جستجوی استاد راهنمای دانشگاهی

• Data Science
• Machine Learning
• Tensor Factorization
• +۵ مورد دیگر

Dr Thiru Balasubramaniam is a Research Fellow at Queensland University of Technology (QUT), working within the Faculty of Science, School of Computer Science. His expertise lies at the intersection of data science, machine learning, and real-world applications, with a specific focus on tensor factorization methods for managing multifaceted data from IoT and Web 3.0 applications. His educational background includes a PhD from Queensland University of Technology and a Bachelor of Engineering from Anna University. Prior to his doctoral studies, he worked as a Research Assistant at the Singapore University of Technology and Design - Massachusetts Institute of Technology (SUTD-MIT) International Design Centre, where he analyzed mobility data to personalize city environments for elderly citizens in Singapore. Dr Balasubramaniam's research interests span multiple areas of data science: Tensor and Matrix Factorization methods Pattern Mining and Text Mining applications Recommender Systems development IoT data processing Web 3.0 applications Real-time analytics for multifaceted data His publication record demonstrates consistent contributions to high-impact venues including IEEE TKDE, ACM TKDD, WWW, WISE, AusDM, and PRICAI. The trend in his recent work shows increasing application of tensor factorization techniques to diverse real-world problems including environmental monitoring, pandemic modeling, social media analysis, and smart grid technology. His research often involves interdisciplinary collaborations, particularly with Professor Richi Nayak at QUT. Scientific recognition includes: QUT-CDS first byte research funding worth 30,000 AUD Dr Balasubramaniam has been actively involved in teaching data analytics subjects at QUT since 2017, including Data Exploration and Mining, Data and Web Analytics, Data Mining Technology and Applications, and Web Computing. His teaching spans both undergraduate and postgraduate levels. His research has been supported through various collaborative grants and institutional funding mechanisms at QUT.

Peyman Kabiriمشاهده پروفایل

مدرس ارشد

• Network Security
• Cybersecurity
• Intrusion Detection and Response
• +۶ مورد دیگر

Peyman Kabiri is a Senior Lecturer in Cyber Security at the De Montfort University School of Computer Science and Informatics , Faculty of Computing, Engineering and Media. His research spans interdisciplinary domains including Network and Cybersecurity (Intrusion Detection/Response) Remote Sensing and Acoustic Emission Analysis Many-Valued Logic and Robotics Sound Analysis and Music Information Retrieval Peyman's academic contributions focus on adaptive error compensation in mechanical systems and security frameworks for cyber-physical environments. His recent publications highlight innovations in Monocular SLAM algorithms for indoor navigation Wavelet packet transform for image processing Signal strength-based wireless intrusion detection Trust modeling in virtualized systems As an educator, he teaches Practical Host & Network Security (PG) Secure Coding (UG) Endpoint Security (UG) with extensive experience in microprocessor design, robotics, and network security modules. Professional memberships include BCS (2024) IEEE (1997-2002, 2012) ACM (2010-2013) SPIE (2005) ISMVIP (2010-2019) ISAV (2017)

Simon Leglaiveمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Audio
• +۳ مورد دیگر

Simon Leglaive is a Professor at the Rennes Institute of Electronics and Telecommunications, leading research in signal processing, machine learning, and audio/speech technologies. His work focuses on advancing methods for speech enhancement, audio source separation, and generative models for audiovisual data. He has contributed extensively to techniques involving variational autoencoders (VAEs), dynamical systems, and probabilistic modeling in noisy and reverberant environments. His research interests span signal processing fundamentals, machine learning applications to audio, and interdisciplinary projects combining speech, audio, and computer vision. Notable contributions include frameworks for unsupervised domain adaptation in speech enhancement, advanced source separation under reverberation, and human motion analysis using latent space modeling. Recent work emphasizes generative models for speech emotion recognition and robust audiovisual fusion. Key technical trends in his publications include: Integration of deep learning with classical signal processing methods Probabilistic modeling of reverberant audio mixtures Unsupervised learning for speech/noise separation Applications of vector quantization and masked autoencoders He has collaborated extensively with researchers from institutions like INSA Rennes and Sorbonne University, contributing to international challenges like the CHiME-7 speech enhancement task. His laboratory develops open-source tools and benchmarks for speech processing research.

Yifeng Liمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Science
• +۶ مورد دیگر

Yifeng Li is an Associate Professor in the Department of Computer Science at Brock University, cross-appointed in Biological Sciences and the Centre for Biotechnology. He holds a Canada Research Chair in Machine Learning for Biomedical Data Science and leads the Brock Biomedical Data Science Lab. His research focuses on AI, machine learning, data science, and their applications in computational biology and bioinformatics. Education: BSc/MSc (Computer Science, Shandong Institute of Light Industry) PhD (Computer Science, University of Windsor) Postdoc (Bioinformatics, University of British Columbia) Research Interests: AI algorithms inspired by neuroscience, deep learning for genomics, drug design, and healthcare applications. He has over 40 peer-reviewed publications and numerous grants, including NSERC and CFI funding. His lab develops tools like MVMF and DECRES for integrative data analysis. He supervises MSc/PhD students in AI, bioinformatics, and computational biology. Awards: Canada Research Chair, NSERC Discovery Grant, NRC Rising Star Award, and 20+ scholarships/honors.

• Computational Biology
• Machine Learning

Nasrin Akhter is an Assistant Professor of Teaching in the Department of Computer Science and Engineering at the University at Buffalo, part of the School of Engineering and Applied Sciences. Her academic career focuses on applying computational methods to biological problems, with particular expertise in protein structure prediction and energy landscape analysis. Dr. Akhter's educational background includes: PhD in Computer Science from George Mason University (2020) MS in Computer Science & Engineering from University of Dhaka, Bangladesh (2007) BSc in Computer Science & Engineering from University of Dhaka, Bangladesh (2005) Her research interests center around computational biology and machine learning, with specific focus on protein structure prediction, energy landscape analysis, and the application of unsupervised and supervised learning techniques to biological problems. Dr. Akhter's work bridges the gap between computer science and biology, developing novel computational approaches to understand protein folding and dynamics. Analysis of Dr. Akhter's publication record shows a consistent focus on protein structure prediction and energy landscape analysis. Her research demonstrates expertise in applying machine learning techniques, particularly non-negative matrix factorization, deep learning, and anomaly detection, to solve complex problems in computational biology. Over time, her work has evolved from foundational methods in decoy selection to more sophisticated approaches integrating multiple learning paradigms. Dr. Akhter has received notable recognition for her research contributions: Best paper award at the Intl. Conf. on Bioinformatics and Computational Biology (BICOB) in 2019 Finalist for best paper award at BICoB in 2018 As a faculty member, Dr. Akhter serves as a reviewer for prestigious journals including IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), PLOS ONE, and Machine Learning with Applications (MLWA). She has also served on program committees for SBP-BRiMS conferences from 2019-2022. Her teaching responsibilities include courses such as CSE 199: UB Seminar, CSE 191: Introduction to Discrete Structures, and previously CSE 116: Introduction to Computer Science II, CSE 331: Algorithms and Complexity, and CSE 469: Introduction to Data Mining. Dr. Akhter's research appears to be conducted in collaboration with a team focused on computational biology and bioinformatics, particularly working with Amarda Shehu and other researchers on protein energy landscape analysis. Her work bridges computer science and biological applications through sophisticated computational methods.

Nizar Bouguilaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Clustering
• +۷ مورد دیگر

Prof. Nizar Bouguila is a Professor at the Concordia Institute for Information Systems Engineering (CIISE), Concordia University, and holds the Concordia University Research Chair in Applied Artificial Intelligence. His research focuses on machine learning, data clustering, smart building systems, and energy management. He actively supervises graduate students in Computer Science, Electrical and Computer Engineering, and Information Systems Security programs. Dr. Bouguila's work integrates probabilistic models, deep learning, and domain adaptation techniques. He has pioneered methods in energy disaggregation, load forecasting, and anomaly detection in smart infrastructure. His recent projects address challenges in occupant behavior prediction, speech emotion recognition, and fake news detection using Arabic datasets. Key research themes include: Probabilistic clustering with Bayesian nonparametric mixtures Smart building analytics via IoT and sensor data Domain adaptation for cross-domain learning Explainable AI in energy systems His 2023-2025 publications emphasize: Advanced transformer networks for load forecasting Graph neural networks in transportation Multimodal data fusion for anomaly detection Topic modeling for short texts Dr. Bouguila's advising spans multiple engineering and computer science disciplines, reflecting his interdisciplinary research profile.

Jiashun Jinمشاهده پروفایل

استاد

• Statistics
• Big Data Analytics
• Network Analysis
• +۴ مورد دیگر

Jiashun Jin is a Professor of Statistics and Affiliated Professor of Machine Learning at Carnegie Mellon University, where he leads research in statistical inference for Big Data with a focus on 'Rare and Weak' signal regimes. He received his Ph.D. in Statistics from Stanford University in 2003. His research interests span: Development of novel methods like Higher Criticism, Graphlet Screening, IF-PCA, and SCORE for high-dimensional data Applications in genomics (cancer classification, SNP analysis), cosmology (non-Gaussian detection in CMB), and network science Theoretical frameworks including phase diagrams to characterize statistical detectability boundaries Recent publications (2021-2023) demonstrate strong emphasis on network community detection, text analysis, genetic association studies, and COVID-19 resource allocation, with consistent focus on statistical optimality in high-dimensional settings. Honors and awards include: NSF CAREER Award (2007) IMS Tweedie Award (2009) IMS Fellow (2011) ASA Fellow (2019) ICCM Distinguished Paper Award (2020) He has developed a significant dataset of 83,381 statistical publications for coauthorship/citation network analysis and has delivered invited lectures including the IMS Medallion Lecture (2015).

Liu Yangمشاهده پروفایل

پژوهشگر ارشد

• Theoretical Machine Learning
• Theoretical Computer Science
• Statistical Learning Theory
• +۵ مورد دیگر

Liu Yang is a postdoctoral fellow in the Computer Science Department at Carnegie Mellon University , with a PhD from CMU under Avrim Blum and Jaime Carbonell. His research focuses on Theoretical Machine Learning and Theoretical Computer Science , exploring areas like Statistical Learning Theory , Property Testing , and Algorithmic Economics . He has contributed to active learning , transfer learning , and online pricing problems through mathematical frameworks. Research Interests : Liu's work bridges Computational Learning Theory with Algorithmic Economics , including: Mathematical theories for active property testing of Boolean functions Transfer learning with applications to online allocation and pricing Analysis of convex losses and statistical identifiability in learning Developing Buys-in-Bulk models for active learning efficiency Service and Teaching : He has served on program committees for ICML 2012-2013 , reviewed for top-tier venues, and taught courses like Graduate Algorithms and Modern Computer Algebra at CMU. He also co-developed the DistLearnKit MATLAB toolkit for distance metric learning.

Jeffrey M. Rosenمشاهده پروفایل

استاد

• Mammary gland development
• Breast cancer
• Wnt signaling
• +۷ مورد دیگر

Dr. Jeffrey M. Rosen is the C.C. Bell Distinguished Service Professor and Vice-Chair of Molecular & Cellular Biology at Baylor College of Medicine. He leads a prominent research laboratory focused on mammary gland development and breast cancer, with particular emphasis on triple negative breast cancer. His work has established him as a leading expert in understanding the molecular mechanisms underlying normal mammary development and their alterations in breast cancer. Dr. Rosen's research centers on defining the mechanisms regulating normal mammary gland development to understand alterations in breast cancer. His lab specifically investigates the interplay of Wnt and Fgf family members in mammary gland development and breast cancer, translational regulation as a therapeutic vulnerability in triple negative breast cancer, and the development of unique preclinical syngeneic mouse models to study tumor response to therapies. A major focus is on understanding tumor initiating cells and the role of the immune microenvironment in treatment response. Analysis of Dr. Rosen's recent publications reveals a strong emphasis on triple negative breast cancer , with particular focus on the tumor immune microenvironment , epigenetic reprogramming , and metastasis . His work increasingly integrates single-cell analysis and spatial pathology to understand tumor heterogeneity. A growing theme is leveraging preclinical models to study metastatic breast cancer and identify novel therapeutic combinations that target both tumor cells and the immune microenvironment. Dr. Rosen has mentored an extensive number of students and postdoctoral fellows throughout his career, many of whom have gone on to become independent investigators, professors, and researchers in academia and industry. His laboratory has been consistently funded through various grants, supporting research on mammary gland development, breast cancer mechanisms, and therapeutic approaches. The lab maintains strong collaborations with other researchers at Baylor College of Medicine and beyond. The Rosen Lab at Baylor College of Medicine comprises a dynamic team of postdoctoral associates, graduate students, and technical staff working collaboratively to advance understanding of breast cancer biology. The lab maintains extensive resources including detailed protocols for mammary gland research and collaborates with other laboratories through regular lab meetings and joint projects. Current research focuses on epigenetic reprogramming of triple negative breast cancer and the tumor immune microenvironment in metastatic disease.

• Computer Science
• Artificial Intelligence
• Information Theory
• +۷ مورد دیگر

Anatoly V. Anisimov is a Professor and current Dean of the Faculty of Computer Science and Cybernetics at Taras Shevchenko National University of Kyiv, with a career spanning over five decades. He previously served as Dean of the Faculty of Cybernetics (2004-2016) and has held academic roles ranging from Assistant Professor to Head of Department of Mathematical Informatics. Candidate of Physical and Mathematical Sciences (1972) Doctor of Science (Dr. Sc.) in Physics and Mathematics (1994) Internship at Stanford University (1976-1977) under Donald Knuth His research interests focus on information theory, cryptography, data compression, natural language processing, and algorithm design , with recent work exploring multidelimiter prefix codes, two-base numeration systems, and lightweight authentication protocols for IoT. Over 150 publications and book chapters reflect his contributions to computational linguistics and theoretical computer science. Key article trends reveal a consistent emphasis on encoding techniques, mathematical modeling, and parallel computing , including applications in error correction, semantic analysis, and tensor factorization . His work bridges classical algorithmic theory with modern cybersecurity and AI challenges. Scientific honors include: Glushkov Prize (1994) Soros Professor Grant (1994-1995) State Prize of Ukraine (1998) Honored Worker of Science and Technique (2005) Distinguished Professor Title (2008) Lebedev Prize (2008) Corresponding Member, National Academy of Sciences (2009) Order of Merit (2018) Advising and grants show collaborations with researchers like I.O. Zavadskyi and O.O. Marchenko. He secured international grants (e.g., Soros) and led projects in computational linguistics, information security, and matrix factorization .

• Cancer and Oncology
• Medical Genetics and Genomics

Bram de Laere is a Research Fellow at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. His research focuses on translational cancer genomics with emphasis on liquid biopsy applications in prostate cancer, contributing to international clinical trials and collaborative projects aimed at advancing precision oncology for advanced prostate cancer patients. His primary research interests center on Cancer and Oncology and Medical Genetics and Genomics . He specializes in circulating tumor DNA (ctDNA) analysis for biomarker discovery in metastatic castration-resistant prostate cancer (mCRPC), investigating genomic alterations driving treatment resistance and disease heterogeneity through next-generation sequencing and computational approaches. Analysis of his publication record reveals consistent focus on liquid biopsy development, particularly ctDNA profiling for prognostic modeling and treatment selection. His work demonstrates strong interdisciplinary collaboration across oncology, genomics, and bioinformatics, with significant contributions to understanding androgen receptor signaling, DNA repair defects, and clinical implementation of genomic biomarkers in prostate cancer management.

• 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.

• Robotics
• Auditory Perception
• Multimodal Perception
• +۳ مورد دیگر

Sylvain Argentieri serves as an Associate Professor at the Institute for Intelligent Systems and Robotics (ISIR) within Sorbonne University, where he has been a member of the ASIMOV research group ("Architectures and Models for Adaptation and Cognition") since 2008. His office is located at ISIR, Campus Pierre et Marie Curie, 4 place Jussieu, BC173, 75005 Paris. He maintains an active research presence with publications spanning from 2006 to 2024. His educational background includes obtaining the highest teaching diploma in France (Agrégation externe) in Electrical Engineering from the École Normale Supérieure in 2002, followed by a Ph.D. in Computer Science from Paul Sabatier University (Toulouse) in 2006, and his Habilitation à Diriger des Recherches (HDR) in 2018. Argentieri's research focuses on artificial perception in robotics with special emphasis on auditory perception, active multimodal perception approaches, and sensorimotor integration. His work demonstrates a consistent trajectory exploring how robots can develop perceptual capabilities through sensorimotor experiences, with particular attention to binaural sound localization, head movement control, and the emergence of spatial representations from uninterpreted sensory signals. His publications reveal a strong interest in how naive agents can structure their understanding of the world through active exploration and sensory prediction. His publication record shows a clear progression from foundational work on sound source localization to more complex investigations of sensorimotor contingencies and embodied cognition. The research spans multiple venues including IEEE conferences, Robotics and Autonomous Systems, and Frontiers journals, with recent work incorporating transformer architectures and neural network approaches to auditory processing. Argentieri maintains active collaborations with researchers including Nicolas Obin, Bruno Gas, and Valentin Marcel, as evidenced by his co-authored publications. His laboratory work takes place within the ASIMOV team at ISIR, which focuses on architectures and models for adaptation and cognition in robotic systems.

Ka Herمشاهده پروفایل

پژوهشگر

• Genomics
• Microbiome Research
• Computational Biology
• +۳ مورد دیگر

Ka Her is a Researcher at the University of California San Francisco (UCSF). Their work focuses on integrating computational methods with genomic and microbiological research to understand complex biological systems. Key areas include chromatin structure analysis, microbiome interactions, and the application of machine learning to genomic data. Ka Her has contributed to advancements in single-cell and multi-omic technologies, particularly in dissecting regulatory elements and their roles in disease contexts such as cancer and inflammatory bowel disease. Their research also explores evolutionary genomics, with studies on human accelerated regions and comparative analyses across species. Publications highlight innovative approaches like CellWalker2 for hierarchical cell-type discovery and MIDAS v3 for microbial gene content estimation. Ka Her’s work frequently bridges basic science and clinical applications, exemplified by studies on fecal microbial transplants and genetic determinants of heart disease. While no formal awards are listed, their extensive publication record reflects significant contributions to computational and experimental genomics.

Kazi Lutful Kabirمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Kazi Lutful Kabir is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University (GMU). He previously served as an Assistant Professor at Stevens Institute of Technology and held a research fellowship at Los Alamos National Laboratory (LANL). His research focuses on applying data mining and machine learning techniques to address biological questions, particularly in protein structure prediction and molecular dynamics analysis. Kabir holds a PhD and MS in Computer Science from GMU and a BS in Computer Science and Engineering from the Military Institute of Science and Technology (MIST), Bangladesh. Education: PhD in Computer Science, George Mason University MS in Computer Science, George Mason University BS in Computer Science and Engineering, MIST, Bangladesh Research Interests: His work bridges computational methods and biological systems, emphasizing machine learning applications in bioinformatics. Key areas include protein structure quality estimation, molecular energy landscape analysis, and antibody dynamics modeling. He also explores quaternion-based methods in pedestrian path analysis and graph theory for structure prediction. Publications: His recent work highlights advancements in non-negative matrix factorization, tensor factorization, and unsupervised learning for molecular structure analysis. Notable themes include protein decoy selection, antibody-antigen interaction dynamics, and integrative approaches combining energy landscapes with Markov state models. Professional Experience: Assistant Professor, Stevens Institute of Technology (prior to GMU) Research Fellow, LANL Labs/Teams: While specific lab affiliations are not detailed, his research aligns with computational biology and data science initiatives at GMU’s College of Science.