جستجوی استاد راهنمای دانشگاهی

Xiaokun Yangمشاهده پروفایل

دانشیار

• FPGA Acceleration
• AI/ML Hardware
• System-on-Chip Design
• +۲ مورد دیگر

Dr. Xiaokun Yang is an Associate Professor of Computer Engineering in the College of Science and Engineering at the University of Houston-Clear Lake. With extensive industry experience at AMD and CEC, his research bridges hardware design and AI acceleration. Research focuses on FPGA acceleration for AI/ML, hardware/software co-design, and IoT system architectures. Publications demonstrate innovations in neural network accelerators, hardware-efficient AI, secure SoC design, and edge computing solutions. Recent work explores ReRAM-based accelerators, blockchain-secured hardware, and federated learning systems. Awards include Best Ph.D Forum Paper at ISVLSI 2014. Education includes a Ph.D. in Electrical and Computer Engineering from Florida International University (2016) and dual M.S. degrees from FIU and Beihang University. Courses taught include Advanced Digital Systems Design, Electronics, and Senior Project. Current research projects investigate FPGA-based AI acceleration and IoT architectures.

Ander Biguriمشاهده پروفایل

پژوهشگر

• Tomographic Reconstruction
• Medical Imaging
• Machine Learning
• +۴ مورد دیگر

Dr. Ander Biguri is a Researcher at the Cambridge Image Analysis (CIA) group within the Faculty of Mathematics at the University of Cambridge. His primary research focuses on tomographic reconstruction techniques, particularly in cone-beam computed tomography (CBCT), and the integration of machine learning into medical imaging workflows. He is the main developer and maintainer of the TIGRE toolbox, an open-source software for iterative CT reconstruction. Key research areas include source-detector trajectory optimization for improved imaging quality, low-dose CT reconstruction, metal artifact reduction, and dynamic PET imaging. His work emphasizes practical applications in clinical and industrial tomography, with contributions to both algorithm development and hardware optimization. Biguri collaborates on projects such as AdvanCT (EURAMET) and has published extensively on topics like motion correction in PET/CT, GPU-based iterative reconstruction, and the design of imaging systems for radiation therapy.

Alan Wagnerمشاهده پروفایل

دانشیار

• Parallel Computing
• High Performance Computing
• Message-Passing Systems (MPI)
• +۵ مورد دیگر

Alan Wagner is an Associate Professor in the Department of Computer Science at the University of British Columbia (UBC), where he has held tenure since 1987. He is a member of the Institute for Computational Intelligence and Cognitive Systems (ICICS). With over 35 years of experience, his research focuses on parallel and distributed computing, high-performance computing (HPC), and message-passing systems (particularly MPI). His work emphasizes latency reduction, scalability, and middleware development using protocols like SCTP and TCP. Education: Ph.D. in Computer Science, University of Toronto (1987) M.Sc. in Computer Science, University of Alberta (1983) B.Sc. (Honors) in Mathematics, Dalhousie University (1977) Research interests include parallel algorithms, distributed systems, and applications in computational finance and data analytics. He has contributed to open-source projects like FG-MPI (a fine-grain MPI implementation) and SCTP-based MPI middleware, enhancing MPI's performance on commodity networks. His work has been supported by grants such as the NSERC I2I Grant (2008) and CFI Leaders Opportunity Fund (2009). He has advised over 20 M.Sc. students and 2 Ph.D. students. His teaching includes courses on parallel algorithms (CPSC 521) and computer networking (CPSC 317). He currently chairs UBC's Computer Science Graduate Admissions Committee.

Luke Seedمشاهده پروفایل

مدرس ارشد

• Holographic Lithography
• VLSI and System Design
• 3-Dimensional System Packaging
• +۵ مورد دیگر

Dr Luke Seed is a Senior Lecturer in the School of Electrical and Electronic Engineering at the University of Sheffield, serving as Deputy Head of Department and Departmental Director of Learning and Teaching since 2012 and 2014, respectively. He holds a BEng (1983) and PhD (1989) in Electronic Engineering from the University of Sheffield, where he joined the academic staff in 1986, advancing to Senior Lecturer in 1997. Research Focus: Holographic lithography, 3D packaging, VLSI design, and computer vision. Key Contributions: Pioneered work on numerically-generated holograms for photolithography since 2002, enabling applications in electronic packaging and non-planar antennas. Professional Memberships: Chartered Engineer (CENG), Member of the Institution of Engineering and Technology (MIET), and Member of the IEEE (MIEEE). Research Interests Seed’s work spans: Development of high-contrast photolithography techniques using computer-generated holograms (CGH). Low-power VLSI design and 3D system packaging innovations. Applications of holographic methods to antenna design and microfabrication. Publications His research has been published in top journals such as IEEE Open Journal of the Computer Society, Applied Optics, and J. Micromech. Microeng. Notable contributions include advancements in phase-seeded CGH algorithms for high-contrast patterns and holographic fabrication of 3D antennas. Teaching & Leadership In addition to research, Seed oversees departmental learning strategies and contributes to curriculum development, emphasizing hands-on FPGA and hardware design education. His work balances academic leadership with cutting-edge engineering innovation.

Timothy D. Craggsمشاهده پروفایل

مدرس ارشد

• Single-Molecule Spectroscopy
• Chemical Biology
• Biophysics
• +۴ مورد دیگر

Dr Timothy D. Craggs is a Senior Lecturer in Biological Chemistry at the University of Sheffield's School of Mathematical and Physical Sciences. He holds a MSci from the University of Cambridge (2002) and a PhD from Cambridge (2007). His career includes a Lindemann Fellowship at Yale University (2010), followed by senior postdoc roles at Oxford and Bristol. He joined Sheffield in 2016 as a Lecturer in Chemical Biology. His research focuses on single-molecule spectroscopy, particularly using FRET techniques to study biomolecular processes. Key areas include DNA replication/repair mechanisms, protein dynamics, and structural biology. The Craggs Lab develops innovative tools like the open-source smfBox platform, enhancing precision in distance measurements (sub-3 nm) and enabling FRET-driven structural studies. Teaching activities span undergraduate and postgraduate levels, including modules on biopolymers, biomaterials, and interdisciplinary biology-chemistry topics. His work bridges fundamental research with applied challenges such as diagnostic methods for infectious diseases (e.g., Buruli ulcer detection) and structural studies of aberrant DNA diseases. Collaborative efforts include multi-laboratory benchmarking of FRET accuracy and contributions to open-source scientific instrumentation. Current projects emphasize real-time observation of biomolecular interactions and the development of advanced microscopy techniques.

Rachel Martinمشاهده پروفایل

استاد

• Analytical Chemical Biology
• Physical Chemistry
• Chemical Physics
• +۳ مورد دیگر

Rachel Martin is a Professor in the Department of Chemistry at the University of California, Irvine (UCI). Her research focuses on analytical chemical biology , physical chemistry , and chemical physics , with a particular emphasis on protein structure, stability, and dynamics. She investigates molecular chaperones, lens crystallins, and extremophilic enzymes, often employing NMR spectroscopy and computational modeling. Dr. Martin's work integrates biophysical chemistry with instrumentation development, such as NMR probe fabrication and automated testing systems. She also explores educational innovations, including specifications grading in chemical biology courses. Her research has implications for understanding protein aggregation in aging, cold adaptation mechanisms, and enzyme function in extreme environments. Recent projects include studying deamidation effects on γ-crystallins, radiation resistance in lens proteins, and collaborative efforts in protein crystallization and structure determination. Her lab leverages open-source tools and maker methodologies for experimental instrumentation.

Sabrina M. Neumanمشاهده پروفایل

استادیار

• Computer Architecture
• Robotics
• Domain-Specific Accelerators
• +۳ مورد دیگر

Sabrina M. Neuman is an Assistant Professor of Computer Science at Boston University, specializing in computer architecture design informed by domain-specific insights, particularly for robotics applications. She holds a PhD in Electrical Engineering and Computer Science from MIT and was a postdoctoral NSF Computing Innovation Fellow at Harvard University. Her research focuses on closing computational gaps in robotics through domain-specific accelerators, leveraging robot morphology for hardware design. She has received honors including the 2021 EECS Rising Star and the Boston University Innovation Career Development Professorship (2023-2026). Education: PhD in Electrical Engineering and Computer Science, MIT M.Eng. in Electrical Engineering and Computer Science, MIT S.B. in Electrical Engineering and Computer Science, MIT Research Interests: Her work emphasizes domain-specific computing for robotics, including automated hardware design flows, FPGA/ASIC acceleration for motion planning, and scalable accelerator deployment across robot platforms. She develops methodologies like Robomorphic Computing, which translates robot morphology into customized hardware accelerators, achieving significant speedups in critical robotics kernels. Awards & Recognition: 2021 EECS Rising Star IEEE Micro Top Picks Honorable Mentions (2022, 2023) 2023-2026 Boston University Innovation Career Development Professorship Grants & Contributions: Developed RobotPerf, an open-source benchmarking suite for robotics computing systems Worked on Grid (GPU-accelerated rigid body dynamics) and RobotCore (ROS 2 hardware acceleration) Labs & Teams: While no specific lab name is mentioned, her work integrates closely with Boston University's robotics and computer architecture groups, emphasizing open collaboration with industry and academia.

Benedetta Piantellaمشاهده پروفایل

دانشگاهی

• Social Impact ICTD
• Participatory Design
• Human-Computer Interaction (HCI)
• +۶ مورد دیگر

Benedetta Piantella is an Industry Associate Professor at the NYU Tandon School of Engineering, affiliated with the Integrated Design & Media Program (IDM). She holds a dual role as an educator and humanitarian technologist, with over two decades of experience in international development, STEM education, and sustainable technology design. Her work focuses on participatory design methodologies to address global challenges in resource equity, climate resilience, and community-driven innovation. Education & Experience: Former Technologist in Residence at Cornell Tech (Connected Experiences Lab, Social Technologies Lab) Technology Architect at Columbia University’s Earth Institute and Quadracci Sustainable Engineering Lab Founder of two R&D companies focused on sustainable solutions for global problems Research Interests: Benedetta’s work bridges technology and society through projects like the Solar Protocol initiative, which explores energy-positive internet infrastructure, and the Low Power Lab’s development of off-grid systems. She emphasizes participatory design, user-centered approaches, and open-source collaboration to create resilient networks and IoT solutions for underserved communities. Key Contributions: Her research addresses equitable access to life-sustaining resources through solar microgrids, real-time monitoring systems, and distributed infrastructure. Recent projects include deploying 24/7 water kiosks in Africa and developing decentralized energy networks for disaster resilience. Labs & Affiliations: Center for Urban Science + Progress (CUSP) Low Power Lab Partnerships with UNICEF, UN, and Millennium Villages Project

Jelmer Borstمشاهده پروفایل

دانشیار

• Computational Cognitive Neuroscience
• Neuroimaging Analysis
• Cognitive Modeling
• +۵ مورد دیگر

Prof. Jelmer Borst is an Associate Professor in Computational Cognitive Neuroscience at the University of Groningen's Faculty of Science and Engineering, affiliated with the Artificial Intelligence department within the Bernoulli Institute. His research focuses on integrating computational models with neuroimaging data to understand cognitive processes like multitasking, working memory, and decision-making. He advises three PhD candidates and collaborates internationally on projects involving EEG/fMRI analysis and cognitive modeling frameworks such as ACT-R and Nengo. Research Interests: Neuroimaging analysis methods, cognitive bottlenecks in multitasking, memory retrieval localization, and adaptive learning systems. Key Projects: Developed the PREDICTOR tool for semi-automated driving response timing, and advanced models linking symbolic process stages to brain activity via MEG/EEG. Recent work includes large-scale evaluations of adaptive learning systems and interventions to mitigate mind-wandering in driving scenarios. He has received the Allen Newell Best Student-led Paper Award (2021) for contributions to cold-start adaptive learning research. His research group maintains active collaborations on datasets involving working memory, decision-making, and cognitive architecture validation through neuroimaging experiments. He also contributes to open-source tools for cognitive modeling and neuroimaging analysis.

Luis Gerhorstمشاهده پروفایل

پژوهشگر

• Distributed Systems
• Operating Systems
• Cyber-Physical Systems
• +۳ مورد دیگر

Luis Gerhorst is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He focuses on systems software, embedded systems, energy-efficient computing, and security mitigations against transient execution attacks like Spectre. His work spans kernel-level optimizations, carbon-aware cloud systems, and embedded system resilience. Education: Completed Bachelor's and Master's theses in system software at FAU, focusing on system-call aggregation and Linux kernel interrupt handling. Research Interests: His primary areas include operating systems, distributed systems, and energy-aware resource management. Notable contributions include the AnyCall system-call aggregation framework and VeriFence , a Spectre defense mechanism for BPF programs. He also explores carbon footprint modeling in cloud environments through projects like carbond . Publications: Recent work addresses energy-efficient embedded systems (vNV-Heap), power-failure resilient network stacks (PfIP), and reverse-engineering Wi-Fi drivers for energy analysis. His research often bridges hardware-software co-design and environmental sustainability. Grants/Advising: Supervised over 10+ theses on topics like carbon-aware timers, BPF sandboxing, and Rust-based alternatives to BPF. Active in open-source projects like Linux kernel contributions and GitHub repositories for systems research. Labs/Teams: Member of Lehrstuhl für Informatik 4, collaborating on projects related to system software and embedded systems resilience. Maintains active GitLab/ GitHub repositories for research tools and prototypes.

Daniel Lohmannمشاهده پروفایل

استاد

• Operating Systems
• Embedded Systems
• Real-Time Systems
• +۴ مورد دیگر

Professor Daniel Lohmann leads the Systems Research and Architecture (SRA) group at Leibniz Universität Hannover since 2017, following his role as Associate Professor (Privatdozent) at Friedrich-Alexander-Universität Erlangen-Nürnberg (2009–2016). His work focuses on configurable system software, emphasizing embedded systems, real-time systems, and dependable architectures. Key projects include danceOS (dependability in embedded OS), Sloth (minimal-effort kernels), and AspectC++ (aspect-oriented C++ extension). His research is funded by DFG and addresses challenges in variability management, fault tolerance, and real-time scheduling. Research interests span operating systems design, software product lines, generative programming, and aspect-oriented development. Notable contributions include the CiAO OS family and the Sloth RTOS, leveraging aspect-oriented techniques for flexibility and efficiency. His work on configuration analysis (e.g., static variability in Linux) and fault mitigation (e.g., AN-Codes for soft errors) highlights his expertise in system reliability and optimization. Publications emphasize real-time systems, embedded software, and software engineering, with awards including the Best Paper Award at SPLC 2010 and an Outstanding Paper at RTAS 2017. He supervises theses on system software tailoring, fault tolerance, and industrial software ecosystems. His groups collaborate on invasive computing (SFB/TRR 89) and hardware-aware OS design.

• Quantum Computing
• Condensed Matter Physics
• Quantum Simulation
• +۳ مورد دیگر

Lex Kemper is an Assistant Professor of Physics at the NC State College of Sciences, specializing in applying quantum computing to solve complex problems in condensed matter physics. His work focuses on advancing quantum simulation techniques, studying quantum materials like cuprate superconductors, and developing noise-resilient quantum algorithms. He has contributed to theoretical frameworks for understanding electron interactions, quasiparticle dynamics, and the optimization of quantum circuits. His research bridges quantum computing, condensed matter physics, and theoretical chemistry, with applications in quantum machine learning and fault-tolerant quantum systems. Key research interests include quantum state preparation, error mitigation in quantum simulations, and the classification of dynamical systems. He has published extensively on topics such as eigenvector continuation, barren plateaus in quantum circuits, and the simulation of strongly correlated systems using the Fermi-Hubbard model. His work often involves collaborations with experimental physicists to validate theoretical models with advanced spectroscopic techniques like RIXS and ARPES. Notable contributions include frameworks for efficient ground-state preparation, denoising response functions, and the development of geometric quantum machine learning algorithms. His research aligns with the broader goals of leveraging quantum computing to tackle unresolved challenges in condensed matter physics and materials science.

• Design Practice and Management
• Visual Arts and Crafts
• Creative Arts and Writing
• +۵ مورد دیگر

Scott Mitchell is a Lecturer in the School of Design at RMIT University's City Campus in Australia. His academic work bridges design theory and practice with a focus on critical and radical approaches to design that examine how objects become sites of social and public action. Mitchell's research spans Design Practice and Management, Visual Arts and Crafts, Creative Arts and Writing, Art Theory and Criticism, Architecture, and Digital Media. His work explores the intersection of design with social contexts, particularly through divergent consumer practices such as hacking and modding. His interdisciplinary approach combines critical design theory with electronic and interaction design practices to examine how designed objects function as sites of social engagement and public action. His publication record from 2014-2025 reveals an evolving research trajectory from foundational explorations of maker culture and material agency to contemporary investigations of energy-conscious design for smart homes and digital twins. A consistent theme across his work is the examination of the relationship between design, materiality, and social contexts, with recent publications focusing on interactive materials, wearable technology, and logo aesthetics. Scott Mitchell is actively engaged in research supervision, being open to Masters Research and PhD student supervision. His current supervisor projects include investigations into logo aesthetics, energy-conscious design for digital twin-driven smart homes, and interactive materials, reflecting his commitment to advancing design theory through practical application.

Constantino Reyes-Aldasoroمشاهده پروفایل

مدرس ارشد

• Biomedical Image Analysis
• Medical Image Processing
• Computer Vision
• +۱۲ مورد دیگر

Constantino Reyes-Aldasoro is a Senior Lecturer in Biomedical Image Analysis at the Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London. His research focuses on the analysis, interpretation, and visualization of biomedical data, particularly in the context of cancer, inflammation, and neurodegenerative diseases. PhD in Computer Science – University of Warwick (2004) MSc in Electrical Engineering – Imperial College London (1994) Bachelor’s in Mechanical and Electrical Engineering – Universidad Nacional Autónoma de México (1993) His research spans image analysis, machine learning, and computational modeling applied to biomedical imaging, with emphasis on electron microscopy, histopathology, and radiology. He has developed algorithms for cell segmentation, vessel tracing, and tumor microenvironment analysis, contributing significantly to open-source tools in the field. His recent publications show a strong trend toward integrating deep learning with traditional image analysis, especially in cancer diagnostics and Alzheimer’s disease assessment. He has also explored topological data analysis and persistent homology for evaluating dataset consistency in colorectal cancer research. Senior Member, IEEE Member Level 1, Sistema Nacional de Investigadores CONACYT (Mexico) He has supervised multiple PhD students in areas such as HeLa cell analysis, coronary plaque detection, and Alzheimer’s imaging. He has secured grants from the Leverhulme Trust, Australian Research Council, and Cancer Research UK. He is an academic editor for journals including PLOS ONE and Journal of Imaging , and has chaired conferences like MIUA and BMVA. He is part of the giCentre research group at City, and actively promotes interdisciplinary collaboration in AI for healthcare.

Jingping Nieمشاهده پروفایل

استادیار

• Artificial Intelligence of Things (AIoT)
• Pervasive Healthcare
• Wireless Sensor Systems
• +۴ مورد دیگر

Jingping Nie is an Assistant Professor at the University of North Carolina at Chapel Hill's School of Data Science and Society. She holds a Ph.D. in Electrical Engineering from Columbia University (2025), under Professors Xiaofan Jiang and Matthias Preindl, and a B.S. in Engineering Science from Smith College (2017). Her research focuses on AIoT systems, wearable devices, and pervasive healthcare, aiming to enhance human wellness through hardware-software co-design. Education highlights include Columbia University (Ph.D. 2025, M.S. 2019) and Smith College (B.S. 2017, magna cum laude). Research spans smart healthcare solutions, smart grid optimization, and emotion recognition via wearable platforms. She leads the NIcE X Lab, focusing on embedded AI and computational IoT systems. Key awards include the ACM MobiSys Best Paper (2024), Apple Scholars in AI/ML Fellowship (2023), and IEEE ITEC Best Paper (2021). Her work bridges AI, cyber-physical systems, and data analytics to create human-centric technologies. Active in professional service as Program Chair of ACM HumanSys 2025 and reviewer for venues like ACM IMWUT. Open to mentoring students in AIoT, wearable tech, and healthcare innovation through her lab openings.