Dr. Kory Lawson Ching is an Associate Professor at the University of California, Davis, specializing in writing technologies, digital literacies, and composition pedagogy. His work emphasizes peer response groups in the writing classroom and the integration of technology in education. Prior to UC Davis, he taught at UC Irvine, the University of Illinois at Urbana-Champaign, and San Francisco State University, where he also instructed graduate courses in composition theory and qualitative research methods. Education: PhD in English (Writing Studies) - University of Illinois at Urbana-Champaign (2008) MA in English - UC Irvine (1997) BA in English (Medieval Studies) - UC Irvine (1994) Research focuses on how digital environments mediate writing processes and teacher preparation. His publications explore topics like distraction-free writing tools, qualitative research, and socio-cultural theories of literacy. He has been recognized with awards including the James L. Kinneavy Award (2008) and K. Patricia Cross Future Leaders Award (2000). Teaching interests include writing in digital environments, ethnographic fieldwork, and game analysis. Grants include the Pathways Core Initiative Hybrid Course Redesign (2020–present) and a California State University technology grant (2014–2015). His work bridges traditional pedagogical practices with innovative digital methodologies.
Maarten De Vos is a Professor at the Department of Electrical Engineering (ESAT) , KU Leuven , with dual appointments in the Faculty of Medicine and Faculty of Engineering Science . He leads interdisciplinary research at the intersection of artificial intelligence and biomedical signal processing.
Karthik Dantu is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York, within the School of Engineering and Applied Sciences. His research focuses on mobile sensor networks, robot networks, networked embedded systems, mobile computing, wireless networks, and embedded operating systems. He leads the Distributed Robotics and Networked Embedded Sensing (DRONES) Lab and has received significant funding including an NSF CAREER Award. Dr. Dantu's educational background includes: PhD in Computer Science from University of Southern California (2009) BE in Computer Science from Sri Jayachamarajendra College of Engineering (1999) His research interests center on algorithmic and systems challenges in Edge Computing Systems, with particular focus on enabling seamless vision sensing in cloud-edge environments. Dantu's work bridges mobile systems and robotics, developing novel approaches for UAV software, visual SLAM, and distributed sensing. His research addresses critical challenges in resource-constrained environments, security, and real-time performance for mobile and robotic systems, with emphasis on practical implementations that solve real-world problems in autonomous systems. Dr. Dantu's publication record shows a strong trajectory in mobile systems and robotics research, with increasing focus on edge computing applications for visual sensing. His recent work demonstrates expertise in adapting visual SLAM to edge environments, securing mobile systems through technologies like Rushmore, and developing novel approaches for UAV software reliability and depth sensing. The research spans theoretical algorithms and practical system implementations, with particular strength in bringing academic research to practical applications in robotics and mobile computing. Dr. Dantu has received several scientific honors: NSF CAREER Award on Enabling Seamless Vision Sensing in Cloud-Edge Systems Outstanding service award from the Office of International Services NSF Travel Grant for SenSys 2005 Conference Travel Grant for SIGCOMM 2002 As an advisor, Dr. Dantu has mentored numerous PhD students to completion, with graduates now working at companies like Samsung Research and Zoox Inc., or continuing academic careers as Assistant Professors. His research is supported by substantial grants including a DARPA OFFSET Sprint 4 award ($470k), an NSF CAREER award ($550k), and multiple NSF collaborative grants totaling over $1.5 million. He serves on numerous conference committees including Mobicom, MobiSys, and ICRA, demonstrating leadership in the mobile systems and robotics research communities. Dr. Dantu leads the Distributed Robotics and Networked Embedded Sensing (DRONES) Lab at UB, which focuses on developing algorithms and systems for mobile sensor networks, robot networks, and embedded sensing applications. The lab's work spans theoretical foundations to practical implementations, with particular expertise in UAV systems, visual SLAM, and edge computing for robotics, maintaining strong collaborations with industry partners and other academic institutions to advance the state of the art in mobile and robotic systems.
Dr. Asier Moneva is a Postdoctoral Researcher at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR) and The Hague University of Applied Sciences , specializing in cybercrime , environmental criminology , and situational crime prevention . His work focuses on offender decision-making in cyberspace, cybercrime victimization patterns, and the application of data science to crime analysis. Education : PhD in Criminology (2020), Master in Crime Analysis and Prevention (cum laude, 2017) from Miguel Hernández University. Current Role : Analyzing cybercrime patterns through environmental criminology frameworks and data science methodologies. Moneva's research examines longitudinal offending patterns in cybercrime, particularly through analyses of web defacement archives ( Zone-H data) and hacker behavior. His studies reveal extreme concentration of cybercrime among chronic offenders, with 2.9% of hackers responsible for 68.5% of defacements. He also investigates repeat victimization dynamics in digital environments and the effectiveness of warning banners as deterrents. Recent publications focus on ransomware payment decisions by SMEs, stolen data markets on Telegram, and the intersection of familial relationships with cybercrime involvement. His work combines quasi-experimental designs , crime scripting , and conjunctive analysis to develop prevention strategies.
Andrea Bunt is a Full Professor and Associate Head (Graduate) in the Department of Computer Science at the University of Manitoba, where she co-directs the HCI lab. She has established herself as a leading researcher in human-computer interaction with significant contributions to software learnability, rural computing, and technologies for children and families. Her work bridges theoretical and practical aspects of HCI, with strong community engagement through student supervision and collaborative projects. Dr. Bunt completed her B.Sc. at Queen's University, followed by an M.Sc. in 2001 and Ph.D. in 2007 at the University of British Columbia. Prior to joining the University of Manitoba, she was a Postdoctoral Fellow at the University of Waterloo in the Human-Computer Interaction Lab. This educational trajectory has provided her with a strong foundation for her interdisciplinary research approach. Her research interests span multiple areas within human-computer interaction, with particular focus on software learnability for diverse user groups, improving computing experiences in rural and remote communities, and designing technologies specifically for children and families. Her work on explainable AI, gender inclusivity in technology, and collaborative learning dynamics represents cutting-edge contributions to the field. Recent projects include Stream Assistant for live streamers, digital interventions for adolescent tech disengagement, and gender-inclusive approaches to online question-and-answer platforms. Analysis of her recent publications reveals a strong emphasis on user-centered approaches to technology design, particularly focusing on vulnerable or underserved populations. Her work consistently bridges theoretical HCI principles with practical applications, demonstrating how technology can be made more accessible, inclusive, and effective for diverse user groups. There's a clear trajectory toward addressing societal challenges through HCI, with increasing focus on ethical considerations in AI systems. CS-Can | Info-Can Young Researcher Award (2018) NSERC Accelerator Supplement (2015-2018) Multiple Best Paper Awards at premier conferences including CHI, Graphics Interface, and FDG Consistent recognition for methodological innovation and impactful research contributions Dr. Bunt actively mentors a diverse group of students at all levels, from undergraduate research assistants to Ph.D. candidates. Her lab receives funding from NSERC Discovery Grants and other sources to support research on intelligent interactive systems. She has successfully guided numerous students through their academic journeys, with many going on to impactful careers in academia and industry. Her collaborative approach extends to interdisciplinary partnerships across computer science, education, and social sciences. The HCI lab she co-directs serves as a vibrant research hub focusing on real-world applications of human-computer interaction principles. Current projects address critical challenges including technology use in rural communities, digital wellbeing for adolescents, and inclusive design practices. The lab fosters a collaborative environment where students and researchers work together to develop innovative solutions to complex HCI problems.
Daniel Coppersmith is an Assistant Professor joining the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst in Fall 2025. His research program is dedicated to advancing the understanding, prediction, and prevention of suicide using innovative technologies such as smartphones and wearable biosensors. He is affiliated with the College of Natural Sciences and operates at the intersection of clinical psychology and computational behavioral science. His research interests center on the dynamic, heterogeneous nature of suicidal thoughts and behaviors. Key areas include: Describing the form and function of suicidal ideation Identifying time-varying risk and protective factors Developing personalized, scalable interventions using real-time data His work emphasizes affect regulation, temporal dynamics, and just-in-time adaptive systems to support individuals at risk. Analysis of his recent publications reveals a strong focus on digital mental health tools, computational modeling of suicide risk, and the development of real-time interventions. His work frequently employs longitudinal data, dynamic systems modeling, and mobile technology to capture intraindividual variability in suicidal thinking. Collaborations with leading figures such as Matthew K. Nock and Emily M. Kleiman underscore his integration into top-tier research networks in clinical and computational psychiatry. Although no scientific awards are currently listed, his publication record in premier journals like Proceedings of the National Academy of Sciences and Behaviour Research and Therapy highlights significant scholarly impact. His future work is expected to expand the reach of precision suicide prevention through scalable, technology-enhanced care models. Dr. Coppersmith advises graduate students in clinical psychology and related fields, though specific advisees are not yet listed. His lab likely focuses on digital phenotyping, suicide risk modeling, and intervention development. He may pursue grants from NIH, NIMH, and private foundations focused on mental health innovation and suicide prevention. His team is expected to include data scientists, clinical psychologists, and software developers working on real-time behavioral monitoring systems.
Nicola Marzari is a Professor of Theory and Simulation of Materials at EPFL, where he also serves as Director of the National Centre for Computational Design and Discovery of Novel Materials (NCCD). He is Chairman of Psi-k, an international network for advanced materials' computational design. Previously, he held the Toyota Chair of Materials Engineering at MIT and leadership roles at the University of Oxford, including Director of the Materials Modeling Laboratory and a Statutory Chair in Materials Modeling. His education includes a Laurea in Physics (summa cum laude) from the University of Trieste, a PhD in Physics from the University of Cambridge under Prof. Michael C. Payne, and postdoctoral work at Rutgers University with Prof. David Vanderbilt. Marzari's research focuses on computational materials science, electronic structure theory, and high-throughput simulations. He develops methods for predicting material properties using first-principles approaches, machine learning, and quantum espresso software. Key areas include energy materials (batteries, thermoelectrics), magnetic materials, and optoelectronic systems. His work bridges fundamental physics and practical material design, emphasizing reproducible workflows and open-source tools like koopmans and AiiDA . His recent articles highlight advancements in machine learning for materials interfaces, dynamical Hubbard functionals, and thermal conductivity modeling. He actively contributes to EuroHPC initiatives for exascale materials design and OPTIMADE standards for materials data exchange. Marzari leads interdisciplinary teams at EPFL and collaborates globally on projects ranging from defect engineering in semiconductors to AI-driven materials discovery. His research aims to accelerate the development of sustainable energy and electronic technologies through computational innovation.
Timothy M. Jones is a Professor of Computer Architecture and Compilation at the University of Cambridge's Computer Laboratory, serving as Director of the Computer Architecture and Semiconductor Design Centre (CASCADE) and Fellow/Director of Studies at Gonville and Caius College. His research focuses on parallelism extraction in applications to enhance performance and address energy efficiency/reliability challenges in compilers, binary translators, and microarchitectures. Current work includes novel cache prefetching techniques, thread-level parallelism schemes, and advanced core prediction methods. He has an Erdős number of 4 and a Dijkstra number of 4 via collaborative networks. Research interests span computer architecture fundamentals, compiler optimizations, hardware security mechanisms, and fault tolerance strategies. Notable contributions include speculative vectorization, heterogeneous parallel error detection (MEEK/FireGuard), and security tools like MarkUs and MineSweeper. CASCADE oversees interdisciplinary projects addressing future microprocessor/system challenges. Jones supervises PhD students through CASCADE's 2025 intake program. Publications emphasize architectural innovations in memory systems, security, and energy efficiency. Key works include MASCOT (memory dependence prediction), Scalar Vector Runahead (2024), and Decoupled Vector Runahead (2023). His work integrates hardware-software co-design principles to tackle real-world processor bottlenecks.
Ahmed Elbeltagi is an Assistant Professor in the Agricultural Engineering Department at Mansoura University's Faculty of Agriculture. His work focuses on hydrology, agricultural water management, and climate change adaptation. Specializes in data-driven modeling for water resource optimization Integrates machine learning with traditional hydrological models Active in climate change impact assessments on agricultural systems Recent research trends include: Developing open-source tools like Aqua-MC for irrigation simulation Applying hybrid deep learning models for evaporation prediction Advancing water quality assessment through multivariate analysis Exploring economic applications of wetlands in arid regions He collaborates with institutions across Egypt, India, China, and Saudi Arabia, with a focus on sustainable water management solutions.
Prof. Michael Weyrich is a faculty member at the Institute of Industrial Automation and Software Engineering (IAS) within the University of Stuttgart , leading the Cluster of Excellence IntCDC . His academic rank is Professor, and he focuses on Industrial Automation , Digital Twins , and Large Language Models (LLMs) for manufacturing and automotive systems. His research explores integrating LLMs into industrial automation for adaptive control, cloud offloading of vehicle functions, and semantic interoperability via Asset Administration Shells . He investigates modular production architectures , connected vehicle systems , and synthetic data generation for autonomous machinery. Recent publications highlight LLM-driven production planning , dynamic sensor calibration , and machine learning for fault detection in electric vehicle powertrains. His work emphasizes real-time data modeling and flexible microservice orchestration .
Thomas K. Uchida is an Associate Professor in the Department of Mechanical Engineering at the University of Ottawa, a position he has held since May 2024. Prior to this promotion, he served as an Assistant Professor at the same institution from October 2018 to May 2024. Before joining the University of Ottawa, Dr. Uchida was an Engineering Research Associate (April 2015-August 2018) and Simbios Distinguished Postdoctoral Fellow (July 2012-April 2015) in the Department of Bioengineering at Stanford University. Dr. Uchida's research focuses on the modeling and simulation of dynamic systems, with particular emphasis on human movement biomechanics. His work spans multiple areas including: Simulation-guided design of assistive devices for improving mobility Modelling musculotendon dynamics and energy expenditure Parameter identification and model reduction methods Impact and contact dynamics Development of computational tools for biomechanical analysis He is a co-author of the book "Biomechanics of Movement: The Science of Sports, Robotics, and Rehabilitation" published by MIT Press, and actively contributes to the development of OpenSim, an open-source software platform for modeling musculoskeletal systems and generating simulations of human and animal movement. His work on OpenSim was featured on the cover of PLoS Computational Biology. Dr. Uchida's recent publications demonstrate strong activity in biomechanics, robotics, and computational modeling. His work bridges engineering principles with biological applications, particularly in understanding human movement mechanics. Key trends include applying machine learning to gait analysis, developing enhanced spine models, analyzing human balance stability with time delays, and advancing musculoskeletal simulation techniques. As an academic advisor, Dr. Uchida currently supervises seven graduate students: Firas Baklouti (expected completion August 2025) Shahin Sharafi Kazem Alambeigi Jiawei Gao Yuzhen Yan Manuel Lucas De Oliveira Blake Scott Miller Dr. Uchida collaborates with research teams focused on biomechanics and movement science. His work with OpenSim places him within an international community of researchers developing computational tools for biomechanical analysis, connecting mechanical engineering with biomedical applications in sports, robotics, and rehabilitation.
Magdalena Szymczyk is a Lecturer in the Department of Biocybernetics and Biomedical Engineering at AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering. Her work bridges embedded systems, biomedical signal processing, and geophysical data analysis. Research focuses on energy-efficient sensor networks, neural networks for GPR data classification, and mathematical transforms in signal analysis Expertise in parallel computing, real-time systems, and biomedical engineering applications Her publications (2015–2025) demonstrate a trajectory from parallel neural networks and S-transform/GPR methodologies to recent work on MicroPython in embedded systems. Key themes include energy optimization in distributed architectures and AI-driven signal processing across biomedical and geophysical domains. She has authored works on deterministic chaos in simulations, GPU image processing, and cybersecurity in microcontroller systems. Her current research emphasizes embedded systems security, medical signal diagnostics, and computational methods for geological analysis. She utilizes tools like OpenCL for GPU acceleration and MATLAB for parallel computing implementations.
Pouya Bashivan is an Assistant Professor in the Department of Physiology at McGill University's Faculty of Medicine. His research focuses on developing computational models to explain and regulate neural responses during visual tasks requiring memory, combining machine learning, neuroscience, and cognitive science. Education : Ph.D. in Computer Engineering (2016), Postdocs in Machine Learning (2020) and Computational Neuroscience (2016-2020) His lab investigates: Topographical neural networks for visual cortex simulation Massively-multitask models for prefrontal cortex Saccade-driven visual exploration models Predictive hippocampus models for episodic memory Recent publications explore adversarial robustness, memory-augmented networks, and brain-state decoding. Current projects emphasize causal models, brain-AI alignment, and translating computational neuroscience into therapeutic applications. The lab is located in the McIntyre Medical Sciences Building, Room 1117, Montreal, Quebec.
Dr. Andrea K. Rorrer is a full-time Professor at the University of Utah's College of Education, Department of Educational Leadership and Policy, and serves as Director of the Utah Education Policy Center (UEPC). With 35 years of education experience, she has held roles as a teacher, principal, policy analyst, and researcher. Her academic career at the University of Utah includes promotions from Assistant Professor (2002-2009) to Associate Professor (2009-2014) and Professor since 2014, alongside serving as Associate Dean for Research from 2014-2023. PhD in Educational Leadership & Policy, University of Texas at Austin (2001) MS in Educational Leadership & Policy, University of Virginia (1995) Dr. Rorrer's research focuses on the intersection of educational leadership, policy implementation, and systemic change with equity as a central theme across early childhood, K-12, and higher education. Her work examines leadership preparation programs, charter school effectiveness, policy mediation, and institutional factors affecting educational outcomes. Recent publications highlight: Leadership preparation program features influencing career intentions (2025) Personalized learning software's impact on teacher-student dynamics (2024) Turnaround reform frameworks (2018) Charter school mobility patterns (2019) Homeschool policy analysis (2012) Scientific recognition includes: UCEA Master Professor Award (2020) College of Education Research Award AERA Dissertation Award (2001) Culbertson Award for early-career contributions Mentorship has been central to her career, with 35 doctoral chairs and 44 committee memberships since 2002. Current teaching activities include Thesis Research and Ed.D. Capstone Project courses.
Dr. Gaël Kermarrec is a researcher at the Boundary Layer Meteorology Group , part of the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover . His work focuses on atmospheric turbulence, GNSS applications, and remote sensing for environmental monitoring. Boundary layer meteorology Turbulence theory GNSS signal processing Terrestrial laser scanning Climate change impacts Geodetic time series analysis His research integrates advanced mathematical models like LR B-splines and Matérn covariance with large eddy simulations to study: Atmospheric turbulence effects on optical/GNSS signals Hydrospheric mass loading Deformation analysis of terrain/port infrastructure Climatic sea-level changes Machine learning for remote sensing The 15 most recent articles (2025-2023) demonstrate his focus on: GNSS-based turbulence detection AI-enhanced climate mapping Advanced surface approximation techniques Multi-sensor data fusion Stochastic modeling of geodetic observations Environmental impacts on optical measurements He has developed tools like the Klimascanner QGIS plugin for urban climate resilience and contributes to: Understanding atmospheric scale lengths Improving TLS/GNSS deformation monitoring Analyzing hydrospheric changes Wavefront modeling Ionospheric corrections
