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Yifan ChenView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Non-parametric Models
• +6 more

Yifan Chen is an Assistant Professor in Computer Science and Mathematics at Hong Kong Baptist University (HKBU), specializing in efficient machine learning with focus on non-parametric models and neural networks involving intensive matrix operations such as Transformers and Graph Neural Networks (GNNs). Affiliation: Department of Computer Science and Mathematics, Hong Kong Baptist University Location: Hong Kong SAR, China Dr. Chen received his Ph.D. in Statistics from the University of Illinois Urbana-Champaign in 2023 under the supervision of Prof. Yun Yang, with additional collaborations with Prof. Ruoqing Zhu, Prof. Heng Ji, and Prof. Jingrui He. Prior to his doctoral studies, he earned his B.S. in Statistics from Fudan University in 2018, advised by Prof. Juan Shen and Prof. Chenghong Zhang. Dr. Chen's research broadly focuses on understanding the statistical structures of modern machine learning algorithms and applying these insights to real-world computational challenges. His work particularly emphasizes non-parametric models and neural networks with intensive matrix operations, including Transformers (language models) and graph neural networks. His research bridges theoretical statistical learning with practical computational efficiency, exploring how to make machine learning algorithms both theoretically sound and computationally feasible for large-scale applications. Analysis of Dr. Chen's publication record reveals a strong focus on computational efficiency in machine learning, with recurring themes in graph-based learning, kernel methods, and optimization techniques. His work demonstrates expertise in both theoretical foundations and practical implementations, with publications spanning top-tier conferences including NeurIPS, ICML, KDD, and AAAI. Many of his papers address fundamental challenges in scaling machine learning algorithms while maintaining statistical guarantees, reflecting his commitment to both theoretical rigor and practical applicability. Scientific awards and recognition include: Dissertation Completion Fellowship from University of Illinois Graduate College (2023) ICML 2023 Grant Award (2023) Shanghai Outstanding Graduate from Shanghai Municipal Education Commission (2018) NSFC Young Scientists Fund (2025) GDSTC General Program funding (2024) RGC Early Career Scheme grant (2024) Dr. Chen actively mentors PhD students and research assistants, currently supervising Yifan Xu and Yujia Yin as PhD students, and Yifan Wu as a visiting research assistant. His group has produced notable alumni including Mingchen Jiang (now PhD student at Institute of Science Tokyo), Jiahao Ma (now PhD student at HKU), Xichen Ye (now incoming PhD student at Fudan), and Guoming Li (now incoming PhD student at NUS). He teaches advanced topics in artificial intelligence and machine learning, as well as applied linear algebra for computing, emphasizing the connections between theoretical foundations and practical applications. Dr. Chen maintains active collaborations with researchers at institutions including University of Illinois Urbana-Champaign, Fudan University, Shanghai University, and industry partners like Amazon Alexa AI and IBM Research.

• Control Theory
• Nonlinear Systems
• Optimization
• +6 more

Dr. Makhin Thitsa serves as Professor of Electrical and Computer Engineering at Mercer University's School of Engineering in Macon, Georgia. She holds a research affiliate position at Georgia Institute of Technology and directs the Cyber-physical Systems and Control Laboratory at Mercer. Her leadership has guided numerous students to prestigious awards including the Barry M. Goldwater Scholarship and MIT's MSRP research program. Her educational background includes: PhD in Electrical & Computer Engineering, Old Dominion University (2011) MSE in Electrical Engineering, Old Dominion University (2007) BSE in Electrical Engineering, Old Dominion University (2005) Dr. Thitsa's research spans nonlinear control theory, non-smooth analysis, optimization, and data-driven control strategies. Her work applies to photonic devices, UAVs, and traffic networks, with current focus on numerically stable feedback optimization for multi-agent systems. She has secured funding from the Air Force Research Laboratory (AFRL) and Georgia Department of Transportation (GDOT). Her recent publications demonstrate expertise in control barrier functions, quantum state observers, and laser pulse shaping, reflecting interdisciplinary work bridging engineering, physics, and public health. Key trends include increasing focus on safety-critical systems and data-driven approaches. Professional honors include: Nomination for Goldwater faculty mentorship award (2023) All-Southern Conference Faculty and Staff Team recognition (2020) As an active researcher and educator, Dr. Thitsa serves on PhD committees at Georgia Tech and MIT, chairs sessions at major conferences like IEEE CDC, and reviews for top journals including Automatica and IEEE Transactions. Her Cyber-physical Systems Lab provides hands-on research experience for students working on cutting-edge control problems.

Narayana Prasad SanthanamView profile

Professor

• Information Theory
• Statistical Learning
• Signal Processing
• +11 more

Narayana Prasad Santhanam is a Professor in the Department of Electrical Engineering at the University of Hawaii, College of Engineering. His research spans theoretical and practical aspects of information theory, statistical learning, and signal processing, with particular focus on high-dimensional and complex problems that cannot be addressed by traditional statistical methods. Santhanam maintains an active research program funded by the National Science Foundation and teaches courses including Probability and Statistics, Linear Algebra and Machine Learning, and Information Theory. Santhanam's research interests center on the intersection of statistical learning and information theory, particularly in high-dimensional settings. His work addresses fundamental questions about when learning is possible, how to characterize non-uniform learning, and how to interpret data from complex sources like slow mixing Markov processes. He has made significant contributions to understanding the limitations of statistical methods in large alphabet scenarios, where traditional approaches fail. His research has important applications in diverse fields including genetic data analysis, risk management, smart grids, and text processing. His recent publications reveal a consistent focus on theoretical foundations with practical applications. Santhanam's work often explores the connections between seemingly disparate fields, bringing combinatorial and probabilistic approaches to bear on complex problems. A notable trend is his development of frameworks for pointwise convergence rather than uniform convergence in statistical estimation, which has significant implications for handling large alphabet problems where traditional methods break down. 2006 IEEE Information Theory Society Best Paper Award 2003 Capocelli Prize Santhanam has successfully secured significant research funding as Principal Investigator on an NSF award of approximately $1.1 million to examine the interplay of statistics and information theory, with applications to document classification and genetic analysis. He has also served as co-PI on multiple NSF awards totaling roughly $800,000 for research on channels with memory and smart grid organization. His research group includes students M. Asadi, A. Esraghi, A. Lee, M. Hosseini, R. Paravi, and G. Tobin, who contribute to projects spanning statistical learning, information theory, and their applications to biological and engineering problems. Santhanam has co-organized three major workshops on large alphabet information theory and statistics, bringing together over 80 researchers from diverse disciplines including biology, computer science, economics, information theory, mathematics, networking, and statistics.

• Nonsmooth Optimization
• Variational Analysis
• Mathematical Programming
• +1 more

Nghia T. A. Tran is an Associate Professor in the Department of Mathematics and Statistics at Oakland University, Rochester, MI, USA. His research focuses on mathematical optimization, variational analysis, and inverse problems, with applications spanning signal processing, statistics, and numerical algorithms. He serves as an Associate Editor for the journal Set-Valued and Variational Analysis . Research Interests: Dr. Tran specializes in: Nonsmooth optimization methods and variational inequalities Stability analysis of convex and non-convex optimization problems Regularization techniques for linear inverse problems Convergence properties of iterative algorithms Publication Trends: His 15 most recent publications (2016–2025) demonstrate a consistent focus on: Geometric characterizations of optimization stability (Lipschitz/tilt properties) Solution uniqueness in regularization frameworks (e.g., Lasso, nuclear norm) Convergence analysis of splitting methods and proximal algorithms Applications to matrix completion, inverse problems, and statistical learning Works predominantly appear in premier journals including SIAM Journal on Optimization, Mathematical Programming, and Journal of Optimization Theory and Applications.

Michael GaeblerView profile

Researcher

• Mind-Body Interaction
• Neuroscience
• Virtual Reality
• +5 more

Dr. Michael Gaebler is a Researcher and Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. His Mind-Body-Emotion Group investigates brain-body interactions in health and disease using advanced technologies like virtual reality (VR), neuroimaging, and psychophysiological methods. Key research topics include emotional arousal decoding, brain-heart coupling, and stress physiology. His work bridges cognitive neuroscience, clinical applications, and immersive technologies, with a focus on translational research in neurorehabilitation and mental health. Education: Dr. rer. nat. in Psychology (2010-2013), Humboldt-Universität zu Berlin & Charité - Universitätsmedizin Berlin MSc Brain & Mind Sciences (2006-2008), University College London & École Normale Supérieure Paris BSc Cognitive Science (2002-2006), Universität Osnabrück Research Interests: The group employs lab-based and real-world methodologies to study mental processes across lifespan and emotional states. Current projects explore: Physiological responses in VR environments Heart-brain interactions during emotional experiences Neuroimaging biomarkers for stress and aging Applications of AI in neuroscience data interpretation Key Tools: AffectTracker: Real-time emotion monitoring in VR Excite-O-Meter: Heart activity integration in VR experiments OpenVirtualObjects: Standardized 3D object library for VR research Grants & Collaborations: Active collaborations with Max Planck Dahlem Campus of Cognition and Leipzig Research Centre for Civilization Diseases (LIFE). Funded projects include EU Horizon and German Research Foundation (DFG) grants.

Gregory MazzaroView profile

Professor

• Non-Linear Radar
• Ultra-Wideband Radar
• RF Electronics and Systems
• +3 more

Dr. Gregory Mazzaro is a Professor in the Department of Electrical and Computer Engineering at The Citadel, School of Engineering. He joined The Citadel in 2013 after working as an Electronics Engineer at the U.S. Army Research Laboratory (ARL) from 2009–2013. His research focuses on nonlinear radar for detecting RF electronics and characterizing materials, with over 100 publications and 10 patents. Education: Ph.D. (North Carolina State University), M.S. (SUNY Binghamton), B.S. (Boston University) Research interests include radar systems (non-linear, ultra-wideband), RF electronics, and electromagnetic material analysis. He received a 2012 U.S. Army R&D Achievement Award for his ring-resonator technique. Teaching responsibilities include Electromagnetic Fields, Antennas & Propagation, and laboratory courses. His recent work explores harmonic radar for smart electronics detection, educational curriculum design, and device-centric radar imaging. Awards: 2012 U.S. Army Research & Development Achievement Award Grants and patents include innovations in radar transceiver design, nonlinear junction detection, and acoustic-radar integration. He advises undergraduate and graduate engineering projects, emphasizing hands-on experimentation.

Devavrat ShahView profile

Professor

• Machine Learning
• Artificial Intelligence
• Data Science
• +7 more

Devavrat Shah is a Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Laboratory for Information and Decision Systems (LIDS). His research focuses on machine learning, causal inference, and algorithmic fairness, with applications in healthcare outcomes, criminal justice reform, and cryptocurrency analysis. He has contributed to groundbreaking work, such as developing a machine-learning algorithm that nearly doubled Bitcoin investments over 50 days using real-time price data analysis. Shah's academic work integrates statistical methods with real-world challenges, including personalized medicine, policy evaluation, and bias detection in algorithms. His recent projects include global clinical studies on lymphoma treatments and frameworks for auditing racial bias in law enforcement systems. His interdisciplinary approach bridges computer science, statistics, and domain-specific applications. Key areas of his research include causal inference frameworks for data-rich environments, algorithmic fairness in decision-making systems, and predictive modeling for healthcare and public policy. His work often combines theoretical rigor with practical impact, addressing societal challenges through data-driven solutions. Shah's contributions to synthetic interventions and causal simulators have advanced trace-driven simulation methodologies, while his collaboration with global consortia like PETAL highlights his commitment to translational research. His students, including Kang Zhang, have contributed to impactful projects in algorithm design and data science.

Yandi ShenView profile

Assistant Professor

• Nonparametric Statistics
• Semiparametric Statistics
• High-Dimensional Inference
• +6 more

Yandi Shen is an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University (CMU), part of the Dietrich College of Humanities and Social Sciences. He holds a Ph.D. in Statistics from the University of Washington (2021), advised by Fang Han and Daniela Witten. Prior to joining CMU, he was a Kruskal Instructor in Statistics at the University of Chicago (2021–2023) and a postdoctoral researcher at Yale University under Zhou Fan. His research focuses on nonparametric and semiparametric statistics, high-dimensional inference, applied probability, and optimization algorithms. Education: Ph.D. in Statistics (University of Washington, 2021); Postdoctoral work at University of Chicago and Yale University. Research Interests: Nonparametric and semiparametric methods High-dimensional statistical inference Applied probability and stochastic processes Empirical Bayes methods Optimization and algorithm design Statistical machine learning Teaching Experience: University of Washington: Teaching Assistant for advanced inference courses (STAT 581-583), probability (STAT 394-395), and applied statistics (STAT 528). University of Chicago: Instructor for courses like STAT 22400 (Applied Regression Analysis) and STAT 33611 (Gaussian Processes). Recent Research Trends: His publications emphasize theoretical advancements in high-dimensional statistics, including empirical Bayes estimation, gradient flows, and optimization in overparameterized models. Key areas include analyzing phase transitions in spline regression, universality in regularized estimators, and nonparametric mixture models under optimal transport distances. Labs/Teams: While no specific lab is named, his collaborative work spans statistical theory and applications, often involving interdisciplinary teams in data science and machine learning.

• Computer Vision
• Optimization Methods
• Medical Imaging
• +5 more

Thomas Pock is a Professor at the Institute of Visual Computing at TU Graz. His research focuses on computer vision, optimization methods, and mathematical models in computer vision, with significant contributions to medical imaging and inverse problems. He leads projects integrating deep learning with traditional variational methods for applications in MRI reconstruction, microscopy, and cardiac signal analysis. His work emphasizes efficient sampling techniques, uncertainty quantification, and algorithmic optimization in computational imaging. Education and academic background are not explicitly detailed in the provided texts, but his extensive publication record indicates expertise in interdisciplinary areas such as biomedical engineering, signal processing, and machine learning. Key research themes include variational networks, total variation methods, and generative adversarial networks (GANs) for image reconstruction and segmentation. His research spans collaborations in both academic and clinical settings, addressing challenges in 3D reconstruction, particle flow estimation, and cardiac electrophysiology modeling. Notable projects include Total Deep Variation and inverse Eikonal methods for medical data analysis. He actively contributes to open-source tools and frameworks for variational optimization and deep learning integration.

• Applied Electromagnetics
• Antennas
• Electrically Small Antennas
• +5 more

Nader Behdad is the McFarland-Bascom Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. His expertise spans applied electromagnetics, with a focus on electrically small antennas, phased arrays, and microwave medical applications. He holds 23 U.S. patents and has advised 29 PhD students, 15 M.S. students, and 33 postdoctoral researchers. His work is supported by agencies like the U.S. Navy, Air Force, and NSF. Education: PhD in Electrical Engineering, University of Michigan (2006) M.S. in Electrical Engineering, University of Michigan (2003) B.S. in Electrical Engineering, Sharif University of Technology (2000) Research Interests: Dr. Behdad’s work addresses challenges in high-power microwaves, bio-electromagnetics, and antenna design. His innovations include novel phased-array architectures, multipactor suppression techniques, and reconfigurable antenna systems. Recent projects emphasize overcoming theoretical limits in antenna performance through non-linear systems and switched-mode topologies. Key Awards: 2025 John Kraus Antenna Award (IEEE) 2021 H. A. Wheeler Prize Paper Award (IEEE) 2011 NSF CAREER Award 2008 URSI Young Scientist Award Advising & Grants: Supervised over 50 students and secured funding exceeding $10M from federal agencies. Current research focuses on high-power microwave systems, beamforming innovations, and biomedical applications of electromagnetic fields. Labs/Teams: Leads the UW-Madison Antenna Research Group, collaborating with institutions like the Naval Research Laboratory and industry partners on advanced antenna and microwave technologies.

Wolf SingerView profile

Professor

• Neuroscience
• Cognitive Science
• Neural Dynamics
• +3 more

Wolf Singer is a renowned neuroscientist and Professor serving as Department Director at the Max Planck Institute for Brain Research in Frankfurt am Main. His research focuses on understanding the neuronal basis of higher cognitive functions, particularly the role of oscillatory dynamics and synchronization in cortical networks. Key areas include how the brain integrates sensory information, processes attention, and forms predictive representations through high-dimensional nonlinear dynamics. His lab investigates population dynamics in the visual cortex of trained monkeys using chronically implanted microelectrode arrays, emphasizing the interplay between bottom-up sensory input and top-down expectations. Singer has contributed significantly to theories of neural synchrony in disorders like schizophrenia and has explored philosophical questions about consciousness and free will. Notable achievements include the 2015 Ehrenplakette der Stadt Frankfurt am Main. His work bridges experimental neuroscience with computational models, often collaborating with researchers across disciplines. The Singer Lab hosts a team including researchers like Igor Dubinin and Andreea Lazar, focusing on topics such as predictive coding, neural plasticity, and EEG/MEG studies of perceptual processes. Singer has authored over 200 publications, with key contributions in journals like PNAS, Neuron, and Trends in Cognitive Sciences. Media engagements include interviews on topics like animal research ethics and the limits of artificial intelligence. His research underscores the importance of nonlinear dynamics and recurrent network architectures in understanding brain function.

Xiangxiong ZhangView profile

Associate Professor

• Numerical Analysis
• Scientific Computing
• Applied Mathematics
• +3 more

Xiangxiong Zhang is an Associate Professor in the Department of Mathematics at Purdue University, specializing in numerical analysis, scientific computing, and applied mathematics. His research focuses on numerical methods for partial differential equations, optimization algorithms, and computational fluid dynamics. He holds a PhD and has been actively involved in teaching graduate and undergraduate courses, including numerical PDEs, optimization, and linear algebra. His work emphasizes high-order numerical methods such as discontinuous Galerkin schemes, finite element methods, and spectral element methods, with applications in fluid dynamics, plasma physics, and compressible flow simulations. Notable contributions include positivity-preserving limiters, bound-preserving schemes, and Riemannian optimization techniques for matrix constraints. Zhang collaborates with researchers in computational mathematics and has published extensively in top journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . His research also addresses challenges in GPU-accelerated computing for large-scale scientific simulations.

• Mechanical Engineering
• Materials Science
• Electrical Engineering
• +7 more

Ignacio Valiente Blanco is an Associate Professor at the Department of Signal Theory and Communications at Universidad de Alcalá. His research focuses on mechanical systems, superconductivity applications, renewable energy optimization, and advanced materials. He holds a PhD from Universidad Carlos III de Madrid (2013), supervised by Dr. José Luis Pérez Díaz, with a thesis on superconducting non-contact linear sliders for cryogenic environments. His work bridges theoretical modeling and practical engineering, addressing challenges in energy efficiency, MEMS fabrication, and space technology. Research Interests: Superconducting magnetic bearings and actuators MEMS and micromotor design Geothermal and solar energy systems Cryogenic engineering Biomedical micromechanical systems Space mission hardware Key Themes in Publications: Recent work emphasizes energy-efficient solar cooling systems, micromagnet integration in MEMS, and ultra-low-power electromagnetic actuators. His research often involves interdisciplinary collaborations, such as the SIROM project for modular space systems and magnetic gear development for aerospace. Grants and Labs: While specific grants are not listed, his involvement in the TM Tecnologías Mecánicas group indicates active participation in funded projects. His lab work includes microfabrication processes and cryogenic testing facilities.

Goran Z. StančićView profile

Professor

• Digital Signal Processing
• IIR Filter Design
• Linear Phase Filters
• +5 more

Goran Z. Stančić is a Full Professor at the Faculty of Electronics, University of Niš, Department of Electronics, where he has been serving since his appointment in 2024. He completed his academic education entirely at the same institution, earning his PhD in 2013, Master's in 1999, and undergraduate degree in 1991, all in Electronics. His research focuses on digital signal processing, particularly in the design and optimization of IIR filters, including fullband differentiators, notch filters, and filters with approximately linear phase. His work emphasizes efficient structures such as parallel all-pass systems and symmetry in magnitude responses. The recent publications show a consistent trend in improving phase linearity, reducing complexity, and enhancing magnitude response in IIR filter designs. His work spans both theoretical filter design and practical modeling, including ultrasonic transducer systems. He is currently participating in two national research projects and has published 11 papers in journals with impact factors. His collaborations include researchers such as Saša V. Nikolić, Ivan Krstić, and Miloš Đurić. While no formal awards are listed, his sustained publication record in high-quality journals reflects strong scholarly impact. He advises students and researchers in digital filter theory and signal processing applications. He is affiliated with the Department of Electronics at the Faculty of Electronics in Niš, contributing to research, education, and national-level scientific projects.

• Randomness and Computation
• Algorithms and Optimization
• Complexity Theory
• +12 more

Avi Wigderson is the Herbert H. Maass Professor at the School of Mathematics, Institute for Advanced Study, Princeton, where he leads research in theoretical computer science and discrete mathematics. He organizes the CSDM (Computer Science and Discrete Mathematics) program at IAS, fostering interdisciplinary research and collaboration. Research Interests: His primary research areas include randomness and pseudorandomness, computational complexity, circuit and proof complexity, quantum computation, cryptography, and distributed computing. His work bridges computer science and pure mathematics, particularly through algebra, analysis, and combinatorics. He has pioneered foundational results in derandomization, lower bounds, and the interplay between computation and mathematical structures. The recent publications highlight his continued leadership in advancing algebraic and analytic techniques in complexity theory. Themes include operator scaling, invariant theory, expander graphs, arithmetic circuits, and the asymptotic analysis of tensors and matrix spaces. These works often unify computational problems with deep mathematical frameworks, demonstrating the power of interdisciplinary approaches. Scientific Awards: Edsger W. Dijkstra Prize in Distributed Computing (2023) for the paper 'Completeness Theorems for Non-cryptographic Fault-tolerant Distributed Computing' Advising and Grants: Wigderson has advised numerous PhD students, primarily at the Hebrew University and Princeton, and hosted over 50 postdoctoral researchers at IAS, including many who have become leading figures in theoretical computer science. While specific grants are not listed, his sustained productivity and influence suggest long-term funding from major sources such as the NSF, Simons Foundation, or IAS internal support. Labs and Teams: He leads the CSDM (Computer Science and Discrete Mathematics) group at the Institute for Advanced Study, organizing seminars, workshops, and collaborative research initiatives that bring together mathematicians and computer scientists from around the world.