جست‌وجوی استاد و پژوهشگر

Yidi Wangمشاهده پروفایل

استادیار

• Real-Time Systems
• Embedded Systems
• Cyber-Physical Systems
• +۵ مورد دیگر

Yidi Wang is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University (SCU). She holds a Ph.D. in Electrical and Computer Engineering from the University of California, Riverside (2023), and previously worked as a Postdoctoral Scholar at UCR (2023–2024). Her research focuses on real-time, embedded, and cyber-physical systems, particularly addressing challenges in GPU scheduling, energy efficiency, and batteryless device operation. She teaches courses such as Introduction to Embedded Systems and Operating Systems at SCU. Education: Ph.D. in Electrical and Computer Engineering, UC Riverside (2023) M.S. in Electrical and Computer Engineering, UC Riverside (2019) B.S. in Electrical Engineering, Huazhong University of Science and Technology (2018) Research Interests: Real-time scheduling for GPU-accelerated applications, energy-efficient computing, heterogeneous platforms, and reliable systems for intermittently powered devices. Her work spans system-level implementations and theoretical analysis, including novel scheduling algorithms and mathematical models for performance optimization. Awards: None explicitly listed in the provided texts. Advising & Grants: Currently mentoring 3 students (2 MS, 1 undergraduate) and recruiting PhD/MS candidates. She serves on technical committees for RTSS and RTAS conferences and has reviewed for journals like Transactions on Computers and Real-Time Systems. Labs/Teams: Leads research on GPU scheduling and batteryless systems, collaborating with industry and academic partners.

Daniel Huttenlocherمشاهده پروفایل

استاد

• Artificial Intelligence + Machine Learning
• AI and Society
• Graphics and Vision
• +۳ مورد دیگر

Daniel Huttenlocher is the Dean of the MIT Stephen A. Schwarzman College of Computing and holds the Henry Ellis Warren (1894) Professorship in Computer Science and Artificial Intelligence + Decision-making (AI+D). He leads the interdisciplinary College of Computing while maintaining academic ties to the Electrical Engineering & Computer Science Department. His research focuses on AI ethics, societal impact of technology, machine learning applications, and vision systems. Key research areas include AI governance frameworks, adaptive public health strategies, and large-scale computer vision algorithms. He has pioneered work on decentralized collaboration systems, social media dynamics, and generative AI's societal implications. Huttenlocher’s recent publications address challenges in epidemic testing optimization and the evolving role of AI in knowledge ecosystems like Wikipedia. His leadership roles include steering MIT’s computing initiatives and fostering industry partnerships, such as the MIT Generative AI Impact Consortium. Despite no explicitly listed awards, his contributions to AI ethics and technical systems reflect significant academic influence. Advising and grant activities remain unspecified in available records. His work bridges technical innovation with societal challenges, emphasizing the ethical deployment of AI across healthcare, education, and digital economies.

Karthika Prasadمشاهده پروفایل

پژوهشگر ارشد

• Carbon Nanoparticles
• Polymer Nanocomposites
• Biotechnology for Agriculture
• +۳ مورد دیگر

Karthika Prasad is a Senior Research Fellow at the Australian National University (ANU), affiliated with the ANU College of Systems & Society and the School of Engineering. She also serves as the Chief Technology Officer (CTO) of BudMore, an agritech company. Her research focuses on carbon nanoparticles, polymer nanocomposites, and biotechnology applications in agriculture, including smart plasma systems for agricultural innovation. Dr. Prasad holds a PhD in Science and Engineering from Queensland University of Technology (QUT, 2019), a Master’s in Nanoscience from VELS University (2014), and a BSc in Nanotechnology from the University of Wollongong (2012). Key achievements include the QUT Executive Dean’s Commendation for Outstanding Doctoral Thesis (2020) and the QUT Higher Achiever Award (2018). Her work spans biomedical applications of nanomaterials, aerospace materials, and sustainable technologies derived from natural products. Recent research trends highlight innovations in nanocomposite materials for medical implants, space technology, and agricultural systems. Professional contributions include postdoctoral research at QUT (2017–2020) and sessional academic roles. She has pioneered studies on plasma-activated solutions for disinfection and explored synergistic effects of plant metabolites with antimicrobial agents. Current projects emphasize functional nanomaterials from waste and low-value natural products, reflecting her commitment to sustainable technological advancements.

• Data-efficient deep learning
• Adversarial attacks
• Deep learning interpretations
• +۴ مورد دیگر

Anshul Thakur is a Departmental Lecturer in Clinical Machine Learning at the University of Oxford's Institute of Biomedical Engineering. His research focuses on advancing data-efficient deep learning techniques, adversarial attacks, and interpretable AI frameworks for healthcare applications. He holds a PhD from IIT Mandi (2020), where his thesis explored audio signal analysis using dynamic kernels and deep learning. Education: PhD in Computing & Electrical Engineering, Indian Institute of Technology Mandi (2020) Research concentrated on bioacoustic signal pattern analysis and ML frameworks for acoustic classification. Research Interests: His work emphasizes clinical AI applications, including federated learning for medical data, multimodal diagnosis systems, and mitigating class imbalance in healthcare datasets. He develops interpretable models for medical practitioners and explores ethical AI deployment in clinical settings. Recent Trends in Publications: Recent work addresses federated learning optimization, multimodal clinical diagnosis, and early disease prediction using biomarker patterns. His studies highlight innovations in EHR analysis, privacy-preserving techniques, and cross-domain medical model adaptation. Labs & Teams: Active in the Institute of Biomedical Engineering, collaborating on projects like the RapiD_AI framework for pandemic preparedness and Continuous Patient State Attention Models for irregular EHR data analysis.

Davood Shiriمشاهده پروفایل

مدرس

• Online and stochastic optimization
• Large-scale optimization
• Graph and network models
• +۳ مورد دیگر

Dr Davood Shiri is a Lecturer in Operations Management and Decision Sciences at the Sheffield University Management School, University of Sheffield. His research focuses on online and stochastic optimization, large-scale optimization, graph/network models, healthcare/humanitarian logistics, and combinatorial optimization. He holds a PhD in Industrial Engineering and Operations Management from Koç University, Turkey, and has prior roles as an Assistant Professor at Medipol University and Bilgi University, and a postdoctoral research fellow at Koç University. Education: BSc and MSc in Industrial Engineering from Sharif University of Technology (Iran), PhD in Industrial Engineering and Operations Management from Koç University (Turkey). His teaching includes modules like MGT3010 (Applications of Operations & Supply Chain Management) and MGT137 (Analysis for Decision Making). Research interests emphasize optimization frameworks for dynamic environments, with applications in disaster response logistics, healthcare systems, and network resilience. His publications address challenges in ambulance routing during disasters, post-disaster road restoration, and algorithmic strategies for interdependent networks. Awards/Grants: No specific awards mentioned in available texts, though his work reflects competitive research funding in operations research domains. Labs/Teams: Active in the Operations Management and Decision Sciences research group at Sheffield Management School, contributing to Triple Crown-accredited programs.

Ruggero Carliمشاهده پروفایل

دانشیار

• Control Systems
• Robotics
• Machine Learning
• +۶ مورد دیگر

Ruggero Carli is an Associate Professor at the Department of Information Engineering, University of Padova. His research focuses on control systems, robotics, and optimization, with emphasis on model-based reinforcement learning, distributed optimization algorithms, and energy systems. His work bridges theoretical advancements with real-world applications, including autonomous robotics, smart grids, and nonlinear control. Key contributions include physics-informed machine learning frameworks, ADMM-based distributed optimization methods, and MPC-driven control solutions for underactuated systems. Research interests include: Model-Based Reinforcement Learning for Robotics Nonlinear Model Predictive Control (NMPC) Distributed Optimization and ADMM Variants Energy Networks and Smart Grids Robot Dynamics and System Identification Recent publications emphasize: Continual learning for driver behavior analysis Physics-informed control for underactuated systems Robust optimization in unreliable networks Autonomous robotic manipulation with large language models His research integrates control theory with modern machine learning techniques, addressing challenges in edge computing, distributed systems, and real-time implementation.

• High-Performance Computing
• Parallel Programming
• Computer Architecture
• +۳ مورد دیگر

Prof. Dr. rer. nat. Matthias S. Müller is a Universitätsprofessor and Director of the IT Center at RWTH Aachen University. His research focuses on High-Performance Computing (HPC), parallel programming models, correctness verification, energy-aware computing, and tools for distributed systems. He leads the High-Performance Computing group, contributing to advancements in HPC resource management, runtime systems, and sustainable computing practices. Key areas of expertise include MPI and OpenMP correctness checking, static and dynamic analysis techniques, performance optimization for heterogeneous architectures, and energy footprint modeling. Müller has extensively collaborated on projects like MUST (MPI correctness tool), OMPT tools, and frameworks for analyzing hybrid parallel applications. His work bridges theoretical computer science with practical implementation challenges in large-scale computing environments. Notable contributions include developing methods for data race detection in Remote Memory Access (RMA) programs, latency-aware power management models, and educational frameworks for HPC lab courses. His research often emphasizes tool development, runtime systems, and interdisciplinary applications of HPC across engineering domains. Müller's lab is part of RWTH Aachen's IT Center, which provides infrastructure and expertise for computational research. He actively publishes in top-tier conferences and journals, addressing challenges in parallel programming, energy efficiency, and distributed computing systems.

• Image and Video Processing
• Multimedia Analysis
• Computer Vision
• +۵ مورد دیگر

Prof. Tan Yap Peng is a Professor and Chair of the School of Electrical & Electronic Engineering at Nanyang Technological University (NTU), Singapore. He holds the President's Chair in Electrical and Electronic Engineering and serves as Associate Vice President (Lifelong Learning – Postgraduate Programmes by Coursework). His research focuses on multimedia analysis, computer vision, machine learning, and data analytics. He earned his B.S. from National Taiwan University and M.A./Ph.D. from Princeton University. He has led major initiatives including the INFINITUS Infocomm Research Centre and contributed to IEEE technical committees. His over 200 publications span image/video processing, neural network robustness, and cross-modal systems. Awards include IEEE Fellow status. Education: B.S. Electrical Engineering (NTU), M.A./Ph.D. (Princeton) Research interests emphasize interactive digital media, content-based analysis, and AI-driven solutions for visual and signal processing. His work addresses challenges in adversarial attacks, video generation, and low-light image enhancement. He has held editorial roles at IEEE Transactions and EURASIP journals. Conference leadership includes chairs for ICME and ICIP. His contributions bridge academia and industry through collaborative research networks.

Qiongkai Xuمشاهده پروفایل

مدرس

• Natural Language Processing
• Machine Learning
• Security
• +۳ مورد دیگر

Dr. Qiongkai Xu is a Lecturer in Natural Language Processing (NLP) at Macquarie University's School of Computing, with an honorary fellowship at the University of Melbourne. He holds a PhD in NLP from the Australian National University (ANU). His research focuses on auditing machine learning models, particularly addressing privacy/security issues in NLP/ML models and developing evaluation frameworks. Key areas include text watermarking, adversarial attacks, data leakage mitigation, and model authentication. Recent publications emphasize robustness against backdoor attacks, generative model watermarking, and healthcare applications of LLMs. His work bridges theoretical security advancements with practical NLP challenges. Awards: DAAD Scholarship (2022), Research Pitching Session Winner (MQ, 2022 & 2024) Grants: Leading the Climate Litigation Risk project on AI-driven greenwashing detection. Labs/Teams: Data Horizons Research Centre, Future Communications Research Centre, and Frontier AI Research Centre.

Bingyao Liمشاهده پروفایل

استادیار

• Computer Architecture
• High-Performance Computing
• Multi-GPU Systems
• +۴ مورد دیگر

Bingyao Li is an incoming tenure-track Assistant Professor at the University of California, Riverside's Computer Science and Engineering Department, starting Fall 2025. She is currently a fourth-year Ph.D. student at the University of Pittsburgh, advised by Dr. Xulong Tang. Her research focuses on computer architecture, particularly multi-GPU systems, virtual memory management, and address translation for high-performance computing. Educational Background: She holds a B.E. (2017) and M.S. (2020) from Tianjin University's College of Intelligence and Computing. Her Ph.D. work at Pitt emphasizes GPU architecture and system-level optimizations for large-scale applications. Research Interests: Her work spans advanced computer architectures, multi-GPU optimization, and parallel computing systems. She explores solutions like object-aware page management (OASIS), TLB enhancements (STAR), and dynamic page placement (GRIT) to improve multi-GPU performance. She also investigates system-level optimizations for large language models and GPU ecosystems from hardware to applications. Awards: She has received the CS50 Outstanding Research Fellowship (2022, 2023) and the SCI Fellowship from the University of Pittsburgh. Her contributions include internships at NVIDIA's Architecture Research Group and talks on multi-GPU computing at NVIDIA and Tianjin University. Future Work: As a faculty member, she plans to mentor PhD students in Winter/Spring/Fall 2026, focusing on next-generation GPU systems and high-performance computing challenges. Her lab will address scalability, efficiency, and emerging applications in parallel computing.

Jay Gottfriedمشاهده پروفایل

استاد

• Human Olfactory Neuroscience
• Cognitive Neuroscience
• Behavioral Neuroscience
• +۳ مورد دیگر

Jay Gottfried is the Arthur H. Rubenstein University Professor at the University of Pennsylvania's Perelman School of Medicine. His research focuses on human olfactory neuroscience, investigating how odor perception is processed in limbic circuits related to emotion, learning, and memory. He employs advanced techniques like functional MRI (fMRI) and intracranial EEG to study olfactory systems, with recent work exploring olfactory navigation using grid-like neural representations and interactions between sensory cortices. Education: A.B. in Molecular Biology from Princeton University; M.D. and Ph.D. in Physiology and Neuroscience from NYU School of Medicine. Postdoctoral training in olfactory neuroscience at University College London. Research Interests include odor coding, oscillations in the piriform cortex, and translational studies linking olfactory dysfunction to neurodegenerative diseases. Notable projects involve odor categorization, fear learning, and sleep-dependent memory consolidation. His lab bridges human and rodent studies to elucidate olfactory mechanisms. Key findings include temporal segregation of nostril odor signals, grid-cell-like representations in olfactory navigation, and the role of piriform cortex in integrating sensory information. He collaborates on developing high-resolution imaging and EEG techniques to map olfactory circuits.

Xiaoyu Xiaمشاهده پروفایل

مدرس

• System Privacy and Security
• AI Privacy
• Parallel and Distributed Computing
• +۴ مورد دیگر

Dr. Xiaoyu Xia is a Lecturer (equivalent to Assistant Professor in North America) in Cybersecurity & Software Systems at RMIT University's School of Computing Technologies. He received his PhD with the prestigious Alfred Deakin Medal from Deakin University, Australia, and has established himself as a leading researcher in distributed systems and cybersecurity with over 50 peer-reviewed publications in top-tier venues including IEEE S&P, ACM WWW, and IEEE Transactions. Dr. Xia's research spans critical areas at the intersection of computing and security: System Privacy and Security Distributed Systems and Edge Computing AI Privacy and Machine Learning Systems Sustainable Computing Cybersecurity and Privacy-Preserving Technologies His recent work demonstrates a clear trajectory toward developing practical privacy-preserving frameworks for emerging technologies, particularly in edge computing environments and large language models. Dr. Xia has made significant contributions to machine unlearning, secure data management in distributed systems, and energy-efficient edge computing solutions that balance performance with sustainability concerns. Dr. Xia has received notable recognition for his scholarly impact: World's Top 2% Scientists by Stanford University (2022-2024) Alfred Deakin Medal for PhD research excellence (2021) Teaching Excellence Award from Swinburne University of Technology (2021) As an active researcher, Dr. Xia currently leads multiple funded projects including an ARC Discovery Project grant worth over $500,000 for developing privacy-aware intelligent digital twins for secure critical infrastructures. He is open to supervising motivated PhD students with interests in system security and privacy, and distributed ML systems. Dr. Xia serves the academic community through editorial roles as Associate Editor for IEEE Transactions on Dependable and Secure Computing and as a Review Board Member for IEEE Transactions on Parallel and Distributed Systems, and regularly participates in program committees for major conferences including ACM WWW and IEEE ICDCS.

• Machine Learning
• Climate Science
• Energy Systems
• +۳ مورد دیگر

Sancho Salcedo Sanz is a Full Professor at the Universidad de Alcalá, affiliated with the Signal Theory and Communications Department and the GHEODE Research Group. His work focuses on applying machine learning and optimization techniques to energy systems, climate science, and environmental modeling. He holds PhDs from Universidad Complutense de Madrid (2019) and Universidad Carlos III de Madrid (2002). Key research interests include deep learning for energy price prediction, spatio-temporal climate analysis, and hybrid models for renewable energy forecasting. His GHEODE group develops optimization algorithms for network design and distributed systems. Recent publications highlight advancements in extreme weather prediction, smart grid optimization, and explainable AI for environmental monitoring. He has pioneered methodologies like Autoencoder-based flow analogues for heatwave reconstruction and multi-method ensembles for energy demand modeling. Labs/Teams: Leader of the GHEODE Group, specializing in modern heuristics and network design. Collaborates extensively on interdisciplinary projects combining AI with environmental and engineering applications.

• High-performance computing
• Simulation software development
• Parallel algorithms
• +۳ مورد دیگر

Michael Bader is a Professor in the Department of Computer Science at the Technical University of Munich (TUM), part of the TUM School of CIT. He leads the research group on hardware-aware algorithms and software for high-performance computing at the Leibniz Supercomputing Center. His work focuses on developing efficient algorithms and software for supercomputing platforms, particularly in geosciences and simulation of earthquakes and tsunamis. His research interests include high-performance computing, simulation software development (e.g., SeisSol and ExaHyPE), parallel numerical algorithms, adaptive mesh refinement, and large-scale geophysical simulations such as earthquake dynamics and tsunami modeling. He emphasizes optimizing algorithms for modern supercomputing architectures to handle complex computational challenges. Professor Bader has supervised numerous PhD students, including Lukas Krenz, Ravil Dorozhinskii, and Sebastian Wolf, among others. His research has been supported by grants from the EuroHPC JU, BMBF, DFG, and other institutions. Notable projects include ChEESE-2P for exascale computing in solid earth sciences and the targetDART project for adaptive task distribution on exascale systems. He is actively involved in teaching, offering courses such as Numerical Algorithms for High Performance Computing and Scientific Computing 1 . His group collaborates extensively with institutions like the Leibniz Supercomputing Center to advance computational methods for simulating natural disasters and geophysical phenomena.

Qin Luمشاهده پروفایل

استادیار

• Signal Processing
• Machine Learning
• Communications
• +۳ مورد دیگر

Qin Lu is an Assistant Professor at the University of Georgia's School of Electrical & Computer Engineering. Her research focuses on robust and adaptive Bayesian methods for decision-making using sequential/networked data, including online Gaussian processes, Bayesian optimization, reinforcement learning, and spatio-temporal inference over graphs. Applications span autonomous systems and mobile edge computing. Education details are not explicitly listed in the provided text. Her work emphasizes scalability and uncertainty quantification, with contributions to edge computing resource management, active learning strategies, and multi-agent systems in IoT. Recent publications highlight advancements in Gaussian process dynamical modeling, adaptive Bayesian optimization, and ensemble learning techniques for online systems. No scientific awards are mentioned in the provided materials. Advising and grants details are not provided, though her research indicates collaborations in autonomous systems, underwater acoustics, and sensor networks. She is affiliated with the Boyd GSRC building and reachable at Qin.Lu@uga.edu.