جست‌وجوی استاد و پژوهشگر

Manos Kapritsosمشاهده پروفایل

دانشیار

• Distributed Systems
• Formal Verification
• Fault Tolerance
• +۳ مورد دیگر

Manos Kapritsos is an Associate Professor in the Department of Computer Science and Engineering at the University of Michigan's College of Engineering. He leads the GLaDOS research group focusing on reliability of distributed systems through formal verification and fault-tolerant replication techniques. His research spans: Formal verification of concurrent and distributed systems Fault-tolerant replication protocols beyond client-server models Automation of verification processes for complex systems Performance verification including latency properties Reliable cryptographic code implementation Analysis of his publications reveals strong emphasis on: developing automated verification tools (Armada, Vale, IronFleet), creating novel replication protocols (Aegean), verifying performance characteristics (Performal), and improving specification reliability (IronSpec). His work consistently bridges theoretical formal methods with practical systems implementation. Awards and honors include: Jay Lepreau Best Paper Award at OSDI 2025 Jon R. and Beverly S. Holt Award for Excellence in Teaching (2022) NSF CAREER Award (2021) Distinguished Paper Award at PLDI 2020 Google Faculty Award (2017) Distinguished Paper Award at USENIX Security 2017 Grant support includes NSF FMitF grants (2020, 2023), NSF Large grant (2021), DARPA grant (2020), and Google Faculty Award (2017). He advises PhD students through the GLaDOS group, focusing on distributed systems verification. He directs the GLaDOS lab at University of Michigan, developing verification frameworks and reliable distributed systems. Current projects include automated proof generation (Basilisk) and efficient communication protocols (Scrooge).

• Internet of Things
• Transport Protocol
• Cybersecurity
• +۸ مورد دیگر

Alan Briones Delgado is a researcher at the La Salle School of Engineering , Universitat Ramon Llull , with a focus on Internet of Things , Cybersecurity , and Transport Protocols . His work spans projects funded by the European Commission and national grants, including EXCEL4HOUSING4.0 , WeB-Nimbus , and NG-SOC , addressing challenges in cloud computing education, ecological monitoring, and security operations. His research integrates Artificial Intelligence and Wireless Sensor Networks for sustainable solutions. Key research areas include Quality of Service in heterogeneous networks, Environmental Conservation via IoT, and Teaching and Learning strategies for Big Data. Projects like EcoSentinel and BTL-COP highlight his commitment to Environmental Monitoring and Community Policing applications. His collaborations extend to institutions in the UK , Albania , and Western Balkans . Contact: alan.briones@salle.url.edu

Patricia D. McGuireمشاهده پروفایل

دانشیار

• Indigenous Sociologies
• Community-Based Research
• Decolonizing Methodologies
• +۵ مورد دیگر

Patricia D. McGuire is an Associate Professor at the School of Social Work , Carleton University , with a PhD in Sociology from the University of Saskatchewan (2013). Her academic work centers on Indigenous knowledge systems, community-based research methodologies, and decolonizing practices within Canadian sociological and social work frameworks. Doctor of Philosophy (Sociology) , University of Saskatchewan (2013) Master of Arts , Lakehead University (2003) Bachelor of Arts (Political Science) and Honours Bachelor of Social Work , Lakehead University (1988) McGuire's research focuses on Indigenous resilience , decolonizing methodologies , and community-driven solutions , particularly examining Anishinaabe societies. She has contributed extensively to sociology and social work literature through book chapters and peer-reviewed articles addressing Indigenous epistemologies, structural barriers in education, and reconciliation processes. Her publications explore topics like urban Indigenous youth safety , land-based education , and Indigenous women's leadership . McGuire has collaborated with the Ontario Native Women’s Association (ONWA) on CIHR-funded projects, including land-based camps and restorative justice practices. Supervision & Education : Supervised PhD and MSW students on CIHR projects (2019–2020), including eight MSW research assistants and one practicum student. Her teaching emphasizes Indigenous community engagement and structural analysis.

David Harrisonمشاهده پروفایل

استاد

• Oncology
• Pharmacology
• Molecular Biology
• +۳ مورد دیگر

Prof David Harrison is a Professor at the University of St Andrews' School of Medicine, affiliated with the Sir James Mackenzie Institute for Early Diagnosis and the Cellular Medicine Division. His research focuses on oncology, pharmacology, molecular biology, and AI-driven medical diagnostics. Key projects include the NuCana Biomed research agreement and the ICECAP initiative investigating immunopathology in severe COVID-19 cases. He supervises multiple PhD students and has contributed to advancements in drug development, cancer biology, and AI applications in pathology. His work spans clinical trials (e.g., RAMPART, NUC-3373 studies) and innovative imaging techniques (e.g., DESI-MSI for cancer metabolomics). Education: PhD (not explicitly stated in texts, inferred from academic rank). Research Interests: Prof Harrison’s expertise includes cancer drug mechanisms, molecular enzymology (e.g., 4-oxo-L-proline reductase), AI-driven pathology automation (e.g., cervical biopsy analysis), and immunological responses in critical illnesses. His lab develops tools for high-resolution histopathological imaging and explores metabolic-phenotypic links in tumors. Publications: Recent work includes studies on enzyme catalysis, clinical trial adaptations, and AI applications in diagnostics. These highlight his interdisciplinary approach to bridging basic science with translational medicine. Awards: None explicitly listed in the provided texts. Grants & Advising: Leads projects funded by NuCana Biomed (£171k) and Medical Research Scotland (£8k). Supervises eight PhD students focusing on drug mechanisms, imaging, and molecular biology. Labs/Teams: Collaborates within the Sir James Mackenzie Institute and cellular medicine teams, contributing to multidisciplinary research in early diagnosis and treatment strategies.

Abdullah Mueenمشاهده پروفایل

استاد

• Temporal Data Mining
• Blockchain Data Mining
• Social Media Mining
• +۵ مورد دیگر

Abdullah Mueen is a Professor and Associate Chair in the Department of Computer Science at the University of New Mexico (UNM), where he has been since 2013. Previously, he worked as a Scientist in the Cloud and Information Sciences Lab at Microsoft Corporation. Research Interests : His work focuses on Temporal Data Mining , with emphasis on efficiency , interactivity , and interpretability . Key areas include Blockchain Data Mining (e.g., BitLink for Bitcoin cluster analysis), Seismic Data Mining (e.g., PAW for aftershock detection), and Social Media Mining (e.g., DeBot for Twitter bot detection). Article Trends : His recent publications span four domains: Seismology : Algorithms for earthquake data analysis (e.g., focal depth inference, aftershock classification). Blockchain : Temporal linkage of Bitcoin addresses (BitLink) and cryptocurrency fraud detection. Traffic Safety : Multi-LiDAR data fusion for real-time road safety monitoring. Time Series Methods : Innovations like MASS similarity search and DAMP anomaly detection for massive datasets. Scientific Awards : ACM SIGKDD Test-of-Time Award (2022) UNM Provost Research Leader Award UNM School of Engineering Junior Faculty Research Excellence Award KDD 2012 Doctoral Dissertation Contest Runner-Up KDD 2012 Best Paper Award Advising and Grants : He has mentored 11 PhD students now employed at institutions like Microsoft, Meta, and Lawrence Livermore National Lab. His research is funded by NSF , NIH , DARPA , AFRL , NEC , Exxon , Microsoft , and LANL .

Long Nguyenمشاهده پروفایل

استاد

• Nonparametric Bayesian Statistics
• Optimal Transport
• Machine Learning
• +۴ مورد دیگر

Long Nguyen is a Professor of Statistics at the University of Michigan, Ann Arbor, with a courtesy appointment in Electrical Engineering and Computer Science. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Vietnam Institute for Advanced Study in Mathematics (VIASM). His research focuses on Bayesian nonparametrics, optimal transport, machine learning, and spatiotemporal data analysis. Nguyen holds a PhD in Computer Science from UC Berkeley and has held postdoctoral positions at Duke University and the Statistical and Applied Mathematical Institute. Education: B.Sc. in Computer Science from Pohang University of Science and Technology; M.Sc. in Mathematics from Arizona State University; Ph.D. in Computer Science from UC Berkeley (2007). Research Interests: Bayesian nonparametric methods, optimal transport theory, statistical inference for complex models, and applications in spatiotemporal data, functional data analysis, and hierarchical modeling. He emphasizes developing scalable algorithms and geometric approaches for statistical learning. Editorial Roles : Annals of Statistics Journal of Machine Learning Research SIAM Journal on Mathematics of Data Science Bayesian Analysis Awards : IMS Fellow, ASA Fellow NSF CAREER Award IEEE Signal Processing Young Author Award L. J. Savage Dissertation Award (via student Aritra Guha) Advising & Collaborations : Guided over 20 PhD students and postdocs, many now in academia and industry. Collaborates on projects in AI ethics, music theory, and environmental data science. Active in organizing summer schools in Vietnam on Bayesian statistics and machine learning. Labs & Teams : Co-leads the Statistical Machine Learning reading group at U-M and collaborates with the VIASM on advanced mathematical research in Hanoi.

Michael Qizhe Shiehمشاهده پروفایل

استادیار

• Large Language Models
• Deep Learning
• Natural Language Processing
• +۴ مورد دیگر

Michael Qizhe Shieh is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the Tree and Rock AI Lab (TRAIL). He holds a PhD and Master's from Carnegie Mellon University (Machine Learning and Language Technologies) and a Bachelor's from Shanghai Jiao Tong University's ACM Class. His research focuses on Large Language Models, Deep Learning, and Natural Language Processing, with notable contributions to semi-supervised learning techniques like Noisy Student and UDA, and the RACE benchmark for reading comprehension. Education: PhD in Machine Learning, Carnegie Mellon University (2020) Master's in Language Technologies, Carnegie Mellon University (2018) Bachelor's in Computer Science, Shanghai Jiao Tong University (2016) His research explores robustness, safety, and scalability of AI systems. He has served as Area Chair for top conferences like NeurIPS, ICML, and ICLR. Current research directions include adversarial robustness, LLM self-evaluation, and alignment mechanisms. His lab, TRAIL, emphasizes foundational AI research. Selected contributions include: Developing UDA and Noisy Student techniques for semi-supervised learning Creating the RACE benchmark for exam-based reading comprehension Advancing methods for LLM safety and adversarial defense Prospective students are encouraged to apply to NUS's PhD program for collaborative research opportunities.

Albert H. Titusمشاهده پروفایل

استاد

• Wearable Sensors
• Biosensors
• Bioinstrumentation
• +۵ مورد دیگر

Albert H. Titus is a Professor in the Department of Biomedical Engineering and an Adjunct Professor in the Department of Electrical Engineering at the University at Buffalo, State University of New York. He serves as Associate Vice President for Regulatory Support in the Office of the Vice President for Research and Economic Development. His research focuses on analog VLSI design for neuromorphic visual processing, biosensors, wearable devices, optoelectronic systems, and neural networks. Education: PhD in Electrical and Computer Engineering, Georgia Institute of Technology (1997) MS in Electrical Engineering, University at Buffalo (1991) BS in Electrical Engineering, University at Buffalo (1989) Research Interests: His work spans wearable and implantable sensors, bioinstrumentation, neural network-based visual processing, analog VLSI implementations, optoelectronics, and electronic packaging. He pioneered CMOS-based neuromorphic systems and developed patented technologies for glare sensing and RF power calorimetry. Publication Trends: His recent articles emphasize CMOS-integrated sensors, machine learning for bioimpedance analysis, implantable medical devices, and xerogel-based optical biosensors. These works bridge biomedical engineering and microelectronics. Scientific Recognition: He is a Fellow of the National Academy of Inventors and has received the SUNY Chancellor’s Award for Excellence in Service (2017), NSF CAREER award, and Western New York Inventor of the Year (2010). His inventions include a patented low-power glare sensor (U.S. Patent 7,586,079) featured in Popular Science’s 2011 Top Ten Inventions. Academic Leadership: As a faculty member, he has supervised nearly 20 PhD and over 40 MS students, while teaching courses in circuits, IC design, sensors, and signal processing across electrical and biomedical engineering disciplines.

David Wrightمشاهده پروفایل

استاد

• Smart Home Technology
• Artificial Intelligence
• Usability Studies
• +۳ مورد دیگر

Dr. David Wright is a Professor in the Department of English and Technical Communication at Missouri University of Science and Technology (Missouri S&T). He joined the faculty in 2007 after prior roles at NASA’s Education Project, Oklahoma state government, and the software industry. He holds a Ph.D. in Technical Communication (Oklahoma State University, 2007), an M.S. in Higher Education Administration (1996), and a B.S. in Organizational Psychology (1993), all from Oklahoma State University. His research focuses on smart home technology and artificial intelligence, particularly examining human-AI interaction through usability and user experience (UX) testing. He also explores technology diffusion, technical communication practices in emerging technologies, and educational methodologies for technical fields. His work integrates interdisciplinary approaches, blending engineering, sociology, and computer science. Recent publications highlight his contributions to IoT usability, smart home adoption challenges, and the intersection of AI ethics with virtual assistants. He has also authored studies on knowledge graph design, technical documentation in software development, and educational initiatives in computer science and healthcare. Dr. Wright teaches courses in technical writing, usability studies, and web-based communication. His academic service includes curriculum development and advising on technical communication pedagogy. While no specific awards are listed, his extensive publication record reflects sustained scholarly impact in his fields.

• Spatial Statistics
• Spatiotemporal Modeling
• Environmetrics
• +۳ مورد دیگر

Philipp Otto is a Professor of Statistics and Data Science at the University of Glasgow. Previously, he was a Reader in Statistics and Data Analytics (2023–2024) and held a Junior Professorship in Big Geospatial Data at Leibniz University Hannover (2018–2023). He earned his PhD in Statistics (summa cum laude) from European University Viadrina in 2016 and a B.Sc. in International Economics, with study visits to Saint Petersburg State University. His research focuses on spatial and spatiotemporal statistics, environmetrics, network modeling, and machine learning applications. Education: PhD in Statistics (2016), European University Viadrina, Frankfurt (Oder) B.Sc. in International Economics (with study visits to Saint Petersburg) Research Interests: Philipp’s work centers on spatial statistics, spatiotemporal volatility modeling, environmental data analysis, and network processes. He develops statistical methods for geo-referenced and network data, with applications in climatology, finance, and environmental risk assessment. His contributions include advancements in GARCH models, spatiotemporal clustering detection, and statistical process monitoring for AI systems. Grants & Projects: He has secured €1,038,847 in research grants, leading projects on historical map time series analysis, agricultural air quality impacts, and high-dimensional spatial dependence structures. Industry collaborations include survival analysis for building information models. Awards: 2017 Fellowship to attend the Lindau Nobel Laureate Meeting (Economic Sciences) 2017 Best Presentation Award (Data Science, Statistics, and Visualisation) Teaching: He teaches statistics and data science across disciplines, including economics, engineering, and mathematics, at both undergraduate and postgraduate levels. Professional Activities: Editorial Boards: Environmetrics (2021), AStA Advances in Statistical Analysis (2020) Member of German Statistical Society (Treasurer, 2013)

Morteza Ghorbaniمشاهده پروفایل

پژوهشگر

• Environmental Engineering
• Fluid Mechanics
• Nanotechnology
• +۳ مورد دیگر

Dr. Morteza Ghorbani is a researcher and faculty member at Sabancı University's Faculty of Engineering and Natural Sciences (FENS), specializing in fluid mechanics and environmental engineering. He leads the AquaCav project, a collaborative effort with Oxford Brookes University, focused on developing sustainable water treatment solutions using hydrodynamic and acoustic cavitation. His research addresses global challenges such as PFAS pollution and wastewater management, with applications in biomedical devices and energy-efficient technologies. Key collaborations include projects funded by the International Science Partnership Fund (ISPF), leveraging his expertise in microfluidic systems and cavitation dynamics. Dr. Ghorbani's work combines experimental and numerical methods to optimize cavitation-based processes for environmental and biomedical applications. His contributions span from fundamental fluid dynamics studies to applied technologies like flexible cystoscopes and clot-on-a-chip platforms. Scientific achievements include the ISPF Research Collaboration Grant (2024) and advancements in PFAS removal, graphene exfoliation, and microalgae cultivation. His research group at Sabancı University explores interdisciplinary solutions at the intersection of engineering, nanotechnology, and sustainability.

Sarah Jonesمشاهده پروفایل

دانشیار

• Thrombosis
• Haemostasis
• Platelet Biology
• +۳ مورد دیگر

Dr. Sarah Jones is an Associate Professor in Thrombosis and Haemostasis at Manchester Metropolitan University, serving as Deputy Director of Research for the Department of Life Sciences. She leads the Thrombosis Group and holds an honorary Reader position at the University of Manchester. Academic Affiliation: Manchester Metropolitan University Secondary Appointment: University of Manchester (Honorary Reader) Over 20 years, Dr. Jones has pioneered research on platelet signaling pathways and their role in thrombosis. Her current work focuses on platelet-endothelial crosstalk in disease states, with emphasis on in vitro thrombosis modeling to replace animal testing. This research, funded by BBSRC, NC3Rs, and BHF, addresses cardiovascular disease mechanisms and translational antithrombotic testing. Her scientific publications demonstrate expertise in platelet biology (Pim kinase signaling, SIRT1 regulation, proteoglycan interactions) and thrombosis modeling. Recent work includes placental perfusion studies for pregnancy-related cardiovascular complications. Grants & Projects: UKRI (NC3Rs) Partnership and Impact Award (2025-2026) UKRI BBSRC/NC3Rs grant for human haemostasis models (2023-2025) British Heart Foundation collaborations (2021-2025) Dr. Jones supervises PhD students (Mishaal Reyman, Poppy White, etc.) and teaches on Biomedical Science, Cellular Science, and Clinical Haematology programs. She has developed innovative endothelialised in-vitro thrombosis models that reduce animal usage in cardiovascular research.

Gerald Quonمشاهده پروفایل

دانشیار

• Computational Biology
• Single Cell Genomics
• Epigenomics
• +۵ مورد دیگر

Gerald Quon is an Associate Professor in the Department of Molecular and Cellular Biology at the University of California, Davis. He is affiliated with the Genome Center and participates in multiple graduate programs, including Integrative Genetics and Genomics, Neuroscience, Computer Science, Biostatistics, and Biomedical Engineering. Education: PhD in Computer Science from the University of Toronto (2012) MSc in Biochemistry from the University of Toronto (2006) Research Interests: Dr. Quon applies computational approaches to genetics and genomics problems, focusing on the genetics of human disease , models of cell population dynamics , and neurogenomics . His lab builds neural network models to understand how genetic variation affects disease risk through molecular and cellular phenotypes, with applications to obesity, Alzheimer’s disease, psychiatric disorders, and Rett syndrome. Recent Research Trends: Recent publications highlight work in neuroplasticity , single-cell multimodal analysis , brain evolution , morphological variation modeling , and microbiome-based classification . His team combines sequencing and imaging technologies to model cellular interactions and gene expression dynamics. Scientific Awards: NIH New Innovator Award (2021) Grants & Collaborations: He received NSF funding (2019) for computational tools in single-cell analysis and collaborates across disciplines, including neuroscience, biomedical engineering, and computational biology. His lab develops software like scProjection , siVAE , and scAlign .

Robert Laganièreمشاهده پروفایل

استاد

• Computer Vision
• Image and Video Analysis
• Visual Surveillance
• +۸ مورد دیگر

Robert Laganière is a Professor at the School of Electrical Engineering and Computer Science at the University of Ottawa, where he has been actively contributing to the fields of computer vision and image analysis. He is a member of the VIVA research laboratory and holds a Ph.D. and M.Sc. from INRS-Telecommunications in Montreal, as well as a bachelor's degree in Electrical Engineering from École Polytechnique de Montréal. Bachelor's in Electrical Engineering: École Polytechnique de Montréal (1987) Master's Degree: INRS-Telecommunications (1990) Doctorate: INRS-Telecommunications (1996) Professor Laganière's research focuses on computer vision, with particular expertise in image and video analysis, visual surveillance, embedded vision systems, and deep learning applications. His work spans fundamental research in feature detection and matching to practical applications in autonomous driving, human recognition, and real-time object tracking. He has made significant contributions to the development of algorithms for pedestrian detection, age and gender recognition, and 3D object localization. His publication trends reveal a consistent focus on practical computer vision applications with a strong emphasis on real-time performance and embedded implementation. Over the past decade, his research has evolved from foundational work in feature matching and homography estimation toward more complex applications in action recognition, human-computer interaction, and intelligent surveillance systems. His work consistently bridges theoretical computer vision with practical engineering constraints, particularly for mobile and embedded platforms. Best Paper Award, IEEE International Conference on Computer and Robot Vision (CRV 2014) Best Paper Award, CVPR Embedded Vision Workshop, Providence, RI, June 2012 Best Real-time Tracker, IEEE International Conference on Computer Vision (ICCV) Workshop on Visual Object Tracking (VOT2015) Professor Laganière has supervised numerous graduate students through the years, with a particular focus on practical applications of computer vision in surveillance, human recognition, and embedded systems. His research has been supported through industry partnerships with companies including CogniVue Corp, NXP, iWatchLife.com, Solink Corp, CBSA Canada, Ross Video, Thales, Habitat Seven, and YouI Labs. He has successfully translated his research into commercial applications through his founding of Visual Cortek (acquired by iWatchLife in 2009) and Tempo Analytics (founded in 2016). As a member of the VIVA research laboratory, Professor Laganière collaborates with colleagues on advanced computer vision projects, particularly those involving intelligent video analytics for security and commerce applications. His work on NAVIRE (Virtual Navigation in Remote Environments) demonstrates his commitment to developing practical solutions for real-world navigation challenges using image-based representations of real environments.

• Solid Mechanics
• Micromechanics
• Dislocation Plasticity
• +۳ مورد دیگر

Professor Dan Balint is the Head of the Mechanics of Materials Division in the Department of Mechanical Engineering at Imperial College London. He holds a Ph.D. in Engineering Sciences from Harvard University (2003), an S.M. in Applied Mathematics from Harvard (2001), and a B.S. in Engineering Mechanics from Michigan State University (1998). Prior to joining Imperial in 2006, he was a Research Associate at the Cambridge Centre for Micromechanics. His research spans theoretical and computational solid mechanics, with focus areas including: Micromechanics of crystalline materials (metals/ceramics) Dislocation-defect interactions and failure mechanisms Discrete dislocation plasticity methods Nuclear cladding materials and zirconium hydrides Thin film failure and metal forming processes Fracture mechanics and material size effects Recent publications (2022-2025) predominantly explore dislocation dynamics, zirconium alloy behavior under nuclear conditions, computational modeling of microstructural stresses, and machine learning applications in materials science. Common themes include thermomechanical degradation, crack initiation mechanisms, and multi-scale modeling approaches. Professor Balint serves as Associate Editor of the European Journal of Mechanics - A/Solids and consults for industrial partners including Rolls Royce, BP, and the US Air Force.