جستجوی استاد راهنمای دانشگاهی

Venceslav Kafedzhiskiمشاهده پروفایل

استاد

• Wireless Communications
• Signal Processing
• Information Theory
• +۶ مورد دیگر

Dr. Venceslav Kafedzhiski is a Professor at the Department of Telecommunications, Faculty of Electrical Engineering and Information Technologies (FEIT) at the Ss. Cyril and Methodius University in Skopje. He holds a Ph.D. from Arizona State University (2000) and has held academic positions since 1983, progressing through ranks including Assistant Professor (2001), Associate Professor (2005), and Full Professor (2010). His roles include coordinating Master's programs in Wireless and Mobile Communications and Wireless Systems, Services, and Applications, and leading the Signal Processing in Telecommunications Laboratory. Education: Bachelor's and Master's in Electrical Engineering from FEIT (1982, 1989) Ph.D. in Electrical Engineering from Arizona State University (2000) Research focuses on wireless communications, signal processing, information theory, MIMO/OFDM systems, and compressed sensing. He has authored over 80 publications in IEEE journals and conferences. Key contributions include work on channel estimation, radar systems, and telecommunication standards (e.g., WiMAX, LTE, 5G). He has led and participated in projects funded by NATO, EU (TEMPUS), and national grants, including initiatives on broadband strategies and spectrum measurement techniques. He serves as Senior Member of IEEE and has organized conferences like the Information Theory and Complex Systems (TINKOS). He has advised students on projects involving video transmission over MIMO channels and interference alignment in distributed storage systems. His leadership roles include chairing national strategy committees for electronic communications and broadband internet, and leading the Technical Committee for Electronic Communications at Macedonia's Standards Institute. Notable awards include honorary membership in Phi Kappa Phi (top academic honor society). His research extends to applied projects like radar systems for landmine detection and MRI signal processing, showcasing interdisciplinary impact in telecommunications and engineering.

• Radio Astronomy
• Medical Imaging
• Compressed Sensing
• +۳ مورد دیگر

Yves Wiaux is a Professor at Heriot-Watt University's School of Engineering & Physical Sciences, affiliated with the Institute of Sensors, Signals & Systems. He specializes in advanced signal processing techniques with applications in radio astronomy and medical imaging. His research focuses on compressed sensing, optimization algorithms, and imaging methods for radio interferometry and MRI. His work integrates mathematical frameworks like wavelet transforms and sparse regularization to address challenges in high-dimensional data reconstruction. Notable contributions include the development of algorithms like SARA (Sparsity Averaging Reweighted Analysis) and PURIFY for radio interferometric imaging, as well as applications in medical imaging such as magnetic resonance fingerprinting. Wiaux collaborates extensively in international projects, including the LOFAR Two Meter Sky Survey. He advises PhD students through the BASP (Biomedical and Astronomical Signal Processing) group and leads research initiatives in scalable imaging techniques for large datasets.

Irtaza Shahidمشاهده پروفایل

پژوهشگر

• Secure and Private Speech Communication
• Generative AI and Speech/Audio Models
• Deepfake and Misinformation Detection
• +۳ مورد دیگر

Irtaza Shahid is a Ph.D. candidate in Computer Science at the University of Maryland, College Park, and a Researcher in the Networking, Mobile Computing, and Autonomous Sensing Lab (iCoSMoS) under Prof. Nirupam Roy. He is also a Founding Deep Learning Engineer at Hearvana.ai, a stealth AI startup under the AI2 Incubator. Ph.D. in Computer Science (University of Maryland, College Park) MS in Computer Science (University of Maryland, College Park) BS in Electrical Engineering (Lahore University of Management Sciences, Pakistan) His research spans Human-Centric Acoustics , focusing on Speech Privacy , Deepfake Detection , and Earable-Based Health Sensing . He has published in top venues like USENIX Security, ACM MobiSys, and IEEE ICASSP. Recent publications highlight Secure Speech Communication (USENIX Security 2025), UWB Localization (NSDI 2025), and Privacy Frameworks for Extended Reality (HotMobile 2025). These works emphasize Edge AI , Low-Power Acoustic Systems , and Signal Integrity . Scientific Awards: Student Travel Grant (USENIX Security 2025), Student Seed Funding (Maryland Cybersecurity Center 2023), Dean's Fellowship (University of Maryland 2020), and multiple Dean's Honor List Awards (LUMS 2016-2020) His projects include VoiceSecure (tamper-proof speech systems), DPOAE Estimation (health sensing via earbuds), and ThermWare (side-channel defense for IoT devices). He has contributed to Audio Engineering Society conferences and collaborated with industry partners like Nokia Bell Labs.

Jemma Brownمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Ultrasound Imaging
• Super-Resolution Techniques
• +۳ مورد دیگر

Jemma Brown is an academic affiliated with King's College London, where she contributes to research in medical imaging and biomedical engineering. She holds a Master of Research in Medical Imaging (King's College London, 2016) and a Master of Physics from the University of Oxford (2015). Her work focuses on super-resolution ultrasound imaging, microbubble/nanodroplet contrast agents, and 3D printing for medical device validation. She has co-authored numerous peer-reviewed articles on topics such as immune ageing, post-COVID-19 recovery, and ultrasound imaging innovations. Her research aligns with UN Sustainable Development Goals related to health and well-being. Education: Master of Research (Medical Imaging), King's College London (2016); Master of Physics, University of Oxford (2015) Research Interests: Super-resolution imaging techniques, medical ultrasound advancements, microbubble dynamics, and translational medical technologies. Her recent work includes developing cardiac phantoms for interventional simulations and studying immune responses in severe COVID-19 cases. She collaborates widely, contributing to large-scale studies like the PHOSP-COVID cohort analysis.

• Information Theory
• Communication Systems
• Radar Systems
• +۴ مورد دیگر

Mark R. Bell is a Professor of Electrical and Computer Engineering at Purdue University, based in the Elmore Family School of Electrical and Computer Engineering. His research focuses on information theory, communication systems, radar signal processing, and free-space optical communications. He holds a BS from California State University, Long Beach (1981), an MS (1982), and a PhD (1988) from the California Institute of Technology. His work spans diverse areas including radar waveform design, signal detection in noisy environments, and interdisciplinary applications like biomedical manufacturing and disaster management. Key research interests include advanced radar systems (e.g., bistatic configurations, LFM chirp processing), communication theory (e.g., time-frequency modulation, interference management), and emerging technologies such as AAV production for biotechnology. His contributions also extend to sensor networks, smart grid monitoring, and statistical modeling of wireless interference. Recent publications highlight innovations in radar resolution enhancement, adaptive threshold detection, and cost-effective manufacturing processes. Collaborative projects include spectrum-sharing frameworks for communications and radar systems, and integration of GPR with surface imaging for civil infrastructure analysis. Prof. Bell’s work has been recognized through sustained contributions to IEEE journals and conferences. His research bridges theoretical signal processing with practical applications in defense, healthcare, and environmental monitoring.

Nick Vannieuwenhovenمشاهده پروفایل

استادیار

• Tensor decompositions
• Numerical analysis
• Riemannian optimization methods
• +۲ مورد دیگر

Nick Vannieuwenhoven is an Assistant Professor at KU Leuven, affiliated with the Department of Computer Science and the NUMA Division. He serves as the Exchange Coordinator for the Master in Mathematical Engineering and is an Associate Editor for The Electronic Journal of Linear Algebra and SIAM Journal on Applied Algebra and Geometry . His research focuses on tensor decompositions, numerical analysis, Riemannian optimization, and applications in data science. He obtained his PhD in 2015 under Professors Karl Meerbergen and Raf Vandebril, funded by the FWO (Research Foundation Flanders). His postdoctoral research (2015–2021) was also supported by FWO fellowships. His research group investigates tensor decompositions, multilinear algebra, and numerical techniques for data science, with a focus on condition number analysis and Riemannian optimization. Collaborators include experts like Carlos Beltrán, Paul Breiding, and Simon Telen. Current students include Jana Jovcheva, Bram Leys, and David Thorsteinsson, working on manifold-valued function approximation, group-invariant networks, and data-based engineering. Key awards include FWO fellowships for his PhD and postdoctoral studies. Grants include support for postdoctoral researchers via MSCA and FWO schemes. Notable projects involve Tucker compression libraries (ATC) and geometric analysis of tensor networks. His work bridges algebraic geometry, numerical analysis, and machine learning, emphasizing stability and computational efficiency.

• Medical Imaging
• Ultrasound Technology
• Machine Learning
• +۳ مورد دیگر

Di Xiao is a Sessional Lecturer at the University of Waterloo. Their work focuses on advancing medical imaging technologies, particularly in ultrasound and MRI, with an emphasis on real-time processing, deep learning integration, and algorithm optimization. They are affiliated with the Sessional lecturers group. Research interests include ultrasound imaging innovations such as speed-of-sound estimation, beamforming techniques, and sparse-array optimizations. Their work also spans MRI reconstruction, signal processing, and applications of machine learning in medical diagnostics. Notable contributions include developing frameworks for live tissue assessment and minimizing image quality loss in sparse data scenarios. Publications highlight trends in real-time ultrasound systems, neural network-driven imaging enhancements, and sparsification strategies for MRI. The articles collectively emphasize interdisciplinary approaches merging AI with traditional medical imaging challenges. No scientific awards or grants are explicitly mentioned. No lab affiliations or student advising records are provided.

Sirisha Rambhatlaمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Representation Learning
• +۴ مورد دیگر

Sirisha Rambhatla is an Assistant Professor at the University of Waterloo, holding appointments in the Management Sciences Department (Faculty of Engineering), Systems Design Engineering, and the David R. Cheriton School of Computer Science (Faculty of Mathematics). She leads the CriticalML lab, focusing on reliable AI for healthcare, aviation, and climate change, leveraging deep learning and spatiotemporal analysis. Her work bridges theory and practice, with publications at top venues like NeurIPS, ICLR, and clinical journals. Affiliations include the Waterloo AI Institute and the Waterloo Institute for Sustainable Aeronautics. Education: Ph.D. and M.S. in Electrical Engineering, University of Minnesota (2019, 2012) B.Tech in Electronics and Telecommunication Engineering, College of Engineering Roorkee (2010) Research Interests: Representation Learning, Spatiotemporal Data Analysis, AI for Healthcare, Computer Vision, and provable algorithms. Her work emphasizes interpretable AI and applications in surgery, intelligent automation, and climate modeling. Awards: 2021 Merit Award for Excellence in Postdoctoral Research E. Bruce Lee Memorial Fellowship University Bronze Medalist (2010) Teaching: Courses include Advanced Machine Learning (MSCI 546/MSE 546), Big Data Analytics (MSCI 623), and Special Topics in Management Engineering (MSCI 598/MSE 598). Labs/Teams: Leads the CriticalML lab, focusing on trustworthy AI systems for critical applications.

• Medical Imaging
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Professor Yalin Wang is a faculty member in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), where he has held roles since 2010. He was promoted to full Professor in 2023, following his tenure as Associate Professor starting in 2017. His research focuses on medical imaging, computer vision, machine learning, and neuroimaging, with an emphasis on Alzheimer’s disease biomarker discovery and geometric modeling. He leads the Geometric & Scientific Computing Lab (GSL), hosting a research website at http://gsl.lab.asu.edu . Education: PhD in Electrical Engineering (University of Washington, 2002), MS and BS in Computer Science (Tsinghua University, 1996 and 1994). Research interests include surface-based morphometry, optimal transport applications in neuroimaging, and deep learning for medical image analysis. He has published over 200 peer-reviewed papers and received the 2016 ASU CIDSE Best Junior Faculty Researcher Award, among other accolades. His grants include NIH-funded projects on Alzheimer’s biomarkers, retinotopic mapping, and pediatric brain development. Collaborations span institutions like Mayo Clinic and ADNI. Notable contributions include developing neurodegeneration biomarkers using low-rank/sparse subspace decomposition and predictive models for cognitive decline via hyperbolic stochastic coding.

Mahdi Soltanolkotabiمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Artificial Intelligence
• +۳ مورد دیگر

Mahdi Soltanolkotabi is a Professor at the University of Southern California (USC) in the Ming Hsieh Department of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering. He serves as the inaugural director of the USC Center on AI Foundations for Science (AIF4S). Previously, he was a postdoctoral researcher at UC Berkeley, advised by Ben Recht and Martin Wainwright, after completing his Ph.D. in Electrical Engineering at Stanford under Emmanuel Candès. His research bridges theoretical foundations of data science with applied AI, focusing on generative AI, machine learning, signal processing, and computational imaging. Education: Ph.D. in Electrical Engineering (2014, Stanford), Postdoc (2014-2015, UC Berkeley). Research interests include developing mathematical frameworks for generative AI, non-convex optimization, high-dimensional probability, and reliable AI systems for healthcare and scientific discovery. His applied work emphasizes trustworthy AI, medical imaging, and interdisciplinary collaborations with domain scientists. Publications highlight contributions to phase retrieval, subspace clustering, federated learning, and neural network optimization. Recent work emphasizes AI reliability (e.g., MediConfusion, Serpent) and scalable image restoration techniques. Awards: Packard Fellowship, NIH Innovator Award, Sloan Fellowship, NSF Career Award, AFOSR-YIP. Grants/Outreach: NIH-funded projects, collaborations with K-12 STEM education programs (Nava College Preparatory Academy, USC Viterbi Adopt-a-School). Labs/Teams: Leads USC’s AIF4S, focusing on foundational AI for science.

Thomas Bonaldمشاهده پروفایل

استاد

• Data Analysis
• Machine Learning
• Graph Theory
• +۴ مورد دیگر

Thomas Bonald is a Professor at Telecom Paris, part of the Institut Polytechnique de Paris, and heads the DIG (Data, Intelligence and Graphs) team within the LTCI laboratory. His research focuses on data analysis, machine learning, graph theory, knowledge bases, and natural language processing. He leads projects like the YAGO knowledge base and develops open-source tools such as scikit-network and torch-kge for graph analysis and knowledge graph embedding. Key achievements include advancing link prediction, knowledge base refinement using LLMs, and contributions to fair resource allocation in networks. His work integrates theoretical insights with practical applications, including enhancing adaptive streaming performance in mobile networks. Scientific awards include recognition of his former students Céline Comte (Telecom Paris award) and Mathieu Feuillet (Gilles Kahn award). Bonald advises numerous PhD students and has supervised over 20 doctoral candidates. His research spans algorithmic fairness, network science, and large-scale data integration. Labs/Teams: DIG team at LTCI, contributing to projects like YAGO and NetSet datasets. His teaching includes probability, statistics, graph learning, and reinforcement learning, with lecture notes on topics like PageRank and spectral embedding.

Dongjin Songمشاهده پروفایل

استادیار

• Continual Learning
• Graph Machine Learning
• Time Series Analysis
• +۲ مورد دیگر

Dr. Dongjin Song is an Assistant Professor in the School of Computing at the University of Connecticut, specializing in continual learning, graph representation, and time series analysis. He holds a PhD from UC San Diego (2016) and previously worked at NEC Labs America. His NSF CAREER award (2024) supports research on evolving graph learning for applications in healthcare, energy, and transportation. Recognized with the Frontiers of Science Award (2024) and UConn AAUP Excellence Award (2025), he develops algorithms addressing catastrophic forgetting and generalization in dynamic systems. Research interests include: Robust representation learning for time-series and graph data Meta-knowledge distillation for heterogeneous systems Privacy-preserving federated learning LLM-enhanced multimodal forecasting He actively contributes as Area Chair at NeurIPS and Associate Editor for Neural Networks . Educational initiatives integrate research into AI/ML courses, and outreach targets K-12 STEM engagement. Current projects explore power outage prediction, clinical time-series analysis, and cross-platform mental health monitoring.

Zhigen Zhaoمشاهده پروفایل

دانشیار

• Bayesian/empirical Bayesian statistics
• High-dimensional data analysis
• Multiple comparison
• +۵ مورد دیگر

Zhigen Zhao is an Associate Professor and Beury Research Fellow at the Fox School of Business and Management, Temple University, within the Department of Statistics, Operations, and Data Science. He holds a Ph.D. from Cornell University (2009) and specializes in Bayesian/empirical Bayesian statistics, high-dimensional data analysis, and bioinformatics. His research is supported by the National Science Foundation. Dr. Zhao’s research focuses on developing statistical methodologies for high-dimensional problems, including selective inference, multiple comparisons, and sufficient dimension reduction. His work bridges theoretical advancements with practical applications in healthcare, genomics, and predictive analytics. Key contributions include Bayesian hierarchical models for electronic health records and novel techniques for controlling false discovery rates in genomic studies. He teaches courses such as Intermediate Statistics, Regression and Predictive Analytics, and Statistical Methods for Business Research at both undergraduate and graduate levels. His recent publications appear in top-tier journals like the Journal of the American Statistical Association and Journal of the Royal Statistical Society, Series B. Dr. Zhao’s grants include NSF funding for high-dimensional statistical research. He advises on methodological challenges in data science and collaborates on projects involving healthcare analytics, genomics, and machine learning applications.

Mathukumalli Vidyasagarمشاهده پروفایل

استاد

• Control Theory
• Systems Biology
• Computational Biology
• +۷ مورد دیگر

Dr. Mathukumalli Vidyasagar is the Cecil & Ida Green Chair in Systems Biology Science at The University of Texas at Dallas (UT Dallas), serving as Professor of Bioengineering and Head of the Bioengineering Department. He holds a part-time appointment as Distinguished Professor at the Indian Institute of Technology Hyderabad through the Jawaharlal Nehru Science Fellowship (2015–present). His academic journey includes roles as Director of the Centre for Artificial Intelligence and Robotics (1989–2000) and Executive Vice President at Tata Consultancy Services (2000–2009). He earned B.S., M.S., and Ph.D. degrees in Electrical Engineering from the University of Wisconsin–Madison (1965–1969). His research focuses on control theory, systems biology, and computational biology, with applications to cancer diagnostics and machine learning. Notable contributions include work on robust control, L1-optimal control, and statistical learning theory. Dr. Vidyasagar has authored over 140 peer-reviewed papers and 11 books. His accolades include Royal Society Fellowship (2012), IEEE Control Systems Award (2008), and the Rufus Oldenburger Medal (2012). He has advised 14 Ph.D. and 12 M.S. students, and sponsored 8 postdoctoral scholars.

• Signal and image processing
• Inverse problems
• Optimization
• +۶ مورد دیگر

Argheesh Bhanot is a Researcher at Savoie Mont-Blanc University, affiliated with Polytech Annecy-Chambéry and the LISTIC laboratory. His email is bhanota@univ-smb.fr , and he is located in office A127 at LISTIC’s campus in Annecy-le-Vieux, France. Research Interests : Bhanot’s work focuses on advanced signal and image processing techniques, including inverse problems, optimization, Markov Chain Monte Carlo (MCMC) methods, artificial intelligence (AI), and graph analysis. His research applications span environmental monitoring (e.g., avalanche dynamics) and medical imaging (e.g., fMRI connectivity analysis). Key Research Contributions : His recent work explores functional connectivity in neuroimaging through graph theory and independent component analysis (ICA), as well as dictionary learning algorithms for signal separation in fMRI and other domains. He also develops energy-efficient remote sensing systems for environmental studies. Labs & Teams : Bhanot is a core member of the LISTIC laboratory, which specializes in interdisciplinary research combining signal processing, AI, and applied mathematics.