جست‌وجوی استاد و پژوهشگر

Cong Shiمشاهده پروفایل

استادیار

• Applied Machine Learning
• Security and Privacy for Mobile Systems
• Robust and Trustworthy ML/AI
• +۴ مورد دیگر

Cong Shi is an Assistant Professor in the Department of Computer Science at New Jersey Institute of Technology (NJIT). His research focuses on applied machine learning, security and privacy for mobile systems, and robust/trustworthy AI. Specific interests include adversarial machine learning, WiFi sensing, and AR/VR security. Education: Ph.D. in Electrical and Computer Engineering from Rutgers University (2022), M.S. in Electrical Engineering from Stevens Institute of Technology (2015), B.S. in Electrical Engineering and Automation from Guangdong University of Technology (2013). Research highlights include developing methods for vital sign monitoring using motion sensors, backdoor defense mechanisms in AI models, and privacy leakage prevention in AR/VR ecosystems. Recent work explores attacks on RF signal classification and vibration-based speech reconstruction. His publications appear at venues like IEEE S&P, ACM CCS, and AAAI. Teaching responsibilities include courses like CS 645 (Security and Privacy in Computer Systems) and CS 785 (Seminar in Computer Science).

Dipo Dunsinمشاهده پروفایل

مدرس

• Digital Forensics
• Cybersecurity
• Incident Response
• +۱ مورد دیگر

Dipo Dunsin serves as Lecturer in Computer Science & Applied Computing at London Metropolitan University, where he also manages the University's Digital Forensics Laboratory. His research develops innovative frameworks for malware investigation and cyber incident response. Dunsin's work applies reinforcement learning to automate threat analysis and evidence extraction, with recent focus on IoT forensics and dark web criminal activities. He creates specialized protocols like D2WFP for analyzing deep/dark web browsing and hybrid methods for detecting steganographic evidence in audio files.

• Machine Learning
• Pain Assessment
• Neurophysiological Sensors
• +۵ مورد دیگر

Raul Fernandez Rojas is an Associate Professor in the Department of AI and Robotics at the University of Canberra. His research focuses on multimodal neurophysiological sensing, machine learning applications in healthcare, and pain assessment using technologies like fNIRS, EEG, and facial expression analysis. He leads projects integrating brain-body interactions in neurological disorders such as Parkinson's disease and dementia, and develops intelligent systems for driver distraction detection. Education: PhD (details unspecified) Research interests include cognitive workload analysis, sensor fusion for biomedical applications, and AI-driven diagnostics for mental and neurological conditions. His work spans clinical pain assessment, human-swarm interaction, and real-time monitoring systems. He has contributed to over 55 peer-reviewed publications and actively supervises PhD candidates in machine learning for neurophysiological applications. Current projects include a dementia detection initiative using machine learning and exercise interventions, funded from 2024–2026. He collaborates internationally on topics like head motion patterns for depression biomarkers and fNIRS-based pain assessment for non-verbal patients. Advising focuses on PhD projects involving neurophysiological sensors (EEG, fNIRS, etc.) and facial expression analysis for pain recognition. Grants include funding for multimodal signal fusion research and wearable sensor systems.

Christophe Royonمشاهده پروفایل

استاد

• Particle Physics
• High-Energy Physics
• Collider Physics
• +۳ مورد دیگر

Christophe Royon is a Foundation Distinguished Professor at the University of Kansas, leading research in high-energy particle physics with a focus on collider experiments at the Large Hadron Collider (LHC). His work primarily involves analyzing data from the CMS experiment to explore fundamental questions in particle physics, such as dark matter detection, Higgs boson properties, and quantum chromodynamics (QCD) dynamics. Research interests include probing new physics scenarios through precision measurements of top quark interactions, vector boson production, and jet substructure. He actively contributes to studies of heavy ion collisions to understand quark-gluon plasma behavior and gluon saturation effects. His experimental expertise spans detector calibration, systematic uncertainty quantification, and machine learning applications in data analysis. Notable achievements include the APS Award for collaboration with Brazil (2017-2018), recognizing his international research contributions. His recent publications (2024-2025) highlight advancements in measuring W boson properties, constraining Higgs Yukawa couplings, and discovering anomalous top quark pair production signals. Dr. Royon collaborates extensively with global physics communities, advancing experimental techniques for future collider projects like the Electron Ion Collider (ECCE). His work bridges theoretical predictions with experimental validation, contributing to both precision Standard Model tests and searches for physics beyond the Standard Model.

• Natural Language Processing
• Machine Learning
• Biomedical Informatics
• +۷ مورد دیگر

Jianlin Shi is an Assistant Professor in the Department of Internal Medicine at the University of Utah , with an adjunct appointment in Biomedical Informatics . His academic journey includes a PhD from University of California, Irvine , medical training at West China Medical Center , and residency at Sichuan University . Primary: Internal Medicine Adjunct: Biomedical Informatics Dr. Shi's research focuses on Natural Language Processing (NLP) in clinical settings , particularly for genetic testing criteria identification , medication disposition extraction , and clinical decision support systems . He has developed medspaCy , a Python NLP toolkit for healthcare, and created hybrid NLP frameworks integrating LLMs with rule-based systems to enhance disease screening and data privacy tools . His recent work spans 2025-2022 , emphasizing applications in surgical infection detection , dementia identification , and clinical cohort generation . While no formal awards are listed, his 15 most recent publications demonstrate sustained contributions to clinical NLP , medical decision support , and health data standardization .

• Civil Engineering
• Materials Engineering
• Resources Engineering and Extractive Metallurgy
• +۴ مورد دیگر

Luis Ibarra is an Associate Professor in the Department of Civil & Environmental Engineering at the University of Utah, where he has been employed since 2010. He was promoted to Associate Professor in July 2016 after serving as an Assistant Professor from 2010 to 2016. He earned his PhD in Civil and Environmental Engineering from Stanford University in 2004. Research Interests Dr. Ibarra's research spans multiple domains of civil engineering with particular emphasis on seismic performance of structures, nuclear safety, and material behavior. His work integrates computational mechanics, experimental testing, and probabilistic risk assessment to address challenges in structural resilience under extreme loading conditions. Primary research themes include: Seismic modeling of nuclear containment structures and fuel rod behavior Development of advanced hysteretic models for structural components Earthquake engineering applications for bridges and buildings Probabilistic risk assessment of structural systems Publications Overview Recent publications (2018-2025) demonstrate Dr. Ibarra's focus on computational mechanics applied to nuclear and structural safety, featuring advanced finite element modeling, probabilistic methods, and experimental validation. Recurring themes include seismic vulnerability of nuclear facilities, deterioration modeling of structural components, and innovative retrofitting techniques for earthquake resilience. The research consistently integrates material science fundamentals with structural engineering applications. Awards and Recognition Ben Jacobsen Kingfisher Bend Ranch Award for exceptional teaching effectiveness (2015) Teacher of the Year Award, CvEEN Department (2014) Teacher of the Year Award in the CvEEN Department (2013) Milek Fellowship Award, American Institute of Steel Construction (2013) Academic Activities Dr. Ibarra maintains an active research program supported by multiple grants including seismic modeling of nuclear structures, machine learning applications for collapse prediction, and performance of retrofitted bridges. He regularly advises graduate students in thesis research and teaches courses in structural dynamics, steel design, and structural analysis. His professional service includes editorial board membership for the Tall and Special Buildings Journal and community outreach through earthquake education programs for high school students.

• Computational Social Science
• Misinformation Studies
• Social Media Algorithm Analysis
• +۴ مورد دیگر

Professor Jana Lasser holds a professorship in Data Analysis at the University of Graz, where she leads the Complex Social & Computational Systems research group at the interdisciplinary IDea_Lab. She serves as Associate Faculty at the Complexity Science Hub Vienna and previously held positions at Graz University of Technology as a Marie Curie Fellow and interim professor at RWTH Aachen. Her educational background includes a PhD in Physics from Georg-August-University Göttingen (2019), conducted at the Max Planck Institute for Dynamics and Self-Organization, with research on salt desert pattern formation. Her academic journey reflects a transition from geophysics to computational social science. Lasser's research centers on emergent phenomena in complex social systems, employing machine learning, natural language processing, and computational modeling. Key interests include misinformation spread on social media, counterspeech effectiveness, social media recommendation algorithms' societal impact, and the fracturing of societal understanding of honesty. Her work combines theoretical frameworks with real-world applications through agent-based simulations and large-scale text analysis. Her recent publications reveal strong thematic coherence across computational social science, with significant focus on political communication dynamics, platform governance, and methodological innovation. The 15 most recent works demonstrate increasing integration of computational methods with social theory, particularly in analyzing truth-concept evolution in political discourse and developing alternative social media architectures. ERC Starting Grant 101160928 DeSiRe for designing social media recommendation algorithms FWF standalone project P 37280-N on conspiracy theory spread netidee SCIENCE prize for digital civic innovation Marie Curie Fellowship for postdoctoral research Lasser actively supervises graduate students in computational social science, with current focus on topics related to her ERC-funded project. She leads the Survey Special Interest Group within the COST Action on Researcher Mental Health, conducting Europe's largest benchmark study on academic mental health. Her lab maintains strong collaborations with the Complexity Science Hub Vienna and international institutions.

Graciela S. Cabanaمشاهده پروفایل

استاد

• Molecular Anthropology
• Science and Technology Studies
• Paleogenomics
• +۷ مورد دیگر

Graciela S. Cabana serves as Professor and Director of Graduate Studies in the Department of Anthropology within the College of Arts and Sciences at the University of Tennessee, Knoxville. She directs the Molecular Anthropology Laboratories (MAL-UTK), a specialized research facility dedicated to DNA analyses of humans with expertise in degraded DNA, ancient DNA, and archaeogenetics. Education: Ph.D. in Anthropology, University of Michigan, Ann Arbor M.A. in Anthropology, University of Michigan, Ann Arbor B.A. in Political Science, University of California, Berkeley As an anthropologist, Dr. Cabana studies the human genome within a biosocial context, drawing insights from history, archaeology, philosophy of science, science and technology studies, and human evolutionary studies. Her research focuses on human movements and their consequences for genome evolution and identity formation. She incorporates tools from ancient and modern DNA analysis, ethnography, and historical analysis in her work on paleogenomics, bioarchaeology, and science & technology studies. Her extensive publication record reveals consistent scholarly contributions in population genetics, migration studies, and interdisciplinary approaches to anthropological questions, with particular emphasis on ancient DNA analysis of historical populations in the Americas, community-engaged research with descendant communities, and the social dimensions of genetic research. Dr. Cabana actively mentors numerous graduate students across various research projects related to paleogenomics, bioarchaeology, and science & technology studies. She teaches courses including Theory in Biological Anthropology, Anthropology & the Genome, DNA Methods, and What is Science? Additionally, she serves as Co-Editor-in-Chief of the Yearbook of Biological Anthropology and Associate Editor for Human Biology and Bioarchaeology International. The Molecular Anthropology Laboratories (MAL-UTK), which she directs, consists of both Main Lab facilities for modern DNA studies and Clean Room Labs specifically designed for ancient/paleogenomic DNA studies of degraded samples. The laboratory complex is USDA certified to receive foreign soils and includes specialized facilities for DNA extraction, amplification, and library preparation.

• Science and Technology Studies
• Anthropology
• Data Ethics
• +۳ مورد دیگر

Katheryn Detwiler is a Teaching Assistant Professor of Science and Technology Studies at Stevens Institute of Technology, affiliated with the School of Humanities, Arts and Social Sciences. She holds a PhD in Anthropology from The New School (2022), an MA in Anthropology from The New School (2010), and a BA in Sociology/Anthropology from Lewis and Clark College (2005). Her research focuses on data practices, scientific knowledge production, and the ethical implications of Big Data in environments like Chile's Atacama Desert. She has held roles including FYE Facilitator and STS Speaker Series Chair at Stevens. Her research explores intersections of computing, astronomy, and resource extraction, analyzing how data becomes a speculative resource. Major grants include a NSF Dissertation Improvement Grant (2014–2015) and a Dick and Sally Roberts Coyote Foundation grant for an experimental film project (2012). Awards include the Inaugural Ann Snitow Award (2019) and Mellon Graduate Fellowship (2016–2017). Key Research Areas : Data Ethics, Environmental Anthropology, Science & Technology Studies Teaching : Courses include Computing and Capitalism , Introduction to Anthropology , and Monsters, Cyborgs, and Robots Professional Engagement : Member of SIAI (Stevens Institute for Artificial Intelligence), co-PI on NSF-funded projects Her work bridges academic disciplines through experimental methods like film and collaborative art, reflecting a commitment to interdisciplinary inquiry and public engagement.

Thomas A. Runklerمشاهده پروفایل

استاد مدعو

• Machine Learning
• Data Analytics
• Pattern Recognition
• +۳ مورد دیگر

Thomas A. Runkler is an Adjunct Professor at the Technical University of Munich (TUM), holding a Chair in the Foundations of Software Reliability and Theoretical Computer Science within the TUM School of Computation, Information and Technology. Since 1999, he has taught computer science at TUM while concurrently serving in various expert and managerial roles at Siemens AG, where he currently holds the position of Senior Principal Research Scientist. His academic journey includes a Master’s and PhD in electrical engineering from TU Darmstadt (1992/1995), followed by postdoctoral research at the University of West Florida (1996–1997). Runkler’s research spans machine learning , data analytics , fuzzy systems , and optimization . He has authored/co-authored over 200 publications, focusing on topics like clustering algorithms, neural networks, and decision-making frameworks. Professional contributions include leadership roles in organizations such as the IEEE CIS committees and the German Association for Computer Science’s Fuzzy Systems group. His teaching includes courses on Data Mining and Knowledge Discovery . Key publications include foundational works on fuzzy preference structures, Bayesian decomposition of dynamical systems, and interpretable reinforcement learning policies. Runkler’s work bridges academia and industry, emphasizing practical applications in automation, recommendation systems, and industrial procurement forecasting. His research often addresses challenges in uncertainty handling, algorithm design, and scalable data analysis.

Timothy Jonesمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Computing
• Compilers
• +۴ مورد دیگر

Timothy Jones is a Professor of Computer Architecture and Compilation at the University of Cambridge Computer Laboratory, with a research focus on extracting parallelism (thread-level, data-level, memory-level) through compilers, binary translators, and microarchitectures to enhance performance, energy efficiency, and reliability. He is also a Fellow and 1956 College Lecturer at Gonville and Caius College, serving as joint Director of Studies for Computer Science. Teaching: Advanced Topics in Computer Architecture (MPhil ACS, Part III), Introduction to Computer Architecture (Part IB), Optimising Compilers (Part II) Research Themes: Computer Architecture, Security, Programming Languages, Parallel Computing Contact: Room SC03, Department of Computer Science and Technology, William Gates Building, Cambridge CB3 0FD His recent publications emphasize heterogeneous parallelism, fault tolerance, speculative execution, and security implications in compiler and microarchitecture design. Notable trends include integrating fine-grained security analysis into superscalar cores and optimizing GPU architectures for scalarization and memory bypassing. He is active on Twitter ( @TJonesCL ) and maintains an Erdős Number of 4.

• Wireless Communication
• Network Optimization
• Machine Learning
• +۳ مورد دیگر

Athanasios Gkelias is a Research Fellow in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. His research focuses on advanced wireless communication systems, network optimization, machine learning applications, and quantum computing methodologies. He contributes to interdisciplinary projects involving IoT coalitions, adversarial signal detection, and cognitive behavior analysis. Key research areas include: (1) Network resource management in Software-Defined Networks (SDN), (2) Localization systems for indoor environments, (3) 3D reconstruction using photometric stereo and origami-based modeling, and (4) distributed optimization frameworks for ad-hoc networks. His recent work explores quantum approaches to combinatorial optimization and self-supervised learning methods. Publications span 20+ years with notable contributions in network coding, vehicular ad-hoc networks (VANETs), and cross-layer design for wireless mesh networks. His work emphasizes practical implementations through frameworks like iVisher for caller ID spoofing detection and cooperative MAC protocols for multi-antenna systems. No scientific awards are listed in the provided information. His research has been applied in healthcare technology through emotion understanding systems for Alzheimer’s patients and in battlefield communication through IoBT coalitions. Active participation in Imperial College's Engineering faculty reflects his commitment to advancing telecommunications and network science.

Ellis Patrickمشاهده پروفایل

دانشیار

• Bioinformatics
• Biomedical Data Science
• Statistical Bioinformatics
• +۶ مورد دیگر

Associate Professor Ellis Patrick is an applied statistician and bioinformatician affiliated with the University of Sydney's School of Mathematics and Statistics, the Westmead Institute for Medical Research, and the Sydney Precision Data Science Centre. His research focuses on developing bioinformatics tools to analyze high-dimensional biomedical data, with applications in disease treatment, precision medicine, and understanding molecular drivers of conditions like Alzheimer’s, MS, and cancer. He holds a PhD from the University of Sydney and postdoctoral experience at Harvard Medical School and The Broad Institute. Education: PhD in statistical bioinformatics from the University of Sydney. Postdoctoral training included joint appointments at Brigham and Women’s Hospital, Harvard Medical School, and The Broad Institute of MIT and Harvard. Research Interests: Specializes in statistical bioinformatics, spatial omics, and high-dimensional data analysis, with emphasis on translational research in infectious diseases, organ transplantation, cardiovascular disorders, neurodegeneration, and oncology. His work combines computational methods with clinical data to uncover disease mechanisms and improve therapeutic strategies. Grants & Collaborations: Recent grants include PrecisionGO (MRFF-funded), White matter lesion transcriptomics (National MS Society), and studies on coronary artery disease and graft failure prediction. Collaborates widely with medical researchers, leveraging interdisciplinary teams to tackle complex health challenges. Labs & Teams: Leads the Bioinformatics Cluster at the Sydney Precision Data Science Centre, integrating computational and experimental approaches to advance precision health initiatives.

• Scientific Visualization
• Data Visualization
• Data Science
• +۵ مورد دیگر

Min Chen is a Professor of Scientific Visualization at the University of Oxford, affiliated with the Department of Engineering Science and Pembroke College. He holds fellowships from the British Computer Society, European Computer Graphics Association, and Learned Society of Wales. His career spans over three decades, with previous roles at Swansea University (1984–2011) and current leadership in visualization research. His research focuses on visualization theory, video visualization, visual analytics, and interdisciplinary applications in fields like epidemiology and cybersecurity. He has authored over 200 publications and led projects such as RAMPVIS during the COVID-19 pandemic. Key roles include editor-in-chief of Computer Graphics Forum and associate editor of IEEE Transactions on Visualization and Computer Graphics. Education: BSc and PhD in relevant fields (details not explicitly stated in texts). Awards include the VGTC Visualization Lifetime Achievement Award (2024). His work emphasizes the theoretical underpinnings of visualization and practical tools for data intelligence.

Ralf Schlüterمشاهده پروفایل

مدرس

• Automatic Speech Recognition
• Machine Learning
• Pattern Recognition
• +۴ مورد دیگر

Ralf Schlüter is a senior researcher and Lecturer (Privatdozent) at RWTH Aachen University, where he leads the Automatic Speech Recognition Group within the Department of Computer Science. His academic roles include serving as Academic Director and leading research initiatives at Lehrstuhl Informatik 6: Human Language Technology and Pattern Recognition. He holds a Dipl. in Physics (1995), a Dr. rer. nat. in Computer Science (2000), and a Habilitation in Computer Science (2019), all from RWTH Aachen University. His research focuses on advanced speech recognition technologies, including end-to-end architectures, acoustic modeling, discriminative training, and machine learning applications. He has led numerous national and international projects, such as EU-funded initiatives like BABEL, BOLT, and GALE. Schlüter has published extensively in top-tier conferences like Interspeech and ICASSP, contributing to advancements in neural network frameworks, feature extraction, and error analysis. He teaches courses on speech recognition, pattern recognition, and signal processing, and serves as a Subject Editor for Speech Communication . His work also involves developing the RWTH ASR toolkit, emphasizing scalable and efficient speech recognition systems. Schlüter’s contributions to speech technology have earned recognition, including the ISCA Best Student Paper Award (2019).