جستجوی استاد راهنمای دانشگاهی

Shi Qiuمشاهده پروفایل

استادیار

• Remote Sensing
• Data Preprocessing
• Cloud Detection
• +۴ مورد دیگر

Shi Qiu serves as an Assistant Research Professor in the Department of Natural Resources & the Environment at the University of Connecticut since 2022. Previously, he held postdoctoral positions at UConn (2019-2022) and visiting research roles at Texas Tech University (2017-2019). His academic credentials include a PhD in Remote Sensing from the University of Electronic Science and Technology of China (2018) and a BE in Spatial Information & Digital Technology from Chengdu University of Technology (2012). Dr. Qiu's research centers on geospatial data processing with specialized expertise in cloud detection, image compositing, land disturbance monitoring, and time series analysis. He applies machine learning techniques to satellite imagery from Landsat and Sentinel missions to address critical environmental challenges including coastal wetland dynamics, forest ecosystem monitoring, and mangrove condition assessment. His methodological focus spans algorithm development for satellite image preprocessing and land cover change detection. Analysis of his recent publications reveals consistent advancement in satellite image time series methodologies, particularly for cloud/shadow detection and land disturbance monitoring. His work demonstrates strong integration of Landsat and Sentinel data across diverse ecosystems, with notable contributions to Fmask algorithm development and operational environmental monitoring systems. Key application areas include coastal wetland change characterization, forest understory mapping, and long-term land cover trend analysis in agricultural landscapes.

• Computational Neuroscience
• Neural Networks
• Computer Hardware
• +۳ مورد دیگر

Professor Liam McDaid serves as Interim Research Director at Ulster University's School of Computing, Engineering and Intelligent Systems (Magee Campus, Derry~Londonderry). His leadership spans computational neuroscience and neuromorphic engineering projects addressing global challenges through the UN Sustainable Development Goals framework. Research focuses on computational neuroscience and neural engineering , with expertise in spiking neural networks, astrocyte modeling, hardware implementations (FPGAs/Networks-on-Chip), and biomedical applications. His work integrates computer hardware with biological systems for fault-tolerant computing and medical diagnostics. Key publications reveal trends in neuromorphic hardware for real-time fault detection (RISC-V systems), medical AI for cardiac/adrenal diagnostics, and event-driven sensing . Research bridges computer engineering with neuroscience to solve healthcare infrastructure challenges. Prize 'Biotechnology' category in Northern Ireland Science Park 25K Award (2011) IgniteNI's global Accelerator Programme (2021) Life and Health Startup Company of the Year 2019 (InventNI) Prize Asthma (2019) Leads major projects including AI-EPOCMON (cardiac diagnostics), Nervous Systems (neural interfaces), and microwave thermal therapy for hypertension. Supervises PhD researchers in neural engineering while directing industry collaborations with healthcare technology focus. Maintains active partnerships across Europe in neuromorphic computing and medical device development. Directs research teams advancing spiking neural networks for hardware security and medical applications, with recent work on adrenal segmentation pipelines and RISC-V watchdog mechanisms. Future initiatives focus on expanding AI-driven point-of-care diagnostics and neuromorphic infrastructure monitoring systems.

• Machine Vision
• Polarization Imaging
• Composite Materials Analysis
• +۳ مورد دیگر

Dr. Gary Atkinson is an Associate Professor in the Department of Engineering, Design and Mathematics at the University of the West of England (UWE). As a member of the Bristol Robotics Laboratory's Machine Vision group, his research focuses on polarization imaging, photometric stereo, and composite materials analysis. He has extensive experience in industrial inspection, security imaging, and 3D reconstruction techniques. Education: MSci in Physics, University of Nottingham (2003) PhD in Computer Science, University of York (2007) Postgraduate Certificate in Teaching and Learning in Higher Education, UWE His research explores polarization properties for surface reconstruction, with applications in composite material inspection, medical imaging, and underwater vision. Recent projects include deep learning-based defect detection in carbon fibre and polarization-driven surface normal estimation for seafloor imaging. Scientific Contributions: Fellow of the Higher Education Academy Principal Investigator for £189k DETI project (2020-2022) Co-investigator on multiple EPSRC/InnovateUK grants Delivered computer vision programs in China (University of Qiqihar) Co-convener of Biometrics Technology Conference (2010) As Programme Leader for BEng Robotics since 2023, he teaches computer vision, component inspection, and engineering mechanics. His work bridges academic research with industrial applications through partnerships like the National Composites Centre.

• Software Engineering
• Sustainability
• Human-Environment Interaction
• +۱۲ مورد دیگر

Ana Leticia de Cerqueira Leite Duboc is a contracted professor at La Salle School of Engineering, affiliated with the Human-Environment Research department. Her work bridges Software Engineering and Sustainability , focusing on education, systems analysis, and cross-disciplinary applications. Key research areas: Software Engineering Sustainability & SDGs Animal Welfare via Acoustic Monitoring Cognitive Robotics ICT for Mental Health Recent projects include Mentaltic (ICT in mental health), CowTalk (cattle vocalization monitoring), and CMES (cross-modal robotic perception). Her collaborations span AGAUR and the Generalitat de Catalunya, with a focus on deep learning and sustainable systems.

Mohamed Abdelfattahمشاهده پروفایل

استادیار

• deep learning systems
• automated machine learning
• hardware-software codesign
• +۷ مورد دیگر

Mohamed Abdelfattah is an Assistant Professor at Cornell Tech and the School of Electrical and Computer Engineering, Cornell University, specializing in machine-learning-centric computer systems for datacenters and mobile devices. Education: B.S., Electrical and Computer Engineering, German University in Cairo (2009) M.S., Electrical and Computer Engineering, University of Stuttgart (2011) Ph.D., Electrical and Computer Engineering, University of Toronto (2016) His research bridges computer architecture and machine learning with core focus areas: Deep learning systems optimization Hardware-aware automated machine learning Reconfigurable computing architectures FPGA-based acceleration He pioneers hardware-software co-design approaches to enhance ML efficiency across computing platforms. Awards: National Science Foundation Early CAREER Award (2024) Vanier Canada Graduate Scholarship Three best paper awards for embedded networks-on-chip for FPGAs His research is funded by the NSF CAREER grant, building on industrial collaborations from prior roles at Intel's Programmable Solutions Group and Samsung where he led hardware-aware ML research teams. His PhD work on FPGA architectures has been adopted by multiple semiconductor companies.

John R Benjaminمشاهده پروفایل

استاد بالینی

• Trauma Medicine
• Expeditionary Medicine
• Malignant Hyperthermia
• +۲ مورد دیگر

John R Benjamin serves as a Clinical Professor in the Department of Anesthesiology at Uniformed Services University of the Health Sciences (USUHS) School of Medicine. He is actively affiliated with Walter Reed National Military Medical Center in Bethesda, MD, where he practices as a Navy Commander. His primary appointment spans military medicine and academic anesthesia. His educational background includes a B.S. from Virginia Tech (1985-1989), M.Sc. from Miami University (1995-1998), M.D. from The Ohio State University College of Medicine (1998-2002), Surgery Internship at Naval Medical Center San Diego (2002-2003), Anesthesiology Residency at NCC (2006-2008), Chief Residency at NCC (2008-2009), and Anesthesia/Critical Care Fellowship at Massachusetts General Hospital (2010-2011). Benjamin's research focuses on Trauma and Expeditionary Medicine and Malignant Hyperthermia , with significant contributions to surgical intensive care unit mobility protocols and postoperative outcomes. His work bridges military medical needs with civilian critical care advancements, particularly in resource-limited environments. His publication trends reveal deep engagement with ICU mobility scoring systems Postoperative care optimization Medical device validation in field settings Interprofessional critical care collaboration spanning high-impact journals including The Lancet and Critical Care Medicine . Scientific recognition includes: Fellow of American Society of Anesthesiologists (F.A.S.A.) As a military-academic physician, Benjamin integrates operational medicine requirements with scholarly research. His grants and operational projects likely focus on expeditionary anesthesia protocols and trauma response systems for military personnel, though specific grants aren't detailed in the source text. He participates in multi-institutional initiatives like the International Early SOMS-guided Mobilization Research Initiative. His work occurs within the military medical ecosystem connecting USUHS, Walter Reed National Military Medical Center, and National Capital Consortium training programs, emphasizing practical applications for battlefield and disaster medicine.

• Functional Neurosurgery
• Neuroimaging
• Deep Brain Stimulation
• +۷ مورد دیگر

Michael Hart is a Senior Lecturer in Neurosurgery at City St George's, University of London, specializing in functional neurosurgery and neuroimaging for movement disorders, pain, and epilepsy. He is affiliated with the Neuroscience & Cell Biology Research Institute and maintains an active clinical and academic practice focusing on deep brain stimulation and related technologies. His educational background includes a medical degree from the University of Edinburgh, specialist neurosurgery training in Cambridge, and a PhD on connectomics from the Department of Psychiatry. He completed fellowships in awake brain surgery (Montpellier), functional neurosurgery (Vancouver), and neuromodulation (St George's Hospital, London). Hart's research interests center on neuroimaging and functional brain mapping, with particular focus on connectomics, ultra-high field 7T MRI, and tractography. He leverages these technologies to understand disease mechanisms and improve treatment targets in functional neurosurgery. His work spans neuro-oncology, movement disorders, and epilepsy, with emphasis on translating technological advances to improved patient care through industry collaborations. His publications demonstrate expertise across neuroimaging techniques, deep brain stimulation accuracy, cognitive assessment in brain tumor patients, and glioma biology. The research shows a clear progression from neuro-oncology applications of connectomics to functional neurosurgery applications, with increasing focus on precision targeting and outcome measurement in neuromodulation procedures. Fellow of The Royal College of Surgeons of Edinburgh (FRCSEd) Associate of The Higher Education Academy (AHEA) European Association of Neurological Surgeons (EANS): Functional Committee British Society of Stereotactic and Functional Neurosurgery (BSSFN): Research & Governance lead Hart provides supervision for medical students, MSc/MRes students, MD(Res), and PhD candidates. He collaborates extensively with movement disorders, epilepsy, and pain teams at SGUL, including Prof Francesca Morgante, Dr Lucia Ricciardi, Prof Mark Edwards, and others. His external collaborations include researchers at Oxford University, University of British Columbia, and University of Seville. He has led industry collaborations with Novo Nordisk, uniQure, and Boston Scientific Inc, and co-founded tech startups including neuroXact for post-operative DBS lead appraisal.

Sarah Rachutمشاهده پروفایل

پژوهشگر

• Digital Law
• E-Government
• Data Protection
• +۷ مورد دیگر

Dr. Sarah Rachut is a Scientific Associate at the Technical University of Munich (TUM), working at the Chair of Digital Law and Security under Prof. Dr. Dirk Heckmann. She serves as Managing Director of the TUM Center for Digital Public Services research institute and is affiliated with the TUM School of Social Sciences and Technology in the Department of Governance. Her work bridges legal scholarship with practical digital governance solutions, focusing on fundamental constitutional issues in digital contexts. Dr. Rachut studied law with a European and international focus at the University of Passau and completed her legal traineeship at the Higher Regional Court of Munich. She earned her doctorate with a dissertation titled "Realization of Fundamental Rights in Digital Contexts," which received the Science Prize of the Society for Data Protection and Data Security in November 2024. She is also a member of the German Society for Law and Informatics (DGRI) and an associate member of the eSports Law Research Center at the University of Augsburg. Her research centers on e-government, e-health, and e-education law, with particular emphasis on how constitutional rights can be realized in digital environments. Dr. Rachut challenges the misconception that law hinders digitalization, arguing instead that legal frameworks enable balanced technological advancement that respects fundamental rights. She advocates for moving from application-based administration toward proactive government services that automatically recognize citizens' legitimate claims without requiring burdensome documentation. Dr. Rachut's extensive publication record demonstrates deep expertise in AI regulation, digital administrative procedures, and electronic examination systems. Her work uniquely combines theoretical legal scholarship with practical implementation experience, as evidenced by her pivotal role in developing Bavaria's Fernprüfungserprobungsverordnung (BayFEV) during the pandemic, which became a national model for legally secure remote examinations. Young Leader in Gov Tech Award by Handelsblatt and Possible (2024) Science Prize of the Society for Data Protection and Data Security (2024) Second place in Young Scientist of the Year award (2025) Talent in BayFiD program (Bavaria's Women in Digital Professions) (2020) Scholarship holder at Graduate School of Bavarian Research Institute for Digital Transformation (2022) As Managing Director of the TUM Center for Digital Public Services, Dr. Rachut mentors students and leads research initiatives focused on digital transformation in government. She has successfully guided student teams to multiple awards in the "Digital Future Challenge" competition. Her leadership extends to shaping policy through expert opinions for legislative bodies including the German Bundestag and various state parliaments, where she has advised on the Bavarian Digital Law and other key digital governance frameworks. Dr. Rachut plays a key role in the TUM Center for Digital Public Services, which serves as an interdisciplinary hub for digital government research. She also serves as spokesperson for the "E-Government Law" working group of the National E-Government Competence Center (NEGZ) and co-edits the journal "Ordnung der Wissenschaft" since October 2024.

• Machine Learning
• Human/AI Interaction
• Uncertainty Quantification
• +۶ مورد دیگر

Douglas A. Talbert is a Professor of Computer Science at Tennessee Tech University. He holds a Ph.D. and M.S. in Computer Science from Vanderbilt University, and a B.S. in Mathematics from Tennessee Tech University. His research focuses on machine learning, human-AI interaction, uncertainty quantification, and healthcare informatics, with applications in clinical decision support systems. He has also contributed to software engineering and human-computer interaction in teaching. His research interests include developing explainable AI models, mitigating bias in machine learning systems, and applying AI to environmental monitoring and biomedical datasets. He has explored topics such as subgroup discovery in sequential databases, scalable reinforcement learning, and anomaly detection in industrial systems. His recent publications highlight advancements in uncertainty quantification for medical models, bias analysis in trauma triage, and semantic segmentation using multispectral satellite imagery. Notable collaborations include work on counterfactual explanations, drug repurposing for rare diseases, and trust-building techniques in biomedical machine learning. Talbert has co-authored numerous papers in conferences like FLAIRS and IEEE, often focusing on practical AI applications. His work bridges theoretical machine learning with real-world challenges in healthcare, cybersecurity, and environmental science.

Enjie Liuمشاهده پروفایل

استاد

• Network Applications
• Healthcare Technology
• Big Data Management
• +۴ مورد دیگر

Dr. Enjie Liu is a Professor of Network Applications at the University of Bedfordshire's School of Computer Science and Technology. She holds leadership roles as Portfolio Leader for the Undergraduate Computer Science and Technology Foundation and IRAC Postgraduate Research Lead. Her expertise spans networked applications in healthcare, big data, IoT, and wireless protocols. With a BSc from Northwest University and a PhD in Telecommunications from Queen Mary, University of London, she has over 100 peer-reviewed publications and led major EU/EPSRC projects like CARRE and Femtocell. Her research addresses healthcare monitoring, network management, and vehicular communication challenges. Enjie's work integrates medical knowledge retrieval, disease prediction, and efficient resource allocation in IoT/VANET systems. She has pioneered projects like MyHealthAvatar for patient-centric health visualization and co-developed reliable MAC protocols for safety-critical vehicular networks. Her industrial experience includes roles in China and the UK prior to joining Bedfordshire in 2003. Education BSc in Computer Science, Northwest University, China PhD in Telecommunications, Queen Mary, University of London Research Interests Dr. Liu focuses on: Healthcare IoT systems and telemedicine applications Big data analytics for disease progression modeling Wireless network optimization in vehicular and femtocell environments Secure and reliable communication protocols for critical infrastructure Her work bridges clinical needs with advanced networking technologies through projects like CARRE (EU FP7), which developed interactive visualization tools for health monitoring. Key Contributions Developed adaptive MAC protocols for VANET safety applications Designed spectrum allocation strategies for cognitive radio IoT systems Advanced femtocell network interference mitigation techniques Labs & Teams Affiliated with the Institute for Research in Applicable Computing (IRAC) and Centre for Computer Graphics and Visualisation (CCGV) . Collaborates across disciplines in health tech and telecommunications.

• Reinforcement Learning
• Learning in Games
• Applied Probability
• +۲ مورد دیگر

Zaiwei Chen is an Assistant Professor in the Edwardson School of Industrial Engineering at Purdue University. His research focuses on developing theoretical foundations for sequential decision-making under uncertainty, particularly in reinforcement learning, multi-agent systems, and optimization. He leads projects on data-efficient algorithms with applications in control and game theory. He holds a Ph.D. in Machine Learning and dual M.S. degrees in Mathematics and Operations Research from Georgia Institute of Technology, and a B.S. in Electrical Engineering from Zhejiang University. His research bridges applied probability, machine learning, and optimization to create robust algorithms for complex systems. Recent work emphasizes convergence guarantees in multi-agent reinforcement learning and scalable solutions for high-dimensional problems. Chen's publications demonstrate a consistent focus on theoretical guarantees for reinforcement learning algorithms, particularly in stochastic and game-theoretic settings. Key trends include Lyapunov stability frameworks, finite-sample analysis, and methods to overcome computational bottlenecks like the "curse of dimensionality." Awards include: Solberg Academic Excellence Scholarship PIMCO Postdoctoral Fellowship in Data Science (2022) Sigma Xi Best Ph.D. Thesis Award Runner-up for SIGMETRICS Doctoral Dissertation Award He actively recruits PhD students to work on reinforcement learning theory and maintains collaborations with Caltech and Georgia Tech. Future work includes scalable multi-agent learning frameworks and real-world applications in industrial systems.

Bimal Viswanathمشاهده پروفایل

دانشیار

• Machine Learning Security
• Privacy
• Adversarial Attacks
• +۲ مورد دیگر

Dr. Bimal Viswanath is an Associate Professor in the Department of Computer Science at Virginia Tech. His research focuses on security and privacy aspects of large online services, particularly at the intersection of machine learning and security. His work covers three main directions: attacks using ML, attacks on ML systems, and ML for improved security/privacy. He has received multiple awards including the Distinguished Paper Award at SOUPS 2014 and Best Paper Award at COSN 2015. Dr. Viswanath completed his Ph.D. in Computer Science at the Max Planck Institute for Software Systems and was a postdoctoral researcher at UCSB's SAND lab. His research group currently includes several PhD students working on security and machine learning projects. His work has been supported by grants from CCI, NSF and 4-VA. Recent research has focused on security challenges in generative AI systems, including deepfake detection, chatbot vulnerabilities, and adversarial attacks on ML systems. His team develops practical defenses against emerging threats in AI-powered systems. Teaching activities include courses on Foundation Models and Security, Secure Computing Capstone, and Security Analytics. Dr. Viswanath serves on program committees for top security conferences including CCS, USENIX Security, and IEEE S&P.

Bo Jiمشاهده پروفایل

دانشیار

• Networking
• Machine Learning
• Security and Privacy
• +۵ مورد دیگر

Bo Ji is an Associate Professor in the Department of Computer Science and a Faculty Fellow in the College of Engineering at Virginia Tech. He holds a Ph.D. in Electrical and Computer Engineering from The Ohio State University (2012) and M.E. and B.E. degrees in Information Science from Zhejiang University (China). Prior to Virginia Tech, he was an Associate Professor at Temple University and a Senior Member at AT&T Labs. Research Interests: His work focuses on the synergy between networking and artificial intelligence/machine learning, addressing challenges in wireless networks, edge computing, data centers, and IoT systems. Key areas include optimizing data freshness (Age of Information), security/privacy in AR/XR, and efficient resource allocation. His research combines theoretical insights with empirical studies, supported by NSF awards and industry collaborations. Awards and Recognition: Bo Ji has received the NSF CAREER Award (2017), IEEE INFOCOM Best Paper Award (2019), IEEE TNSE Excellent Editor Award (2021-2024), and Dean's Faculty Fellow Award (2023). He is a Senior Member of IEEE/ACM and serves on editorial boards for journals like IEEE/ACM Transactions on Networking and ACM SIGMETRICS. Grants and Labs: Leads the Synergistic Networking and Augmented Intelligence Lab (SNAIL). Active in securing grants from NSF, Amazon, and ICAT/CHCI for projects on AR privacy, AI-augmented decision-making, and federated learning systems. Collaborates on interdisciplinary projects with experts in cybersecurity and human-computer interaction. Teaching: Teaches courses in algorithms (CS 4104/6104) and online learning at Virginia Tech, with prior experience at Temple University.

Ken Choiمشاهده پروفایل

استاد

• Ultra-Low-Power VLSI Design
• Nanometer Circuit Design
• Design Automation
• +۳ مورد دیگر

Ken Choi is a Professor of Electrical and Computer Engineering at Illinois Institute of Technology's Armour College of Engineering. He joined Illinois Tech in 2007 after working as a senior CAD engineer and technical consultant at Samsung and Sequence Design. His research focuses on ultra-low-power VLSI design, nanoscale circuit optimization, and carbon nanotube FET applications. Education: Postdoctoral Research Associate at University of Tokyo (2005), Ph.D. in ECE from Georgia Tech (2003). Key research interests include Design for Power (DFP) and Design for Manufacturing (DFM) in VLSI systems, as well as low-power SOC design for multimedia applications. He has received multiple awards, including Best Paper Awards from the International Symposium on Wireless Sensor Networks and IEEE ISOCC conferences. His work spans topics like radiation-hardened latch designs, energy-efficient encoder architectures, and clock-gating techniques for ultra-low-power applications.

Neda Mohammadiمشاهده پروفایل

پژوهشگر

• Smart Cities
• Digital Twins
• Generative AI
• +۶ مورد دیگر

Neda Mohammadi is a Senior Research Engineer at the Georgia Institute of Technology, affiliated with the College of Engineering. Her research focuses on Smart City Digital Twins, Generative AI, and Cyber-Physical Systems, aiming to enhance urban infrastructure resilience and sustainability. She holds a PhD from Virginia Tech, an MSc from the University of Surrey, and a BSc from Sharif University of Technology. Dr. Mohammadi has led NSF-funded projects and received the 2022 Outstanding Faculty Achievement in Research award from the College of Engineering. Her work explores human-infrastructure interactions, disaster response systems, and AI-driven solutions for energy, mobility, and communication systems. Key projects include flood prediction models, cyberattack scenario generation using NLP, and community-centric crisis communication platforms like CARE. She emphasizes leveraging generative AI for realistic scenario modeling and data synthesis in complex urban systems. Dr. Mohammadi’s research bridges theoretical frameworks with practical applications, collaborating with governments and stakeholders to advance smart city initiatives. Her publications (2020–2025) span urban resilience, digital twin implementations, and data-driven decision-making. She leads interdisciplinary teams focused on sustainable infrastructure, energy benchmarking, and real-time crisis management systems. Education: PhD in Civil Engineering, Virginia Tech MSc in Civil Engineering, University of Surrey BSc in Civil Engineering, Sharif University of Technology Awards: Outstanding Faculty Achievement in Research (College of Engineering, 2022) Grants: Multiple NSF-funded projects on smart city digital twins and infrastructure resilience Labs/Initiatives: Smart City Digital Twins Research Group CARE Integrated Crisis Communication System