جست‌وجوی استاد و پژوهشگر

Peter Schneider-Kampمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Peter Schneider-Kamp is a Professor of Data Science at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on Artificial Intelligence, Machine Learning, Privacy-Preserving Techniques, and Algorithms. He has led projects such as the Danish Foundation Models initiative and the PREPARE cardiovascular disease project. His work spans synthetic data frameworks (e.g., Syntheval), quantization-aware neural networks (BitNet), and autonomous drone systems (Drones4Safety). He has been honored with awards including the Researcher Award 2014 and the Friedrich-Wilhelm-Preis 2009. Key research interests include optimizing sorting networks, termination analysis, and UAV-based infrastructure inspection. He has contributed to over 112 publications and actively participates in academic activities like organizing the 13th ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming. Schneider-Kamp also engages in educational roles, teaching courses such as DS806 and DM564 on database systems. His projects highlight interdisciplinary impact, including cardiovascular disease risk estimation (PREPARE) and AI-driven health advice analysis. He collaborates internationally, with recent work featured in venues like the International Conference on Agents and Artificial Intelligence.

• Coding Theory
• Information Theory
• Forward Error Correction (FEC)
• +۵ مورد دیگر

Mohsen Moradi is a Postdoctoral Research Associate in the Department of Electrical and Computer Engineering at Northeastern University. His research focuses on advanced coding theory, particularly in the development of Polarization-Adjusted Convolutional (PAC) codes, forward error correction, and decoding algorithms. He explores intersections between reinforcement learning and signal processing to enhance code performance. His work emphasizes practical applications such as bounded-complexity sequential decoding, tree pruning techniques, and Monte-Carlo-based code construction. Key contributions include optimizing PAC codes through search-constrained algorithms and analyzing their performance alongside Reed-Solomon and Reed-Muller codes. Recent publications (2020–2025) highlight trends in PAC/SC/Polar code design, with a focus on computational efficiency, metric optimization, and algorithmic innovation. His research bridges theoretical insights with real-world implementation challenges in communication systems. Moradi’s explorations extend to hybrid coding systems (e.g., concatenated codes) and stochastic methods like guessing-based decoding. He collaborates with academic and industry partners to advance next-generation communication protocols requiring robust error correction at high data rates.

• Machine Learning
• Neural Networks
• Quantum Computing
• +۳ مورد دیگر

Zheng Zhang is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. His research focuses on neural networks, quantum computing, uncertainty quantification, and optimization, with particular emphasis on tensor networks, low-rank compression methods, and hardware-efficient machine learning systems. He leads efforts in developing memory-efficient training algorithms for large language models (LLMs), tensorized optical networks, and physics-informed neural PDE solvers. Key contributions include FLAT-LLM for LLM compression, FETTA hardware accelerators, and DeepOHeat for thermal simulation in 3D-IC design. His work spans cross-disciplinary areas such as quantum-inspired algorithms, stochastic control, and yield-aware optimization of photonic ICs. He holds a faculty position in the College of Engineering and is affiliated with the ECE department. Research trends in his 2025 publications emphasize scalable training techniques for transformers, zeroth-order optimization methods, and optical computing integration. His work consistently addresses computational efficiency, memory constraints, and hardware acceleration across domains like AI, quantum computing, and electronic design automation. Notable grants and lab affiliations include projects on FPGA-based neural training, quantum circuit simulation, and tensor-compressed PDE solvers. He advises on edge computing, neuromorphic systems, and uncertainty-aware design tools for integrated circuits.

Yuchen Zhaoمشاهده پروفایل

مدرس

• Privacy-Preserving Machine Learning
• Edge Computing
• Privacy Metrics
• +۴ مورد دیگر

Dr. Yuchen Zhao is a Lecturer in the Cyber Security and Privacy Research Group at the University of York's Department of Computer Science. His research focuses on privacy-preserving machine learning, edge computing, and healthcare technology, particularly in smart healthcare systems using federated learning. He holds a Ph.D. from the University of St Andrews and has worked at institutions such as Imperial College London and the University of Southampton. Education: Ph.D. in Computer Science, University of St Andrews (2017) M.Sc. in Information Security, Wuhan University (2013) B.Eng in Information Security, Huazhong University of Science and Technology (2011) His research interests include user-centric systems powered by personal data and privacy protection mechanisms in IoT and edge environments. Notable projects include AI Testing Innovation (Innovate UK) and Building a Minimal Viable Digital Identity (EPSRC SPRITE+). He collaborates with interdisciplinary teams to advance usable privacy and security in healthcare and smart systems. Dr. Zhao is actively recruiting PhD students interested in privacy-preserving machine learning, edge computing, and usable privacy. His work bridges theoretical advancements with practical implementations, aiming to reform real-world systems through secure, privacy-aware technologies.

Sofia Fatigoniمشاهده پروفایل

پژوهشگر

• Astrophysics
• Cosmology
• Cosmic Microwave Background (CMB) Experiments
• +۳ مورد دیگر

Sofia Fatigoni is a Researcher at the California Institute of Technology (Caltech), affiliated with the Division of Physics, Mathematics, and Astronomy and the Department of Physics. Her work focuses on experimental cosmology and instrumentation, particularly in the development and optimization of detectors for the BICEP/Keck CMB polarization experiments. She contributes to advancing next-generation CMB observatories, such as the BICEP Array, and addresses technical challenges in cryogenics, calibration, and data analysis. Her research aims to constrain inflationary models through precise measurements of cosmic microwave background polarization. Key interests include polarization calibration techniques, detector module design, and suppression of systematic errors in CMB observations. She collaborates on projects involving vacuum window materials, multiplexing strategies, and cryogenic systems for low-temperature environments. Fatigoni’s work also explores astrophysical foreground characterization, such as dust polarization and gravitational lensing effects, to improve the accuracy of cosmological measurements.

Feng Wangمشاهده پروفایل

استادیار

• Next Generation Internet
• Network Performance Measurement
• Big Data Verification
• +۶ مورد دیگر

Dr. Feng Wang is an Assistant Professor in the School of Engineering at Liberty University, specializing in network reliability, interdomain routing, and software-defined networking. His research addresses challenges in next-generation internet architectures, network performance measurement, and secure IoT device management. Education: B.E. in Electrical and Computer Engineering from Zhejiang University (China) M.S. in Electrical and Computer Engineering from Yanshan University (China) Ph.D. in Electrical and Computer Engineering from the University of Massachusetts, Amherst Research focuses on: - Detecting transient routing failures and improving network performance through real-time diagnostic systems (e.g., collaboration with AT&T). - Designing scalable addressing schemes for IoT and wireless sensor networks. - Developing intrusion detection systems (e.g., MOCA framework) and network anomaly mitigation techniques. His publications (2015-2023) emphasize network security, routing protocol optimization, and scalable internet architectures. Notable work includes BGP rerouting solutions, real-time routing failure diagnosis, and variable-length addressing for 6LoWPAN. No scientific awards were explicitly listed in the provided texts. Collaborations include Agilent, AT&T, and Intel, focusing on practical network reliability and security solutions. Dr. Wang has advised no listed students or managed grants in the provided texts. His research extends to lab implementations of lightweight routers (SoC-based) and stability-aware protocols for RPL networks.

Johannes Bauerمشاهده پروفایل

استاد

• Applied Cryptography
• Embedded Systems Security
• Vulnerability Assessment
• +۷ مورد دیگر

Prof. Johannes Bauer is a Professor of Computer Science at Cooperative State University Mannheim (DHBW Mannheim) since 2022. He holds a Ph.D. in Computer Science from Friedrich-Alexander-Universität Erlangen-Nürnberg (2016), focusing on cost-effective security measures for embedded systems. His research spans applied cryptography, vulnerability assessment, side-channel analysis, and hardware security modules (HSMs). Professional Experience: 2022–Present: Professor at DHBW Mannheim 2018–2021: Principal Designated Engineer for cybersecurity standards at Underwriters Laboratories 2016–2018: Product Security Officer at Robert Bosch Smart Home GmbH Research Interests: Embedded systems security Physical-layer attacks and covert channels Cold-boot attacks and memory forensics Hardware-software co-design for security Smart grid cybersecurity Publications Highlight: His 2016 Ph.D. thesis introduced novel methods for timing side-channel detection and anti-EMI feature abuse. Recent work includes analyzing demand response capabilities in smart grids (2019). Labs/Teams: Active in cybersecurity research groups, contributing to standards like ETSI EN 303 645 and UL MCV 1376. Collaborates with industry partners on embedded system hardening.

• Computer Vision
• Machine Learning
• Image Processing
• +۵ مورد دیگر

Dr. Xin Lin is a Professor at the School of Computer Science and Technology, University of Science and Technology of China in Hefei. With an extensive publication record spanning computer vision, machine learning, and artificial intelligence, Dr. Lin leads a research group focused on solving challenging problems in image processing, robotics, and wireless communications. His work bridges theoretical advancements with practical applications across healthcare, autonomous systems, and industrial manufacturing. Dr. Lin's research interests encompass computer vision, machine learning, image processing, and artificial intelligence, with particular expertise in image restoration, 3D object detection, and human pose estimation. His laboratory develops innovative approaches to handle multiple image degradations simultaneously and create lightweight, efficient vision systems suitable for real-world deployment. The research demonstrates strong interdisciplinary connections, applying computer vision techniques to medical imaging, satellite communications, and industrial IoT applications. Analysis of Dr. Lin's recent publications reveals a strong focus on multi-task learning approaches that address multiple image degradation problems simultaneously. His work shows increasing sophistication in handling complex real-world scenarios, from low-light conditions to rain interference, while maintaining computational efficiency. The research trajectory demonstrates a clear path from fundamental image processing techniques to practical applications in autonomous driving, healthcare, and industrial systems. Dr. Lin has received recognition for his contributions to the field through numerous publications in top-tier venues including CVPR, IEEE Transactions, and ACL. His work on image restoration, particularly the Dual Degradation Representation framework, has gained significant attention in the computer vision community. Dr. Lin actively supervises graduate students and collaborates with researchers worldwide. His laboratory works on cutting-edge projects involving digital twins for manufacturing, satellite communications, and medical imaging applications. Current research directions include developing more robust and efficient models for real-world deployment scenarios, with particular attention to resource-constrained environments.

Robert Calderbankمشاهده پروفایل

استاد

• Information Theory
• Quantum Computing
• Wireless Communications
• +۶ مورد دیگر

Robert Calderbank is a distinguished academic and researcher at Duke University, holding professorships in Computer Science, Electrical and Computer Engineering, and Mathematics. He serves as Director of the Information Initiative at Duke and is affiliated with the Duke Quantum Center. His interdisciplinary work bridges information theory, quantum computing, and biomedical applications. Education: Ph.D. from California Institute of Technology (1980) Previous Institution: Princeton University (Distinguished Professor) Calderbank's research spans wireless communications, distributed storage systems, machine learning, and quantum information theory. Recent work focuses on two-dimensional magnetic recording, quantum error correction, and biomedical imaging with pump-probe microscopy. His publications demonstrate sustained innovation in constrained coding, matrix completion, and subspace classification. Scientific contributions include grants from the National Science Foundation and collaborative projects with the University of Maryland. Awards include Fellowships from the Royal Society, IEEE, and AAAS. His teaching and mentorship at Duke and Princeton have shaped next-generation signal processing and computer science research.

Ciarán Doneganمشاهده پروفایل

پژوهشگر ارشد

• Regulatory Genomics
• Explainable AI
• Geometric Deep Learning
• +۱ مورد دیگر

Ciarán Donegan is a Research Fellow in the Machine Learning group at the Technical University of Berlin and BIFOLD (Berlin Institute for the Foundations of Learning and Data), with a concurrent guest researcher position in the Ohler Lab at the Max Delbrück Center for Molecular Medicine. His primary research focuses on applying deep learning methodologies to elucidate gene regulation mechanisms within molecular biology. Education: M.A.I. in Electronic & Computer Engineering from Trinity College Dublin (2021) B.A.I. in Electronic & Computer Engineering from Trinity College Dublin (2020) His research program integrates Regulatory Genomics with cutting-edge machine learning techniques, emphasizing Explainable AI frameworks and Geometric Deep Learning architectures. This interdisciplinary approach targets complex biological systems, particularly in modeling transcriptional regulation and epigenetic modifications through neural network interpretability. Applications extend to disease mechanism analysis and therapeutic target identification in life sciences contexts. Publication trends reveal dual-domain expertise: the 2022 sports broadcasting paper demonstrates real-time computer vision capabilities for automated production systems, while the 2021 VPU-specific CNN research pioneers hardware-aware neural architecture search. These works collectively underscore a methodological thread in optimizing deep learning for constrained environments—whether computational (edge devices) or operational (live sports)—while maintaining biological research as his core focus area. No scientific awards were documented in the source materials. Advisory activities and grant funding details remain unspecified in available records. He operates within collaborative research ecosystems spanning TU Berlin's Machine Learning group, BIFOLD's interdisciplinary data science initiatives, and the Ohler Lab's genomic regulatory studies at the Max Delbrück Center, facilitating cross-institutional innovation in AI-driven life sciences.

Aude Olivaمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Generative Models
• +۵ مورد دیگر

Aude Oliva serves as MIT director of the MIT-IBM Watson AI Lab and director of strategic industry engagement at the MIT Schwarzman College of Computing. As a Senior Research Scientist at MIT CSAIL, she leads the Computational Perception and Cognition group, driving interdisciplinary research at the intersection of human intelligence and artificial systems. Her roles position her at the forefront of translating academic AI research into real-world applications through major industry partnerships. Dr. Oliva earned her MS and PhD in cognitive science from Institut National Polytechnique de Grenoble, France, establishing her foundation in human perception and computational modeling. Her research integrates computer vision, deep learning, and cognitive neuroscience to understand visual information processing in both biological and artificial systems. She develops computational models that mimic human visual recognition while creating AI systems capable of compositional reasoning and efficient video understanding. Current work emphasizes neuroscience-inspired architectures, resource-efficient deep learning, and multimodal representation learning, with applications spanning healthcare, robotics, and human-computer interaction. Her cross-disciplinary approach uniquely bridges theoretical neuroscience with practical AI development. Analysis of recent publications reveals a clear trajectory toward tighter integration of neuroscience and AI, particularly through brain imaging datasets like BOLD Moments. Her group consistently advances efficient deep learning techniques (Trans-LoRA, VA-RED²) while exploring fundamental questions in visual cognition through projects like the Algonauts Challenge. The work demonstrates increasing industry relevance with strong representation in NeurIPS and Nature Communications. Her major recognitions include: NSF Career Award in computational neuroscience Guggenheim fellowship in computer science Vannevar Bush Faculty Fellowship in cognitive neuroscience As director of the $240M MIT-IBM Watson AI Lab, Dr. Oliva oversees substantial research funding while advising graduate students through MIT's EECS department. Her lab benefits from unique industry-academic synergy, with students gaining access to IBM resources and real-world deployment challenges. The collaborative environment fosters innovation in efficient AI systems with tangible societal impact. The Computational Perception and Cognition group operates as a dynamic hub where computer scientists, neuroscientists, and cognitive scientists collaborate on fundamental questions of intelligence. Current projects focus on making AI systems more human-like in visual reasoning while ensuring computational efficiency for real-world deployment, leveraging the unique resources of the MIT-IBM partnership.

Jose A. Boludaمشاهده پروفایل

دانشیار

• Computer Vision
• Hardware Design
• Image Processing
• +۴ مورد دیگر

Dr. Jose A. Boluda is an Associate Professor in the Department of Computer Engineering at the University of Valencia's School of Engineering. He holds a B.S. in Physics (Electricity, Electronics and Computer Science, 1992) and a Ph.D. (2000) from the same institution. With 6 teaching quinquenios and 4 research sexenios, his academic career spans over two decades. His research focuses on: Computer vision hardware design Smart vision sensors and neuromorphic computing Reconfigurable architectures (FPGA) High-speed motion analysis and change-driven processing Biomimetic visual sensing strategies He has authored over 50 international publications and participated in 15 research projects. His publications predominantly explore: Event-based vision sensors Hardware-accelerated image processing Neuromorphic engineering applications Real-time systems for robotics and 3D scanning Low-power circuit design for computer vision Awards include: Fernando Sapiña Award 2023 for Valencian teaching materials Fernando Sapiña Award 2021 for English teaching materials Fernando Sapiña Award 2021 for Valencian teaching materials He has directed 2 doctoral theses and supervised multiple master's projects. Research includes international collaborations at the University of Virginia, University of Macedonia, and Universitat Jaume I. His lab focuses on developing novel vision sensors and processing architectures.

Dan Fuمشاهده پروفایل

استادیار

• Machine Learning
• Computer Systems
• Efficient Algorithms
• +۳ مورد دیگر

Dan Fu is an Assistant Professor at the University of California, San Diego (UCSD) in the Computer Science and Engineering Department and a Distinguished Research Scientist at Together AI. He leads the SandyResearch Lab and is affiliated with the MLSys group, focusing on making machine learning models faster and more efficient through hardware-aware algorithms and subquadratic architectures. Research interests include: Efficient ML architectures (Chipmunk, Hyena, Monarch Mixer) Hardware-aware systems algorithms (ThunderKittens, FlashAttention) Long-sequence modeling and GPU optimization His recent work spans training-free Transformer acceleration (Chipmunk), convolutional language models (Hyena), and hardware-aware attention optimizations (FlashAttention). Projects are deployed in production at Together AI and integrated with frameworks like PyTorch. Scientific awards include: NDSEG Fellowship (2025) Best Paper at ICML Hardware Aware Workshop (2022) Best Student Paper Runner Up at UAI (2022) Best Poster at ENLSP Workshop (NeurIPS 2023) He contributes to open-source projects (Safari repository, FlyingSquid) and teaches courses on machine learning systems (Stanford CS 324/528, Harvard CS 61/152).

David Lowenthalمشاهده پروفایل

استاد

• Parallel and distributed computing
• Operating systems
• Run-time systems

David Lowenthal is a Professor at the University of Arizona specializing in parallel and distributed computing, operating systems, and run-time systems, with his office located in GS 705. His research directly addresses critical challenges in modern high-performance computing infrastructure. His educational background includes: Ph.D. in Computer Science from the University of Arizona (1996) Professor Lowenthal's research centers on optimizing large-scale computing systems through innovative scheduling algorithms, power management techniques, and network optimization strategies. Key contributions include mitigating inter-job interference in cluster environments, developing quality-of-service mechanisms for MPI applications, and pioneering coscheduling approaches like the Jigsaw scheduler. His work bridges theoretical computer science with practical system implementation to enhance resource utilization under power constraints. Analysis of his 15 most recent publications (2016-2023) reveals three dominant research thrusts: intelligent job scheduling (particularly coscheduling and backfilling), power-aware computing for energy-constrained HPC systems, and network performance optimization in dragonfly/fat-tree topologies. These publications consistently demonstrate collaborative NSF-funded research with emphasis on real-world system implementation and performance evaluation. No scientific awards were documented in the provided text. While no student advisees were listed, his research program has secured substantial NSF funding through collaborative grants including "Collaborative Research: SHF: Medium: Co-Optimizing Computation and Data Transformations for Sparse Tensors" (2022) and "Collaborative Research: OAC Core: Improving Utilization of High-Performance Computing Systems via Intelligent Co-scheduling" (2021), indicating active grant management and interdisciplinary collaboration. No specific laboratories or research teams were mentioned in the source material.

Dimitrios Soudrisمشاهده پروفایل

استاد

• Computer Engineering
• Embedded Systems
• Reconfigurable Architectures
• +۵ مورد دیگر

Dimitrios Soudris is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), leading the Microprocessor and Digital Systems Lab (MicroLab). Previously, he served as Lecturer, Assistant, and Associate Professor at Democritus University of Thrace from 1995 to 2008. Diploma in Electrical Engineering (1987), University of Patras PhD in Electrical Engineering (1992), University of Patras His research focuses on Embedded Systems , Reconfigurable Architectures (FPGAs) , Hardware Accelerators for data centers/space/cloud, Edge Computing , and Low Power VLSI Design . Recent publications highlight trends in: AI/ML acceleration for edge and space applications Secure FPGA architectures for 6G networks Energy-efficient heterogeneous memory systems Approximate computing techniques Transformer optimization for low-power contexts Scientific Awards: INTEL and IBM awards (project LPGD #25256) HiPEAC, DAC, and ISCA awards (2010–2024) XILINX Open Hardware Design Contest (2017, 2019, 2021) He has coordinated >70 R&D projects funded by the European Commission, ENIAC-JU, ESA, and industry partners. As Associate Editor of ACM TODAES and conference chair (PATMOS, VLSI-SOC), he contributes to academic leadership. His lab, MicroLab, specializes in hardware-software co-design for emerging computing paradigms.