جست‌وجوی استاد و پژوهشگر

Xiangnan Kongمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Biomedical Informatics
• +۵ مورد دیگر

Xiangnan Kong is an Associate Professor in the Department of Computer Science at Worcester Polytechnic Institute (WPI), serving as the Graduate Coordinator of the Data Science Program. His academic journey includes a BS and MS from Nanjing University (2006, 2009) and a PhD from the University of Illinois at Chicago (2014). He maintains an active research program with significant contributions to data mining and machine learning. Dr. Kong's primary research interests focus on data mining and machine learning, with emphasis on addressing data science problems in biomedical and social applications. His work specifically targets data variety issues across multiple domains including neuroscience, biomedical informatics, social networks, and business intelligence. He has published extensively in top-tier conferences and journals such as KDD, ICDM, SDM, WWW, WSDM, CIKM, and TKDE. His recent publications demonstrate a strong trend toward interdisciplinary applications, with significant work in neuroimaging analysis, healthcare applications, urban traffic estimation, and efficient deep learning architectures. His research bridges theoretical advances in graph mining, heterogeneous information networks, and deep learning with practical applications in biomedical domains and social computing. Dr. Kong has successfully advised numerous PhD and Master's students, with former students now working at prominent organizations including Facebook Research, VISA Research, MIT CSAIL, and Amazon AI. His research is supported by multiple grants including NSF awards, Adobe Gift Grant, Huawei Research Grant, and WPI TRIAD Grant.

Neil Spencerمشاهده پروفایل

استادیار

• Network Analysis
• Bayesian Modeling
• Forensic Statistics
• +۲ مورد دیگر

Dr. Neil Spencer is an Assistant Professor in the Department of Statistics at the University of Connecticut. His research integrates Bayesian inference, network analysis, and computational statistics, with applications ranging from forensic science to neurological disorders. He earned a PhD in Statistics and Machine Learning from Carnegie Mellon University, MSc from University of British Columbia, and BScH from Acadia University. Research focuses on developing novel methods for network data analysis (latent position models, efficient MCMC), robust Bayesian inference, and forensic statistics. Publications demonstrate consistent innovation in computational techniques for complex data structures and interdisciplinary applications. Teaching includes STAT5410 (Statistical Computing) and STAT3345Q (Probability Models for Engineers). He co-advised PhD candidate Tolani Olarinre and participates in the New England Statistical Society's NextGen committee. Research publications emphasize methodological innovations in network modeling, Bayesian computation, and experimental design, with significant applications in neuroscience and forensic science.

Hua Wangمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Medical Image Computing
• +۶ مورد دیگر

Hua Wang is a Professor and Graduate Director in the Department of Computer Science at the Colorado School of Mines. He joined the university in 2012 after completing his Ph.D. in Computer Science & Engineering at the University of Texas at Arlington. Prior to academia, he worked in industry as a Signal Processing engineer at Motorola for four years. Education B.S. in Electronic Engineering, Tsinghua University (Beijing, 1999) M.S. in Signal Processing, Nanyang Technological University (Singapore, 2003) Ph.D. in Computer Science & Engineering, University of Texas at Arlington (2012) Research Interests : Dr. Wang focuses on developing efficient machine learning and data mining algorithms with theoretical guarantees for large-scale data challenges. His work spans applications in medical image computing, health informatics, cheminformatics, material informatics, additive manufacturing, bioinformatics, and computer vision. He emphasizes practical solutions for interdisciplinary problems such as drug discovery, disease diagnosis, and robotics. Advising & Grants : While no specific student advisees or grant details are listed, his publications reflect extensive collaboration across domains like healthcare, materials science, and robotics. His work often involves multi-institutional efforts addressing real-world data-driven challenges. Labs & Teams : His research group likely focuses on algorithmic development for medical imaging and multi-modal data fusion, though specific lab affiliations are not explicitly mentioned in the text.

Junsong Yuanمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Video Analytics
• +۶ مورد دیگر

Junsong Yuan is a Professor and Director of the Visual Computing Lab at the Department of Computer Science and Engineering, University at Buffalo (SUNY). He previously served as an Associate Professor and Nanyang Assistant Professor at Nanyang Technological University (NTU), Singapore. His research focuses on computer vision, pattern recognition, video analytics, and large-scale visual search. He holds a Ph.D. from Northwestern University, M.Eng. from National University of Singapore, and B.Eng. from Huazhong University of Science and Technology. Education : Ph.D., Electrical and Computer Engineering, Northwestern University, 2009 M.Eng., Electrical and Computer Engineering, National University of Singapore, 2005 B.Eng., Communication Engineering, Huazhong University of Science and Technology, 2002 Research Interests : Dr. Yuan's work spans computer vision, video analytics, gesture/action analysis, and multimodal learning. He emphasizes scalable visual search, 3D reconstruction, and federated learning for medical imaging. His lab develops algorithms for autonomous systems, generative media processing, and human-AI interaction. Awards : IEEE Fellow (2021) IAPR Fellow (2018) SUNY Chancellor's Award for Excellence in Scholarship (2022) Advising & Grants : He has advised numerous PhD students, including Sudhir Yarram, Nan Xi, and Tianyu Luan. His research is supported by grants focusing on video generation, medical image analysis, and federated learning. He serves as Editor-in-Chief of JVCI and on editorial boards of T-PAMI, T-IP, and T-CSVT. Labs & Teams : Director of the Visual Computing Lab, leading projects in generative models, 3D scene understanding, and healthcare AI. Collaborates with industry partners like Meta, Amazon, and Accenture on applied vision systems.

Gopal Guptaمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Parallel and Distributed Processing
• +۴ مورد دیگر

Dr. Gopal Gupta is the Erik Jonsson Chair Professor of Computer Science at The University of Texas at Dallas (UT Dallas), leading the Department of Computer Science since 2009. He holds a Ph.D. from the University of North Carolina at Chapel Hill (1991) and has held academic roles since 1992, including Department Head and Associate Head at UT Dallas. His research focuses on Programming Languages, Software Engineering, Parallel Processing, Human-Computer Interaction, and Artificial Intelligence , with notable contributions in logic programming, constraint programming, and neuro-symbolic AI. He has developed influential systems such as the NEXUS parser , DAWN , and VoxBoox , and co-founded Interoperate , a startup translating legacy code into modern formats. His honors include the Junior Faculty Enhancement Award (1992) and multiple best paper awards. Gupta’s work bridges theory and application, emphasizing explainable AI, commonsense reasoning, and system assurance. Key Projects : Leader of the Condicutve Logic Programming initiative Co-developer of the E-plan system for chemical hazard response Pioneer in voice-based accessibility tools like VoxBoox Advocate for entrepreneurial ventures in academia His publications span logic-based AI, parallel computing, and software verification, with recent work in trustworthy AI, causal counterfactuals, and neuro-symbolic frameworks.

Guangliang Chenمشاهده پروفایل

دانشیار

• Subspace/manifold clustering
• Dictionary clustering
• Classification
• +۶ مورد دیگر

Guangliang Chen is an Associate Professor in the Department of Mathematics and Statistics at San José State University (SJSU), part of the College of Science. His research focuses on subspace/manifold clustering, dictionary learning, and classification with applications in image and document analysis. He earned a Ph.D. in Applied Mathematics from the University of Minnesota (2009) and a B.S. in Mathematics from the University of Science and Technology of China (2003). Key research contributions include scalable spectral clustering algorithms, geometric multi-resolution analysis, and advancements in compressive sensing. His work has been recognized with a Best Paper Award at the 2009 ICCV workshop for Kernel Spectral Curvature Clustering (KSCC). He teaches courses in applied statistics, machine learning, and data visualization at both undergraduate and graduate levels. Dr. Chen's recent projects involve developing efficient SVM classification techniques, large-scale spectral clustering frameworks, and MATLAB implementations for scalable algorithms. His research also extends to anomaly detection in hyperspectral imaging and functional genomics analysis through multiscale methods.

Qi Zhengمشاهده پروفایل

دانشیار

• High-dimensional data analysis
• Semiparametric and nonparametric models
• Varying effects survival analysis
• +۴ مورد دیگر

Qi Zheng, PhD, is an Associate Professor in the Department of Bioinformatics and Biostatistics at the University of Louisville's School of Public Health and Information Sciences (SPHIS) . Joined in Fall 2015, Dr. Zheng's methodological research focuses on high-dimensional data analysis, semiparametric models, survival analysis, personalized treatments, tree-based learning, and statistical applications in epidemiology and biomedical sciences. Postdoctoral Fellow (2015), Bioinformatics and Biostatistics, Emory University PhD (2013), Mathematical Science, Clemson University BS (2006), Mathematics, Nanjing University, China Research interests span statistical methodology development for complex biomedical data. Key areas include high-dimensional regression, quantile regression, survival analysis, and machine learning applications in public health. Recent publications (2022–2013) demonstrate expertise in statistical hypothesis testing, survival modeling, and high-dimensional data methods. Articles address observational studies, censored quantile regression, time series change-point detection, and generalized linear models with complex covariates. Contact: qi.zheng@louisville.edu | Personal Website

Aniket Didolkarمشاهده پروفایل

پژوهشگر

• Deep Learning
• Cognitive Science
• Large Language Models
• +۵ مورد دیگر

Aniket Didolkar is a Ph.D. student at Mila and The University of Montreal, advised by Prof. Yoshua Bengio, Dr. Anirudh Goyal, and Prof. Michael Mozer. He is also a visiting researcher at Meta, working with Dr. Nicolas Ballas. Education: Pursuing Ph.D. in Deep Learning and Cognitive Science at The University of Montreal. Research Focus: Developing cognitive science-inspired deep learning techniques, including metacognitive LLMs for mathematical problem solving, hybrid recurrent-transformer architectures for long-context modeling, and object-centric visual representation learning. Article Trends: His work spans object-centric learning (CVPR 2025, ICLR 2025, ICLR 2024), LLM reasoning (NeurIPS 2024), hybrid neural models (NeurIPS 2022), and NLP/speech augmentation (COLING 2020, Interspeech 2020). Scientific Awards: Unique Excellence Scholarship ($15,000) at Mila (December 2023). Scholarship by University of Alberta and Amii ($1,500) for Amii AI Week (April 2022). Scholarship by University of Montreal and Quebec Ministry of Higher Education ($4,000) (April 2022). Collaborations: Worked with institutions including Valence Labs, Microsoft Research NYC, IISc Bangalore, IIIT Delhi, Preferred Networks, and Ubisoft India Studios.

• Statistical learning in high dimensions
• Statistical learning on networks
• Functional data analysis
• +۱۲ مورد دیگر

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur, Nice, France. He holds a Chair in Artificial Intelligence and serves as Director of the Institut 3IA Côte d'Azur, while also leading the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. Université Côte d'Azur: Full Professor of Statistics (2019-present) Institut 3IA Côte d'Azur: Director (2019-present) Inria: MAASAI Team Leader (2019-present) His research focuses on statistical learning methodologies for high-dimensional data, network analysis, functional data modeling, and deep latent variable approaches. Key applications include medical diagnosis through radiomics, ecological monitoring, astrophysics data analysis, and humanities data science. Recent publications emphasize dynamic co-clustering techniques for count data streams, deep latent position models for network analysis, and discriminative clustering frameworks using generalized mutual information. His work integrates Bayesian sparsity, probabilistic PCA, and robust classification methods. Software development remains a core activity with tools like HDclassif, FunLBM, and Linkage.fr platform for network analysis with textual edges. He has mentored over 20 PhD/postdoc researchers in statistical learning and AI applications.

Rachel Cardell-Oliverمشاهده پروفایل

استاد

• Sensor Networks
• Data Analytics
• Environmental Monitoring
• +۷ مورد دیگر

Rachel Cardell-Oliver is a Professor and Head of Department in the Computer Science and Software Engineering department at the School of Physics, Maths and Computing , University of Western Australia (UWA). She is also affiliated with the UWA Data Institute and School of Social Sciences, Planning and Transport Research Centre. As President of the Computing Research and Education Association of Australasia (CORE), she leads national academic initiatives. In 2024, she became a Fellow of the Australian Computer Society . Her education includes a PhD in Computer Science (University of Cambridge) , and BSc and MSc (University of Western Australia) . She has held roles at the University of Essex, SRI Cambridge, and UWA. Her research focuses on sensor networks , distributed intelligence , and applications in environmental monitoring (e.g., soil moisture, rammed earth buildings), public transport systems, smart water meters, wastewater treatment, and privacy-preserving data analytics. Her work aligns with UN Sustainable Development Goals, particularly Education and Environment . Key achievements include pioneering smart metering systems and data-driven urban infrastructure solutions . She has authored over 90 publications and secured 28 grants, including major projects like the ARC Training Centre for Transforming Maintenance through Data Science . Grants & Projects : Smart water treatment, transport systems, stormwater management, and sensor network deployments. Labs/Teams : BuildSense (building monitoring), RailSmart (transport planning), and LoRa Underground Farm Sensor Network datasets.

Jeff Krichmarمشاهده پروفایل

استاد

• Neurorobotics
• Computational neuroscience
• Biologically inspired learning
• +۳ مورد دیگر

Jeffrey Krichmar is Professor in Cognitive Sciences and Computer Science at the University of California, Irvine, where he directs the Cognitive Anteater Robotics Laboratory. His research develops neurorobotic systems that embody principles from neuroscience to create adaptive, intelligent machines. Krichmar investigates how neural architecture influences system function, creating biologically plausible models of learning, memory, and decision-making. His work bridges computational neuroscience with robotics, developing spiking neural networks for navigation, continual learning, and adaptive behavior inspired by biological systems from hippocampus to cephalopod cognition. Current projects include neuromorphic computing toolboxes, vector-based navigation models, and multi-robot coordination strategies. Krichmar's lab evaluates cognitive architectures through robotic platforms like the Toyota HSR, developing neurorobots that demonstrate how neural principles can address real-world challenges in navigation, learning, and human-robot interaction.

Andreas Maierمشاهده پروفایل

استاد

• Machine Learning
• Medical Image Processing
• Deep Learning
• +۳ مورد دیگر

Prof. Dr.-Ing. habil. Andreas Maier is a Professor and Head of the Pattern Recognition Lab (PRL) at the Department of Computer Science, Friedrich-Alexander-University Erlangen-Nuremberg. His research focuses on machine learning, medical image processing, and deep learning applications in healthcare. He leads interdisciplinary projects in medical imaging systems, including advancements in CT reconstruction, MRI analysis, and anomaly detection in power systems. Key roles: Head of PRL, Editor of 'Bildverarbeitung für die Medizin' conference proceedings Research interests: Medical image analysis, deep learning algorithms, multimodal data fusion Publications: Over 30+ 2025 papers in top journals/conferences on topics like CT artifact reduction, tumor pH mapping, and neural network architectures His work bridges theoretical machine learning with practical medical applications, emphasizing explainable AI and ethical AI practices. He actively contributes to the scientific community through Medium articles discussing peer review challenges, machine learning pitfalls, and open-source AI advocacy.

D. Mitchell Wilkesمشاهده پروفایل

دانشیار

• Digital Signal Processing
• Image Processing
• Computer Vision
• +۳ مورد دیگر

D. Mitchell Wilkes is an Associate Professor in the Department of Electrical Engineering and Computer Science at Vanderbilt University's School of Engineering. His research focuses on digital signal processing, image processing, and robotics, with applications in medical imaging, manufacturing automation, and cognitive systems. He holds a Ph.D. in Electrical Engineering from Georgia Institute of Technology (1987), an M.S. from the same institution (1984), and a B.S. from Florida Atlantic University (1981). Education: Ph.D., Electrical Engineering, Georgia Tech, 1987 M.S., Electrical Engineering, Georgia Tech, 1984 B.S., Electrical Engineering, Florida Atlantic University, 1981 Research: His work spans signal modeling, sonar systems, and robotics, with notable contributions to friction stir welding automation and neuromorphic decision-making in robots. Over 40 peer-reviewed articles highlight interdisciplinary applications in computer vision, medical imaging, and autonomous systems. Grants & Patents: He secured NSF funding for adaptive robotics control systems and holds patents in welding process control. His lab collaborations include Vanderbilt's Intelligent Systems Center and interdisciplinary medical engineering projects. Labs/Teams: Active in the Intelligent Robotics Lab and collaborates with biomedical and manufacturing teams, advancing human-robot interaction and industrial automation.

Siamak Ardekaniمشاهده پروفایل

مدرس ارشد

• Medical Imaging
• Cardiovascular Disease
• Neurological Disorders
• +۳ مورد دیگر

Siamak Ardekani is a Senior Lecturer in the Department of Biomedical Engineering and the Department of Medicine at Johns Hopkins University. He holds a PhD in Biomedical Engineering from UCLA (2006), an MD from Shiraz University of Medical Sciences (1993), and an MS in Biomedical Engineering from Drexel University (2000). His research focuses on computational medical imaging, particularly MRI, to develop statistical shape and function models for early disease detection and therapy monitoring in neurological and cardiovascular disorders. His work emphasizes extracting clinically relevant data from medical images to aid in patient risk assessment and treatment efficacy evaluation. Ardekani's contributions include advancements in cardiac MRI analysis, diffusion tensor imaging (DTI), and automated anatomical modeling. He received the Johns Hopkins Discovery Award in 2020 for multidisciplinary research. His affiliations include the Center for Imaging Science at Johns Hopkins. Ardekani’s research spans computational methods for cardiac motion analysis, MRI-based disease phenotyping, and image registration techniques. He collaborates on projects such as the Cardiovascular Research Grid (CVRG), enhancing data sharing and analysis in cardiovascular studies. His work bridges medicine and engineering, with applications in both clinical and preclinical settings.

• Harmonic Analysis
• Information Theory
• Signal Processing
• +۳ مورد دیگر

Sinan Gunturk is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on harmonic analysis, information theory, and signal processing, particularly on efficient digital representations of analog signals. He holds a Ph.D. in Applied and Computational Mathematics from Princeton University (2000) and a B.S. in Mathematics and Electrical Engineering from Bogazici University, Turkey (1996). His work bridges sampling theory and quantization, with notable contributions to sigma-delta modulation and compressive sensing. He has held academic roles at NYU since 2001, including Director of Graduate Studies in the Mathematics Department. Gunturk is a recipient of the Alfred P. Sloan Research Fellowship (2006) and the Monroe H. Martin Prize (2005). His publications emphasize quantization theory, sparse recovery, and signal reconstruction, often addressing practical applications in digital signal processing. He has organized major conferences like SampTA 2009 and contributed to editorial boards of journals such as Foundations and Trends in Signal Processing .