Award (2012) As a Senior Editor for Information Systems Journal since 2021 (previously Associate Editor 2016-2020), Dr. Chan has significantly contributed to the academic community. He has served as Track Co-Chair for major conferences including International Conference on Information Systems and Pacific Asia Conference on Information Systems. His consulting work with United Nations ESCAP on digitalization of tax administrations in Asia demonstrates the real-world impact of his expertise. Dr. Chan teaches courses in Research Design, Quantitative Research Methods, and Digital Business at ESSEC.
Prof. David Ham is a Professor of Computational Mathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on high-level abstractions for scientific computation, particularly in geophysical fluids and numerical software. He leads the Firedrake project and co-developed the dolfin-adjoint framework, which received the 2015 Wilkinson Prize for Numerical Software. Ham holds a BSc (Mathematics) and LLB from The Australian National University, and a PhD from TU Delft. His career includes roles as a NERC Independent Research Fellow and Grantham Research Fellow at Imperial College. He is affiliated with the Grantham Institute, Mathematics of Planet Earth, and Software Performance Optimisation groups. His research spans computational science, including finite element methods, adjoint-based inversion, and parallel computing. Recent work emphasizes differentiable programming integration with machine learning and geophysical modeling. Ham has contributed to numerous grants and projects, including EPSRC and NERC-funded initiatives. He leads development of software tools like Firedrake and Thetis, advancing computational methods for oceanography and geodynamics.
