جست‌وجوی استاد و پژوهشگر

Thirimachos Bourlaiمشاهده پروفایل

استاد

• Multispectral Imaging
• Biometrics
• Facial Recognition
• +۷ مورد دیگر

Thirimachos Bourlai is a Professor in the School of Electrical and Computer Engineering at the University of Georgia. He concurrently holds adjunct roles at West Virginia University (Lane Department of Computer Science and Engineering and School of Medicine, Department of Ophthalmology) and the Savannah River National Labs. As founder and director of the Multi-Spectral Imagery Lab, his research focuses on biometric systems, multispectral imaging, and human authentication in challenging conditions. He has published extensively and serves on editorial boards and professional organizations like the IEEE Biometrics Council. His academic journey includes a Ph.D. from the University of Surrey’s CVSSP Group and postdoctoral work at Methodist Hospital/University of Houston (thermal imaging) and WVU. He has held various academic ranks, including Associate Professor at UGA (2020–2024) and WVU (2017–2020). His research spans facial recognition across spectral bands (UV, visible, IR), liveness detection, and forensic algorithms for law enforcement. Key contributions include datasets like MycorrhiSEE and systems like FaceSensor-ID for sensor origin identification. Awards include the Distinguished Faculty Fellow title and Student Career Success Influencer Award from UGA. His work bridges academia and industry, addressing real-world security challenges through AI and computer vision. Bourlai has presented globally and reviews for top journals like TPAMI and IEEE Transactions. Recent articles emphasize thermal imaging, GAN-based image synthesis, and cross-spectral authentication. His labs collaborate with national labs and agencies, addressing issues like occlusion-resistant face recognition and concealed object detection in surveillance systems.

Martin Dillerمشاهده پروفایل

پژوهشگر

• Formal Argumentation
• AI Systems
• Probabilistic Argumentation
• +۳ مورد دیگر

Martin Diller is a Researcher at the International Center for Computational Logic (ICCL) at TU Dresden, where he has been since May 2019. He is part of the 'Logic Programming and Argumentation' group led by Sarah Gaggl. His current research focuses on formal models of argumentation and their application in AI systems, particularly within the SEMECO interdisciplinary cluster on AI-assisted regulatory workflows for medical systems and cybersecurity. Education: Holds a joint MSc in Computational Logic from TU Wien, TU Dresden, and University of Bolzano (via the EMCL program with an Erasmus Mundus scholarship). Previously completed a MA in Philosophy (Logic & Epistemology) and BSc in Computer Science at Universidad Nacional de Córdoba, Argentina, with postgraduate funding from CONICET. Also conducted research internships at University of Aberdeen, UCL, NICTA (Canberra), and others. Key Projects: Active in the Transregional Collaborative Research Center ‘Foundations of Perspicuous Software Systems’ (2019–2022), and the Center for Scalable Data Analytics and Artificial Intelligence (2023). Currently leads research in probabilistic argumentation and flexible dispute derivations for assumption-based arguments. Key Contributions: Developed the 'flexABle' system for argumentation framework analysis, contributed to admissibility criteria in probabilistic argumentation, and advanced methods for integrating natural language processing with formal argumentation systems. Grants & Funding: Involved in EU-funded interdisciplinary projects and German collaborative research initiatives. Labs/Teams: Core member of the Logic Programming and Argumentation group at ICCL, collaborating with institutions worldwide.

Suresh Raja Neethirajanمشاهده پروفایل

دانشیار

• Digital Agriculture
• Artificial Intelligence
• Big Data Analytics
• +۱۱ مورد دیگر

Dr. Suresh Raja Neethirajan is an Associate Professor and Dalhousie University Research Chair in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. His research program focuses on digital livestock farming, integrating artificial intelligence, big data analytics, and advanced sensor technologies to improve animal health, welfare, and sustainable agricultural practices. His research interests span Digital Agriculture , Artificial Intelligence , Big Data Analytics , Animal-Computer Interaction , and Sensors & Bio-Instrumentation . He leads the Mooanalytica research lab, which develops innovative tools such as facial recognition systems for emotion detection in cows and pigs, rapid diagnostic sensors for avian influenza and food allergens, and wearable devices for non-invasive physiological monitoring. The recent publications reflect a strong trend in developing AI-driven, sensor-based solutions for precision livestock farming. These include emotion recognition in pigs, sex detection in chicken eggs, and circadian rhythm modeling in dairy cows, all leveraging computer vision, machine learning, and multimodal data fusion to advance animal welfare and farm efficiency. Dalhousie University Research Chair Dr. Neethirajan supervises BSc, MSc, and PhD students and is actively involved in teaching and guest lectures on sensor technologies. His lab has access to state-of-the-art research facilities in the Netherlands and collaborates across veterinary, agricultural, and biomedical domains. He has previously held academic and research positions at Wageningen University, the University of Guelph, Oak Ridge National Laboratory, and the National Agricultural Research Organization in Japan. His team has developed numerous industrial-ready solutions, including on-farm detection tests for ketosis and coronavirus, and is focused on transforming laboratory innovations into practical applications for the agri-food industry. Future work continues to emphasize resilience sensing, data management systems, and non-contact vital sign technologies.

• Nutritional Epidemiology
• Dietary Assessment Methods
• Food Frequency Questionnaire (FFQ)
• +۱۲ مورد دیگر

A. Geelen is a senior academic at Wageningen University & Research, affiliated with the Graduate School for Food and Nutrition (VLAG). His research is centered on nutritional epidemiology, dietary assessment methodologies, and public health nutrition, with a focus on chronic disease prevention through diet. Wageningen University & Research – Faculty Member, VLAG Graduate School His research interests include dietary intake assessment, food frequency questionnaire (FFQ) development and validation, adherence to dietary guidelines, micronutrient status, and the role of diet in conditions such as type 2 diabetes, obesity, and gestational diabetes. He employs large-scale cohort studies like Lifelines and NEO, integrating epidemiological methods with clinical and imaging data. The recent articles highlight a consistent focus on validating dietary tools, analyzing dietary patterns, and assessing the impact of nutrition on metabolic health. Trends show a strong emphasis on methodological rigor in dietary assessment, longitudinal analysis of dietary habits, and translation of findings into public health recommendations. While no specific scientific awards are mentioned in the provided data, his work has been referenced in policy sources and picked up by multiple news outlets, indicating societal and policy impact. Dr. Geelen has actively supervised PhD candidates across diverse topics including gestational diabetes, dietary assessment methods, and micronutrient deficiencies. His projects often involve interdisciplinary collaboration and external funding. He has contributed to major consortia such as EU-CHANCES, focusing on aging and health. He is involved in research teams working on cohort studies, digital nutrition tools, and public health interventions targeting vulnerable populations such as low-SES pregnant women. His work bridges methodological innovation with real-world application in nutrition science.

• Wave Energy Conversion
• Renewable Energy
• Hydrodynamic Modeling
• +۹ مورد دیگر

Jens Engström is a Senior Lecturer at the Department of Electrical Engineering, Uppsala University, where he is affiliated with the Division of Electricity. His research is centered on wave energy conversion systems, particularly point-absorbing wave energy converters, with a strong emphasis on hydrodynamic modeling, survivability under extreme conditions, and control strategies. His research interests include wave energy conversion, hydrodynamic modeling, control systems for wave energy converters, marine renewable energy, and the application of machine learning to optimize energy extraction and survivability. He also investigates the integration of wave energy with desalination systems for sustainable freshwater production, especially in off-grid and island contexts. Recent publications show a clear trend toward the application of artificial intelligence and deep learning in enhancing the performance and survivability of wave energy converters. His work combines experimental validation, numerical simulation (including CFD), and field measurements, particularly from the Lysekil Research Site. The research spans from fundamental hydrodynamics to system-level optimization of wave energy farms. He has contributed to numerous high-impact journals such as Renewable Energy , Ocean Engineering , and IEEE Journal of Oceanic Engineering , and has co-authored review articles on wave energy park optimization. His research is highly collaborative, often involving teams from Uppsala University and international partners. Engström completed his doctoral thesis in 2011 on hydrodynamic modeling of point-absorbing wave energy converters and has been actively publishing since 2007. He is involved in advising students and supervising research projects, though specific names are not listed. His work is supported by experimental and numerical infrastructure at Uppsala University, particularly related to the Lysekil wave energy research site.

John McCoyمشاهده پروفایل

استادیار

• Mathematical models of judgment and decision making
• Computational cognitive science
• Crowd wisdom
• +۵ مورد دیگر

John McCoy is an Assistant Professor of Marketing at the Wharton School of the University of Pennsylvania. He is also affiliated with the Wharton Neuroscience Initiative and Penn's MindCORE hub. His academic work bridges marketing, cognitive science, and computational modeling. Dr. McCoy's research focuses on mathematical models of judgment and decision making , computational cognitive science , crowd wisdom , and forecasting . Methodologically, he uses a combination of behavioral experiments and computational modeling, drawing on ideas and techniques from psychology, economics, marketing, Bayesian statistics, and computer science. Much of his current work centers on better ways to aggregate judgments from multiple individuals, including in situations where the majority may be wrong and the truth may be unverifiable. His research has gained significant attention, with popular accounts appearing in the Wall Street Journal , NPR , and the New Yorker . A key contribution is the "surprisingly popular answer" method for extracting wisdom from crowds, which selects not the "most popular" answer but the "surprisingly popular" one by eliciting both answers and predictions about others' answers. Dr. McCoy teaches graduate courses including Consumer Research Topics and Marketing Management. His teaching focuses on contemporary topics in consumer research, drawing from marketing, psychology, and economics literature to help students develop research ideas and understand theoretical and methodological approaches to consumer behavior. His scholarly work spans multiple domains including crowd wisdom, individual-level decision making, semantic cognition, and graph theory. His research has important applications in marketing, healthcare communication, and forecasting methodologies.

Panos Louvierisمشاهده پروفایل

استاد

• Cybersecurity
• Data and Information Fusion
• Defence Analytics
• +۷ مورد دیگر

Panos Louvieris serves as Professor of Information Systems at Brunel University London within the Department of Computer Science, College of Engineering, Design and Physical Sciences. He leads the Defence & Cyber Security (DCS) research group and holds co-directorship roles at the Brunel Intelligent Digital Economy and Society (IDEAS) Research Centre and the Trusted Open Models Institute (TOMI) at the Hartree Centre. His academic profile includes committee membership in the EPSRC Digital Personhood Network, EPSRC ITaaU+ Network, and NEMODE+ Network, reflecting his influence in UK research infrastructure. His research spans cybersecurity, data fusion, and computational finance with emphasis on cyber situational awareness, explainable AI, and distributed ledger technologies. Recent work integrates agent-based simulation for pandemic modeling, Markov models for threat prediction, and social media analysis for security awareness. This interdisciplinary approach bridges computer science, operations research, and social sciences to address defense, finance, and public health challenges through advanced computational models. Analysis of his publication trajectory reveals evolving focus from foundational information fusion (2005-2010) to cyber-physical systems (2010-2015), with current emphasis on digital personhood markets (2020-present). His most recent publications demonstrate methodological innovation in blockchain applications for tax compliance and geospatial pandemic modeling, while maintaining core expertise in cyber defense analytics. Louvieris actively supervises research and collaborates with institutions including the Hartree Centre and EPSRC networks. His grant portfolio features the EPSRC-funded Digital Personhood project (2013-2016), Innovate UK's Contact Tracing project (2020-2021), and current leadership of the EPSRC Making Tax Smart initiative (2022-2023) exploring distributed ledger technology for central bank digital currency compliance. He directs the Defence & Cyber Security research group and co-leads the IDEAS Research Centre, fostering cross-disciplinary work on digital economy challenges. Current projects involve HSBC sandbox testing of Open Banking prototypes and development of futures markets for digital personhood data, positioning his team at the intersection of cybersecurity, FinTech, and societal digital transformation.

Diana Suleimenovaمشاهده پروفایل

مدرس

• Agent-Based Modelling
• Forced Displacement Prediction
• Verification, Validation and Uncertainty Quantification (VVUQ)
• +۷ مورد دیگر

Dr Diana Suleimenova is a Lecturer in Computer Science at Brunel University London , affiliated with the Modelling & Simulation Group and Computer Science for Social Good research groups. Her work focuses on agent-based modelling , forced displacement prediction , and verification, validation, and uncertainty quantification (VVUQ) of multiscale applications on exascale platforms .

Gianluca Bonifaziمشاهده پروفایل

پژوهشگر

• Network Analysis
• Social Media Dynamics
• Cybersecurity
• +۳ مورد دیگر

Gianluca Bonifazi serves as a Researcher in the Department of Information Engineering at Marche Polytechnic University's College of Engineering. His academic profile shows active engagement in cutting-edge research with publications spanning network science, social media analysis, and artificial intelligence applications. His research interests focus on network analysis methodologies , particularly examining social media dynamics on platforms like Threads and TikTok, developing frameworks for cybersecurity in IoT ecosystems, and creating novel approaches for explainable AI. Bonifazi's work bridges theoretical network science with practical applications in social media analysis and security systems. Analysis of his recent publications reveals a strong emphasis on multilayer network frameworks applied across diverse domains - from characterizing user behavior in social platforms to interpreting neural network representations. His work demonstrates consistent innovation in adapting network theory to contemporary digital challenges, with particular attention to user influence dynamics, platform security, and AI transparency. Bonifazi maintains an active research trajectory with publications appearing through 2025, indicating ongoing scholarly contributions. His work shows interdisciplinary reach across computer science, data analytics, and social network theory.

• Biomedical Engineering
• Medical Informatics
• Pulmonology
• +۸ مورد دیگر

Keywan Sohrabi is a Professor at Technische Hochschule Mittelhessen (THM) , specializing in Biomedical Engineering and Medical Informatics . His work bridges Pulmonology , Sleep Medicine , and Digital Health through innovative applications of 3D Imaging , Acoustic Analysis , and Machine Learning . Contact: keywan.sohrabi@ges.thm.de

• Smart Grids
• Energy Communities
• Data Mining Applications in Power Systems
• +۷ مورد دیگر

Sérgio Filipe Carvalho Ramos serves as Coordinator Professor at the School of Engineering (ISEP) of the Polytechnic Institute of Porto (IPP), where he has held academic positions since 2001. Since 2021, he directs the Bachelor Program in Electrical Engineering - Electrical Power Systems and previously served as deputy director of the Electrical Engineering Department (2018-2020). He maintains active research collaborations with international institutions including Universidade de Vigo and Universidade da Coruña in Spain, and UNESP in Brazil. His educational background includes a PhD (2015) and MSc (2006) in Electrical and Computer Engineering from the University of Lisbon - IST. His research focuses on smart grids, energy communities, data mining applications in power systems, and renewable energy optimization . He has pioneered work in smart buildings optimization, shared PV generation, and flexible power contracts through significant R&D projects. The analysis of his recent publications reveals strong trends in energy community modeling , with 8 of the 15 most recent papers addressing peer-to-peer energy sharing, community optimization, and local market design. His work increasingly integrates machine learning for forecasting (particularly for EV integration and PV generation) and employs advanced optimization techniques including game theory and MBNLP methods. The research consistently targets practical implementation in smart grid contexts with emphasis on real-world applicability. Premio REN 2008 Scientific Publication Merit from IPP (2011, 2015, 2023) Ramos has supervised 2 PhD students (ongoing), 29 MSc, 6 ERASMUS, and 116 BSc students. His grant portfolio demonstrates exceptional success in securing competitive funding, including FCT projects BENEFICE and SPET as PI/Co-PI, the EU-funded SATCOMM project coordinating Atlantic energy pilots, and multiple Honda Research Institute consultancies. His research group has successfully coordinated over 20 national and international R&D projects with total funding exceeding €5M. As a core researcher in the GECAD research group, he leads initiatives in smart buildings and energy communities. He serves as General Secretary for IPP at ATHENA European University and participates in the CARPE Network's Sustainable and Smart Cities working group, demonstrating institutional leadership beyond his departmental responsibilities.

Elizabeth Akinyi Ondulaمشاهده پروفایل

استادیار

• Artificial Intelligence
• Reinforcement Learning
• Decision Theory
• +۳ مورد دیگر

Elizabeth Akinyi Ondula is an Assistant Professor of Computer Science at Winthrop University , with a Ph.D. in Computer Science from the University of Southern California where she was part of the Autonomous Networks Research Group under Prof. Bhaskar Krishnamachari. Her research focuses on AI systems operating under uncertainty , integrating reinforcement learning , decision theory , and control-theoretic perspectives to design reliable AI for high-stakes environments . Research Interests She specializes in: Evaluating AI decision-making under stochastic environmental and model variability Developing domain-specific frameworks for AI deployment in physical/social contexts Designing interpretable and safe AI through coordination structures and information pattern analysis Her work spans epidemic response systems (SafeCampus-RL, Classroom-MDP), multi-agent fairness frameworks (Fair-MARL), and agricultural optimization tools for Kenyan farmers. Teaching Roles (Fall 2025) CSCI 475: Software Engineering I CSCI/DIFD 141: Introduction to Web Application Design CSCI 245: Front End Web Development Her email is eondula@usc.edu .

Paul De Koninckمشاهده پروفایل

استاد

• Molecular Neurosciences
• Synaptic Plasticity
• Hippocampus
• +۷ مورد دیگر

Paul De Koninck is a Professor in the Department of Biochemistry, Microbiology and Bioinformatics at Université Laval and Deputy Scientific Director of Fundamental Research at the CERVO Brain Research Center. His work bridges molecular neuroscience with advanced neurophotonics technologies to unravel mechanisms of synaptic plasticity and neuronal signaling. Academic Affiliation: Université Laval (Faculty of Science and Engineering) Leadership Role: Deputy Scientific Director, CERVO Brain Research Center Research focuses on synaptic plasticity , neurophotonics , and optical nanoscopy to study how neurons decode signals and remodel circuits for learning and memory . His lab develops cutting-edge tools like STED microscopy and single molecule imaging to track synaptic molecules in real time. Recent publications highlight his dual focus on neurodegenerative disease mechanisms (e.g., Huntington's disease) and microscopy innovation (machine learning-enhanced STED, fluorescence lifetime imaging). Articles span zebrafish neurobiology , mitochondrial transport regulation , and neurovascular framework development . Scientific Awards: 2018 Université Laval "Professeur étoile" Prize for teaching excellence His lab contributes to major collaborative initiatives, including the Canadian Optogenetics and Vectorology Foundry grant (2021: $4.3M from Brain Canada) and the Greenspine Lab for neurophotonics research. The team combines patch clamp electrophysiology , biochemistry , and quantum dot imaging to study synaptic signaling.

Sebastian Ahnertمشاهده پروفایل

دانشیار

• Algorithmic Information Theory
• Network Analysis
• Genotype-Phenotype Maps
• +۴ مورد دیگر

Dr. Sebastian Ahnert is an Associate Professor at the Department of Chemical Engineering and Biotechnology, University of Cambridge, and a Senior Research Fellow at The Alan Turing Institute. He leads the Structural Complexity research group and is a Fellow of King's College, Cambridge. Current Positions: Associate Professor (Cambridge), Senior Research Fellow (Alan Turing Institute), Fellow (King's College) Research Interests: Algorithmic information theory, network analysis, genotype-phenotype maps, interdisciplinary applications in biology, humanities, and food science His work spans the quantification of biological complexity using algorithmic descriptions, including protein quaternary structure classification , RNA sequence-structure maps , and symmetry in evolutionary systems . Network analysis applications extend to historical correspondence (Tudor Networks of Power), food science (flavor compound networks), and connectomes . Recent publications emphasize non-deterministic genotype-phenotype maps , neutral set thermodynamics , and automated phenotyping technologies. Award highlights: 2024 Richard Deswarte Prize in Digital History Shortlisted for 2024 SHARP Book History Prize Current students include PhD candidates in computational biology and plant science, with former advisees contributing to RNA evolution and network analysis studies. Collaborations span quantum physics, plant developmental biology, neuroscience, and digital humanities.

Siqi Wuمشاهده پروفایل

استادیار

• Computational Social Science
• Web Data Analysis
• Social Media Research
• +۷ مورد دیگر

Siqi Wu is an Assistant Professor of Information and Library Science in the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington. Previously, they were a postdoc research fellow in the Center for Social Media Responsibility at the University of Michigan School of Information. Dr. Wu is a computational social scientist who collects, models, and analyzes web data at scale, with research focusing on understanding social phenomena through large-scale empirical measurements and designing next-generation sociotechnical systems via data-driven policies and interventions. Dr. Wu's educational background includes: Ph.D. in Computer Science from the Australian National University M.S. in Information Technology from the University of Melbourne B.E. in Electronics Engineering from Tianjin University Dr. Wu's research spans computational social science, large-scale web data analysis, and social media systems. Their work examines YouTube recommendation networks, Twitter data analysis, prevalence estimation techniques, and cross-platform attention dynamics. Recent projects investigate user control over recommendations, harmful content detection, and cross-partisan communication patterns, combining computational methods with social science theories to understand algorithmic influence on online experiences and develop practical interventions. Dr. Wu's publications demonstrate consistent excellence in social media analysis, with particular expertise in measuring attention dynamics across platforms, estimating class prevalence using black box classifiers, and understanding recommendation systems. Their work bridges technical innovation with social science questions, resulting in practical implications for platform design and policy across YouTube, Twitter, and other social media ecosystems. Notable recognitions include: Google PhD Fellowship (2018) ICWSM Best SPC (2022-2024) CSCW 2019 Best Paper Honorable Mention (top 5%) ICWSM 2021 Spotlight Paper selection (top 8) Dr. Wu actively mentors students interested in computational social science, seeking those with strong programming skills, data analysis experience, and passion for understanding social phenomena through computational methods. They serve on program committees for ICWSM, CSCW, and CHI, and their research has practical applications for social media platform design, content moderation policies, and understanding information ecosystems. Dr. Wu has developed several software tools including pyquantifier for prevalence estimation and tools for Twitter and YouTube data collection. Dr. Wu leads research on social media analysis with focus on YouTube and Twitter ecosystems, developing methods to understand recommendation systems, user engagement patterns, and cross-platform dynamics. Their work involves collecting and analyzing large-scale datasets to inform platform design and policy decisions related to algorithmic transparency and user control.