جست‌وجوی استاد و پژوهشگر

Geoffroy Peetersمشاهده پروفایل

استاد

• Audio Signal Processing
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Geoffroy Peeters is a Full Professor at Télécom Paris since 2018, affiliated with the Signal, Statistics and Learning (S2A) team within the Image, Data, Signal (IDS) department of the Laboratoire Traitement et Communication de l'Information (LTCI) . He received his PhD (2001) and Habilitation (2013) in audio signal processing from University Paris-VI . Education : PhD in Computer Science (2001), HDR (2013) from University Paris VI; Master in electronic engineering (1995) from Polytechnique Louvain-la-Neuve His research focuses on signal processing and machine learning (including deep learning) applied to audio and music data analysis , covering areas such as: Timbre description and audio feature extraction Singing voice detection and source separation Beat/downbeat/tempo estimation Chord/key/multi-pitch estimation Music structure/summary analysis Audio identification and auto-tagging Corpus creation and evaluation methodologies With over 200 publications and several patents, his work contributed to the ISO MPEG-7 audio standard . He has held leadership roles in academic conferences including: Co-general-chair: DAFx-2011, ISMIR-2018 Co-program-chair: ISMIR-2019 ISMIR Board member (2016-2017, 2018-2019) Elected ISMIR President (2022-2023) IEEE Task Force on Computational Audio Processing His teaching focuses on signal processing , deep learning , and music information retrieval through various projects and workshops.

Stephan Sigristمشاهده پروفایل

استاد

• Neuroscience
• Molecular Biology
• Biochemistry
• +۳ مورد دیگر

Stephan Sigrist is a Full Professor of Biochemistry at Freie Universität Berlin and Charité Universitätsmedizin Berlin since 2008, holding the prestigious Einstein Professorship from the Einstein Foundation Berlin (2014–present). His research focuses on molecular mechanisms of synaptic transmission, neuroplasticity, and aging using Drosophila models, with significant contributions to understanding active zone organization and neurotransmitter release. His academic background includes: Chemistry studies at Technische Universität Berlin (1986–1988) Biochemistry Diploma from Eberhard Karls Universität Tübingen (1988–1992) PhD in Biology from Universität Tübingen (1997) Habilitation in Biochemistry from Georg-August-Universität Göttingen (2005) Professor Sigrist's research centers on synaptic biology, investigating how proteins like Bruchpilot, Unc13, and RIM-binding protein organize release sites for precise neurotransmission. His work reveals autophagy's role in protecting synapses from aging and links polyamine metabolism to memory preservation. Using super-resolution microscopy and genetic approaches, his lab dissects molecular dynamics at nanometer scales to understand neurodegeneration mechanisms. Analysis of his 10 most recent publications shows a clear trajectory from foundational synaptic machinery studies (2006–2011) toward aging-related neuroprotection (2013–2019), with consistent emphasis on Drosophila models and increasing integration of autophagy pathways. His work bridges molecular neuroscience with translational implications for age-related cognitive decline. His major scientific recognitions include: Einstein Professorship of the Einstein Foundation Berlin Best Habilitation Award from University Medical Center Göttingen EMBO Long-term Fellowship HFSP Long-term Fellowship Professor Sigrist leads a research group within the SFB/TRR186 consortium (Heidelberg-Berlin Molecular Switches), directing investigations into spatiotemporal control of cellular signaling. His laboratory at Takustraße 6 employs advanced imaging, electrophysiology, and behavioral assays to study synaptic protein dynamics in live organisms.

Stefano Scanzioمشاهده پروفایل

پژوهشگر

• Industrial Communication Systems
• Wireless Networks
• Real-Time Networks
• +۷ مورد دیگر

Stefano Scanzio is a Senior Researcher at the National Research Council of Italy (CNR-IEIIT) and teaches computer science courses at Politecnico di Torino . With over 60 publications in wireless networks, real-time communication, and industrial IoT, he serves as Associate Editor for Ad Hoc Networks , IEEE Access , and Electronics journals. Ph.D. in Computer Science (Politecnico di Torino, 2008) Laurea in Computer Science (Politecnico di Torino, 2004) His research focuses on industrial wireless networks , particularly Wi-Fi and IEEE 802.15.4 TSCH, with emphasis on: Energy-saving mechanisms in wireless sensor networks Predictive models for Wi-Fi channel quality Reliable communication through seamless redundancy Machine learning integration for network optimization Clock synchronization under non-Gaussian noise Recent publications analyze Wi-Fi 7's multi-link operation, executable QR codes for IoT, and AI-driven network self-configuration. His work has been recognized with multiple best paper awards .

Simone Campanoniمشاهده پروفایل

دانشیار

• Compilers
• Computer Architecture
• Runtime Systems
• +۵ مورد دیگر

Simone Campanoni is an Associate Professor in the Department of Computer Science at Northwestern University, with a courtesy appointment in Electrical and Computer Engineering. His research focuses on compilers, computer architecture, runtime systems, and parallel computing. He leads the research lab, emphasizing co-design approaches to address hardware and application trends. Education: Ph.D., M.S., and B.S. in Information Technologies and Computer Engineering from Politecnico di Milano. Research Interests: Compilers, memory-centric optimizations, parallelization of sequential code, hardware-software co-design, and resilient architectures. His work includes the NOELLE compiler framework, LLVM extensions, and energy-efficient parallelization techniques. Recent Work: Contributions include energy-efficient bitwidth speculation, GPU scheduling (GhOST), and compiler-based memory protection (CAMP). His projects often involve collaborations with industry (e.g., Google) and academic partners. Awards: Best Teacher Award (2018), HiPEAC awards (2014, 2012), and multiple top-tier conference recognitions. His research has been funded by NSF, DOE, and industry grants totaling over $10 million. Grants & Labs: Leads NSF-funded projects on parallelism, compiler-architecture co-design, and neural network training. His lab develops tools like NOELLE and GINO, open-sourced to advance compiler research. Team & Students: Advises PhD and Master’s students in parallel computing, memory systems, and compiler design. Notable advisees include Riley Boksenbaum, Tommy McMichen, and Enrico Deiana.

César A. Uribeمشاهده پروفایل

استادیار

• Distributed Optimization
• Decentralized Control
• Algorithm Analysis
• +۵ مورد دیگر

César A. Uribe is the Louis Owen Assistant Professor in the Department of Electrical and Computer Engineering at Rice University, part of the George R. Brown School of Engineering. He also serves as a Visiting Professor at the Moscow Institute of Physics and Technology (MIPT). His research focuses on distributed optimization, decentralized control, algorithm analysis, and computational optimal transport, with an emphasis on fundamental limits of distributed optimization and scalable algorithms for networked systems. Uribe holds a BSc in Electronic Engineering from Universidad de Antioquia (2010), MSc in Systems and Control from Delft University of Technology (2013), an MSc in Applied Mathematics from the University of Illinois at Urbana-Champaign (2016), and a PhD in Electrical and Computer Engineering from UIUC (2018). He was a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) before joining Rice in 2021. His research explores distributed learning algorithms, non-asymptotic analysis of social learning, and optimal transport theory. Key themes include geometric convergence rates in distributed inference, resilient optimization under adversarial conditions, and applications in networked systems such as epidemics and control systems. Uribe has received numerous awards, including the 2020 INFORMS DEI Ambassadors Program Award and the 2019 Yahoo! FREP Award. His work spans over 50 peer-reviewed publications and includes collaborations on distributed algorithms for machine learning, signal processing, and control theory. He actively mentors students in PhD and postdoctoral programs, emphasizing diversity and inclusion in STEM. Current research initiatives include optimal transport methods for network regression, competitive virus spread models over hypergraphs, and PID-based neural network architectures for adaptive control.

• Computer Engineering
• Embedded Systems
• Signal Processing
• +۲ مورد دیگر

Joseph Young is an Assistant Teaching Professor in the Electrical and Computer Engineering (ECE) department at Rice University, serving as Director of the Professional Master's Program. He joined Rice in 2021, focusing on overseeing capstone projects and advising MECE students. His research interests span computer engineering, embedded systems, signal processing, neuroengineering, and information theory. Education: B.S. in Electrical Engineering, North Carolina State University (2015) M.S. in Electrical and Computer Engineering, Rice University (2018) Ph.D. in Electrical and Computer Engineering, Rice University (2020) Research focuses on applying information theory to neural data analysis, embedded systems design, and signal processing. Recent work includes validation of passive TV viewing measurement systems, functional connectivity analyses in neuroscience, and LED device optimization. Notable awards include the NSF IGERT fellowship (2017-2019), Rice ECE Distinguished Student Service Award (2017), and an undergraduate research grant from NC State (2015). He advises the MECE capstone program and has held internships at RTI International and Sandia National Laboratories. His professional roles emphasize bridging academic research with practical applications in engineering education and industry collaboration.

Paul Soمشاهده پروفایل

استاد

• Dynamical Systems
• Neuroscience
• Physics
• +۱۱ مورد دیگر

Paul So is a Professor at George Mason University affiliated with the Department of Physics & Astronomy in the College of Science and the Neuroscience Program . He specializes in applying dynamical systems analysis and statistical physics to understand neuronal dynamics, information processing in the brain, and pathological conditions like epilepsy and Parkinson’s disease. Education : PhD in Nonlinear Dynamics from University of Maryland at College Park (1995) BS in Physics & Math from Harvey Mudd College (1988) His research focuses on high-dimensional chaotic systems, quantum chaos, synchronization, coherence, and unstable periodic orbit reconstruction for neural networks. He has published extensively on topics such as the Kuramoto model, chaotic synchronization, and dynamical interdependence in neural ensembles. Professor So has over 20 years of experience teaching introductory and advanced physics courses and advising graduate students at the university. His work bridges theoretical physics and neuroscience to uncover mechanisms underlying brain function and disease.

Se Young Yoonمشاهده پروفایل

دانشیار

• Control Systems
• Active Magnetic Bearings
• Multi-Agent Systems
• +۵ مورد دیگر

Se Young Yoon is an Associate Professor in the Department of Electrical and Computer Engineering at the University of New Hampshire's College of Engineering and Physical Sciences. He earned his B.S. and M.S. in System Science and Mathematics from Washington University in St. Louis (2005) and a Ph.D. in Electrical Engineering from the University of Virginia (2011). His research focuses on control systems, active magnetic bearings, multi-agent systems, and nonlinear dynamics, with applications in robotics, energy systems, and mechatronics. He is a member of IEEE, IEEE-CSS, Tau Beta Pi, and Kappa Mu Epsilon. Education: Ph.D., Engineering, University of Virginia (2011) M.S., Systems Science and Theory, Washington University (2005) B.S., Systems Science and Theory, Washington University (2005) Research Interests: Robust control of systems with delays and uncertainties Cooperative control of multi-agent systems Active magnetic bearing (AMB) systems for turbomachinery Surge control in compressors Data-driven and model-based control algorithms His recent work emphasizes stabilization of dynamic systems with uncertain equilibria and distributed control under communication constraints. He has collaborated on experimental platforms like rotor-AMB test rigs for energy storage flywheel emulation and compressor surge suppression. Labs/Teams: Previously affiliated with the Rotating Machinery and Controls (ROMAC) Lab at University of Virginia.

Manuel E. Acacioمشاهده پروفایل

استاد

• Computer Architecture
• Hardware Transactional Memory
• Chip Multiprocessors
• +۱ مورد دیگر

Manuel E. Acacio is Full Professor in the Computer Engineering Department at the University of Murcia, Spain, leading the Computer Architecture & Parallel Systems (CAPS) research group. His research advances multiprocessor systems through innovations in cache coherence protocols, hardware transactional memory, and synchronization mechanisms. Recent work explores efficient concurrency management, speculative execution, and hardware support for deep neural networks.

Celia Clarkeمشاهده پروفایل

مدرس

• Physiology
• Gait Rehabilitation
• Spinal Cord Injury
• +۳ مورد دیگر

Dr. Celia Clarke is a Lecturer in Physiology at the University of East Anglia's School of Health Sciences. She holds a Ph.D. in Bioengineering (2010), an MRes in Evidence of Motor Unit Synchronization (2005), and a BSc in Applied Human Physiology (1999). As module coordinator for Anatomy and Physiology for Trainee Nursing Associates, she teaches physiology and pathophysiology across undergraduate, postgraduate, and post-registration programs. Her research focuses on gait rehabilitation following spinal cord injury and acquired brain injury, with emphasis on developing quantitative assessment tools and technology-assisted interventions. She has investigated concussion assessment in athletes, virtual-reality based rehabilitation, and biomechanical factors in falls among arthritis patients. Current projects include enhancing walking recovery through telerehabilitation devices and improving physiological outcome measures. Collaborative grants include NIHR-funded projects on concussion assessment prototypes and telerehabilitation devices, alongside industry partnerships with Norwich City Football Club. She co-led the STaRLab Modernization Project supported by the Wolfson Foundation.

• Computer Vision
• Deep Learning
• Cybersecurity
• +۳ مورد دیگر

Marcella ASTRID is a Researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), specifically within the CVI2 department. Her research focuses on advanced machine learning techniques applied to cybersecurity and computer vision challenges, particularly in the detection of synthetic media (deepfakes) and anomaly detection systems. She specializes in developing robust models that address vulnerabilities in adversarial scenarios and improve generalizability across diverse datasets. Key research areas include deepfake detection through spatio-temporal analysis, anomaly detection via novel training paradigms, and efficient neural network compression for resource-constrained environments. Her work bridges theoretical advancements in machine learning with practical applications in surveillance systems, autonomous robotics, and cybersecurity infrastructure. Recent contributions emphasize leveraging autoencoder weaknesses, pseudo anomaly generation, and localized attention mechanisms to enhance detection accuracy and model robustness. Marcella’s research also explores cross-modal learning (audio-visual synchronization) and semi-supervised methodologies to tackle data scarcity issues in critical domains like battery thermal imaging and multi-camera traffic classification. She actively publishes in top-tier venues and collaborates on interdisciplinary projects addressing real-world security and reliability challenges.

Lu Pengمشاهده پروفایل

استاد

• Computer Architecture
• GPU Computing
• Quantum Computing
• +۲ مورد دیگر

Lu Peng holds the Yahoo! Founder Chair in Science and Engineering at Tulane University. His research spans computer architecture with focus on GPU optimization, quantum computing, and blockchain acceleration. Peng develops specialized hardware architectures for emerging computing paradigms, including quantum processors, blockchain processing units (BPUs), and efficient GPU memory systems. Recent innovations include GeauxTrace blockchain contact tracing and high-throughput GPU database indexing techniques. Awarded the ORAU Powe Award (2007) and IEEE conference best papers (2019, 2001), Peng maintains active research in computer systems design. He teaches graduate and undergraduate courses in computer architecture while directing research on next-generation computing paradigms.

• Complex Systems
• Statistical Physics
• Network Theory
• +۶ مورد دیگر

Hernan Makse is a Distinguished Professor of Physics at the City College of New York (CCNY) , affiliated with the Levich Institute and Department of Physics. He directs the Complex Networks and Data Science Lab , pioneering research in statistical physics approaches to complex systems. His work bridges brain networks, biological synchronization, social media dynamics, and granular materials. Research Focus: Makse's lab develops emergent laws for complex systems using statistical mechanics, network theory, and machine learning. Key areas include: Brain network optimization via Collective Influence algorithms for neural integration and control Symmetry fibrations in biological systems (e.g., C. elegans connectome) COVID-19 contact tracing through superspreader k-core analysis Election/fake news forecasting via Twitter data mining Granular matter phase transitions and jamming physics Publication Trends: His recent articles focus on network-based prediction systems (elections, pandemics), neural synchronization mechanisms, and AI-driven solutions for biological and social systems. Earlier foundational work established fractality and renormalization in complex networks. Awards & Honors: Elected Member, Academia Brasileira de Ciencias (2023) Academic Leadership: He mentors PhD students in physics/data science and collaborates with Memorial Sloan Kettering (brain tumors), University of Vienna (neural symmetry), and Brazilian health agencies (COVID apps). Grants include NSF-CRCNS and NIH Brain Initiative funding. Lab Initiatives: The lab develops open-source tools for COVID-19 contact tracing and neural symmetry analysis. Current projects include MRI-based breast cancer prediction and neurotransmitter dynamics modeling.

Poras T. Balsaraمشاهده پروفایل

استاد

• Power Electronics
• Energy Efficient Circuits and Systems
• VLSI Design
• +۵ مورد دیگر

Poras T. Balsara is a Professor and Vice Dean in the Electrical and Computer Engineering Department at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His research focuses on power electronics, energy-efficient circuits, VLSI design, and renewable energy systems. He co-directs the Renewable Energy and Vehicular Technology (REVT) and Integrated Design, Engineering, and Algorithmics (IDEA) labs. Balsara holds an IEEE Fellowship and has authored over 200 publications in top-tier journals and conferences. Education: Ph.D. in Electrical Engineering, Penn State University, 1989 M.S. in Electrical Engineering, Penn State University, 1985 B.E. (Electrical) with distinction, University of Bombay, India, 1983 L.E.E. (Electronics), Victoria Jubilee Technical Institute, India, 1980 Research Highlights: Balsara's work spans power electronics, wireless charging systems, and energy-efficient architectures. Notable contributions include breakthroughs in dynamic wireless power transfer (DWPT) for electric vehicles, high-efficiency DC-DC converters, and digital control systems for renewable energy integration. His labs pioneer innovations in smart energy systems and VLSI design. Awards & Recognition: IEEE Fellow (2011) Member of Phi Kappa Phi and Tau Beta Pi honor societies Advising & Grants: Balsara has advised over 50 graduate students, many of whom have contributed to impactful research in power systems and VLSI. His funding includes grants from NSF, industry partnerships, and a startup (Cirasys Corporation) spun off from his research. Labs & Teams: REVT Lab: Focuses on vehicular technologies and renewable energy systems. IDEA Lab: Develops advanced computational and algorithmic solutions for engineering challenges.

• Applied Mathematics
• Stochastic Processes
• Neural Networks
• +۲ مورد دیگر

Pedro Vilanova-Guerra is a Teaching Assistant Professor in the Department of Mathematical Sciences at the Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology. Located in North Building 222, he can be reached at (201) 216-3771 or pguerra@stevens.edu. His research focuses on mathematical modeling of complex systems including neural networks and biochemical oscillators, employing techniques from stochastic processes and kinetic theory. His recent publications investigate population dynamics in neural networks and synchronization phenomena in stochastic biochemical systems. Professional affiliations include membership in the AMS Mathematical Reviews. His teaching portfolio spans probability, statistics, optimization, and numerical methods courses.