Włodzimierz Kasprzak is a Professor at the Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology. His research focuses on computer vision, robotics, human-computer interaction, and machine learning. He has contributed to advancements in human action classification, skeleton-based feature analysis, and multimodal interface design. Research Highlights: Development of lightweight classification models for human actions in video using skeleton-based features. Advances in multi-stream fusion techniques for image and video analysis. Design of embodied agent systems for cybersecurity event visualization and control. Awards and Recognition: 2024: Individual First Class Rector's Award for Scientific Achievements (2022-2023) 2021: Medal of the Commission of National Education 2011: Golden Cross of Merit His work integrates theoretical contributions with practical applications in robotics, surveillance systems, and human-centered technologies.
Paul McKenna is a Professor in the Department of Physics, Faculty of Science, at the University of Strathclyde, where he currently serves as Deputy Associate Principal (Research & Knowledge Exchange). He previously held leadership roles as Vice Dean (Research) in the Faculty of Science (2021–2023) and Head of the Department of Physics (2018–2021). His work is central to advancing ultra-intense laser-plasma science and its applications. His research focuses on ultra-intense laser-plasma interactions , particularly the development of laser-driven particle and radiation sources , plasma optics and photonics , and high field science . His work bridges fundamental physics with practical applications in medicine, materials science, and fusion energy. He is actively involved in major international laser facilities, serving on advisory boards such as the Program Advisory Committee for the Extreme Light Infrastructure-Nuclear Physics (ELI-NP) and previously at the Central Laser Facility, Harwell. Recent publications highlight a strong trend in laser-driven proton acceleration , beam diagnostics using machine learning , plasma-based collimation , and structured light generation . His work increasingly integrates computational methods, such as Bayesian optimization and neural networks, to enhance experimental outcomes in high-energy-density physics. Fellow of the Royal Society of Edinburgh (2020) High Power Laser Science and Engineering Outstanding Contribution Award (2023) McKenna has secured significant research funding, notably from EPSRC, and leads multiple active projects including those on relativistic plasma apertures and Bayesian optimization in fusion simulations. He contributes extensively to researcher development and postgraduate research strategy. He has supervised numerous early-career researchers and PhD students, though specific names are not listed in the provided data. He is also involved in interdisciplinary efforts to foster collaborative research cultures in technological universities. He leads or participates in advanced research facilities such as the SCAPA (Scottish Centre for the Application of Plasma-based Accelerators) and contributes to the development of high-repetition-rate laser systems. His lab’s work is highly collaborative, involving partnerships across the UK and internationally, with strong ties to institutions like Queens University Belfast and national laboratories.
Kun Chen is a Professor in the Department of Statistics at the University of Connecticut's College of Liberal Arts and Sciences. His research bridges advanced statistical methodology with critical applications in healthcare, environmental science, and mental health. His research focuses on large-scale statistical learning , machine learning optimization , and healthcare analytics , particularly in suicide risk prediction using electronic health records and health information exchanges. Recent work integrates natural language processing with social determinants of health for veteran suicide prediction and develops novel tensor regression methods for longitudinal data with missing observations. Analysis of his 15 most recent publications reveals a dominant trend in mental health data science (73% of articles), with significant contributions to statistical methodology (53%) including reduced-rank regression extensions and sparse factor modeling. His environmental statistics work (20%) focuses on nanomaterial applications in contaminated agriculture and microbiome-environment interactions. Scientific Recognition: Co-authored seminal 2023 Springer monograph Multivariate reduced-rank regression: theory, methods and applications (2nd Edition) Developed rrpack R package for reduced-rank regression (2019) His collaborative work spans UConn Health, Veterans Affairs, and multiple national consortia, with recent grants supporting data fusion techniques for suicide prevention and Parkinson's disease progression modeling. Current projects include transfer learning frameworks for hospital suicide risk prediction and gut microbiome analysis in neurological disorders. Dr. Chen maintains active leadership in statistical ecology applications and serves on editorial boards for biostatistics journals, with recent work on quantum dot analysis demonstrating methodological versatility across physical and health sciences.
Vahab Khoshdel is an Assistant Professor in the Department of Electrical and Computer Engineering at the Price Faculty of Engineering, University of Manitoba. His research bridges machine learning, deep learning, computer vision, robotics, and medical imaging, with applications in rehabilitation robotics, microwave/ultrasound imaging, stored grain monitoring, and precision agriculture. Education: 2021, Ph.D. Biomedical Engineering, University of Manitoba 2017, Ph.D. Mechanical Engineering, Ferdowsi University of Mashhad 2013, M.Sc. Mechatronic Engineering, University of Shahrood 2011, B.Sc. Robotics Engineering, University of Shahrood Research Interests: Khoshdel specializes in applying generative AI, neural networks, and optimization techniques to medical imaging and robotics. His work includes microwave/ultrasound breast imaging, impedance control for rehabilitation robots, and AI-driven agricultural monitoring systems. Publication Trends: His recent articles emphasize machine learning workflows for medical diagnostics, deep learning in multimodal imaging, and neural networks in rehabilitation robotics. Key subfields include 3D imaging, inverse scattering, tissue classification, and sEMG signal analysis. Contact: Vahab.Khoshdel@umanitoba.ca
Dr. Kevin A. Adkins is a Professor in the College of Aviation at Embry-Riddle Aeronautical University , where he teaches aerodynamics, aircraft performance, and uncrewed aircraft systems (UAS) courses. He pioneered the first collegiate Advanced Air Mobility (AAM) course in the U.S. in 2023 and directs two labs: the Advanced Air Mobility Research and Innovation Lab (AAMRIL) and the Uncrewed Vehicle and Atmospheric Investigation Lab (UNVAIL) . Education : Ph.D. in Aerospace Engineering (Mississippi State University), M.Eng. and B.S. in Aerospace Engineering (University of Michigan-Ann Arbor) His research focuses on atmospheric boundary layer meteorology using UAS, AAM concepts of operation (ConOps), and flight test engineering. He collaborates extensively on sensor development for environmental monitoring, including low-cost particulate matter sensors and bioaerosol sampling mechanisms. Recent publications emphasize UAS applications in wildfire detection, urban microclimate analysis, and wind farm humidity studies. Dr. Adkins serves on advisory committees for the Florida Department of Transportation's AAM initiative and ASTM International's UAS standards. Awards : Fellow of the Royal Aeronautical Society, ERAU Researcher of the Year (2020), PIEoneer Real Life Learning Award (2019), AUVSI Best Paper Award (2019) He mentors numerous student projects on UAS sensor development and atmospheric research, with teams winning symposium awards. His labs integrate experiential learning with international fieldwork in Puerto Rico, Norway, and Lithuania.
Florence Haegel is a University Professor of Political Science at Sciences Po, where she is affiliated with the Center for European Studies and Comparative Politics (CEE) and the Department of Political Science. She previously served as director of both the CEE and the Department of Political Science, demonstrating strong institutional leadership. Her research spans several core areas in political science, with a focus on political parties and party systems , especially the transformation of the French partisan right. She investigates political socialization , civic participation , and citizens’ perceptions of Europe , employing innovative qualitative methods such as collective interviews. Her recent work includes analyzing activism’s intrinsic rewards and political engagement among marginalized populations. The trajectory of her publications reveals a sustained interest in party dynamics, public discourse, and democratic participation, with methodological rigor underpinning empirical work across France, Belgium, and the UK. Her scholarship combines theoretical depth with comparative and ethnographic insights. Member, Editorial Board, Revue Française de Science Politique Member, Board of Directors, French Association of Political Science (AFSP) Member, MaxPo Joint Council (since 2019) Florence Haegel supervises graduate research through the Doctoral School and teaches in the Master's program in Comparative Politics, as well as the undergraduate Introduction to Political Science course. She is currently leading the PICRI project "Precarity, Participation, Politics," examining civic engagement among vulnerable populations. Her work continues to influence both academic and public debates on democracy and political belonging. She is actively involved in research coordination and academic governance, contributing to the advancement of political science in France and Europe.
Weiran Wang is an Assistant Professor in the Department of Computer Science at the University of Iowa. Previously, he worked as a Staff Research Scientist at Google (2021-2024), Senior Research Scientist at Salesforce Research (2019-2020), and Amazon Alexa (2017-2019). He completed his PhD in 2013 at UC Merced under Miguel A. Carreira-Perpinan and postdoctoral research at Toyota Technological Institute at Chicago (2014-2017) with Karen Livescu and Nathan Srebro. PhD: EECS Department, UC Merced (2013) MS: Computer Science, Chinese Academy of Sciences (2008) BS: Computer Science, Huazhong University of Science and Technology (2005) His research focuses on machine learning algorithms for speech processing , multi-view representation learning , and optimization . Key contributions include advancements in end-to-end speech recognition, stochastic canonical correlation analysis, and deep variational methods for multi-modal data. Notable work includes improving WER metrics for telephony speech and developing GPU/TPU implementations for ASR biasing. The 15 most recent publications span 2024-2018, emphasizing speech recognition (2024 NAACL/Interspeech), multi-view learning (2022 ICLR), self-training (2020 Interspeech), and acoustic modeling (2018). Trends include deep learning, attention mechanisms, and distributed optimization techniques. No scientific awards are explicitly mentioned in the provided texts. Current teaching includes CS4980: Deep Learning (Spring 2025) and CS4420: Artificial Intelligence (Fall 2024).
W. Patrick McCray is a Professor in the History Department at the University of California, Santa Barbara, specializing in the histories of technology and science since 1945. He holds a Ph.D. from the University of Arizona (1996). His research bridges disciplines, examining intersections of art, technology, and science, with a focus on Cold War collaborations, computing history, and futurism. Currently, he is completing a book titled *README* (MIT Press, 2025), analyzing how computers became culturally pervasive through their representation in books. He is also a Kluge Chair in Technology and Society (2025–2026). McCray’s work spans books like *Making Art Work* (2020), exploring artist-engineer collaborations, and *The Visioneers* (2013), which won the Watson Davis Prize. His honors include fellowships from the AAAS and APS, and roles at the Smithsonian’s Lemelson Center and the Canadian Institute for Advanced Research. His teaching includes courses on technology history, computing, and the atomic age. McCray’s research also addresses environmental history, space exploration, and nanotechnology governance. Key projects include exploring habitability’s intersections with space and environmental science, and studying mountain cultures in the American West. His writing extends to public venues like the *Los Angeles Review of Books*, where he critiques tech culture and innovation narratives. He is a renowned voice on topics like Silicon Valley’s societal impacts and the myth of the 'independent inventor.'
Rong Chen is a Distinguished Professor and Chair of the Department of Statistics at Rutgers University, within the School of Arts and Sciences. With a Ph.D. from Carnegie Mellon University, Professor Chen has established himself as a leading researcher in statistical time series analysis, Monte Carlo methods, and their applications across various fields. Professor Chen's research focuses on: Nonlinear and Multivariate Time Series Analysis Monte Carlo Methods, Statistical Computing and Bayesian Analysis Statistical Applications in Science, Engineering and Business His research trajectory has evolved significantly over the years, beginning with foundational work on nonlinear time series and moving toward more complex high-dimensional tensor time series analysis. Recent publications show a strong emphasis on matrix and tensor factor models for high-dimensional time series, reflecting the growing importance of analyzing complex structured data in modern applications. His work bridges theoretical statistical innovation with practical problem-solving across finance, engineering, and computational biology. Professor Chen has been recognized for his contributions to the field with prestigious fellowships: ASA Fellow (American Statistical Association) IMS Fellow (Institute of Mathematical Statistics) As Chair of the Department of Statistics, Professor Chen oversees academic programs including the Master in Financial Statistics and Risk Management (FSRM) and the Master in Data Science (Statistics Track) programs. His leadership extends to guiding research directions in the department and fostering collaborations across disciplines. Professor Chen has secured numerous research grants supporting work in time series analysis, statistical computing, and applications in finance, engineering, and bioinformatics. Professor Chen's research group maintains active collaborations with researchers in finance, engineering, and computational biology, applying statistical innovations to real-world problems including financial time series analysis, protein folding studies, HIV infection dynamics modeling, wind power forecasting, and nuclear material detection systems.
Mai Elshehaly is a Lecturer in Computer Science at City, University of London, where she is affiliated with the giCentre in the Department of Computer Science, School of Science and Technology. She focuses on designing, developing, and evaluating visualisation solutions that support data-driven decision-making, particularly for non-technical users in healthcare and public sectors. Her work involves close collaboration with healthcare practitioners, local authorities, and schools to co-design tools grounded in real-world needs. Her research interests center on information visualisation and human-computer interaction , with a strong emphasis on user-centered and co-design methodologies . She investigates how lived experiences of stakeholders can shape visual analytics systems, especially in healthcare quality improvement, population health management, and digital transformation initiatives. Her approach bridges technical innovation with social impact, promoting equitable access to data insights. Her recent publications highlight trends in narrative visualization, integration of lived experience with electronic records (e.g., CLEVER framework), and adaptable dashboards for healthcare (QualDash). These works are published in top venues such as IEEE VIS, IEEE TVCG, and Computer Graphics Forum, reflecting a consistent focus on usability, evaluation, and real-world deployment of visual analytics tools. Department-level achievement award during PhD at Virginia Tech University-level achievement award during PhD at Virginia Tech Mai has secured research funding from the Bradford Institute for Health Research (BIHR), NIHR, and internal university sources including EPSRC doctoral training funds. She has led and contributed to interdisciplinary projects such as the Digital Makers Programme and Connected Bradford, which involve partnerships across academia, healthcare, and local government. While no formal PhD students are listed, she mentors through collaborative research and advisory roles. She is actively involved in applied research through leadership positions: Co-Director of the Digital Makers Programme, Deputy Director of the Workforce Observatory for West Yorkshire, member of the advisory board at the Wolfson Centre for Applied Health Research, and cross-theme visualisation expert at the Yorkshire and Humber Patient Safety Research Centre. These roles underscore her commitment to translating research into practice within health and education systems.
Soukaina Filali Boubrahimi serves as an Assistant Professor in the Computer Science Department within the College of Engineering at Utah State University. Her academic appointment is based in the SER 332 building located at 4205 Old Main Hill, Logan, UT 84322-0001. She maintains a research-active position with a focus on computational methods for complex temporal data analysis. Dr. Filali Boubrahimi's research program centers on time series analysis , machine learning , and space weather prediction , with particular emphasis on solar flare forecasting and counterfactual explanation systems. Her work bridges theoretical machine learning advancements with practical applications in heliophysics, hydrology, and social media analysis. The research portfolio demonstrates significant expertise in handling imbalanced temporal datasets, developing novel data augmentation techniques, and creating interpretable AI systems for critical prediction tasks. Analysis of her recent publication trajectory reveals consistent contributions to counterfactual explanation frameworks for time series data (Info-CELS, M-cels, ACTS), space weather prediction systems (solar flare and energetic particle event forecasting), and generative modeling approaches (AVATAR, ChronoGAN). Her work frequently addresses the challenges of severely imbalanced datasets through contrastive learning and sophisticated preprocessing techniques, demonstrating methodological innovation in handling rare but critical space weather events. While no specific awards are documented in the available information, her research program appears substantial based on the volume and quality of recent publications spanning multiple high-impact domains. The research demonstrates strong interdisciplinary connections between computer science, space physics, and environmental science. Her laboratory activities focus on developing machine learning frameworks for temporal data analysis, with particular attention to space weather prediction systems. The research group appears to specialize in creating robust models for rare event prediction, explainable AI systems for time series classification, and novel data augmentation techniques for imbalanced temporal datasets. Current projects likely include the development of multimodal fusion approaches for solar energetic particle prediction and spatio-temporal modeling for hydrological applications.
Dilip Sarkar serves as an Associate Professor in the Department of Computer Science at the College of Arts and Sciences, University of Miami. His academic profile demonstrates a strong research focus across multiple domains of computer science with particular emphasis on theoretical foundations and practical applications. Dr. Sarkar's research interests include: Artificial Intelligence and Cybernetics Parallel Algorithms and Programming Combinatorics Image Processing and Compression Internet of Things Security Machine Learning Applications in Healthcare Quantum Computing Approaches His publication record from 2018-2024 reveals a researcher engaged with cutting-edge computational challenges. Recent work spans quantum tensor networks for time series analysis, security frameworks for single sign-on systems, efficient computation in belief theoretic models, and machine learning applications for traumatic brain injury recovery prediction. His research demonstrates consistent integration of theoretical computer science with practical problem-solving across diverse application domains. As a member of CCS (Center for Computational Science) at the University of Miami, Dr. Sarkar participates in interdisciplinary research initiatives that bridge computational methods with scientific discovery across multiple fields. His technical expertise spans both theoretical frameworks and practical implementations, with notable contributions to image compression techniques, particularly for medical applications, and security protocols for modern web and IoT environments.
David Bermbach is a Full Professor at Technische Universität Berlin , leading the Scalable Software Systems group since 2023. His research focuses on distributed systems, serverless computing, and benchmarking, with significant work on edge and fog computing architectures. He is affiliated with the Einstein Center Digital Future and co-chairs interdisciplinary projects like SimRa for bicycle traffic safety. Full Professor, Scalable Software Systems (2023–present) ECDF-Professor, Mobile Cloud Computing (2017–2023) Postdoctoral Researcher (2014–2017) Education : Diploma in Business Engineering (2010) – Karlsruhe Institute of Technology (KIT) PhD in Computer Science (2014, summa cum laude) – KIT Research Interests span distributed systems with emphasis on cloud, edge, and fog computing, serverless architectures, IoT platforms, and benchmarking frameworks. His work addresses consistency-performance trade-offs, resource placement, and interdisciplinary applications in urban mobility and satellite edge computing. Article Trends show a focus on serverless computing (12/15), edge-cloud integration (9/15), and benchmarking methodologies (7/15). Key themes include optimizing function placement, federated learning architectures, and low-earth orbit computing systems. Scientific Awards Best Paper Award – ShutPub (2024) Best Workshop Paper – A Research Perspective on Fog Computing (2017) Best Paper Runner Up – Benchmarking Eventual Consistency (2014) Summa Cum Laude PhD Thesis (2014) Advising & Grants include mentoring students like Tobias Pfandzelter and Trever Schirmer, leading funded projects through the Einstein Center Digital Future, and contributing to 6G network research. His team works on cloud federation, serverless optimization, and real-world IoT applications.
Aleksandra Sarcevic is a Professor of Information Science at Drexel University's College of Computing & Informatics (CCI), where she directs the Interactive Systems for Healthcare (IS4H) Research Lab. She earned her PhD (2009) and MLIS (2005) from Rutgers University's School of Communication and Information, and holds a BA in Film and TV Production from the University of Arts, Belgrade. PhD, Communication, Information and Library Studies MLIS, Library and Information Science BA, Film and TV Production Her research focuses on computer-supported cooperative work (CSCW) , human-computer interaction (HCI) , and healthcare informatics , with specializations in: Collaboration in high-risk environments Medical team coordination Context-aware systems for critical care Crisis informatics Socio-technical system design Recent publications examine AI-enabled decision support , PPE compliance monitoring , and real-time clinical alert systems , with funding from NIH , NSF , and AHRQ . She received the NSF CAREER award in 2013 and mentors both current and graduated PhD students in interdisciplinary research. The IS4H lab develops interactive healthcare systems for trauma resuscitation and infection control, employing ethnographic methods and sensor-based activity recognition to improve medical team performance.
Carlos Platero Dueñas is a Full Professor at the Department of Electrical, Electronic and Automatic Engineering and Applied Physics at the Universidad Politécnica de Madrid (UPM), where he has served for 31 years. He leads the research group Tecnologías para Ciencias de la Salud since 2015 and contributes to interdisciplinary research at the intersection of biomedical engineering, neuroscience, and artificial intelligence. Department: Electrical, Electronic and Automatic Engineering and Applied Physics Research Group: Tecnologías para Ciencias de la Salud (Health Science Technologies) Teaching: 34 years of academic experience, including 128 final projects supervised His research focuses on applying computational methods to neurodegenerative diseases , particularly Alzheimer's and Parkinson's, through neuroimaging analysis, predictive modeling, and hippocampal segmentation. Recent work includes AT(N) profiles for dementia prediction and machine learning techniques for clinical data modeling. The 15 most recent publications reveal a strong emphasis on Alzheimer's disease progression , hippocampal segmentation , and predictive analytics using neuroimaging and clinical markers. Key methodologies involve graph cuts algorithms, longitudinal modeling, and label fusion techniques applied to MRI and CT scans. Teaching contributions include: 128 final projects supervised (undergraduate and master's) 2 doctoral theses directed Active participation in university governance through the School Council and Researcher Staff Committee
