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Naoki SaitoView profile

Professor

• Applied and Computational Harmonic Analysis
• Graph Signal Processing
• Multiscale Transforms
• +6 more

Naoki Saito is a Professor in the Department of Mathematics at the University of California, Davis, and the Director of the UC Davis TETRAPODS Institute of Data Science (UCD4IDS). His research lies at the intersection of applied mathematics, signal processing, and data science, with a focus on multiscale analysis and harmonic analysis on graphs and networks. His research interests include Applied and Computational Harmonic Analysis , Graph Signal Processing , Multiscale Transforms , Wavelets , Spectral Graph Theory , and Mathematical Data Representation . He develops theoretical frameworks and practical algorithms for analyzing complex datasets, particularly through the use of Laplacian eigenfunctions and multiscale basis dictionaries. The recent publications reflect a strong trend toward graph-based signal processing , scattering transforms , and topological data analysis . His work emphasizes the construction of natural, adaptive bases for signals on graphs and simplicial complexes, enabling efficient and interpretable data analysis. The integration of harmonic analysis with machine learning techniques is a recurring theme. Although no specific scientific awards are listed in the provided texts, his sustained scholarly output and leadership in the field are evident. Dr. Saito advises a number of students and postdoctoral researchers, including J. Irion, Y. Shao, H. Li, and others. His research has been supported by various grants, though specific funding sources are not detailed in the provided materials. He leads the UCD4IDS, a research institute focused on data science, indicating active involvement in collaborative, interdisciplinary research and academic leadership.

• Machine Learning
• Domain Adaptation
• Transfer Learning
• +3 more

Dr. Sinno Jialin Pan is a leading researcher in machine learning and artificial intelligence at Nanyang Technological University, Singapore. His work focuses on domain adaptation, sentiment analysis, and efficient neural network optimization. Key research areas: Machine Learning, Domain Adaptation, Reinforcement Learning, Sentiment Analysis Recent publications demonstrate expertise in time-series classification (2025) using hierarchical domain adaptation, LLM efficiency (2025) through expert pruning, and graph generation (2024) via spectral diffusion. His work spans both theoretical advancements and practical applications in neural architecture optimization and adversarial learning. Scientific contributions include: 2025: Virtual-label hierarchical domain adaptation 2024: Spectral diffusion for graph generation 2024: Multilingual jailbreak analysis in LLMs Current trends show increasing focus on large language model optimization and robust neural architectures , with applications in fault diagnosis, recommender systems, and misinformation detection.

Theodore KimView profile

Professor

• Computer Graphics
• Computer Animation
• Solid Mechanics
• +3 more

Theodore Kim is a Professor of Computer Science at Yale University, where he co-leads the Computer Graphics Group with Julie Dorsey and Holly Rushmeier. His research focuses on physics-based simulation, including fluid dynamics, solid mechanics, and fractal growth structures. He holds a PhD from the University of North Carolina at Chapel Hill and has held academic positions at UCSB and the University of Saskatchewan. His work has been applied in over two dozen films, earning him SciTech Oscars in 2012 and 2022. He previously served as a Senior Research Scientist at Pixar, contributing to projects like *Cars 3*, *Coco*, and *Incredibles 2*. Education: Ph.D., Computer Science, University of North Carolina at Chapel Hill (2006) M.S., Computer Science, University of North Carolina at Chapel Hill (2006) B.S., Computer Science, Cornell University (2001) Research Interests: Kim’s work bridges academia and industry, emphasizing practical applications of physics-based simulation. Notable areas include hair and skin simulation for animation, fluid dynamics, and the historical context of computer graphics innovations. His research also addresses racial biases in graphics, such as in hair and skin modeling. Articles Trends: Recent work emphasizes diverse representation (e.g., Black hair simulation), biomechanical accuracy (feather modeling), and historical analysis of technical contributions (e.g., Búi Tướng Phong’s legacy). Earlier publications focus on fluid subspace methods, wavelet turbulence, and efficient simulation techniques. Awards: Academy Award for Scientific and Technical Achievement (2012, 2022) NSF CAREER Award (2013–2018) UCSB Harold J. Plous Award (2015) Best Paper Awards at SCA (2011, 2016, 2018) Grants & Labs: Leads Yale’s Critical Computing Initiative and directs undergraduate studies in CS. His lab collaborates with industry (e.g., Pixar) and emphasizes open-source software. Current projects include fractal design tools and anti-racist graphics research.

Hari SundarView profile

Associate Professor

• Parallel Algorithms
• High Performance Computing
• Computational Sciences
• +3 more

Hari Sundar is an Associate Professor in the Department of Computer Science at Tufts University, holding the Ada Lovelace Associate Professorship. Previously, he served as an Associate Professor at the Kahlert School of Computing, University of Utah. His research focuses on developing parallel algorithms for computational sciences and high-performance computing, addressing challenges in biosciences, geophysics, computational fluid dynamics, and computational relativity. He leads efforts in adaptive mesh refinement, geometric multigrid methods, and scalable scientific computing frameworks like Dendro-GR for numerical relativity. Education: Ph.D. in Computer Science from the University of Pennsylvania (2009), and a Bachelor of Engineering from the University of Delhi (2000). Postdoctoral work at the Oden Institute, University of Texas at Austin. Research Interests: Parallel algorithms, high-performance computing architectures, computational relativity (binary black hole simulations), multiphase flow modeling, and domain-specific languages for scientific computing. His work emphasizes scalability and efficiency on modern supercomputers. Key Contributions: Development of the Dendro-GR platform for gravitational wave simulations, scalable PDE solvers, and GPU-optimized algorithms for phonon transport and genomic sequence alignment. His recent work includes advancements in gravitational waveform modeling for LISA space missions and thermodynamically consistent two-phase flow simulations. Grants & Collaborations: Active in NSF-funded projects on computational relativity, multiphase flow algorithms, and scalable PDE solvers. Collaborates across disciplines in astrophysics, materials science, and bioinformatics.

Luca FredianiView profile

Professor

• Theoretical and Computational Chemistry
• Density Functional Theory (DFT)
• Multiwavelet Methods
• +4 more

Luca Frediani is a Professor in Theoretical and Computational Chemistry at the Hylleraas Center, Department of Chemistry, UiT The Arctic University of Norway. His research focuses on advanced quantum chemistry methods, including density functional theory, multiwavelet basis sets, and solvation modeling. He actively develops computational tools like MRChem and VAMPyR for molecular electronic structure calculations. Current affiliation: UiT The Arctic University of Norway Research group: Theoretical and Computational Chemistry Teaching: KJE-2001 Theoretical Chemistry and Spectroscopy His work spans relativistic quantum chemistry, numerical methods for response properties, and benchmarking of basis set limits. Publications emphasize eliminating basis set errors, multiwavelet applications, and polarizable continuum models for solvation. He collaborates extensively on software development for quantum chemistry. Recent articles highlight multiwavelet-based DFT at the basis set limit, noise-tolerant force calculations, and relativistic effects in electronic structure. Sub-fields include scalar relativity, cavity-free solvation, and metal-ligand interaction accuracy.

Len GelmanView profile

Professor

• Signal Processing
• Condition Monitoring
• Vibration Analysis
• +4 more

Len Gelman is a Professor and Chair in Signal Processing and Condition Monitoring at the University of Huddersfield's Department of Engineering within the School of Computing and Engineering. He also serves as Director of the Centre for Efficiency and Performance Engineering. His research focuses on advanced signal processing techniques for fault diagnosis in electromechanical systems, vibration analysis, and predictive maintenance. He is actively involved in PhD supervision and has authored over 100 publications, achieving 1499 citations and an h-index of 21. Key research areas include digital twin technology, nonlinear spectral analysis, and machine learning for industrial diagnostics. His work addresses challenges in non-stationary signal processing, motor current signature analysis, and condition monitoring under varying operating conditions. Collaborations include interdisciplinary projects with the Centre for Efficiency and Performance Engineering. Recent studies highlight innovations in fault diagnosis frameworks for rotating machinery, conveyor belt systems, and wind turbines. His contributions bridge theoretical advancements with practical industrial applications, emphasizing explainable AI and adaptive diagnostics. Gelman's research has been presented at major conferences like the World Congress on Engineering and published in specialized journals. Education: Not explicitly stated in the provided text. Awards: High citation count and h-index reflect his significant academic impact. Grants/Advising: Supervised 2 PhD projects; accepting new students in diagnostic engineering and condition monitoring. Labs/Teams: Leads the Centre for Efficiency and Performance Engineering and collaborates with the Department of Engineering's research groups.

Pierre DuchesneView profile

Professor

• Time Series Analysis
• Sampling Theory
• Multivariate Analysis
• +2 more

Pierre Duchesne is a Full Professor in the Department of Mathematics and Statistics at the University of Montreal . He serves as Professor-responsibility for the M.Sc. and Ph.D. in Statistics programs (2000-2004). His research focuses on applied statistics with emphasis on: Time Series Analysis (univariate and multivariate models, serial correlation testing, portmanteau statistics) Sampling Theory (robust estimation methods, calibration estimators) Multivariate Analysis (ARCH effects, vector autoregressive models, causality testing) Applications in Econometrics and Financial Econometrics His work combines theoretical development with practical implementation through: Wavelet-based diagnostic methods Simulation studies for model validation Software development (S-PLUS/SAS) for statistical analysis Collaboration with organizations like Statistics Canada and Canadian Journal of Statistics He has served as Associate Editor for journals including Computational Statistics & Data Analysis (CSDA) and Canadian Journal of Statistics (CJS/RCS) .

Jian KangView profile

Professor

• Biostatistics
• Bayesian Methods
• Gaussian Processes
• +11 more

Jian Kang is a Professor and Associate Chair for Research at the University of Michigan School of Public Health , specializing in Biostatistics . His work focuses on developing advanced statistical methods for large-scale biomedical data , with applications to precision medicine , neuroimaging , and genomics . Education: PhD in Biostatistics, University of Michigan (2011) MS in Mathematics (Statistics), Tsinghua University (2007) BS in Statistics, Beijing Normal University (2005) Research Interests include Bayesian nonparametric methods , deep learning for medical imaging , ultra-high-dimensional variable selection , and graphical models for network inference . His 2025-2023 publications demonstrate expertise in Bayesian hierarchical modeling , spatial statistics , and machine learning for healthcare . Scientific Awards : Michigan SPH Excellence in Research Award (2025) ICSA President's Citation Award (2024) Statistics in Biopharmaceutical Research Best Paper (2023) Best Paper in Biometrics by IBS Member (2022) Fellow, American Statistical Association (2021) Grants include NSF-IIS (2021-2025) for BCI statistical learning , NIGMS (2020-2022) for metabolomics biomarker selection , NIDA (2020-2025) for imaging data analysis , and NIMH (2014-2025) for multidimensional neuroimaging methods . Labs and Teams develop Bayesian computational tools for neuroimaging and spatial transcriptomics , collaborating with institutions like Emory University and University of North Carolina.

Subrahmanya Sastry ChallaView profile

Professor

• Wavelets
• Sparse Optimization Theory
• Inverse Problems
• +7 more

Professor Subrahmanya Sastry Challa is affiliated with the Department of Mathematics at Indian Institute of Technology Hyderabad. His academic journey includes a PhD from IIT Kanpur under Prof. P. C. Das, an M.Sc(Tech) from JNT University, and a B.Sc from Hindu College, Machilipatnam. Research Focus: He specializes in Wavelets and Sparse Optimization Theory Frame Theory and Data-driven Learning Methods Applications in Medical Imaging and Signal Processing His recent work explores sparsity-driven optimization techniques with applications in tomography, ECG signal recovery, and machine learning algorithms. Publications & Collaborations: He has contributed to advancements in compressive sensing, inverse problems, and numerical linear algebra through collaborations with researchers like Dr. Phanindra Jampana and Dr. Praveen Pradhan. Key journals include IEEE Transactions on Signal Processing , Inverse Problems , and Neurocomputing . Teaching: Courses taught include Wavelets & Applications, Compressive Sensing, Numerical Linear Algebra, and Mathematics Behind Machine Learning, emphasizing both theoretical and applied aspects. Administrative Roles: Served as Associate HoD/HoD (2010-2014), Chief Vigilance Officer (2015-2019), and participated in policy-drafting committees during IIT Hyderabad's formative years.

Bin HanView profile

Professor

• Computational Mathematics
• Numerical Partial Differential Equations (PDEs)
• Applied Harmonic Analysis
• +4 more

Bin Han is a Professor of Mathematics at the Department of Mathematical and Statistical Sciences , University of Alberta, Canada. He holds a PhD (1998), MSc (1994), and BSc (1991) in Mathematics from the University of Alberta, Chinese Academy of Sciences, and Fudan University, respectively. Research Interests: Computational Mathematics: High-order finite difference methods, numerical solutions of PDEs (Helmholtz, elliptic interface, Burgers' equations), and Fourier/wavelet-based algorithms. Applied Harmonic Analysis: Framelets/wavelets with applications in image processing, data sciences, and deep learning, focusing on directional and quasi-tight properties. Wavelet Theory: Construction of wavelets on bounded intervals for boundary value problems, Gibbs phenomenon analysis, and stability of refinable functions. Computer Aided Geometric Design (CAGD): Subdivision schemes, spline approximation, and isogemetric analysis. Article Trends: His recent work (2021-2022) emphasizes high-order finite difference methods for Helmholtz and interface problems, directional tensor product complex tight framelets for image processing, and quasi-tight framelets with balancing orders for robustness and sparsity. Scientific Awards: NSERC Postdoctoral Fellowship (1999-2000) Advising & Grants: He has supervised PhD students Qiwei Feng, Michelle Michelle, Ran Lu, and Chenzhe Diao. His research is supported by NSERC, Westgrid, Compute Canada, and MITACS.

• Wireless Communication
• Machine Learning
• Signal Processing
• +4 more

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

HanQin CaiView profile

Assistant Professor

• Mathematical Optimization
• Data Science
• Machine Learning
• +5 more

Dr. HanQin Cai is the Paul N. Somerville Endowed Assistant Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF), also serving as Director of the Data Science Lab. He holds a joint appointment with the Department of Computer Science. His research focuses on theoretical and algorithmic foundations of mathematical optimization, data science, and machine learning, with emphasis on non-convex algorithms, adversarial attacks, signal/image processing, and deep learning integration. He has secured NSF grants totaling over $2.6M, including a $121K single-PI grant and a $2.49M co-PI grant. His work has been recognized with the UCF OSCaR Award (2025) and IEEE Senior Member status (2024). Education: PhD in Applied Mathematical and Computational Sciences from University of Iowa (2018), with M.S. in Mathematics (2014) and M.C.S. in Computer Science (2017). Previously served as a Postdoc at UCLA Mathematics Department under Dr. Wotao Yin. Research highlights include: query-efficient zeroth-order optimization, robust signal processing with corrupted data, adversarial attacks on neural networks, and tensor-based methods for high-dimensional data analysis. His recent publications explore advanced techniques in matrix recovery, tensor decompositions, and explainable AI. Grants & Awards: NSF DMS-2304489 (2022–2025), NSF DUE-2321986 (2024–2029), UCF OSCaR Award, IEEE Senior Membership. Labs & Teams: Directs UCF's Data Science Lab, collaborates across disciplines in statistics, computer science, and engineering.

Jayneel ParekhView profile

Research Fellow

• Neural Network Interpretability
• Audio Processing
• Computer Vision
• +3 more

Jayneel Parekh is a Postdoctoral Researcher in the MLIA (Machine Learning and Artificial Intelligence) team at ISIR (Institut des Sciences et Industries du Réel), Faculty of Science, Sorbonne University, working with Prof. Matthieu Cord. His research focuses on understanding and enhancing large multimodal models, with applications across audio, visual, and multimodal domains. Parekh completed his PhD at LTCI, Telecom Paris under Prof. Florence d'Alche and Prof. Pavlo Mozharovskyi, researching neural network interpretability applied to image and audio data. He earned his undergraduate degree in Electrical Engineering from IIT Bombay, where he worked with Prof. Preeti Rao and Prof. Yi-Hsuan Yang on Speech-to-Singing conversion. His research spans neural network interpretability, audio processing, computer vision, and multimodal models, with emphasis on explainable AI. His work demonstrates a consistent trajectory from foundational audio/image interpretability methods to cutting-edge large multimodal model analysis, showing increasing complexity and impact across NeurIPS, ICML, and ICCV publications. L2I paper awarded 2nd prize for STIC Best Scientific Contribution 2023 Top Reviewer at NeurIPS 2023 Parekh actively contributes to the academic community through workshop organization (ICCV on Explainable Computer Vision, ELLIS Unconference on Robustness/Fairness/Explainability) and presentations at institutions including IIT Jodhpur, Deezer Research, and IBM Research. His collaborative network spans MPI Informatics, TU Darmstadt, TU Munich, and Télécom Paris.

Shujuan MaoView profile

Assistant Professor

• Environmental Seismology
• Hydrogeophysics
• Geothermal Energy
• +5 more

Shujuan Mao is an Assistant Professor in the Department of Earth and Planetary Sciences at the University of Texas at Austin, part of the Jackson School of Geosciences. She holds a B.S. from Peking University, a Ph.D. from MIT, and conducted postdoctoral research at Stanford University and the Institut des Sciences de la Terre in France. Her research focuses on environmental seismology, hydrogeophysics, and geothermal energy, with an emphasis on understanding subsurface fluid dynamics using seismic interferometry. Key areas include groundwater monitoring, carbon sequestration, and volcanic unrest. She leads the 'Seismo4D' research group, which develops cutting-edge seismic techniques for 4D subsurface imaging. Mao's work spans academic contributions (over 15 peer-reviewed articles), student supervision (two current advisees), and international collaborations. Her group actively recruits PhD students and postdocs interested in environmental seismology and energy transition challenges. Her mailing address is Jackson School of Geosciences, Austin, TX 78712-1692, with contact via smao@jsg.utexas.edu or shujuan.c.mao@gmail.com.

• Computer Animation
• Geometry Processing
• Fluid Dynamics
• +2 more

Chris Wojtan is a Professor at the Institute of Science and Technology Austria (ISTA) , leading the Visual Computing Group . His research focuses on geometric and numerical algorithms for computer animation and geometry processing , particularly in simulating solid and fluid dynamics , controlling physics simulations, and computing with 3D shapes. Research Interests : Computer Animation, Geometry Processing, Fluid Dynamics, Solid Mechanics, 3D Shape Processing. Notable Awards : ERC Consolidator Grant (2022) SIGGRAPH Significant New Researcher Award (2016) Eurographics Young Researcher Award (2015) ERC Starting Grant (2014) Education : PhD in Computer Science from Georgia Institute of Technology (2010). Funding : Recipient of NSF Graduate Research Fellowship (2005-2008), and principal investigator for ERC grants.