جست‌وجوی استاد و پژوهشگر

Chinwe Ekennaمشاهده پروفایل

دانشیار

• Robotics
• Machine Learning
• Computational Biology
• +۴ مورد دیگر

Chinwe Ekenna is an Associate Professor in the Department of Computer Science at the University at Albany, State University of New York (SUNY) and directs the Robotics, Algorithm and Computable Systems (RACS) Laboratory . She serves as a faculty advisor for UAlbany's ACM-W chapter , promoting women in robotics and STEM. PhD in Computer Science from Texas A&M University (2016) MSc in Computer Science from Covenant University, Nigeria (2008) BSc in Computer Science from Covenant University, Nigeria (2006) Her research focuses on intelligent motion planning for robotics and proteins, integrating machine learning to enhance planning efficiency and accuracy. Key areas include computational biology , robotic navigation , and topological modeling . Her recent publications explore transformer-based motion planning , attention mechanisms for molecular analysis , and discrete Morse theory for robotics . These works bridge artificial intelligence , robotics , and biomedical applications . As a leader, she organized the Meet the Women in Robotics Workshop at RSS 2019, funded by the National Science Foundation. Her teaching includes courses in theory of computation and algorithms . At the RACS Laboratory , her team investigates path planning , adversarial robustness , and robotics in dynamic environments , emphasizing interdisciplinary collaboration and innovation.

Frank Shihمشاهده پروفایل

استاد

• Image Processing
• Deep Learning
• Medical Imaging
• +۴ مورد دیگر

Frank Shih is a Professor of Computer Science at the New Jersey Institute of Technology (NJIT). His research focuses on image processing, deep learning, medical imaging, and cybersecurity , with applications in healthcare, agriculture, and transportation. He holds a Ph.D. in Electrical and Computer Engineering from Purdue University (1987), an M.S. from Stony Brook University (1984), and a B.S. from National Cheng Kung University (1980). His work includes developing novel techniques for medical image enhancement (e.g., X-ray contrast improvement), crop freshness assessment via deep learning, and robust defense against adversarial attacks in neural networks. He has also pioneered methods in land cover segmentation, drug toxicity prediction, and real-time vehicle counting systems. Shih’s research integrates interdisciplinary approaches, combining computer vision with domain-specific challenges in healthcare, agriculture, and infrastructure monitoring. His laboratory focuses on advancing AI-driven solutions for real-world problems, including automated diagnosis systems and secure image processing frameworks.

Zhi Weiمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• Machine Learning
• +۶ مورد دیگر

Zhi Wei is a Distinguished Professor of Computer Science at the New Jersey Institute of Technology (NJIT). His research integrates computational methods with biological data to address challenges in cancer genomics, immune system modeling, and drug interaction prediction. He holds a Ph.D. in Bioinformatics from the University of Pennsylvania (2008), M.S. in Computer Science from Rutgers University (2004), and B.S. in Computer Science from Wuhan University (2000). Key research interests include single-cell analysis, multi-omic data integration, machine learning applications in genomics, and viral oncogenesis mechanisms. His work has led to tools like GigaAssay for high-throughput mutagenesis assays and MONTAGE for detecting mosaic copy number variations. Current projects focus on tumor heterogeneity, immune checkpoint blockade therapies, and AI-driven game strategy optimization. Publications emphasize computational methodologies (e.g., scDILT framework for single-cell data integration) and translational research in cancer immunotherapy response prediction. He has advised numerous interdisciplinary studies but no specific student names listed. Funding and grants focus on NIH-supported projects in genomic medicine and bioinformatics. Laboratory work involves collaborations with medical institutions to translate computational findings into clinical applications, particularly in melanoma and glioma therapies. His lab develops open-source tools for processing spatial transcriptomics and virome analysis in oncology.

Xiaojing Yuanمشاهده پروفایل

استاد

• BioMedical Signal and Image Processing
• Intelligent System Modeling and Simulation
• Distributed Sensor Networks
• +۳ مورد دیگر

Dr. Xiaojing (Jane) Yuan is a Professor in the Department of Engineering Technology at the University of Houston’s Cullen College of Engineering. She specializes in biomedical signal processing, intelligent systems, and wireless sensor networks. Her research focuses on applications in health informatics, disease management, and sensor network optimization. Education Ph.D. in Robotics and Automation, Tulane University (2003) M.S. in Computational Intelligence, Tulane University (2002) M.S. in Computer-Aided Automation, University of Science and Technology of China (1997) B.S. in Electrical Engineering, Hefei University of Technology (1994) Research Interests Dr. Yuan’s work spans biomedical signal/image processing, intelligent system modeling, and distributed sensor networks for health monitoring. She develops algorithms for skin cancer detection and wireless sensor network protocols for chronic disease management. Her lab, the Intelligent Sensor Grid and Informatics Lab, integrates hardware and software solutions for real-world applications. Grants & Projects Principal Investigator (PI) of projects totaling over $200k in funding, including grants from NASA, NSF, and the Texas Emerging Technology Fund Co-PI for interdisciplinary initiatives like the NSF NeTS-NOSS program and NASA’s ISHM testbed Labs & Teams Director of the Intelligent Sensor Grid and Informatics Lab and advisor for the International Society of Automation (ISA).

Qiang Chengمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۲ مورد دیگر

Qiang Cheng is an Associate Professor at the University of Kentucky with joint appointments in the Division of Biomedical Informatics and the Stanley and Karen Pigman College of Engineering. His research bridges computational methods with biomedical applications, focusing on machine learning, data mining, and AI-driven solutions for precision medicine. Research interests center on developing novel algorithms for biomedical data analysis, including tabular data processing, gene expression modeling, drug response prediction, and temporal pattern recognition in healthcare contexts. His work frequently integrates deep learning architectures with domain-specific challenges in omics and clinical informatics. Recent publications demonstrate strong focus on generative models (diffusion networks, autoregressive architectures), efficient learning methods (sparse attention, lightweight networks), and biological applications (circadian rhythm prediction, molecular generation). Methodological innovations consistently target high-dimensional biomedical data challenges through multi-view learning, tensor decomposition, and causal inference frameworks.

Chris Joslinمشاهده پروفایل

استاد

• Computer Graphics
• Medical Imaging
• Virtual Reality
• +۵ مورد دیگر

Chris Joslin is a Professor at the School of Computer Science, Carleton University. His office is located in 4302 Canal Building, and he can be reached at Chris.Joslin@carleton.ca. He specializes in interdisciplinary research areas including computer graphics, medical imaging, virtual reality, computer vision, and human-computer interaction. His work bridges theoretical advancements with practical applications in animation, 3D modeling, and medical visualization. Research interests include developing novel techniques for 3D editing, medical image processing, and immersive virtual environments. Notable contributions include advancements in 3D Gaussian splatting, AI-driven MRI analysis, and robust sensor fusion for autonomous systems. His publications span from foundational studies on motion retargeting to applied work in procedural audio generation for soft-body simulations. Recent trends in his articles emphasize integration of deep learning with traditional computer vision tasks, optimization of medical imaging workflows, and enhancing accessibility in virtual reality systems. Despite prolific output, no scientific awards are explicitly mentioned in the provided texts. Advising and grant details remain unspecified, though his involvement in collaborative projects like VPARK and ISIS suggests engagement with interdisciplinary teams. His work is anchored at Carleton’s Herzberg Laboratories, a hub for advanced computational research.

Hadi Ali Akbarpourمشاهده پروفایل

استادیار

• Artificial Intelligence
• Computer Vision
• Autonomous Systems
• +۷ مورد دیگر

Hadi Ali Akbarpour is an Assistant Professor in the Department of Computer Science at Saint Louis University's School of Science and Engineering . He earned a Ph.D. in Electrical and Computer Engineering from the University of Coimbra, Portugal (2012). Research focuses on Artificial Intelligence and Computer Vision at the intersection of Autonomous Systems , Remote Sensing , and Robotics Key topics: Deep Learning , 3D Reconstruction , Homography Modeling , and Sensor Fusion Recent publications highlight advancements in: Structure-from-Motion for aerial imagery Deep Learning for novel view generation Georegistration accuracy in wide-area contexts 3D Point Cloud integration Agricultural Monitoring via aerial mosaicking Professional accolades include: Best Paper , Presentation , and Challenge Awards Co-chaired the 51st IEEE Applied Imagery Pattern Recognition Workshop Guest Editor for Sensors Journal special edition (2021) His funded projects include roles as Principal Investigator ( $1.1M ), co-PI ( $8M ), and task leader in $6M SBIR projects .

• Machine Learning
• Artificial Intelligence
• Graph Neural Networks
• +۲ مورد دیگر

Maoying Qiao is a Lecturer at Australian Catholic University's Peter Faber Business School, specializing in machine learning and artificial intelligence. Her research spans graph neural networks, computer vision, and probabilistic modeling, with applications in network analysis and 3D vision. Key publications focus on improving graph convolutional networks through negative sampling, adapting stochastic block models for power-law networks, and developing conditional graphical lasso methods for multi-label image classification. Marine science applications include automated catch detection systems for fisheries.

Fei Wangمشاهده پروفایل

استاد

• Power Electronics
• Power Systems
• Motor Drives
• +۳ مورد دیگر

Fei Wang is a Professor and Condra Chair of Excellence in Power Electronics at the University of Tennessee, Knoxville (UTK), affiliated with the Tickle College of Engineering and the Min H. Kao Department of Electrical Engineering and Computer Science. He serves as Technical Director of CURENT and holds a joint appointment at Oak Ridge National Lab. His expertise spans power electronics converters, motor drives, wide bandgap devices, and renewable energy systems. Education: B.S. in Electrical Engineering, Xi’an Jiaotong University, 1982 M.S. and Ph.D. in Electrical Engineering, University of Southern California, 1985 and 1990 Research Interests: Focuses on design, modeling, and control of advanced power electronics systems, including grid integration of renewable energy, electric vehicle drivetrains, and wide bandgap semiconductor applications. He has authored over 500 publications and holds 20 patents, with funding exceeding $65M and personal share over $18M. Key Contributions: Developed three-level NPC medium voltage drives at GE Co-founded CURENT and led its technical strategy Recipient of IEEE IAS Gerald Kliman Innovator Award (2018), Dushman Award (1998), and multiple research excellence awards Grants & Advising: Supervised 13 Ph.D. and 8 M.S. students, advised postdocs, and hosted over 35 visiting scholars. Active in IEEE standards development and editorial roles. Labs & Teams: Leads the CURENT Engineering Research Center, focusing on ultra-wide-area power grid resilience through advanced power electronics and system integration.

Fan Yeمشاهده پروفایل

استاد

• Mobile and embedded sensing systems
• AI/ML algorithms
• Data-centric wireless communication
• +۳ مورد دیگر

Fan Ye is a Professor in the Department of Electrical and Computer Engineering at Stony Brook University, serving as Graduate Program Director. His research focuses on mobile/embedded sensing systems, AI/ML for Computational Screening and Surveillance (CSS), data-centric wireless communication, edge computing, and IoT. He leads projects in health monitoring infrastructure (e.g., Proteus) and vehicular peer model training (e.g., Roadtrain), emphasizing scalability and manageability. His work bridges hardware-software integration for real-world applications like non-contact vital signs monitoring and aging-in-place sensor tech. Research trends reflect a strong emphasis on practical systems: 2024 publications highlight RF-based health monitoring innovations, edge computing optimizations, and user-centric sensor development. Earlier work includes contributions to decentralized machine learning, self-calibrating indoor tracking, and UWB-depth sensor fusion. Collaborations span academia and industry, addressing challenges in privacy, energy efficiency, and real-time data processing. Ye’s lab develops scalable solutions for pervasive edge environments, addressing issues like multicast rate control, optimal producer selection (OPSEL), and blockchain resource allocation. His teaching contributions include collaborative approaches to aging technology education. Current projects aim to engage older adults in shaping low-cost sensor systems and improving post-COVID health monitoring through unobtrusive home sensors. Notable infrastructure includes the Proteus health monitoring platform and SCALING for plug-and-play indoor tracking. His systems emphasize robustness and adaptability, leveraging acoustic, radar, and vision modalities for multi-factor authentication and indoor mapping.

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +۵ مورد دیگر

Dr. Guanjin Wang serves as a Senior Lecturer in the School of Information Technology at Murdoch University, conducting cutting-edge research at the intersection of artificial intelligence, machine learning, and interdisciplinary applications. Her work bridges theoretical advancements with real-world implementations in health informatics and precision agriculture. Education: Joint Ph.D. in Artificial Intelligence, The Hong Kong Polytechnic University and University of Technology Sydney (2018) Her research program centers on developing novel methodologies for learning from complex and imperfect data environments while prioritizing model explainability. Key focus areas include transfer learning for low-resource settings, multimodal learning architectures, fuzzy system innovations, and co-designed AI solutions that integrate domain expertise. Current projects emphasize Aboriginal perinatal mental health prediction and agricultural applications like barley genotype-to-phenotype modeling. Analysis of her recent publications reveals a strong trend toward interdisciplinary collaboration, particularly in healthcare AI where explainability is critical for clinical adoption. Her work consistently addresses data heterogeneity challenges through federated learning, multi-view frameworks, and robust fuzzy systems, with significant contributions to imbalanced data classification and spatiotemporal modeling. Scientific Awards: Google Inclusion Research Award Google Academic Research Award Dr. Wang actively supervises honors and master's students while welcoming PhD inquiries in AI/ML domains. Her research program is supported by approximately 2.2 million AUD in competitive funding from diverse sources including: Google Research (multiple awards) Western Australia Department of Health FHRI Fund Australian Medical Research Future Fund (MRFF) Hong Kong Innovation and Technology Fund German DAAD Healthway Murdoch University Vice Chancellor's Grant She contributes to academic leadership as IEEE Western Australia Section Chapter Chair since 2021 and collaborates with Murdoch's Ngangk Yira Institute for Change on community-engaged research projects.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۲ مورد دیگر

Dongjie Wang is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas, School of Engineering. His research focuses on artificial intelligence, machine learning, and data science applications in bioinformatics, transportation planning, and data-centric AI. Wang's research interests span several key areas: Development of reinforcement learning frameworks for biological pathway analysis and gene selection Generative AI applications in urban transportation systems Multi-modal representation learning for cross-domain retrieval Neuro-symbolic approaches for feature engineering His recent publications demonstrate a consistent focus on developing novel AI methodologies for real-world applications, particularly in transportation systems optimization, biological data analysis, and tabular data processing. The articles collectively show strong emphasis on reinforcement learning architectures, graph neural networks, and multimodal integration techniques. Wang advises graduate students in computer science and leads research in data-centric AI applications. His laboratory focuses on developing scalable machine learning solutions for complex systems.

Zachary Pattersonمشاهده پروفایل

استاد

• Transportation Engineering
• Data Science
• Machine Learning
• +۵ مورد دیگر

Zachary Patterson is a Professor at the Concordia Institute for Information Systems Engineering, Concordia University. His research focuses on emerging technologies in transport-related data collection, computational geospatial data processing, and the application of statistical and artificial intelligence techniques to transportation and environmental challenges. He specializes in discrete choice experiments, greenhouse gas emission prediction, and smart mobility solutions. Dr. Patterson's work integrates machine learning, graph neural networks, and geospatial analysis to address complex issues such as public transit optimization, passenger flow prediction, and environmental impact assessment. He has developed frameworks for privacy-preserving data collection and innovative solutions for on-demand transit systems. His interdisciplinary approach bridges computer science, transportation engineering, and environmental science. Notable contributions include the TMS-GNN model for traffic-aware passenger flow prediction, HydroVision for hydrometric forecasting, and frameworks for electric vehicle adoption analysis. His research often emphasizes interpretability of black-box machine learning models and the ethical use of data in urban systems. Dr. Patterson has authored over 50 publications, with a focus on transportation data analytics, sustainability, and smart cities. His work has been applied in real-world contexts such as Montreal’s transportation infrastructure and environmental policy design.

• Machine learning
• Computer vision
• Signal processing
• +۵ مورد دیگر

Jenni Raitoharju is an Assistant Professor at the University of Jyväskylä's Faculty of Information Technology. Her research focuses on developing novel machine learning algorithms with applications in environmental studies, autonomous systems, and computer vision. Key areas include deep learning, uncertainty estimation, one-class classification, and statistical machine learning. She leads projects such as the 2024–2026 Ministry of Environment-funded study on aquatic biodiversity monitoring and a 2023 Finnish National Agency for Education project on morphotaxonomic classification. Her work bridges theoretical advancements with practical implementations, as seen in publications like Linear-Time One-Class Classification with Repeated Element-Wise Folding (2024) and AquaMonitor: A multimodal multi-view image sequence dataset (2025). She actively contributes to maritime computer vision initiatives, including organizing the 2023 MaCVi Workshop. Jenni's research also involves ecological instrumentation, such as the Riverine Organism Drift Imager (RODI) for aquatic organism studies. Her projects emphasize interdisciplinary collaboration, combining machine learning with environmental science and engineering. While no awards or grants are explicitly listed, her 17+ publications since 2011 reflect sustained academic productivity. She coordinates lab efforts focusing on AI-driven ecological monitoring and autonomous systems.

Wei Xiangمشاهده پروفایل

استاد مدعو

• Internet of Things
• Machine Learning for IoT
• Big Data Analytics
• +۳ مورد دیگر

Wei Xiang is an Adjunct Professor and Founding Chair of Electronic Systems and Internet of Things Engineering at James Cook University. He leads the development of Australia's first accredited IoT Engineering program and conducts research at the intersection of IoT, machine learning, and big data analytics. Research encompasses: IoT systems for environmental monitoring Machine learning applications for sensor data Edge computing architectures Wireless communication protocols Smart agriculture technologies Publication analysis reveals focus areas: Smart transportation systems (25%) Agricultural IoT (20%) Medical AI applications (15%) Industrial IoT (15%) Network optimization (25%) Honors include Pearcey Entrepreneurship Award, Engineers Australia Cairns Engineer of the Year, and TNQ Innovation Award for establishing Australia's first IoT Engineering degree. Current projects include closed-loop irrigation systems for sugarcane farms and marine monitoring using distributed deep learning.