جست‌وجوی استاد و پژوهشگر

Ruqi Zhangمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Probabilistic Methods
• +۵ مورد دیگر

Ruqi Zhang is an Assistant Professor in the Department of Computer Science at Purdue University. Previously, she was a postdoctoral fellow at the Institute for Foundations of Machine Learning, UT Austin (2021-2022), and earned her PhD in Statistics from Cornell University (2016-2021). Her research focuses on scalable probabilistic methods for machine learning, including alignment of foundation models, uncertainty quantification, and Bayesian deep learning. She has received awards such as the Ross-Lynn Research Scholar Fund and ICML Best Reviewer recognition. Education: PhD in Statistics, Cornell University (2021) MS in Computer Science, Cornell University (2021) Bachelor of Science in Mathematics, Renmin University of China (2012-2016) Research Interests: Trustworthy AI: Safety of LLMs/VLMs, alignment mechanisms Probabilistic Inference: Bayesian methods, MCMC, variational inference Generative Models: Diffusion models, energy-based models Uncertainty Estimation: Calibration, out-of-distribution detection Notable Contributions: Developed the Discrete Langevin Sampler for high-dimensional discrete spaces Pioneered Low-Precision SGLD for efficient Bayesian neural networks Designed DP-Fast MH for privacy-preserving Bayesian inference Teaching: CS57800 - Statistical Machine Learning (2022-Present) CS37300 - Data Mining and Machine Learning (2024) CS59200 - Probabilistic Machine Learning (2022)

Gwan Choiمشاهده پروفایل

دانشیار

• Fault-tolerance
• Verification simulation
• High-performance VLSI circuits
• +۷ مورد دیگر

Gwan Choi is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on fault-tolerance, verification simulation, high-performance VLSI circuits, radiation testing, design for dependability, and software engineering. He holds a Ph.D., M.S., and B.S. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (1988–1994). Education: Ph.D., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (1994) M.S., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (1989) B.S., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (1988) Research Interests: Dr. Choi’s work spans fault-tolerant computing, radiation-hard electronics, and high-performance VLSI design. He has contributed to LDPC decoding architectures, MIMO communication systems, and energy-efficient circuit designs. His research emphasizes practical implementations in aerospace and embedded systems. Awards: National Science Foundation (NSF) Career Award (1997) Labs & Activities: He has collaborated with NASA Langley Research Center as a visiting scientist and contributed to industry through roles at Cray Research and Tandem Computers. His work bridges academic research with real-world applications in fault tolerance and low-power systems.

Maximilian Starkمشاهده پروفایل

مدرس

• Communication Systems
• Machine Learning
• Information Theory
• +۳ مورد دیگر

Dr. Maximilian Stark is a Lecturer at the Institute of Communications, TU Hamburg. His research focuses on machine learning-driven advancements in communication systems, particularly applying the information bottleneck method to decoding algorithms, signal processing, and quantization techniques. His work bridges theoretical information theory with practical implementation challenges in coding and channel design. Key research areas include LDPC and polar codes optimization, low-bitwidth decoding architectures, and distributed signal processing frameworks. Recent publications emphasize adaptive learning systems for error resilience under quantization constraints and hardware-efficient receiver design. Dr. Stark's academic contributions span over 20 peer-reviewed articles since 2016, with a strong emphasis on integrating machine learning principles into traditional communication engineering problems. Notable themes include neural decoding paradigms, resource-constrained quantization strategies, and distributed information compression methods. No scientific awards explicitly listed. Academic advising details and lab affiliations are not provided in source materials.

Karystinos Georgeمشاهده پروفایل

استاد

• Communication theory and systems
• Coding theory
• Adaptive signal processing
• +۶ مورد دیگر

Prof. George Karystinos is a Professor at the Technical University of Crete (TUC), School of Electrical and Computer Engineering (ECE), where he leads the Telecommunications Laboratory. His academic journey includes a Ph.D. in Electrical Engineering from SUNY Buffalo (2003) and a Diploma in Computer Engineering from the University of Patras (1997). He previously held a faculty position at Wright State University before joining TUC in 2005. His research focuses on communication theory, coding, adaptive signal processing, wireless systems, and neural networks. Key interests include spreading code design, interference suppression, adaptive antenna arrays, and noncoherent detection techniques. His work has led to advancements in power line communication, MIMO systems, and low-complexity decoding algorithms. Prof. Karystinos has received prestigious awards, including the 2003 IEEE Transactions on Neural Networks Outstanding Paper Award and the 2001 IEEE International Conference on Telecommunications Best Paper Award. He is an active member of IEEE societies in Communications, Signal Processing, and Information Theory. He teaches core courses such as Signals and Systems , Information Theory and Coding , and Probability and Random Processes . His laboratory emphasizes practical applications of theoretical concepts, with recent projects on smart photovoltaic systems and cascaded H-bridge converter control. His research spans 20+ years, yielding over 80 publications in top journals/conferences. Current efforts address challenges in IoT communication, noncoherent detection for RFID, and energy-efficient signal processing. He collaborates internationally on projects funded by Greek and EU research programs.

Lei Youمشاهده پروفایل

استادیار

• Mathematical Optimization
• Distributed Data Analytics
• Data-Driven Optimization
• +۹ مورد دیگر

Lei You is an Assistant Professor in Applied Mathematics at the Department of Engineering Technology, Technical University of Denmark (DTU), located in Ballerup, Denmark. With a Ph.D. in Computer Science specializing in Mathematical Optimization from Uppsala University (2019), Dr. You has established expertise in distributed data analytics, data-driven optimization, and machine learning interpretability. Dr. You's research focuses on the theories of interpretability and efficiency of machine learning models toward On-Device AI. Key areas of investigation include: Strategies to streamline complex models without performance loss Unraveling the intricate mechanisms of decision-making models Understanding the synergy between model simplification and explainability Distributed data analytics and federated learning systems Optimization techniques for wireless communication and edge computing Dr. You's recent publications demonstrate a strong focus on counterfactual explanations, optimal transport theory, neural network pruning, and multi-objective optimization for federated learning. The research spans applications from explainable AI to 6G-satellite systems and energy-efficient base station management. A notable trend is the integration of causal inference and distributional analysis in machine learning optimization problems, with significant contributions to federated learning efficiency and robustness. Scientific awards and recognition: Doctoral Dissertation Award for Operations Research in Telecommunications and Network Analytics awarded by INFORMS in 2020 Dr. You actively supervises student projects and research activities, with recent supervisions including work on remote sensing assessment, network pruning techniques, Microsoft Graph API optimization, and applications of large language models in business contexts. Current research projects include 'Machine Learning for Beehives Monitoring' and 'InnoTech - TaskForce,' focusing on practical applications of machine learning in environmental monitoring and digital transformation. Dr. You maintains active industry connections through previous roles at Bolt, Wolt (Doordash), and The Boston Consulting Group, ensuring research relevance to real-world challenges.

• Industrial Engineering
• Operations Research
• Data Analysis
• +۵ مورد دیگر

Dr. Anne LOUIS is a Research Director at CESI, leading the application domain of 'Industry of the Future' and heading the Research & Innovation Department for CESI Nord-Ouest. She holds an HDR in Electronics from the University of Rouen (2006) and a PhD in Microwaves from the University of Limoges (1998). Her work focuses on decision support systems for industrial tasks, predictive maintenance analytics, and Industry 5.0 demonstrators. She leads a research platform on industrial performance (2015–2020) and manages a team in Engineering and Numerical Tools. Research Interests: Decision support tools for task scheduling, maintenance operations analysis, and multi-agent systems for industrial systems. Current Programs: Industry 5.0 demonstrator development and industrial performance platforms. Her research spans robotics optimization in horticulture, bio-waste management systems, and SME open innovation strategies. She supervises four ongoing PhD projects on AI-driven maintenance, multimodal transport supervision, and open innovation sustainability. Completed theses include robotic task optimization (2022), bio-waste treatment (2021), and SME innovation practices (2021). Scientific Leadership: Member of LINEACT Scientific Council, COMUE Normandie's Numerical Sciences pole, and doctoral school committees. Over 25+ publications since 2018, focusing on optimization algorithms, multi-agent simulations, and innovation frameworks.

Sheng Yangمشاهده پروفایل

استاد

• Information Theory
• Wireless Communications
• MIMO Systems
• +۶ مورد دیگر

Sheng Yang is a full professor at CentraleSupélec, University of Paris-Saclay, and holds an honorary associate professorship at the University of Hong Kong. His research focuses on information theory and its applications to wireless networking, including MIMO systems, non-coherent communication, interference mitigation, and physical layer security. He has authored numerous influential papers and received the 2015 IEEE ComSoc Young Researcher Award. His teaching includes courses on digital communication, information theory, and wireless communications. Educational Background: BE in Electrical Engineering (Jiaotong University, Shanghai, 2001), Engineer Degree and M.Sc. (Telecom ParisTech, 2004), and Ph.D. (Université Pierre et Marie Curie, 2007). Research Interests: Multi-antenna (MIMO) communications, non-coherent systems, hardware impairments, coded caching, federated learning, and the integration of machine learning with communication systems. Notable Contributions: Developed novel rate-splitting strategies for MIMO broadcast channels, analyzed capacity limits of 1-bit quantized MIMO systems, and pioneered CSI compression schemes using context trees. He has supervised over 10 PhD/postdoc students, many now in academia and industry leadership roles. Awards: IEEE ComSoc Young Researcher Award (2015), Associate Editor roles at IEEE Transactions on Information Theory and IEEE Transactions on Wireless Communications. Labs/Teams: Active in the MODESTY, COMEDY, and SYCOMORE research groups at L2S, focusing on system modeling and control. His work intersects energy-efficient communication, distributed computing, and future network architectures.

Paris Smaragdisمشاهده پروفایل

استاد

• Audio Processing
• Machine Learning
• Signal Processing
• +۷ مورد دیگر

Paris Smaragdis is a Professor and Associate Head in the Department of Computer Science at the University of Illinois Urbana-Champaign, with a secondary affiliation in Electrical and Computer Engineering. His research focuses on audio processing, machine listening, and signal processing using machine learning. He holds over 40 patents and has led major initiatives in IEEE Signal Processing Society roles. Notable roles include Editor-in-Chief of the ACM/IEEE Transactions on Audio, Speech, and Language Processing, and former leadership in MERL and Adobe Research. Education: PhD (2001), MS (1997), and postdoc at MIT under Barry Vercoe, specializing in computational audition. Research Interests: Audio source separation, deep learning for signal processing, on-device processing, graph models, and neural network efficiency. His work has been commercialized in products used by millions. Awards: IEEE Fellow (2015), TR35 (2006), multiple Best Paper Awards, and recognition for teaching excellence. Professional Activities: Chair of multiple IEEE committees, including Audio and Acoustics Signal Processing, and leadership roles in data science initiatives. Collaborates with Fortune 500 companies on audio technologies.

Mitchell Dayمشاهده پروفایل

دانشیار

• Auditory Neurophysiology
• Sensory Coding
• Binaural Hearing
• +۷ مورد دیگر

Mitchell Day is an Associate Professor in the Department of Biological Sciences at Ohio University's College of Arts and Sciences. He is based in the Life Sciences Building and is a member of the Quantitative Biology Institute. His research integrates neurophysiology, psychophysics, and computational modeling to study how the brain processes auditory information, particularly sound localization and binaural hearing. Ph.D., Neural Science, New York University B.S., Physics and Mathematics, University of Iowa Dr. Day's research focuses on sensory coding , particularly how neurons in the auditory system encode spatial information through spike trains. His lab investigates binaural hearing , which allows humans and animals to localize sounds and separate sources in complex environments. Using neurophysiological recordings from the medial superior olive and inferior colliculus , combined with psychophysical testing and computational models, his work explores how factors like sound level, frequency, and noise-induced hearing loss affect neural coding of sound location. His recent publications reveal a strong emphasis on neural population coding , sound source separation , and the impact of hearing loss on binaural processing . Themes across his work include the robustness of auditory representations, cue integration in space perception, and the neural mechanisms underlying auditory scene analysis. His research often employs rabbit models and examines both peripheral and central auditory pathways. Scientific contributions include key insights into: How hearing loss degrades sensitivity to interaural time differences Frequency-dependent dominance of localization cues in the inferior colliculus Neural decoding of concurrent sound sources Role of subthreshold potassium dynamics in temporal coding precision Dr. Day teaches courses in physiology, human physiology, and neuroscience at both undergraduate and graduate levels. He leads an active research lab investigating auditory processing and trains students in electrophysiological techniques, data analysis, and computational neuroscience. While specific grants and awards are not listed, his sustained publication record in high-impact journals indicates active funding and scholarly engagement. His lab is located in Life Sciences Building 220, and he collaborates with researchers at institutions including Harvard Medical School and Massachusetts Eye and Ear.

Michael Tangermannمشاهده پروفایل

دانشیار

• Machine Learning
• Neurotechnology
• Brain-Computer Interfaces (BCI)
• +۵ مورد دیگر

Dr. Michael Tangermann is an Associate Professor at Radboud University and Principal Investigator at the Donders Institute for Brain, Cognition and Behaviour. He leads the Data-Driven NeuroTechnology Lab, focusing on developing machine learning algorithms for neural and behavioral data analysis. His work integrates brain-computer interfaces (BCI), closed-loop neurotechnologies, and deep brain stimulation (DBS) to address clinical and non-clinical challenges. Education: PhD in Computer Science (2007, University of Tübingen) and Diplom in Computer Science (2000, University of Tübingen). Former positions include Head of the Brain State Decoding Lab (University of Freiburg, 2013–2020) and Substitute Professor for Autonomous Intelligent Systems (2019–2020). Research interests span machine learning for time-series analysis, neurotechnological applications (e.g., BCI for communication/rehabilitation, adaptive DBS), and translational neuroscience. His lab collaborates with institutions like the University Medical Center Freiburg and MindAffect. Publications (H-index: 40) emphasize decoding brain states, optimizing BCI paradigms, and developing open-source tools like the Dareplane platform. Teaching includes advanced BCI courses and digital signal processing at the graduate level.

• Wireless Communications
• Sparse Signal Recovery
• Communication Systems
• +۴ مورد دیگر

Ashok BANDI is a Researcher at the Signal Processing & Satellite Communications group (SIGCOM) within the Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg. His research focuses on Sparse Signal Recovery and Wireless Communications, particularly in structured sparsity modeling and signal recovery in communication systems. Education: Master’s degree from National Institute of Technology (NIT Trichy), India (2012). Research Interests: Design of physical layer systems for WLAN standards (802.11a/n/ac). Development of efficient algorithms for structured sparse signal recovery in satellite and wireless communication systems. Integration of sparsity principles into on-board beamforming for high-throughput satellite systems. Advisors: Prof. Björn Ottersten and Dr. Bhavani Shankar MYSORE RAMA RAO. Labs/Teams: Part of the Signal Processing & Satellite Communications (SIGCOM) research group led by Prof. Björn Ottersten.

Galen Reevesمشاهده پروفایل

استادیار

• Information Theory
• Machine Learning
• Statistical Signal Processing
• +۳ مورد دیگر

Galen Reeves is an Assistant Professor at Duke University with a joint appointment in the Department of Electrical and Computer Engineering and Department of Statistical Science since Fall 2013, reflecting his interdisciplinary expertise bridging engineering and mathematical sciences. His research establishes rigorous theoretical frameworks at the intersection of information theory, machine learning, and statistical signal processing, focusing on fundamental limits in high-dimensional inference problems. His academic background features elite training across top institutions: PhD in Electrical Engineering and Computer Sciences, University of California, Berkeley (2011) MS in Electrical Engineering, University of California, Berkeley (2007) BS in Electrical and Computer Engineering, Cornell University (2005) Reeves' research centers on mathematical foundations of data science, with seminal contributions to compressed sensing, tensor estimation, and coding theory. He investigates information-theoretic bounds for estimation problems, develops efficient algorithms like approximate message passing, and analyzes generative AI model behavior under recursive training conditions. His work demonstrates how statistical physics approaches solve complex problems in communication theory and high-dimensional statistics. Analysis of his 2021-2025 publications reveals three dominant trends: breakthroughs in channel capacity using Reed-Muller codes, theoretical analysis of generative models and diffusion sampling, and fundamental limits in tensor/matrix estimation. These works consistently integrate information theory with machine learning, emphasizing scalability challenges in high dimensions and algorithmic robustness under heteroskedasticity. His scientific recognition includes: NSF VIGRE fellowship supporting postdoctoral research at Stanford University (2011-2013) NSF CAREER award (2018) for Theoretical Foundations for Probabilistic Models with Dense Random Matrices While no specific students are documented in the source text, his faculty position entails graduate mentorship in both ECE and Statistical Science departments. Research funding primarily stems from the NSF CAREER grant advancing probabilistic modeling, complemented by earlier fellowship support. His collaborations span Stanford University, EPFL, TU Delft, and Microsoft Research. Though no dedicated lab is mentioned, his joint appointment fosters cross-departmental research at Duke, particularly in projects like 'Modeling Traffic with Self Driving Cars' which applies statistical learning to autonomous systems. His work maintains strong ties to industry through past Microsoft Research internships and ongoing computational applications in communications and AI.

• Tinnitus
• Neuromodulation
• Auditory Nerve Implants
• +۳ مورد دیگر

Professor Hubert H Lim is a leading researcher in the Department of Otolaryngology at the University of Minnesota , where he also serves as Director of the Earl E. Bakken Medical Device Center within the Institute for Engineering in Medicine . His work bridges auditory neuroscience and bioelectronic medicine with a focus on neuromodulation , ultrasound technology , and machine learning applications. Research interests include: Tinnitus treatment via multimodal sensory stimulation Autonomic neuromodulation for cardiovascular control Development of auditory nerve implants Ultrasound-triggered drug delivery systems Deep learning in clinical decision-making Preclinical models for medical device testing Recent publications highlight innovations in gold-shell hydrogels , closed-loop vagus nerve stimulation , and feline models for auditory implants. His work extensively involves clinical trials for tinnitus therapy and in vitro platforms for cellular neuromodulation. Collaborative grants include projects funded by the National Institutes of Health , American Heart Association , and 3M , focusing on neural recording , regulatory approval for implants, and non-invasive neuromodulation . Media coverage (2020) highlights his novel tinnitus treatment featured in 27 news outlets.

Alireza Sheikhمشاهده پروفایل

پژوهشگر

• channel coding
• optical communication
• wireless communication
• +۳ مورد دیگر

Alireza Sheikh is a University Researcher in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), specializing in Signal Processing Systems. He is affiliated with the ICT Lab at TU/e and has been actively involved in research projects such as FUN-NOTCH: Fundamentals of the Nonlinear Optical Channel. Dr. Sheikh received his B.Sc. (Hons.) degree in electrical engineering from Ferdowsi University of Mashhad, Iran, in 2011, his M.Sc. (Hons.) degree in communication systems engineering from the University of Tehran, Iran, in 2014, and his Ph.D. degree in electrical engineering from Chalmers University of Technology, Sweden, in 2019. Prior to joining TU/e, he was a visiting researcher at the Institute of Communications and Navigation, German Aerospace Center (DLR), Germany, in 2017-2018. Dr. Sheikh's research focuses on communication systems with particular emphasis on data protection and efficient communication. His primary research interests include channel coding , optical communication , wireless communication , information theory , machine learning , and estimation theory . He is particularly interested in designing low-complexity coding schemes for next-generation communication systems that can protect data against imperfections. His recent publications demonstrate a strong focus on improving communication systems through advanced coding techniques and signal processing methods. His work spans optical communications (addressing nonlinearity issues), wireless communications (particularly Massive MIMO systems), and fundamental information theory problems. A common thread in his research is the development of practical, high-throughput solutions for real-world communication challenges, with significant contributions to product codes, error correction, and nonlinearity-tolerant communication schemes. Dr. Sheikh has contributed to significant research projects, most notably as a project member of FUN-NOTCH (Fundamentals of the Nonlinear Optical Channel), which runs from 2018 to 2025. This project focuses on addressing challenges in optical communications, particularly related to nonlinearity, modulation formats, and product codes. He is actively involved in teaching, specifically in the course "Applications of Information Theory (5LSF0)" at TU/e, demonstrating his commitment to sharing knowledge with the next generation of engineers and researchers.

Benjamin Van Durmeمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Semantics
• Streaming Algorithms
• +۱۰ مورد دیگر

Benjamin Van Durme is an Associate Professor in the Department of Computer Science at Johns Hopkins University's Whiting School of Engineering, with secondary appointments as Senior Research Scientist at the Human Language Technology Center of Excellence (HLTCOE) and affiliation with the Center for Language and Speech Processing (CLSP). He leads Natural Language Understanding research at HLTCOE and serves as research lead at Microsoft Semantic Machines. His educational background includes B.S. and B.A. (2001), M.S. in Language Technologies (2004), M.S. in Computer Science (2006), and Ph.D. (2009), all from the University of Rochester. Van Durme's research spans multiple facets of artificial intelligence with primary focus on natural language processing, computational semantics, and information seeking systems. His work addresses fundamental challenges in machine learning, reasoning agents, multimodal understanding, factuality, and legal reasoning. Current projects include Decomp.io for decompositional semantics, IterX for structured information extraction, Nellie and Treewise for neuro-symbolic reasoning, and MultiVENT for event detection in videos. Analysis of his recent publications reveals a strong emphasis on improving large language models through context compression, safety alignment, and multilingual capabilities. His work bridges theoretical advances in NLP with practical applications in legal reasoning, scientific communication, and information retrieval systems. Van Durme actively collaborates across multiple institutions and leads research efforts that address critical challenges in AI safety, factuality verification, and efficient reasoning systems. His lab produces work that spans from foundational ML methods to cognitive science applications, with particular attention to creating models that can extract structured information, make logical inferences, and handle uncertainty in multilingual contexts.