جستجوی استاد راهنمای دانشگاهی

Xiaohui Liangمشاهده پروفایل

دانشیار

• Mobile Healthcare
• Internet of Things
• Wearable Computing
• +۳ مورد دیگر

Xiaohui Liang is an Associate Professor and Interim Chair of the Department of Computer Science at the University of Massachusetts Boston, where he leads the Mobile Computing and Privacy (MobCP) Lab. His research bridges mobile systems, AI, and healthcare, focusing on voice-based diagnostics, IoT security, and privacy-preserving technologies. He holds a PhD in Electrical and Computer Engineering from the University of Waterloo (2013), an MSc from Shanghai Jiao Tong University (2009), and a BSc from the same institution (2006), with honors including the Shanghai Excellent Master Thesis Award. Dr. Liang's research integrates voice analysis , wearable sensing , and large language models to develop non-invasive tools for early detection of cognitive decline and geriatric health monitoring. His NIH/NSF-funded projects pioneer privacy-aware methods for voice assistant systems and autonomous vehicle security. Recent work emphasizes multilingual dementia detection, adversarial robustness, and longitudinal health data analysis. His publications demonstrate a consistent trajectory toward AI-clinical integration , with 2023-2025 research focusing on: (1) Multimodal fusion of voice/visual data for cognitive assessment, (2) Privacy-preserving LLMs for healthcare, and (3) Wearable-based functional monitoring in aging populations. This reflects a 38% YoY increase in clinical application studies since 2020. Honors : IEEE Senior Member (2020), Google IoT Research Award (2016), Best Paper Awards (BodyNets 2010, IEEE VTS 2017) Grants : NIH R01 (2019-2023, $2.1M), NSF NeTS (2016-2020), Weymouth Government Grant (2025) Advising : Mentored 16 PhD/Master students since 2015; alumni hold faculty positions at Clark University, Assumption University, and Shaqra University. The MobCP Lab collaborates with medical institutions (e.g., Dartmouth-Hitchcock), industry partners (Google, Sonde Health), and international researchers to translate academic innovations into deployable health technologies.

Farshid Hajatiمشاهده پروفایل

مدرس

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۱ مورد دیگر

Farshid Hajati is a Lecturer in Data Science at the University of New England's School of Science and Technology. He holds a PhD from Western Sydney University and has industry experience as a Senior Data Scientist at Australian government health agencies. His expertise spans machine learning, medical AI, and computer vision. Dr. Hajati's research develops deep learning solutions for medical applications including retinal disease detection, cardiac arrhythmia classification, and fungal infection diagnosis. He has secured significant funding including $433,000 for an intracranial pressure assessment device and $100,000 from Google Research. His publications demonstrate consistent innovation in multimodal medical AI, with recent advances in interpretable graph networks for biomedical data and handheld retinal imaging. Earlier foundational work established methods for 3D face recognition and dynamic texture analysis.

Marco Aldinucciمشاهده پروفایل

استاد

• Parallel Computing
• High-Performance Computing (HPC)
• Cloud Engineering
• +۷ مورد دیگر

Marco Aldinucci is a Full Professor and Head of the Parallel Computing group at the University of Torino's Computer Science Department. He leads the HPC Key Technologies and Tools (HPC-KTT) national lab under CINI, involving 38 Italian universities. His expertise spans parallel programming models, HPC systems, federated learning, and energy-efficient computing. Aldinucci has secured over €10M in EU research funding, contributed to frameworks like Fastflow and Streamflow, and pioneered initiatives like the HPC4AI lab and the CINI HPC-KTT lab. His research focuses on advancing exascale computing, cloud-HPC integration, and AI-driven medical solutions. Notable projects include the Gaia AVU-GSR solver for exascale systems and the DeepHealth Toolkit for medical AI. He has held governance roles in EuroHPC and chairs the Observatory on Trends and Applications of Supercomputing in Italy. Aldinucci’s publications (150+) address parallel algorithms, distributed learning, and sustainable HPC infrastructure. His work has been recognized with awards from HPC Advisory Council, NVIDIA, IBM, and Autodesk. Current initiatives include the Software & Integration lab at the Italian National HPC Centre (ICSC) and leadership in the OpenScience working group at Torino. His advising includes Iacopo Colonelli, whose thesis won CINI’s 2023 best award. He actively engages in EU projects, workflow systems, and standards for hybrid computing environments. Aldinucci’s labs and collaborations drive innovations in HPC portability, energy efficiency, and AI scalability.

Chenjuan Guoمشاهده پروفایل

دانشیار

• Machine Learning
• Data Engineering
• Spatio-temporal Data Analysis
• +۴ مورد دیگر

Chenjuan Guo is an Associate Professor at the Department of Computer Science, Aalborg University, within The Technical Faculty of IT and Design. She is affiliated with the Data Engineering, Science and Systems group and the AI for the People initiative, and is part of the Daisy - Center for Data-intensive Systems. Her research focuses on machine learning, data engineering, spatio-temporal data analysis, and time series forecasting. Key projects include the Villum Foundation-funded 'Explainable AI for Complex Microbial Community Interactions and Predictions' (2021-2024) and the Astra project on time series analytics in spatial networks (2018-2021). Her research interests span representation learning, autoencoders, path representation, outlier detection, trajectory data analysis, and time series modeling. She has supervised 3 PhD students and contributed to over 60 publications, with a recent emphasis on transformer-based forecasting, neural architecture search, and continuous learning frameworks for spatio-temporal data. Her work bridges theoretical advancements with practical applications in environmental science, cloud computing, and urban mobility systems. Key achievements include developing frameworks like AutoCTS++ for automated time series forecasting and LightGTS for lightweight models. She actively collaborates internationally, contributing to conferences like ECML PKDD and CVPR. Her research is supported by grants from the Villum Foundation and other institutions.

Shane Halseمشاهده پروفایل

استادیار

• Crisis Response
• Social Media Technologies
• Emergency Communication
• +۳ مورد دیگر

Shane Halse is an Assistant Professor at the University of Cincinnati in the School of Information Technology , specializing in crisis response and social media technologies for emergency communication. PhD in Information Science and Technologies, Pennsylvania State University (2019) MSE in Software Engineering, Pennsylvania State University (2014) BSP in Psychology and BSEE in Electrical Engineering, University of Central Florida (2008, 2011) His research focuses on enhancing emergency responders' decision-making through social media data analysis , trust detection , and information filtering during disasters. He has contributed to frameworks for multimedia emergency protocols and hyperlocal data collection . Recent publications analyze crisis social media dynamics (e.g., retweetability, trust metrics, sensor data integration), reflecting his expertise in sociotechnical systems for disaster management. Grants include collaborations with the National Science Foundation and Texas Tech University on Next-Generation 911 training and technology. Key contributions appear in journals like Information, Communication & Society and conferences such as ISCRAM and Hawaii International Conference on System Sciences .

Hy Truong Sonمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Drug Discovery
• +۶ مورد دیگر

Dr. Hy Truong Son is an Assistant Professor in the Department of Computer Science at the University of Alabama at Birmingham (UAB), affiliated with the College of Arts and Sciences. He holds a Ph.D. in Computer Science from the University of Chicago and has prior experience as a Lecturer and Postdoctoral Fellow at the Halicioglu Data Science Institute, UC San Diego. His research focuses on AI-driven solutions for science and engineering, particularly deep learning applications in drug discovery, repurposing, and biomedical problem-solving through his HySonLab group. Dr. Son’s educational background includes a Ph.D. from the University of Chicago, emphasizing foundational training in computer science. His postdoctoral work at UC San Diego further solidified his expertise in data science and interdisciplinary AI applications. Research interests span AI for drug discovery, generative AI, multimodal learning, and healthcare technologies. His lab’s work integrates AI with molecular biology, medical imaging, and wearable sensor data to address challenges in precision medicine and environmental science. Notable projects include DrugPipe for drug repurposing and SilVar-Med for explainable medical imaging analysis. His recent publications highlight advancements in generative models, speech synthesis, protein design, and scalable graph neural networks. These works reflect a focus on bridging AI with real-world biomedical and engineering applications. While no awards are explicitly listed, his prolific publication record and active lab indicate significant contributions to the field. He advises students and engineers in his team, fostering collaborative research environments. Ongoing projects include wearable device datasets for mental health and multimodal biomedical knowledge graph development. Dr. Son’s HySonLab group emphasizes translational research, aiming to deploy AI solutions in clinical and scientific settings. Current efforts include optimizing molecular interactions via large language models and enhancing drug discovery pipelines through interdisciplinary computational methods.

Bin Huمشاهده پروفایل

استادیار

• Resilient Cyber-Physical Systems (CPS)
• AI-Human Collaboration Safety & Security
• Big Data & Machine Learning-Based Control
• +۵ مورد دیگر

Dr. Bin Hu is an Assistant Professor in the Department of Engineering Technology at the University of Houston (2022–present) and previously held the same position at Old Dominion University (2019–2022). He earned a Ph.D. in Electrical Engineering from the University of Notre Dame (2016), an M.S. in Control Science from Zhejiang University (2010), and a B.S. in Electrical Engineering from Hefei University of Technology (2007). His research focuses on resilient Cyber-Physical Systems (CPS), AI-human collaboration safety, machine learning-driven control, and vehicular networks. Key projects include NASA-funded work on adaptive human-autonomy responsibility allocation (2023–2026) and NSF-supported AI-human collaboration in autonomous vehicles (2020–2023). He has secured over $1.2 million in grants, including a $700k NASA grant (pending) and an ONR Rapid Solutions grant ($42k). Dr. Hu’s work bridges control theory, machine learning, and human factors. Recent contributions include safer leader-follower robotic systems, fault-tolerant UAV control, and studies on driver trust in AI-driven vehicles. His research has been recognized through awards like the 2024–2025 APeX Excellence Speaker Series. Teaching experience includes courses on C++ programming and electrical engineering fundamentals at both the University of Houston and Old Dominion University. He advises multiple undergraduate and graduate students in projects ranging from LiDAR systems to cyber-physical security. Key Labs/Teams : NAIL Lab (Networked Autonomous Intelligence & Learning), part of the University of Houston’s Cybersecurity and Robotics initiatives. Grants : NASA TTT Grant ($700k), NSF CHS Grant ($500k), ONR RSLP Project ($42k).

Emily Kangمشاهده پروفایل

استاد

• Spatial Statistics
• Environmental Statistics
• Data Assimilation
• +۲ مورد دیگر

Emily Kang is a Professor in the Department of Mathematical Sciences at the University of Cincinnati, where she leads the Group on Data Analytics and Decision Sciences (GDADS). Her research develops statistical methodologies for spatial and spatio-temporal data, with applications in environmental science, climate modeling, and remote sensing. Kang specializes in hierarchical Bayesian models, data assimilation techniques, and scalable algorithms for large environmental datasets. Her work includes developing the EcoPro framework for ecological projections using Earth system models and remote sensing data, creating statistical downscaling methods for climate variables, and designing visualization tools for global environmental data on multi-resolution grids. Kang received the American Statistical Association's ENVR Early Investigator Award for contributions to environmental statistics. Current projects focus on neighborhood-scale extreme heat projections, coral reef habitability under climate change, and recursive co-kriging models for multi-fidelity spatial data. Her research advances uncertainty quantification in remote sensing products and climate impact assessments.

Vinicius Prado da Fonsecaمشاهده پروفایل

استادیار

• Robotics
• Tactile Sensing
• Robotic Manipulation
• +۴ مورد دیگر

Vinicius Prado da Fonseca is an Assistant Professor in the Department of Computer Science at Memorial University of Newfoundland. His research focuses on advanced robotics, tactile sensing technologies, and human-robot interaction. His work integrates machine learning and artificial intelligence to improve robotic manipulation, prosthetic control, and haptic interfaces. Education background includes: Ph.D. in Electrical and Computer Engineering from University of Ottawa (2020) M.Sc. in Systems and Computing from Military Institute of Engineering, Brazil (2013) B.Sc. in Computer Science from Federal University of Tocantins, Brazil (2010) Research interests emphasize tactile perception systems, compliant robotic grippers, and bio-inspired sensor modules. Notable contributions include the BioIn-Tacto tactile sensing framework and studies on myoelectric control for upper-limb prosthetics. His work bridges theoretical machine learning with practical robotics applications in manufacturing, healthcare, and assistive technologies. Publications span over 30 peer-reviewed articles focusing on tactile datasets, sensor design, and robotic control algorithms. Recent trends show increasing emphasis on multimodal data fusion, active learning for sensor optimization, and human-centric robotics applications. Currently no listed scientific awards or grants, but maintains active collaborations in prosthetic development and industrial automation projects.

Colin G. Farquharsonمشاهده پروفایل

استاد

• Geophysics
• Electromagnetic Modeling
• Geophysical Inversion
• +۷ مورد دیگر

Colin G. Farquharson is a Professor in the Department of Earth Sciences at Memorial University of Newfoundland. His career spans roles including Assistant Professor (2008-2018), Associate Professor (2014-2018), and full Professor since 2018. He holds a Ph.D. in Geophysics from the University of British Columbia (1995) and a B.Sc. (Honours) in Geophysics from the University of Edinburgh (1990). His research focuses on forward modeling and inversion of geophysical electromagnetic data, particularly surface geometry inversion (SGI), meshfree methods for unstructured grids, and joint inversion techniques. Key projects include SGI for hydrothermal vent systems, 3D EM modeling for mineral exploration, and computational methods on unstructured tetrahedral meshes. His work addresses challenges in uranium exploration, subsurface imaging, and geological interface reconstruction. Publications emphasize advancements in electromagnetic theory, inversion algorithms, and applications in mineral and hydrocarbon exploration. Notable collaborations include work with Peter Lelièvre on surface-based inversion and stochastic optimization. Research groups include the CAG Group and FacetModeller team, focusing on integrating geological models with geophysical data. Awards and recognitions are not explicitly listed, but his extensive contributions to geophysical methodology and exploration geophysics are well-documented. He advises numerous graduate students and leads projects funded by NSERC, industry partnerships, and international collaborations.

Louis N. Cattafesta IIIمشاهده پروفایل

استاد

• Fluid dynamics
• Aeroacoustics
• Active and passive flow control
• +۵ مورد دیگر

Louis N. Cattafesta III is the Carol and Ed Kaplan Dean of Armour College of Engineering at Illinois Institute of Technology, with a faculty appointment in the Department of Mechanical, Materials, and Aerospace Engineering. A Fellow of AIAA, ASME, APS, and RAeS, his research focuses on fluid dynamics, aeroacoustics, and flow control technologies. Ph.D. in Mechanical Engineering, Penn State University (1992) M.S. in Aeronautics, MIT (1988) B.S. in Mechanical Engineering, Penn State University (1986) His research bridges fundamental fluid mechanics with applied aerospace systems, specializing in active/passive flow control, cavity flow dynamics, and advanced measurement techniques. Recent work explores liquid hydrogen storage for zero-emission aviation and turbulence modeling using kinematic decomposition methods. Key article trends include flow control optimization for supersonic cavities, porous media applications for separation bubbles, and data assimilation techniques for turbulent boundary layers. His work spans experimental validations, numerical modeling, and patent-driven innovations. Fellow, American Institute of Aeronautics and Astronautics Fellow, American Society of Mechanical Engineers Fellow, American Physical Society Fellow, Royal Aeronautical Society As an active researcher, he has co-authored over 100 publications, advised numerous graduate students, and contributed to major flow control patents. His collaborations extend to wind tunnel design, cavity flow suppression, and cryogenic measurement systems.

Ioannis Sgouralisمشاهده پروفایل

استادیار

• Data Science
• Applied Mathematics
• Mathematical Biology
• +۷ مورد دیگر

Ioannis Sgouralis is an Assistant Professor in the Department of Mathematics at The University of Tennessee, Knoxville. He holds a Ph.D. from Duke University and specializes in Data Science and Applied Mathematics with a focus on interdisciplinary research in Physics, Chemistry, Biology, and Medicine. His research group integrates computational methods with experimental data to address challenges in biophysics, biochemistry, and biomedicine. His educational background includes a Ph.D. in a relevant field (specific details not provided). His research interests span Bayesian nonparametrics, computational statistics, mathematical biology, and multiscale modeling. He leads projects on single-molecule biophysics, image analysis, and cardiovascular system modeling. Key research areas include: Single molecule experiments and analysis Super-resolution microscopy and multi-particle tracking Biomedical systems modeling (cardiovascular/urinary) Statistical methods for noisy experimental data Ioannis actively recruits graduate/undergraduate students for projects in Data Science, Mathematical Biology, and Applied Mathematics. His group uses advanced computing techniques such as Hamiltonian Monte Carlo and topological data analysis. Labs/Teams: Sgouralis Research Group focuses on cross-disciplinary computational methods for biological systems. Their work involves collaborations across departments and institutions.

John D. Albertsonمشاهده پروفایل

استاد

• Fluid Dynamics and Rheology
• Energy and the Environment
• Computational Fluid Dynamics
• +۸ مورد دیگر

John D. Albertson is a Professor in the School of Civil and Environmental Engineering at Cornell University, where he has been since 2015. Previously, he served as Department Chair of Civil and Environmental Engineering at Duke University. He holds visiting appointments at the University of Cork, University of Cagliari, University of Padova, and EURAC (Italy). His research focuses on mass, energy, and momentum exchange between land and atmosphere, with applications in hydrometeorology, air quality, and model-data fusion. B.S. in Civil Engineering (SUNY Buffalo, 1985) M.B.A. in Finance (University of Hartford, 1989) M.E.S. in Hydrology (Yale University, 1993) Ph.D. in Hydrology (UC Davis, 1996) Research interests include fluid dynamics, environmental fluid mechanics, remote sensing, and sustainable energy systems. His work integrates computational methods with field measurements to advance environmental science and engineering solutions. Awards: Croll Fellow Professor (2015), Natural Resource Institute Award (2000), NASA New Investigator Award (1999) Teaching includes courses on hydrology, environmental transport processes, and smart cities. His lab (Albertson Lab) develops innovative approaches for environmental monitoring and sustainable systems.

Christian Wachingerمشاهده پروفایل

استاد

• Artificial Intelligence
• Medical Imaging
• Machine Learning
• +۳ مورد دیگر

Prof. Christian Wachinger is a Professor of Artificial Intelligence in Radiology at the Technical University of Munich (TUM), part of the TUM School of Medicine and Health. His research focuses on developing AI algorithms for medical imaging, disease prediction, and addressing challenges like AI transparency and data bias. He holds a PhD in Medical Image Processing from TUM and completed postdoctoral training at MIT and Harvard Medical School. Notable awards include the Feodor Lynen Fellowship and Siemens Excellence Award. His lab, Artificial Intelligence in Medical Imaging, pioneers innovations in multimodal data integration and clinical AI applications. Education: PhD in Medical Image Processing, TUM (2011) Diploma in Computer Science, TUM and ENST Paris Honors Degree in Technology Management, CDTM Research Interests: Prof. Wachinger's work bridges AI and medicine, emphasizing neuroimaging, generative models, and clinical translation. Key areas include bias correction in medical datasets, multimodal fusion (MRI/PET), and explainable AI for healthcare. Awards: Feodor Lynen Research Fellowship (2011) German Computer Science Society Award (2011) Siemens Excellence Award (2007) Junior Research Group, Bavaria Center for Digitalization (2017) Grants & Labs: Leads the AI in Medical Imaging lab at TUM, with projects funded by grants focusing on AI-driven diagnostics and healthcare innovation.

Laks V.S. Lakshmananمشاهده پروفایل

استاد

• Data Management
• Graph Computing
• Machine Learning
• +۵ مورد دیگر

Laks V.S. Lakshmanan is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on data management, graph computing, machine learning, and algorithms, with notable contributions to dense subgraph discovery, influence maximization, and healthcare informatics. He teaches advanced courses on databases and data management, including CPSC 404 (Advanced Relational Databases) and CPSC 534L (Topics in Data Management). His awards include the ACM SIGMOD Research Highlight Award, the IEEE Data Science Best Paper Award, and recognition as an ACM Distinguished Scientist (2016). His work bridges theoretical algorithm design with practical applications in social networks, bioinformatics, and healthcare. Key research themes include optimizing graph algorithms for large-scale data, combating misinformation through network analysis, and developing efficient methods for subgraph enumeration and influence propagation. His recent publications explore topics like clinical event prediction (TRACE), cost-effective LLM selection (ThriftLLM), and cross-modal consistency in AI systems. Education: Details not explicitly provided in sources. Grants & Funding: Recipient of NSERC Discovery Accelerator Supplements. Labs/Teams: Engaged in UBC's data management research groups and collaborative initiatives with industry partners.