Charles F. Harvey is a Professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), where he investigates hydrology, carbon cycling, and groundwater contamination. His work spans tropical peatlands, arsenic pollution in South Asia, and coastal groundwater dynamics, with field sites in Borneo, Bangladesh, and Vietnam. Education: B.A. in Mathematics (Oberlin College, 1986), M.S. in Applied Earth Science (Stanford University, 1992), Ph.D. in Geological and Environmental Sciences (Stanford University, 1996). Harvey’s research focuses on hydrogeology , carbon sequestration , and contaminant transport . He explores how groundwater flow and biogeochemical processes interact in tropical peatlands and coastal systems, with significant implications for climate change and water security. His recent publications emphasize tropical peatland hydrology , arsenic mobilization , and coastal groundwater exchange , integrating field experiments, remote sensing, and computational models. Scientific Awards AGU Fellow GSA Fellow Meinzer Award (GSA) Prince Sultan bin Abdulaziz International Prize for Water M. King Hubbert Award for Hydrology NSF Young Investigator Award Harvey leads the Harvey Lab , mentoring students and researchers in projects ranging from groundwater arsenic mitigation to coastal marsh biogeochemistry . His work bridges terrestrial and marine systems , addressing critical planetary challenges.
Rosa I. Arriaga is an Associate Professor in the School of Interactive Computing at Georgia Institute of Technology, where she directs the Ubicomp Health and Wellness Lab. Her research focuses on Human-Computer Interaction (HCI), Ubiquitous Computing, and mHealth, with applications in mental health, chronic disease management (e.g., diabetes), and pediatric asthma care. She designs technology systems to improve patient engagement, continuity of care, and clinician-patient communication. Research Areas: Chronic Care Management PTSD Therapy Support (PECSS project) Diabetes Management (DUCSS project) mHealth System Design AI in Healthcare Key Projects: PECSS: Leverages ubiquitous computing and machine learning to address clinical challenges in PTSD therapy. DUCSS: A diabetes management system to streamline patient education and self-monitoring, particularly for underserved populations. ZenVR: A virtual reality system for meditation education, validated through longitudinal studies. Teaching: Teaches undergraduate courses in HCI and graduate courses in Human-Centered Computing (HCC). Her Coursera course Intro to User Experience Design has over 50K learners. Grants & Partnerships: Funded by GT-Emory AI Humanity, American Diabetes Association, and Georgia Center for Diabetes Translation Research. Collaborations with Emory University, University of Rochester, and medical professionals. Labs & Teams: Leads the Ubicomp Health and Wellness Lab, fostering interdisciplinary research with graduate and undergraduate students focusing on health technology innovation.
Junjian Qi serves as the Hohbach Endowed Associate Professor in the Department of Electrical Engineering and Computer Science at South Dakota State University's College of Engineering, holding this position since 2023. His academic journey includes prior appointments as Assistant Professor at Stevens Institute of Technology (2020-2023) and University of Central Florida (2017-2020), along with research roles at Argonne National Laboratory and University of Tennessee. His educational background includes: Ph.D. in electrical engineering from Tsinghua University, Beijing, China (2013) B.E. in electrical engineering from Shandong University, Jinan, China (2008) Dr. Qi's research centers on electric power systems resilience, with particular expertise in cascading failure mechanisms, microgrid control architectures, cyber-physical security vulnerabilities, and synchrophasor applications. His work integrates advanced data analytics and machine learning techniques to enhance grid stability against extreme weather events and cyber threats. Current investigations focus on developing distributed control strategies for inverter-dominated grids and modeling system interdependencies during failure propagation. Analysis of his 15 most recent publications (2021-2024) reveals a strong methodological shift toward data-driven approaches for power system challenges. Key trends include machine learning applications for cascading failure prediction, novel distributed control frameworks for AC/DC microgrids, and cybersecurity enhancements for inverter-based resources. His work consistently bridges theoretical models with real-world utility data, particularly evident in multiple Best Paper Award-winning publications analyzing actual outage sequences. Dr. Qi's scientific recognition includes: NSF CAREER Award (2020) Three consecutive Best Paper Awards at IEEE PES General Meetings (2022-2024) World's Top 2% Scientist designation in energy (2020-2023) IEEE PES Outstanding Working Group Award (2023) Multiple journal Best Paper Awards (IEEE Transactions on Power Systems, Journal of Modern Power Systems) He currently leads significant research initiatives including an NSF CAREER project ($500k) on cascading failure analysis and an NSF collaborative grant ($219k) for grid stability, alongside previous DOE funding ($1.8M) for cybersecurity of distributed energy resources. His service includes editorial roles for IEEE Transactions on Power Systems and IEEE Power Engineering Letters, plus leadership in IEEE PES technical committees focused on voltage control and smart grid security.
Andrea Bonfiglio is an Associate Professor at the Department of Naval, Electrical, Electronic and Telecommunication Engineering (DITEN) within the School of Engineering at the University of Genoa. His work focuses on power systems, smart grids, and renewable energy integration with a particular emphasis on innovative control strategies and energy storage solutions. Teaching areas: Electrical Systems, Industrial Measurements, Energy Security Key research themes: Smart distribution networks, synthetic inertia, microgrid control, vehicle-to-grid technologies, battery energy storage Recent work explores virtual energy partitioning, load flow optimization, and inertia allocation in transmission networks Contact: a.bonfiglio@unige.it His publications highlight advanced control methodologies using machine learning and sliding mode control for both transmission and distribution networks, with applications to renewable integration and grid stability challenges.
Ron Fedkiw is the Canon Professor of Computer Science at Stanford University's School of Engineering. He holds a PhD in Applied Mathematics from UCLA. His research focuses on computational algorithms for applications in computational fluid dynamics, computer graphics, biomechanics, and machine learning. Fedkiw has pioneered techniques for simulating natural phenomena in film and video games, earning two Academy Awards for his contributions to visual effects. He leads the PhysBAM lab and collaborates with industry through consulting roles at Epic Games and former work with Industrial Light & Magic. Education: PhD in Applied Mathematics, UCLA (1996). Notable awards include the National Academy of Science Award, Packard Fellowship, and multiple teaching honors. His lab has graduated 40 PhD students, many of whom have made significant impacts in academia and industry. Research interests span fluid dynamics, cloth simulation, facial animation, and integrating machine learning with physical models. Key contributions include algorithms for two-way fluid-solid coupling, muscle-based facial modeling, and neural network approaches for cloth and deformable bodies. Current projects explore physics-informed machine learning and real-time interactive simulations. Scientific Awards include two Oscars, PECASE, and Okawa Foundation grants. His work bridges computational physics and visual effects, with over 140 research papers and a textbook on level set methods. Advising and grants: Supervised 40 PhD students, securing funding through NSF, ONR, and industrial partnerships. Lab collaborations include SAIL (Stanford AI Lab) and Epic Games. Future work focuses on AI-driven physical simulations and biomedical applications.
Francesco Pilati is an Associate Professor at the Department of Industrial Engineering, University of Trento, where he serves as local coordinator for the scientific field ING-IND/17 (Industrial Plants and Logistic Systems). He chairs the research group on Industrial Plants, Production Systems, and Logistics, and teaches courses in Industrial Plants and Design of Digital Production and Assembly Systems. As coordinator of the Master's program in Management and Industrial Systems Engineering and University Coordinator for the EIT double degree in Zero-Defect Manufacture, Pilati bridges academic leadership with advanced manufacturing research. He has also served as Invited Lecturer at universities in Vienna and Göttingen. His research focuses on integrating environmental sustainability with technical-economic criteria through multi-objective optimization and impact assessment. Key areas include: Distribution networks and warehousing systems Manufacturing and assembly line design Hybrid energy production systems Digitization of manual production processes using depth cameras Recent publications highlight applications of Industry 4.0 technologies to pandemic safety, logistics optimization, and smart manufacturing. Pilati has received significant recognition including the Philip Morris Italia Empowering Research Award (2016) and Autostrade per l'Italia academic recognition. His editorial contributions include guest editing special issues on Digital Twins and Smart Factories in Q1 journals.
Rong Zheng is a Professor in the Department of Computing and Software and a member of the School of Biomedical Engineering at McMaster University, Canada. She holds a Tier-1 Canada Research Chair in Mobile Computing and serves as Acting Chair of the Computing and Software department from July to December 2025. She is also an Associate Member of the Electrical & Computer Engineering department. Education: Ph.D. in Computer Science, University of Illinois, Urbana-Champaign, USA Master of Engineering (thesis) in Electrical Engineering, Tsinghua University, Beijing, China Bachelor of Engineering in Electrical Engineering, Tsinghua University, Beijing, China Dr. Zheng's research lies at the intersection of mobile computing, wireless networking, and machine learning, with a strong focus on applications for aging populations. She directs the NSERC Smart Mobility for the Aging Population CREATE program. Her work encompasses sensor development, wireless network design, and mobile data analytics to address real-world challenges in healthcare, mobility, and data center monitoring. She has developed innovative solutions like the MacQuest campus navigation app and has captured first prize in indoor localization competitions. Her recent publications demonstrate a clear trajectory toward applying wireless sensing technologies (particularly acoustic, Wi-Fi, and mmWave) to health monitoring and mobility assessment for older adults. There's a strong emphasis on developing efficient edge computing solutions that can process data in real-time on resource-constrained devices, as exemplified by her TeamNet framework for collaborative inference on the edge. Her work bridges theoretical advances with practical applications that have social impact. Scientific Awards: Tier-1 Canada Research Chair in Mobile Computing US National Science Foundation CAREER Award (2006) Joseph Ip Distinguished Engineering Fellow (2015-2018) Dr. Zheng leads the Wireless System Research Group (WiSeR) at McMaster University, which has secured significant funding including a $1.65M NSERC CREATE grant for smart mobility research for older adults. Her research has been supported by multiple funding agencies including NSERC, NSF, UH GEAR, and DURIP. She actively mentors graduate students and has developed specialized courses including CAS 772 (Mobile Data Analytics) and CAS 781 (Mobility in the Aging Population). The WiSeR group conducts impactful research on communication, networking, and data analytics issues in Cyber Physical Systems, with applications spanning healthcare, smart infrastructure, and data center monitoring. Their work on data center infrastructure monitoring networks has been featured in EurekAlert and Data Center Dynamics, and they've made significant contributions to indoor localization technology.
Prof. Marc Stamminger is a Professor of Visual Computing at FAU since 2002, leading the Chair of Computer Science 9 (Computer Graphics). His work focuses on algorithms for synthesizing and analyzing images through 3D modeling, LiDAR/Radar capture, and light simulation. He co-leads FAU Solar, applying 3D modeling for environmental lighting analysis under varying conditions. Stamminger has published over 250 papers, winning prestigious awards like the Siggraph Test-of-Time Award. He holds executive roles in Eurographics and is Vice Dean of FAU's Technical Faculty. Research interests span neural rendering , 3D reconstruction , radar imaging , and medical visualization . Recent work emphasizes radiance field rendering (e.g., VR-Splatting, INPC) and radar-based human motion tracking. His lab's FAU Solar project integrates large-scale 3D models with environmental lighting simulations. Publications trends highlight neural rendering optimizations , radar-MIMO systems , and agricultural digital twins . Key collaborations involve medical imaging (e.g., vocal fold reconstruction) and autonomous driving data generation. Awards: Siggraph Test-of-Time (2023?), 2× Siggraph Best-Of-Show Grants/Teams: FAU Solar Lab, Eurographics leadership, FAU Vice Dean Labs: Chair of Computer Science 9, FAU Solar Initiative
Professor Guy Wallis is a Professor and Director of Research at the School of Human Movement and Nutrition Sciences, Faculty of Health, Medicine and Behavioural Sciences at the University of Queensland. He is also an Affiliate of the Centre for Sensorimotor Performance. His work bridges visual neuroscience, computational modeling, and applied human factors research, with significant contributions to understanding visual recognition and visuomotor behavior. Education: Bachelor (Honours) of Engineering in Electrical and Electronic Engineering from Imperial College London PhD in Visual Neuroscience from University of Oxford, UK Prof. Wallis's research program combines computational modeling with behavioral studies, many conducted in computer-controlled virtual environments. His work spans visual neuroscience, object recognition, visuomotor control, and simulator-based training. He has made significant theoretical contributions to understanding how visual recognition is achieved in biological systems and how everyday visuomotor tasks are regulated, challenging existing paradigms and offering new insights. His recent publications reveal a strong focus on virtual reality applications, visual-motor integration challenges, and cross-species cognitive studies. There's a clear trend toward investigating how virtual environments can be optimized for training and assessment, with particular attention to visual perception limitations and how humans adapt to these environments across diverse contexts from surgical training to aviation. Scientific Awards and Recognitions: Elected Fellow of the Queensland Academy of Arts and Sciences (2022) ARC Medical Research Advisory Group (2022-2024) ARC College of Experts (2019-2021) CSIRO CSS Human Research Ethics Committee member (2020-2022) UQ Health and Behavioural Sciences Faculty, HDR Supervision Award (2018) ARC Future Fellowship (2011-2014) ARC QEII Fellowship (2003-2007) UQ Postdoctoral Fellowship (2001-2003) Prof. Wallis has successfully secured funding from major organizations including the Australian Research Council, the Human Frontier Science Program, and the Wellcome Trust. His industry partnerships span diverse sectors such as construction training, mining, healthcare, and aerospace. His research has led to the development of novel training programs for health professionals, impacted the design of man-machine interfaces for mining equipment, and informed the design parameters for pilot training systems. As Director of Research, he oversees the research direction of the School of Human Movement and Nutrition Sciences, fostering interdisciplinary collaborations and supporting early-career researchers through his leadership in the Centre for Sensorimotor Performance.
Kristofer Pister is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He co-directs the Berkeley Sensor and Actuator Center (BSAC) and the Ubiquitous Swarm Lab. His career spans groundbreaking innovations in Micro/Nano Electro Mechanical Systems (MEMS), Control Systems, and Low-Power Circuits, with a focus on Smart Dust and synthetic insects. Education: Ph.D. and M.S. in EECS from UC Berkeley (1992, 1989); B.A. in Applied Physics from UC San Diego (1986). His research areas include MEMS , Control Systems , Robotics , and Integrated Circuits , with recent work on self-powered micro-sensors, crystal-free radios, and interplanetary swarm networks. Key awards include the ISA Albert F. Sperry Founder Award (2009) , Alexander Schwarzkopf Prize (2006) , and the NSF CAREER Award (1996) . He has authored numerous influential publications in wireless sensor networks and microrobotics. His lab, Ubiquitous Swarm Lab , explores distributed robotics and swarm intelligence. Pister emphasizes open collaboration in research, ethical conduct in academia, and efficient resource utilization for graduate students.
Pinar Keskinocak serves as the H. Milton and Carolyn J. Stewart School Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. She co-founded and directs the Center for Health and Humanitarian Systems, demonstrating leadership in both academic administration and research innovation. Her career includes prior roles as College of Engineering ADVANCE Professor and interim associate dean for faculty development, alongside industry experience at IBM T.J. Watson Research Center. Her educational foundation includes a Ph.D. in Operations Research from Carnegie Mellon University and M.S. and B.S. degrees in Industrial Engineering from Bilkent University. This rigorous training underpins her interdisciplinary approach to complex societal challenges. Dr. Keskinocak's research pioneers operations research applications for societal impact , with dual emphases on health systems and humanitarian logistics . She develops advanced models for infectious disease dynamics (including COVID-19, malaria, and Guinea worm), vaccination strategies, disaster response, and supply chain optimization. Her work uniquely bridges engineering analytics with real-world implementation through partnerships with the CDC, American Red Cross, Carter Center, and Children's Healthcare of Atlanta. Analysis of her 2023-2025 publications reveals intensifying focus on eradication program modeling for polio and Guinea worm, with increasing integration of machine learning for predictive accuracy in vaccine distribution. A pronounced trend addresses health equity through targeted interventions for under-immunized populations in Sub-Saharan Africa, emphasizing resource allocation under constraints and cross-border coordination challenges. Her scientific recognition includes: INFORMS Fellow (2015) NSF CAREER Award (2001) Outstanding Professional Education Award, Georgia Tech (2018) Denning Award for Global Engagement, Georgia Tech (2016) Women in Engineering Excellence Teaching Award (2005) Moving Spirit Award, INFORMS (2005) Dr. Keskinocak has secured extensive project funding through collaborations with federal agencies and NGOs, directing research on pandemic response systems, vaccine logistics, and disaster debris management. While specific advisees aren't publicly cataloged, her leadership in the Center for Health and Humanitarian Systems mentors numerous graduate researchers through interdisciplinary projects. She significantly influences national policy via National Academies committees addressing supply chain resilience and vaccine distribution infrastructure. The Center for Health and Humanitarian Systems operates as a dynamic hub where engineering rigor meets humanitarian action, coordinating multi-institutional teams to develop deployable solutions for global health crises and disaster response systems.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
Dr. Yunjie Yang is an Associate Professor at the University of Edinburgh's School of Engineering, with affiliations at the Edinburgh Futures Institute (EFI), the Edinburgh Generative AI Laboratory (GAIL), and the Edinburgh Centre for Robotics. He previously held the Chancellor's Fellow in Data Driven Innovation (2018-2023) and Bayes Innovation Fellow (2023-2024) positions. His research focuses on AI-powered sensing and imaging, machine learning, and soft sensors & electronics for robotics. Yang received his PhD in Engineering Electronics from the University of Edinburgh, MSc in Control Science & Engineering from Tsinghua University, and BEng in Measurement & Control Engineering from Anhui University. After his PhD, he worked as a Postdoctoral Research Associate in Chemical Species Tomography before securing his lectureship. His research interests center on developing intelligent sensing systems that replicate human perception capabilities for robotics and intelligent systems. He pioneers flexible sensing and imaging technologies across various scales through innovative multi-modal sensors, soft electronics, and their modeling using machine learning approaches. His work aims to enable autonomous physical artificial intelligence by bridging the gap between robotic systems and human-like perception. Analysis of his recent publications reveals a strong focus on soft robotics perception, particularly through electrical impedance tomography (EIT) and transformer-based architectures. His research spans medical imaging applications, digital twin modeling for industrial processes, and machine learning approaches for sensor data interpretation. The trend shows increasing integration of physics-informed deep learning with traditional tomographic techniques to achieve higher accuracy and efficiency. European Research Council (ERC) Starting Grant (2024) IEEE J. Barry Oakes Advancement Award (2024) IEEE I&M Society Graduate Fellowship Award (2015) Multiple Best Paper Awards Senior Member of IEEE Fellow of the International Society for Industrial Process Tomography Fellow of the Higher Education Academy ESI highly cited papers Dr. Yang serves as Associate Editor for IEEE Transactions on Instrumentation and Measurement and holds editorial positions with Scientific Reports and IEEE Sensors Journal. His research has been licensed to overseas research institutes and industry partners and received wide media coverage including BBC, EFE, USA Today, and STV. He has secured significant grant funding including the prestigious ERC Starting Grant. He leads the Edinburgh SMART Lab (Sensing/imaging + Machine Learning + Robotics), which aims to replicate human perception capabilities for robotics and advance flexible sensing technologies through innovative multi-modal sensors and machine learning approaches. The lab focuses on enabling autonomous physical artificial intelligence with applications spanning medical diagnostics, industrial monitoring, and advanced robotics systems.
Meng Wu is an Assistant Professor in the School of Electrical, Computer and Energy Engineering (ECEE) at Arizona State University, specializing in advanced optimization, control, and machine learning methods for integrating distributed energy resources (DERs) into power systems. Her work addresses critical challenges in power system planning, operations, stability, and electricity markets under high DER penetration. Education: Ph.D. in Electrical and Computer Engineering, Texas A&M University, 2017 M.Eng. in Electrical and Computer Engineering, Cornell University, 2011 B.Eng. in Electrical Engineering & Automation, Tianjin University, China, 2010 Research Interests: Dr. Wu's research focuses on DER integration through transmission-distribution coordination , spatio-temporal price forecasting , and optimal market participation strategies. Key areas include: Physics-guided machine learning for DER-penetrated distribution systems Computational algorithms for wholesale-distribution market coordination Dynamic modeling of DERs and composite loads for voltage stability Optimal bidding strategies for energy storage and DER aggregators Publication Trends: Her 2021-2024 publications reveal a concentrated effort on DER market integration using parametric programming and deep learning, with emphasis on real-time locational marginal price forecasting, transmission-distribution coordination, and degradation-aware energy storage operations. Scientific Awards: Best Paper Award, IEEE PES General Meeting (2021) Best Conference Paper Award, North American Power Symposium (2019) Invited Participant, US Frontiers of Engineering Symposium, NAE (2021) Advising and Grants: Dr. Wu mentors multiple PhD and Master's students, including recent graduates Zhongxia Zhang (PhD) and Sayyid Mohssen Sajjadi (MS). Her group secured PSERC funding for projects on DER aggregation and adaptive transmission-distribution modeling, with industry partnerships at ISO New England and Quanta Technology. Research Group: Leading an active research team at ASU, she collaborates with the Power Systems Engineering Research Center (PSERC) on DER integration challenges, advising students through FURI, MORE, and Barrett Honors College programs.
Lorin Schöni is a PhD candidate at ETH Zürich, actively contributing to the Security, Privacy and Society research group since October 2022. Concurrently, he serves as a Lecturer at the Department of Humanities, Social and Political Sciences, where he teaches the course Focus on the Human: Human-Centered Security and Privacy Lab (Autumn Semester 2025, Unit 851-0391-00L). His research focuses on human-centered approaches to cybersecurity , particularly through three key domains: Human-Computer Interaction - Investigating design decisions that influence user behavior in digital security contexts Extended Reality Applications - Developing immersive training systems for phishing prevention Usability Engineering - Creating intuitive security interfaces with explainable AI components Recent publications highlight his work at the intersection of autonomous motivation theory and personalized cybersecurity training , with notable presentations at top venues including CHI 2025 and SOUPS 2024. His research aims to transform security from a restrictive framework into a collaborative partnership between users and digital systems, as emphasized in his award-winning work on human-centered cybersecurity.
