جستجوی استاد راهنمای دانشگاهی

• Compiler Optimization for High Performance Computing
• Optimization of Sparse/Dense Matrix/Tensor Computations
• Scalable Machine Learning
• +۱ مورد دیگر

P. (Saday) Sadayappan is a Professor in the School of Computing at the University of Utah. He serves as a lead researcher in high-performance computing, with a focus on compiler optimization and algorithm-architecture co-design. His current projects include NIH SBIR Phase 2 funding for large-scale image analysis and NSF grants for tensor applications and cyber-infrastructure for AI. Research Interests : Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Research Trends in Publications : His work emphasizes optimizing computational workflows for emerging hardware architectures, with a focus on accelerating machine learning and scientific computing through compiler-level innovations. Recent trends include co-design for CNNs, sparse matrix optimizations, and distributed algorithms. Scientific Awards : ACM SIGPLAN Most Influential PLDI Paper Award (2018) Grants & Projects : NSF (2022–2027): Comprehensive Framework for Tensor Applications NSF AI Institute ICICLE (2021–2026): Cyber-infrastructure for environmental AI NIH SBIR (2023–2025): Next-gen machine learning for image analysis Labs & Teams : Collaborates with institutions like Ohio State University and RNET Technologies on projects involving parallel computing, sparse algorithms, and compiler design.

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

Roland Badeau is a Full Professor in the Signal, Statistics and Learning (S2A) team within the Image, Data, Signal (IDS) Department at Télécom Paris, Institut Polytechnique de Paris. His primary affiliation is with the Information Processing and Communication Laboratory (LTCI). Research Interests: Badeau specializes in statistical modeling of non-stationary signals, with core expertise in adaptive high-resolution spectral analysis and Bayesian extensions to Non-negative Matrix Factorization (NMF). His work spans room acoustics (stochastic reverberation models), data representation (dimensionality reduction, time-frequency analysis), probabilistic latent variable modeling, and algorithm development (Bayesian estimation, optimization methods, fast adaptive algorithms). Applications focus on audio/music processing including source separation, denoising, dereverberation, multipitch estimation, and automatic music transcription, with extensions to biomedical data analysis and digital communications. Key Trends in Publications: Recent work centers on Statistical Wave Field Theory, establishing mathematical frameworks for reverberation modeling using energy-stress tensor formalism and Riemannian geometry. His publications demonstrate a progression from foundational signal processing algorithms (e.g., YAST, ESPRIT) to physics-informed approaches for polyhedral rooms and frequency-dependent attenuation, with strong emphasis on Bayesian and alpha-stable distribution methods for robust audio separation. Academic Leadership: Badeau supervises doctoral and master’s theses while leading teaching units at Télécom Paris. He serves as the TSIA study track supervisor (Signal Processing for Artificial Intelligence) and Master ATIAM correspondent. His team (S2A) develops tools like DESAM for joint source separation and multi-track coding.

Carlo Colantuoniمشاهده پروفایل

استادیار

• Developmental Neuroscience
• Systems Neuroscience
• Cognitive Neuroscience
• +۱۲ مورد دیگر

Carlo Colantuoni is an Assistant Professor in the Department of Neurology at the Johns Hopkins University School of Medicine. He is based at Johns Hopkins Hospital in Baltimore, MD, and leads a research lab focused on functional genomics and computational neuroscience. His work is affiliated with the Neuroscience Training Program and centers on understanding human brain development and disease through multi-omic data analysis. His research interests include Developmental Neuroscience , Systems and Computational Neuroscience , Neurobiology of Disease , and Functional Genomics . He develops and applies advanced computational methods such as Structured Joint Decomposition (SJD) and projectR to analyze large-scale transcriptomic datasets. Colantuoni's recent publications focus on neocortical development , stem cell variation , neuroinflammation , and host immune responses . His work integrates data from in vivo mammalian development and in vitro cerebral organoid models to identify conserved molecular dynamics and human-specific features. He has contributed to public resources like NeMO Analytics , enabling broad access to curated datasets and analytical tools. He collaborates with leading researchers such as Gabriel Santpere, Nenad Sestan, Pasko Rakic, Flora Vaccarino, and Dimitri Avramopoulos on projects involving neurodevelopmental risk genes and neuronal identity. His lab also works with applied mathematicians Don Geman and Laurent Younes on cell type marker identification using the CellCover method.

Alexander Duncanمشاهده پروفایل

پژوهشگر

• Materials Science
• Electron Microscopy
• Machine Learning for Materials Analysis
• +۳ مورد دیگر

Dr. Alexander Duncan is a Senior Scientific Collaborator at École polytechnique fédérale de Lausanne (EPFL), holding multiple roles across departments. Affiliated with the School of Basic Sciences (SB), Institute of Physics (IPHYS), and Laboratory of Electron Microscopy and Spectroscopy (LSME), he also serves in educational units like EDMX-ENS and SMX-ENS. His research focuses on advanced electron microscopy techniques, oxide heterostructures, and machine learning applications for nanoscale analysis. Materials Science Electron Microscopy Machine Learning for Materials Analysis Nanoscale Imaging Ferroelectric Materials Oxide Heterostructures Duncan's recent publications demonstrate expertise in EDX spectral denoising , symmetry breaking interfaces , and machine learning-aided phase quantification . He has developed novel algorithms for hyperspectral unmixing and atomic-scale reconstruction techniques, with applications in neuromorphic computing and deep mantle mineral analysis. He supervises PhD students including Aebersold Arthur Brian and Sblendorio Gabrielle Anne Laguisma , while actively contributing to open-source software development ( espm and emtables ). Duncan also participates in EPFL's Physics PhD program committee and teaches advanced electron microscopy courses.

Christian Rohlfingمشاهده پروفایل

پژوهشگر

• Signal Processing
• Data Compression
• Audio Source Separation
• +۳ مورد دیگر

Dr.-Ing. Christian Rohlfing is a Researcher at the Institute of Communications Engineering , RWTH Aachen University . His work spans genomic data compression , audio source separation , and video coding . Rohlfing’s research focuses on efficient genomic sequence compression using entropy coding, context-based methods, and standardization within MPEG-G . He also advances point cloud compression via graph transforms and adaptive entropy coding, and audio/video coding with non-negative matrix factorization (NMF), dictionary learning, and convolutional neural networks (CNNs). His publications (2011–2023) reflect expertise in signal processing for multimedia and bioinformatics , with applications in spatial audio coding , informed source separation , and gene variation compression . Rohlfing contributes to international standards like ITU-T and ISO/IEC MPEG , particularly in Versatile Video Coding (VVC) and MPEG-G for genomic data.

Loyal Goffمشاهده پروفایل

دانشیار

• Developmental Neuroscience
• Cellular and Molecular Neuroscience
• Single-Cell Genomics
• +۸ مورد دیگر

Loyal Goff is an Associate Professor in the Department of Neuroscience and the McKusick-Nathans Institute of Genetic Medicine at Johns Hopkins University School of Medicine. His research bridges developmental neuroscience, genomics, and computational biology, focusing on cell-type diversity and transcriptional regulation in the mammalian nervous system. Research Interests: Dr. Goff's work centers on understanding cellular heterogeneity in the central and enteric nervous systems. Using single-cell RNA sequencing and spatial transcriptomics, his lab investigates how gene expression programs define cell identity during development, how neural circuits are established, and how disruptions contribute to neurodevelopmental and neurodegenerative disorders. Key areas include retinal development, cortical layering, microglia function, and the role of long non-coding RNAs in neural differentiation. Publications and Methodology: He has contributed to foundational studies in single-cell genomics, including the development of analytical tools such as CoGAPS and projectR. His recent publications span topics from defining neuronal subtypes to modeling disease mechanisms like microcephaly and autism, demonstrating a strong integration of experimental and computational approaches. Scientific Contributions: Co-developer of CoGAPS, a Bayesian NMF algorithm for single-cell data analysis. Contributor to RNA velocity and transfer learning frameworks in genomics. Key role in elucidating transcriptional networks in retinal and cortical development. Advising and Collaborations: Dr. Goff mentors trainees and collaborates across disciplines, including bioinformatics, developmental biology, and neurology. His lab receives support from NIH and other research grants, enabling high-impact studies on neural development and disease. He is actively involved in training programs at Johns Hopkins, shaping the next generation of neuroscientists. Laboratory and Resources: The Goff Lab maintains a robust research environment with expertise in single-cell genomics, mouse models, and computational analysis. They develop and share software tools and datasets publicly, contributing to open science in neuroscience and genomics.

Zachary DeBruineمشاهده پروفایل

استادیار

• Machine Learning
• Genomics
• Bioinformatics
• +۳ مورد دیگر

Zachary DeBruine serves as an Assistant Professor in the Department of Computer Science at Grand Valley State University's College of Computing. He holds a Ph.D. in Bioinformatics and Biochemistry from Van Andel Institute (2020) and a B.S. in Biochemistry and Molecular Biology. Dr. DeBruine's research focuses on the intersection of machine learning and genomics, specializing in building multimodal machine learning models, developing foundation models for genomics data, and creating large language model (LLM) solutions that interpret complex genomic information through natural language. His work bridges computational techniques with biological applications, particularly in single-cell analysis and genomic data interpretation. His recent publications and GitHub repositories demonstrate expertise in non-negative matrix factorization (NMF), sparse matrix algorithms, and multimodal variational autoencoders. These works primarily employ C++ and Python for high-performance computing applications in bioinformatics. GVSU Distinguished Early-Career Scholar Award Graduate Student Association's Outstanding Graduate Teaching Award Dr. DeBruine leads a vibrant research lab with over ten undergraduate and graduate students working on multiple funded projects. He actively mentors students through product development, manuscript writing, and real-world problem solving. He serves on GVSU's University R&D Committee and is an active member of both the Human Cell Atlas Network and the Chan Zuckerberg Initiative Network. He founded and serves as CEO of Herd Social, a company building social networking solutions for the rare disease community, demonstrating his commitment to applying computational approaches to real-world healthcare challenges.

• Signal Processing
• Machine Learning
• Acoustics
• +۵ مورد دیگر

Roland Badeau is a Full Professor at Télécom Paris (part of Institut Polytechnique de Paris) within the Signal, Statistics and Learning (S2A) team of the Image, Data, Signal (IDS) Department. His work focuses on statistical modeling of non-stationary signals, particularly in audio and music processing, with applications including source separation, denoising, dereverberation, and automatic music transcription. He has authored over 30 journal papers, 130 conference papers, 4 patents, and a book chapter. Education : Habilitation à Diriger des Recherches (2010), PhD in Signal Processing (2005) from Télécom Paris Research Themes : Stochastic reverberation models, time-frequency analysis, probabilistic latent variable models, audio coding, and biomedical data applications Teaching : Applied Mathematics and Signal Processing at Télécom Paris; Music Signal Processing at Sorbonne Université; Audio-Frequency Analysis at ENS Paris-Saclay Awards : Featured in Stanford's Top 2% Researchers (2024); contributed to a best student paper award at ICASSP 2020 Responsibilities : Supervision of doctoral theses, teaching units, and academic programs like TSIA and Master ATIAM

Sixin Zhangمشاهده پروفایل

دانشیار

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

Sixin Zhang is an Associate Professor at INP-Toulouse, specifically affiliated with the ENSEEIHT school. He holds a Ph.D. from the Courant Institute of Mathematical Sciences, New York University (2012), following undergraduate studies. His postdoctoral research spanned institutions including ENS Paris, Peking University, and IRIT in France. His research focuses on machine learning, optimization, signal/image processing, statistics, dynamical systems, and high-dimensional data analysis. Key research areas include representation learning for recognition and inverse problems, optimization algorithms, and applications in industrial systems and astrophysics. He contributed to the development of the Kymatio library for scattering transforms and co-authored foundational work on transform learning in non-negative matrix factorization. His work bridges theoretical mathematics with applied machine learning, including contributions to generative models, data assimilation networks, and optimization techniques for neural networks. Notable projects include the MeteoNet AI challenge and the RWST statistical framework for interstellar medium analysis.