ChanMin Kim is a Professor in the Learning and Performance Systems department at Penn State College of Education. With over 80 publications and 2553 citations, their work focuses on integrating artificial intelligence , robotics , and educational technology into science and computer science education. Research interests: Science writing, AI-human partnerships, robotics in education, equity-focused technology design Key methodologies: Natural Language Processing, learning analytics, scaffolding strategies Recent work explores large language models in education, debugging processes in pre-service teacher training, and automated assessment systems for science explanations. While the provided data doesn't show specific scientific awards or student advisees, their publications in venues like British Journal of Educational Technology and Journal of Science Education and Technology demonstrate significant contributions to learning sciences. Kim collaborates extensively with researchers in AI, educational technology, and equity-focused domains.
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.
Gireeja Ranade is an Assistant Teaching Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She previously served as a Researcher at Microsoft Research AI in the Adaptive Systems and Interaction Group. Her educational background includes a PhD in Electrical Engineering and Computer Science from UC Berkeley and an undergraduate degree from MIT. Research Focus Prof. Ranade's research spans control theory, information theory, and machine learning, with applications in wireless communication, algorithmic fairness, and misinformation analysis. Her work addresses fundamental challenges in system stabilization under uncertainty, real-time control optimization, and equitable resource allocation. She maintains strong collaborations across disciplines, resulting in publications at premier venues like IEEE Transactions on Automatic Control, PNAS, and The Web Conference. Her recent publications demonstrate a consistent focus on robustness in control systems, fairness in algorithmic decision-making, and analysis of information propagation in online ecosystems. The work frequently combines theoretical rigor with practical implementations in robotics, networking, and social systems. Awards and Recognition 2017 UC Berkeley Electrical Engineering Award for Outstanding Teaching 2020 UC Berkeley Award for Extraordinary Teaching in Extraordinary Times Academic Leadership Prof. Ranade leads a dynamic research group including PhD candidates, master's students, and undergraduates. She has advised over 25 students on projects ranging from neural network controllers to fairness metrics in resource allocation. She founded the CalMentors program, which connects UC Berkeley students with K-12 learners for tutoring support during the COVID-19 pandemic. Educational Innovation She co-designed and teaches UC Berkeley's introductory EECS 16A/B sequence, integrating linear algebra with applications in machine learning and circuit design. She has also developed courses on optimization (EECS127/227A) and data science (Data 102), with publicly available lecture videos demonstrating her teaching methodology.
Mickaël CAMPO is an Associate Professor at the University of Burgundy (Université Bourgogne Franche-Comté) in Dijon, France, specializing in sports psychology. He is affiliated with the Faculty of Sports and Physical Education (UFR STAPS) and the Socio-Psychology and Sports Management Laboratory (Laboratoire Psy-DREPI). His educational background includes a Doctorate in Sports Psychology. Prior to his current position, he served as an Associate Professor at Université de Rouen Normandie from 2013 to 2015 and completed his PhD studies at Claude Bernard University Lyon 1 from 2007 to 2010. Dr. CAMPO's research focuses on the interpersonal emotional process (IEP), particularly examining: Relationships between social psychological variables such as social identity and emotions in group contexts Interpersonal emotion regulation in sports settings Emotional contagion phenomena in team sports Psychological adaptation processes in performance and learning contexts Emotional intelligence development and application in sports His work primarily centers on team sports, with a special emphasis on rugby. He has developed theoretical frameworks like the Interpersonal Emotional Process (IEP) model and has conducted extensive research on how emotions affect performance in competitive sports environments. Dr. CAMPO has received recognition for his research, including the 1st Young Researcher Prize in 2010. His scientific contributions span numerous publications in prestigious journals, with his work focusing on the intersection of social psychology and sports performance. As an academic, Dr. CAMPO has supervised multiple research projects and has been invited to present at various conferences, including the clinical medico sport congress in Lyon. His work bridges theoretical research with practical applications for athletes, coaches, and sports psychologists.
Prof. Dr. Bernd Skiera is a leading Marketing Professor at Goethe University Frankfurt since 1999 and a member of the managing board of the efl - The Data Science Institute. His work bridges information systems and marketing, with a focus on data-driven decision making and digital transformation.
Aniello Murano is a Professor of Computer Science at the Department of Electrical Engineering and Information Technologies , University of Naples Federico II. He serves as Scientific Director of the ASTREA (Automated Strategic Reasoning) Laboratory and leads cutting-edge research in Artificial Intelligence, Strategic Reasoning, Multi-Agent Systems , and Formal Verification . Research Interests : Strategic reasoning under perfect/imperfect information, specification/verification/synthesis of reactive systems, temporal/modal logics, automata theory, parity games, game theory, mechanism design, and formal languages. Notable Projects : PNRR Research Unit Coordinator (2023-2025) on Resilient AI, PRIN 2020 Unit Coordinator (RIPER: Resilient AI-Based Self-Programming and Strategic Reasoning), H2020-MSCA SEAL (Principal Coordinator). Awards & Honors : JPMorgan Faculty Research Award (2022), Royal Society Award (2016), Best Paper PRIMA (2015), INDAM Project Leader (2023), Italian Scientific Habilitation (2017-2018). Students & Postdocs : Supervised 6 PhD students (e.g., Silvia Stranieri, Vadim Malvone) and mentored postdocs such as Munyque Mittelmann and Bastien Maubert. Laboratory : Leads ASTREA Lab, focusing on automated strategic reasoning and resilient AI systems.
Anna Abrahams is a Senior Programmer Artist Film and Artistic XR at Eye Filmmuseum in Amsterdam and teaches Moving Image at the Royal Academy of Art, The Hague, within the Photography department. She has been active in experimental film since 1989 as a co-founder of Rongwrong, an independent production foundation. Education: Studied film theory at the University of Amsterdam. Her research and practice focus on experimental film, virtual reality (XR), and moving image art. She bridges traditional film techniques (35mm, 16mm) with digital innovations, emphasizing immersive storytelling and artistic exploration of landscapes, urban spaces, and sociocultural themes. The films she has directed or co-directed, spanning 1995–2022, explore topics like psychogeography, counterculture, and digital experimentation. Her 2022 VR work Angels of Amsterdam highlights her engagement with extended reality technologies. As a co-author, she contributed to publications including Warhol Films , mm2. Experimental Film in the Netherlands , and film³ [kyü-bik film] , documenting avant-garde film practices and theoretical frameworks.
Stacey A. Sinclair holds a dual appointment as Professor in the Department of Psychology within Princeton University's Faculty of Arts and Sciences and the Princeton School of Public and International Affairs. Her research investigates how culturally embedded prejudices translate into individual cognition through interpersonal dynamics, challenging institutional-focused bias models by examining micro-level social processes. Her work centers on two groundbreaking frameworks: Social Tuning : Demonstrates unconscious adjustment of prejudice and self-views toward interaction partners' perceived attitudes during liking or uncertainty states Implicit Homophily : Reveals interpersonal attraction to others with congruent implicit (but disavowed) intergroup bias These phenomena collectively indicate that individuals may inhabit self-reinforcing social networks with uniform bias levels, creating invisible pathways for prejudice transmission. Current projects explore how such networks impact health and intellectual performance among stigmatized groups, particularly through unrecognized environmental reinforcement mechanisms. Her publication record (2014-2016) shows consistent focus on implicit racial bias operating beneath conscious awareness across contexts like speed perception, educational settings, and ingroup bonding. This work establishes that prejudice persists not only through explicit systems but via subtle interpersonal feedback loops that bypass conscious control, with implications for policy interventions targeting unconscious bias channels. No scientific awards were specified in the source materials. While student advising details were omitted, her lab operates as a nexus for social psychological research on bias transmission. Funding sources remain unmentioned, though her affiliation with Princeton's School of Public and International Affairs suggests policy-relevant applications of her findings. Her laboratory environment actively investigates how micro-interactions generate macro-level inequality patterns, currently developing projects to quantify bias immersion effects on cognitive performance and physiological health outcomes in marginalized populations through controlled social network experiments.
Duncan Wilson is a Professor of Connected Environments at the Bartlett Centre for Advanced Spatial Analysis (CASA) at University College London. His work bridges academia and industry, focusing on IoT, AI, and spatial analysis to enhance understanding of built and natural environments. Current role: Professor of Connected Environments at UCL Education: PhD in Artificial Intelligence and Machine Vision (UCL, 1997), BEng (Hons) in Electrical Engineering (Loughborough University, 1993) Research interests include: Cognitive computing at the network edge Extraordinary sensory systems for data capture Spatial reasoning and digital twins IoT for healthcare and biodiversity Edge AI and TinyML Recent articles span digital twin development , IoT for biodiversity monitoring , and smart healthcare infrastructure . He has received recognition for collaborative R&D approaches during his directorship at Intel's Sustainable Connected Cities institute. Teaching: Leads MSc Connected Environments and modules on IoT ethics, AI on microcontrollers, and sensor network deployment Projects: IoT Living Lab at UCL, Project Hercules for eye clinic analytics, and Shazam for Bats environmental monitoring Professional activities: Former Director of Intel Collaborative Research Institute (2012-2018), ex-member of Smart London Board (2017-2022)
Robert M. Weikle, II is a Professor in the Charles L. Brown Department of Electrical and Computer Engineering at the University of Virginia, with a courtesy appointment in the Department of Physics. He earned his B.S. from Rice University (1986), M.S. (1987), and Ph.D. (1992) in Electrical Engineering from Caltech, followed by postdoctoral work at Chalmers University of Technology (1992). His research focuses on millimeter-wave and terahertz electronics , applied electromagnetics, integrated antennas, low-noise sensors, and heterogeneous integration of compound semiconductors. His work bridges electronics and photonics for spectrum access, with applications in astronomy, spectroscopy, and metrology. He has published extensively on micromachined silicon substrates, superconducting materials, and emerging technologies. Scientific Awards: IEEE Microwave Prize (1993) David A. Harrison III Award (1999) University of Virginia All-University Outstanding Teaching Award (2000) Edlich-Henderson Innovator of the Year (2016) Fulbright Scholar (2001) As Chief Technology Officer and co-founder of Dominion Microprobes, Inc., he commercializes micromachined wafer probes for high-frequency metrology. His lab, located in E220 Thornton Hall and the Jesse W. Beams Physics Building, has produced 15+ recent publications on submillimeter-wave devices, THz probes, and calibration techniques.
Qiaoning Carol Zhang serves as Assistant Professor of Human Systems Engineering within The Polytechnic School at Arizona State University's Ira A. Fulton Schools of Engineering. Her research investigates the critical intersection of human perception, social contexts, and emerging technologies including artificial intelligence, robotics, and automated vehicles, with emphasis on creating intuitive, user-friendly, and inclusive systems. Her academic foundation includes: Ph.D. in Information, University of Michigan (2023) M.S. in Industrial and Operations Engineering, University of Michigan (2018) B.S. in Industrial Engineering, Hunan University (2016) Dr. Zhang's research program centers on understanding how individual differences and social dynamics shape technology interactions. Key focus areas include Human-AI Collaboration , Human-Robot Interaction , Human Factors in Automated Vehicles , and User Experience Design . Her work employs interdisciplinary methodologies to ensure technology adapts to diverse user needs across complex socio-technical environments, particularly in transportation and healthcare robotics. Analysis of her 15 most recent publications (2021-2025) reveals dominant themes in trust dynamics within automated vehicles, with significant attention to explainable AI interfaces. Research consistently examines how voice characteristics (gender, similarity), explanation modalities, and individual differences (age, personality) impact cognitive and affective trust. Recent work extends to healthcare robotics for elderly populations using Kano model analysis to identify critical user requirements. No scientific awards are documented in the provided materials. Dr. Zhang actively recruits Ph.D. candidates and undergraduate/master's researchers with backgrounds in human-computer interaction, data science, and interdisciplinary fields (design, computer science, cognitive science). She emphasizes opportunities in transportation technology, healthcare robotics, AI, and UX research/design, requiring applicants to submit CVs, research statements, and representative work samples. While specific grants aren't detailed, her research scope indicates substantial funding in human factors and emerging technology domains. Her research team focuses on developing empathetic technology through projects examining trust calibration in automated vehicles and healthcare robot design for older adults. Current initiatives include voice interface optimization for diverse user groups and Kano model applications in home healthcare robotics, aiming to bridge technical capabilities with human-centered design principles.
Marco Bertini is a Professor in the Marketing Department at Esade School of Business Administration and Management , affiliated with the Institute for Data-Driven Decisions . His research focuses on pricing, marketing, and consumer behavior, particularly in low-cost strategies and decision-making frameworks. Research Interests: Bertini’s work explores pricing psychology, consumer experience, and behavioral economics. He investigates how pricing strategies influence customer trust and how friction in retail interactions can enhance engagement, with applications in nonprofit finance and food waste reduction. Articles: Recent publications highlight value-based sales, beneficiary pricing in nonprofits, and the impact of price promotions on food waste. His research spans interdisciplinary topics, integrating data-driven insights with behavioral interventions. Projects: He is a member of the JUICE research group, focusing on judgment and decisions in the marketplace, and contributes to the ESADE D3 initiative on data-driven decision-making.
Boris Jukic is a Professor and Director of Applied Data Science at the Reh School of Business , Clarkson University. With a PhD from the University of Texas at Austin, he teaches courses such as Visual Basic Programming for Business Applications and Development of Business Applications on the Internet. Dr. Jukic's research focuses on: Management and pricing of networks and telecommunication services Application of data management and presentation strategies in e-business and e-commerce IT architecture's impact on organizational success metrics His recent publications highlight trends in data warehousing , IT architecture , and business intelligence . Notable subfields include data modeling , incentive-compatible pricing , and process-data integration . Contact: Email: bjukic@clarkson.edu Phone: 315/268-3884 Office: 227 Bertrand H. Snell Hall, Clarkson University
Fatma Deghim is a Research Fellow at the Technical University of Munich , affiliated with the Chair of Energy Efficient and Sustainable Design and Building. Her work focuses on urban microclimate, building energy simulation, and data-driven methods for sustainability. Education : Master’s Degree in Civil Engineering (2019–2022) and Bachelor’s Degree in Civil Engineering (2015–2019), both from TUM. Research Interests include: Urban microclimate and indoor-outdoor interactions Building energy simulation and comfort analysis Integration of green infrastructure in climate-resilient design Data-driven methods for environmental monitoring Publications highlight her expertise in applying machine learning to occupancy modeling, thermal comfort prediction, and multi-objective optimization frameworks for sustainable building design. Her work emphasizes uncertainty analysis, resource efficiency, and computational methods. Teaching contributions include assisting in courses on sustainable architecture, building energy principles, and urban water systems at TUM.
Yu Xia is a Post Doc at the Department of Chemistry, Stockholm University, Sweden. He is affiliated with the Tom Willhammar Research Group, focusing on advanced electron microscopy and diffraction techniques for structural characterization of materials. PhD (2019–2023) from a joint program between the University of Birmingham (UK) and the Southern University of Science and Technology (China). Research emphasizes fabrication of metallic nanoparticles with non-equilibrium structures and shapes using gas-phase condensation and thermal shock methods. Specializes in scanning transmission electron microscopy (STEM), in-situ heating experiments, and electron energy loss spectroscopy (EELS) for nanoparticle analysis. Current work prioritizes 4DSTEM imaging for electron beam-sensitive materials and Python-based post-processing of electron microscopy datasets. Yu Xia's research spans Materials Science , Nanotechnology , and Electrocatalysis , with applications in photocatalytic hydrogen evolution , graphene composites , and advanced electron microscopy techniques . His work often integrates computational image processing with structural characterization to optimize material properties. Publications highlight innovations in heterostructure engineering , metallic alloy catalysts , and electron beam-sensitive material imaging . No scientific awards are explicitly mentioned in the provided text. Yu Xia's technical expertise includes Python scripting for image analysis, in-situ electron microscopy , and multifunctional graphene-based materials .
