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Qing QuView profile

Assistant Professor

• Signal Processing
• Data Science
• Machine Learning
• +4 more

Qing Qu is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan – Ann Arbor, with affiliations to the Michigan Institute for Data Science (MIDAS), the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM), and the Michigan Institute for Computational Discovery and Engineering (MICDE). He earned a B.E. from Tsinghua University (2011), a M.Sc. from Johns Hopkins University (2012), and a Ph.D. from Columbia University (2018) under the supervision of John Wright. From 2018–2020, he was a Moore-Sloan Fellow at NYU Center for Data Science. Research Focus: At the intersection of signal processing, data science, machine learning, and numerical optimization, with emphasis on deep representation learning and diffusion models. Awards: Microsoft Ph.D. Fellowship (2016), NSF Career Award (2022), Amazon AWS AI Award (2023), UM CHS Junior Faculty Award (2025), Google Research Scholar Award (2025), and Best Paper at NeurIPS’23 Workshop on Diffusion Models. Publications: Recent work explores generalization in diffusion models via Probability Flow Distance , low-dimensional subspace clustering, controllable image editing, and watermarking techniques. His research also investigates neural collapse theory and optimization landscapes in deep learning. Collaborations: Co-organizer of ICCV’25 and ICML’25 tutorials with experts like Yi Ma, Yuxin Chen, and Liyue Shen. Former students include Peng Wang, Yifu Lu, Jinfan Zhou, and Siyi Chen, who have joined institutions like Princeton, UChicago, Duke, and University of Macau. Grants: Funded by NSF, ONR, ARO, Amazon Research, KLA Corporation, MICDE, and MIDAS.

Junliang DongView profile

Assistant Professor

• Terahertz Technology
• Imaging and Sensing
• Communications
• +4 more

Dr. Junliang Dong is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Arkansas, College of Engineering, where he joined as a tenure-track faculty member in Fall 2024. His research bridges fundamental science with practical applications in terahertz (THz) radiation technology. Dr. Dong's educational background includes: Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology, USA M.Sc. in Control Science and Engineering from Tsinghua University, China His research spans multiple disciplines with particular focus on THz imaging and sensing, THz communications, and THz biophotonics. He has authored approximately 30 peer-reviewed journal articles and 40 conference papers, and holds 5 patents. His work has been featured in Optics & Photonics News (cover feature), The New York Times, American Scientist, Phys.org, and Science Daily. Dr. Dong's publications reveal a strong emphasis on advancing terahertz imaging capabilities, particularly for non-transparent media and cultural heritage applications. His research demonstrates innovative approaches to single-shot ultrafast terahertz photography, metal-wire waveguide technology, and applications spanning from art conservation to biomedical imaging. His notable achievements include: Swiss THz Prize at IRMMW-THz 2023 Mitacs Elevate Fellowship As an educator, Dr. Dong teaches Electromagnetics (ELEG 37004), Introduction to Terahertz Technology and Applications (ELEG 4870V/5870V), and Introduction to Antennas (ELEG 47803/57803). His research group focuses on computational THz imaging, photonic-inspired devices for THz communications, THz biophotonics, and ubiquitous THz applications across aerospace, automotive, marine, and energy sectors, as well as in art conservation and archaeology.

Weiping PeiView profile

Assistant Professor

• Security and Privacy
• Crowdsourcing
• Machine Learning
• +3 more

Weiping Pei is an Assistant Professor in the School of Cyber Studies at The University of Tulsa, specializing in security and privacy, crowdsourcing, machine learning, and human-computer interaction. Her work bridges technical security mechanisms with human behavioral aspects to develop practical privacy-preserving systems. Education: Ph.D. in Computer Science, Colorado School of Mines (2022) M.Eng. in Software Engineering, Sun Yat-sen University (2017) B.Eng. in Microelectronics, Sun Yat-sen University (2015) Dr. Pei's research centers on three interconnected pillars: secure and privacy-preserving crowdsourcing (with focus on receipt transcription systems and third-party privacy), robust machine learning (particularly adversarial attacks against vulnerability detection and content-preserving text manipulation), and usable security (including GDPR policy analysis and online grooming detection). Her approach uniquely integrates human behavioral modeling with cryptographic and machine learning techniques to address real-world security gaps in crowd-powered systems. Analysis of her 2023-2025 publications reveals a strategic shift toward AI-driven security solutions, particularly using LLMs for behavioral analysis in digital forensics and adversarial machine learning for vulnerability assessment. Her work consistently targets high-impact venues like IEEE S&P and ACM CCS, demonstrating both theoretical rigor and practical applicability in privacy policy compliance and crowdworker security. Scientific Awards: NSF CAREER Award (2025) for foundational cybersecurity research NSF SaTC 2.0 RES Award (2025) for privacy-preserving technologies NSF CRII Grant recipient (2023) Distinguished Rath Award nominee for societal-impact PhD thesis (2022) Outstanding CS Ph.D. Student Award at Mines (2021) Grace Hopper Celebration Scholarship (2018) Dr. Pei actively mentors four students across degree levels while leading multiple NSF-funded projects totaling $1.3+ million. Her research group develops tools for privacy policy analysis (PolicyChecker), adversarial vulnerability testing (AdVul), and online safety monitoring. She teaches core cybersecurity courses including Data Security and Cryptography, with future work focusing on AI security foundations through her NSF CAREER grant.

• Numerical Analysis
• Computational Mathematics
• Sparse Signal Processing
• +4 more

Dr. Toni Volkmer is a researcher affiliated with the Faculty of Mathematics at Chemnitz University of Technology. His work focuses on computational mathematics and signal processing, particularly in high-dimensional data analysis and sparse Fourier transforms. His research intersects numerical analysis, approximation theory, and algorithm design for efficient data processing. Key research areas include Sparse spectral estimation Rank-1 lattice sampling Multivariate function approximation High-dimensional data analysis Fast matrix-vector operations Graph Laplacian computations His publications demonstrate expertise in developing sublinear-time algorithms for harmonic analysis and creating efficient numerical methods applicable to modern data science challenges. While no formal awards are listed, his contributions to scientific computing have been documented in multiple peer-reviewed publications since 2012.

• Machine Learning
• Signal Processing
• Blind Source Separation
• +4 more

Iván Durán Díaz is a full professor in the Department of Signal Theory and Communications at the University of Seville. His research focuses on advanced signal processing techniques, machine learning, and wireless communication systems. Collaborative roles in brain-machine interfaces and emotion recognition projects Key contributions to blind source separation and component analysis Research interests include: Development of machine learning algorithms for signal processing Analysis of complex-valued signals in wireless communications Time-frequency domain speech separation techniques His recent publications demonstrate expertise in: Neural signal processing for emotion detection Alpha-Beta divergence applications in clustering Component analysis for medical imaging and speech signals

• Integrated Sensing and Communication
• Wireless Communications
• Signal Processing
• +6 more

Dr. Fan Jiang serves as an Associate Senior Lecturer at Halmstad University's School of Information Technology, where he leads research at the intersection of wireless communications and localization technologies. His academic profile reflects strong technical expertise in next-generation wireless systems with practical applications in vehicular networks and satellite communications. His research program focuses on integrated sensing and communication (ISAC) frameworks, with particular emphasis on: Massive MIMO and millimeter wave system design Wireless localization/synchronization algorithms Integration of sensing functionalities within communication protocols Applications for vehicular positioning and satellite networks Analysis of his 15 publications from 2022-2024 reveals consistent output in high-impact venues, with growing emphasis on satellite communications and reconfigurable intelligent surfaces. His work demonstrates strong mathematical rigor in channel modeling and algorithm design while addressing practical implementation challenges like synchronization errors and hardware constraints. Dr. Jiang maintains active research collaboration through co-authorship networks and participates in experimental validation efforts, as evidenced by his 2022 field study on 5G mmWave positioning. His technical approach balances theoretical innovation with real-world applicability across multiple wireless domains.

Joakim Andén-PanteraView profile

Associate Professor

• Signal Processing
• Statistical Data Analysis
• Machine Learning
• +6 more

Joakim Andén-Pantera is an Associate Professor in the Division of Probability, Mathematical Physics and Statistics at the Department of Mathematics, KTH Royal Institute of Technology. His research spans signal processing, statistical data analysis, and machine learning with applications in cryo-electron microscopy, biomedical signal analysis, and audio classification. His educational background includes advanced training in mathematics and signal processing leading to his current academic position. Though specific degree details aren't provided in the text, his research profile indicates deep expertise in mathematical methods for signal representation. Professor Andén-Pantera's work focuses on developing mathematical frameworks that extract discriminative information from signals while remaining invariant to irrelevant variations like translation, frequency-shifting, and noise. His research bridges theoretical mathematics with practical applications across multiple domains: Developing wavelet scattering transforms for robust signal representation Applying these techniques to cryo-EM for molecular structure analysis Creating methods for audio and music classification through time-frequency analysis Designing algorithms for biomedical signal processing, particularly ECG analysis Contributing to computational methods for cosmological parameter estimation His publication record shows consistent contributions in both theoretical signal processing and practical implementations. The research trajectory demonstrates increasing sophistication in applying scattering transforms and deep learning to diverse signal processing challenges across biology, medicine, and acoustics. Notable scientific recognition includes: Best Paper Award (2nd Place) at IEEE International Workshop on Machine Learning for Signal Processing (2015) Best Paper Award (1st Place) at International Conference on Digital Audio Effects (2012) Best Paper Award at IPDPS for cuFINUFFT implementation Professor Andén-Pantera advises graduate students on degree projects in financial mathematics, mathematical statistics, and engineering mathematics. His research group develops computational tools including ASPIRE for cryo-EM and Kymatio for wavelet scattering transforms. He teaches courses in Applied Statistics, Probability Theory, and Statistical Learning at KTH. He leads the development of several influential open-source software projects that have become standard tools in their respective fields, with Kymatio particularly gaining widespread adoption across multiple research communities.

• Statistical Signal Processing
• Machine Learning
• Optimization
• +6 more

Ali Pezeshki is a Professor at Colorado State University with joint appointments in the Department of Electrical and Computer Engineering and the Department of Mathematics . His research spans statistical signal processing , machine learning , optimization , and applied harmonic analysis , with applications to radar, bioimaging, and network systems. B.Sc. and M.Sc. in Electrical Engineering from University of Tehran (1999, 2001) Ph.D. in Electrical Engineering from Colorado State University (2004) He has served on the editorial board of IEEE Access (2012-2018) and received the IEEE SP Society Best Young Author Award (2011). His students include PhD candidates like Yifan Yang and alumni such as Pooria Pakrooh (Qualcomm) and Wenbing Dang (Argo AI). His recent publications focus on greedy optimization strategies , submodular functions , adaptive radar , and single-pixel imaging . Keywords across his work include Compressed Sensing , Network Learning , and Statistical Estimation .

Karystinos GeorgiosView profile

Professor

• Telecommunications theory and systems
• Coding theory
• Adaptive signal processing
• +7 more

George N. Karystinos is a Professor at the School of Electrical and Computer Engineering , Technical University of Crete , where he has served since 2005. Since 2021, he also holds the position of Dean of the School. Previously, he was an Assistant Professor at Wright State University (2003–2005). Education: Ph.D. in Electrical Engineering, State University of New York at Buffalo , 2003 Diploma in Computer Engineering and Science, University of Patras , 1997 Additional studies in Piano, National Conservatory of Athens and State University of New York at Buffalo Research Interests: His research spans telecommunications theory and systems , coding theory , adaptive signal processing , wireless communications , MIMO systems , neural networks , and optimization with limited data . He is particularly known for contributions to L1-norm PCA , noncoherent detection , RFID systems , and code design for CDMA . Scientific Awards: IEEE Transactions on Neural Networks Outstanding Paper Award (2003) IEEE ICT Best Paper Award (2001) IEEE ISWCS Best Paper Award (2013) IEEE ICASSP Best Student Paper Award (2015) IEEE RFID-TA Second Best Student Paper Award (2011) IEEE MOCAST Best Student Paper Award (2018) Laboratory and Teaching: He leads research at the Telecommunications Laboratory and teaches courses such as Signals and Systems , Information and Code Theory , and Probability and Random Process Theory . Grants and Projects: While specific grant details are not listed, his extensive publication record and award history indicate active participation in funded research projects, particularly in wireless communications and signal processing.

Peter ManoharView profile

Researcher

• Theoretical Computer Science
• Algorithms
• Coding Theory
• +4 more

Peter Manohar is a postdoctoral researcher in the Computer Science and Discrete Math group at the Institute for Advanced Study , focusing on Theoretical Computer Science with emphasis on algorithms, coding theory, and cryptography. His work explores spectral algorithms for semirandom and smoothed instances of NP-hard constraint satisfaction problems, linking these methods to coding theory, extremal combinatorics, and cryptography. Education: PhD in Computer Science from Carnegie Mellon University, advised by Venkatesan Guruswami and Pravesh K. Kothari B.S. in EECS from UC Berkeley, advised by Alessandro Chiesa and Ren Ng Research Trends: His recent publications highlight advancements in spectral refutation techniques, locally decodable/correctable codes, and connections between complexity theory and coding. Articles span venues like FOCS, STOC, APPROX, and arXiv, reflecting his interdisciplinary approach. Awards: He has received prestigious NSF and Cylab Presidential Fellowships, along with ARCS scholarships during his PhD. His work on quantum proofs (TCC 2019) and constraint satisfaction problems has been recognized in invited journal special issues. Teaching & Collaboration: Peter has taught courses at Carnegie Mellon, including Quantum Computing and Computer Graphics. He interned at TTIC in Summer 2023 and co-organized CMU's Theory Club, demonstrating active engagement in academic communities.

Benjamin Bejar HaroView profile

Researcher

• Machine Learning
• Signal Processing
• Computer Vision
• +3 more

Dr. Benjamin Bejar Haro is a researcher at the Paul Scherrer Institute (PSI) , specializing in computational methods for scientific applications. His work spans machine learning, signal processing, and computer vision, with recent publications focusing on EUV mask inspection, sparse signal recovery, and biomedical video analysis. Research Interests Deep learning for optical imaging Multi-channel signal processing Algorithm development for crystallography Biomedical video analysis Sparse signal reconstruction Ensemble forecasting systems Publications His recent work includes contributions to EUV mask inspection using neural networks (2025), cross-channel unlabeled sensing (2025), and kilohertz serial crystallography algorithms (2024), reflecting interdisciplinary expertise in computational modeling and scientific data analysis.

Coralia CartisView profile

Professor

• Numerical Optimization
• Algorithm Design and Analysis
• Nonlinear Optimization
• +6 more

Professor Coralia Cartis holds a Professorship in Numerical Optimization at the University of Oxford's Mathematical Institute, where she also serves as a Tutorial Fellow at Balliol College. Additionally, she is a Turing Fellow at The Alan Turing Institute in London, focusing on advanced optimization research with applications spanning machine learning and climate science. She obtained her PhD in Mathematics from the University of Cambridge in 2005, establishing the foundation for her expertise in algorithmic optimization. Research Focus: Her work centers on designing and analyzing algorithms for linear and nonlinear optimization problems, both convex and nonconvex, with emphasis on complexity theory and dynamical systems connections. Key application areas include compressed sensing, sparse approximation, machine learning, and inverse problems in climate modeling, where she develops methods for ocean biogeochemical model optimization and data assimilation. Publication Evolution: Recent publications (2024-2025) demonstrate expansion into high-order tensor methods and quartic regularization models, while maintaining her foundational work on cubic regularization. Her research consistently bridges theoretical complexity analysis with practical climate science applications, showing increasing interdisciplinary collaboration. Honors: Leslie Fox Prize in Numerical Analysis (2005, second place) Mathematical Programming Computation Journal Best Paper Prize (2019) INFORMS Simulation Society Outstanding Paper Prize (2021) ICM Invited Speaker (2022) EUROPT Fellow (2023) SIAM Fellow (2023) Academic Leadership: She actively contributes to Oxford's Numerical Analysis and Machine Learning research groups, supervises graduate students (though specific names aren't listed), and secures research funding for projects involving climate modeling and optimization theory. Her work with the Alan Turing Institute facilitates cross-institutional collaborations in data science. Research Infrastructure: Her optimization research is conducted within Oxford's Mathematical Institute, leveraging resources from both the Numerical Analysis group and the Machine Learning and Data Science research cluster, with additional computational support through The Alan Turing Institute.

• Telecommunications
• Compressive Sensing
• Cognitive Radio
• +4 more

Ricardo Blasco-Serrano is a researcher affiliated with KTH Royal Institute of Technology , specifically within the School of Electrical Engineering (EES) and the Communication Theory department. His work focuses on telecommunications , with emphasis on compressive sensing , cognitive radio , and network coordination . He has contributed to theoretical frameworks for interference management and sparse signal recovery. Education : Doctorate in Telecommunications from KTH Royal Institute of Technology (2013). His research explores the tradeoff between measurement rates and estimation accuracy in compressive sensing, interference coordination in cognitive radio channels, and polar codes for network compression. Key collaborations include work with Ragnar Thobaben, Mikael Skoglund, and Eduard Jorswieck, supported by EU FP7 grant 257626. Notable publications include work on vehicle-to-everything (V2X) communication , partial support recovery , and MISO cognitive channel strategies . His methods have implications for machine-to-machine communication and network densification .

Giuseppe Carlo CalafioreView profile

Professor

• Convex Optimization
• Control Systems
• Randomized Algorithms
• +6 more

Giuseppe Carlo Calafiore is a Full Professor at the Department of Electronics and Telecommunications (DET) of the Polytechnic University of Turin , where he has been since 1998. He has held visiting positions at institutions like Stanford University , University of California at Berkeley , and Vietnam National University . His research focuses on convex optimization , control systems , and randomized algorithms , with applications in finance , robotics , and machine learning . Education : Laurea in Electrical Engineering (1993) and Ph.D in Information and System Theory (1997) from Politecnico di Torino. Research Interests : Span large-scale optimization , robust portfolio optimization , and distributed algorithms , with emphasis on sparsity-based methods and data-driven control for autonomous systems. Recent Publications include works on nonlinear model predictive control , financial network robustness , and adaptive gain control in population games. His scientific awards include the IEEE CSS George S. Axelby Outstanding Paper Award (2008) and Fellow of the IEEE (2018) . He has advised students in artificial intelligence and communications engineering programs. His projects include research on smart energy allocation and financial risk modeling .

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +10 more

Robert Krauthgamer holds The Harry Weinrebe Professorial Chair of Computer Science at The Weizmann Institute of Science, where he also serves as Department Head of the Department of Computer Science & Applied Mathematics within the Faculty of Mathematics and Computer Science. His distinguished career includes editorial leadership as Editor-in-Chief of SIAM Journal on Computing (2019-2025) and significant service on numerous program committees for major theoretical computer science conferences. Dr. Krauthgamer's primary research focuses on Analysis of Algorithms , with particular expertise in Data Analysis and Massive Data Sets, Combinatorial Optimization, Approximation Algorithms and Hardness of Approximation, Average-case Analysis and Heuristics, Embeddings of Finite Metrics, and Routing and Peer to Peer networks. His work also spans broader areas of Discrete Mathematics and High-Dimensional Geometry, reflecting his deep mathematical approach to algorithmic problems. His recent publications reveal a strong emphasis on sparsification techniques, high-dimensional computational geometry, streaming algorithms, and graph-theoretic problems. Throughout his career, Krauthgamer has supervised numerous graduate students at both MSc and PhD levels, with recent theses addressing cutting-edge topics in algorithm design and analysis. His service contributions include steering committee roles for major conferences like SODA, ESA, and HALG, as well as committee membership for the prestigious Gödel Prize (2019-2021). As an educator, he regularly teaches advanced courses including Randomized Algorithms and Sublinear Time and Space Algorithms, often in collaboration with other leading researchers. He also organizes Weizmann's TheoryLunch and has coordinated the Foundations of Computer Science seminar for many years, demonstrating his commitment to fostering academic community and intellectual exchange.