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Liqiang WangView profile

Professor

• Big Data Analytics
• High-Performance Computing (HPC)
• Parallel Computing
• +3 more

Liqiang Wang is a Professor in the Department of Computer Science at the University of Central Florida (UCF), where he directs the Big Data Lab. Previously, he served as faculty at the University of Wyoming (2006-2015). He holds a Ph.D. in Computer Science from Stony Brook University (2006) and spent a visiting research period at IBM T.J. Watson Research Center (2012-2013). His research focuses on big data analytics, high-performance computing, parallel systems optimization, and applying deep learning to detect programming errors and enhance model robustness. Education: Ph.D., Computer Science, Stony Brook University (2006); Visiting Researcher, IBM Watson (2012-2013). Research Interests: Improving accuracy and security of big data models, optimizing parallel computing systems (HPC, Cloud, GPUs), program analysis for concurrency errors, and deep learning applications in anomaly detection and adversarial robustness. Notable projects include scalable LSQR algorithms for seismic tomography and the OpenMP Analysis Toolkit (OAT) for concurrency error detection. Key Awards: NSF CAREER Award (2011), Castagne Faculty Fellowship (2013-2015), UCF Mid-Career Refresh Award (2020), and grants including a $50K NSF CIVIC-PG grant (2022) and Google/Meta donations. Advising and Grants: Supervises over 20 Ph.D./M.S. students and has secured grants totaling over $100K. Notable collaborations include seismic tomography with NCAR and cloud computing optimization. Labs/Teams: Director of UCF’s Big Data Lab, collaborating on projects like Parallel LSQR and Anti-Neuron Watermarking.

Masatoshi OkutomiView profile

Professor

• Medical Imaging
• Computer Vision
• Deep Learning
• +5 more

Professor Masatoshi Okutomi is affiliated with the Department of Systems and Control Engineering at the School of Engineering, Institute of Science Tokyo. His research focuses on advanced medical imaging techniques, particularly in endoscopy and 3D reconstruction, leveraging deep learning and neural networks. Key interests include virtual chromoendoscopy for cancer detection, image restoration, and stereo matching under challenging conditions. His work bridges computer vision and healthcare, addressing real-world applications such as MRI reconstruction and foggy stereo matching. Notable contributions include developing lightweight medical segmentation networks for edge devices and advancing neural radiance fields (NeRF) for novel view synthesis. His research spans diverse domains: from improving video quality assessment to enhancing object detection in high-dynamic-range images. Collaborative projects emphasize practical solutions for medical diagnostics and robust image processing in adverse environments. Recent articles highlight advancements in temporally-consistent video restoration, few-shot view synthesis, and degraded image classification using knowledge distillation. These innovations underscore his commitment to pushing boundaries in both theoretical computer vision and applied medical technology.

Kourosh DavoudiView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Deep Learning
• +3 more

Kourosh Davoudi is an Associate Professor of Computer Science at Ontario Tech University's Faculty of Science. He holds a PhD in Computer Science from York University with a focus on Machine Learning and Data Mining. Prior to joining Ontario Tech in 2019, he was a postdoctoral research fellow at the University of Waterloo's Department of Management Sciences. His research interests span Natural Language Processing, Deep Learning, Reinforcement Learning, Graph Mining, and Machine Learning. He actively supervises graduate students in these areas and teaches courses such as Data Mining and Artificial Intelligence. His research emphasizes practical applications of AI techniques in areas like outbreak detection, sentiment analysis, and automated grading systems. Recent work includes innovations in neural document segmentation, vision-language models, and hybrid outbreak detection using social media data. His publications consistently address challenges in algorithm design, explainable AI, and domain-specific NLP applications. Dr. Davoudi has contributed to conferences such as COLING, EMNLP, and IEEE transactions, focusing on interdisciplinary applications of machine learning. His work bridges theoretical advancements with real-world problems in healthcare, education, and social media analysis.

• Deep Learning
• Medical Image Analysis
• Biomedical Engineering
• +2 more

Maria Chiara Fiorentino is a Research Fellow at the Department of Information Engineering, Polytechnic University of Marche, Italy. Her work focuses on applying deep learning techniques to medical image analysis, particularly in ultrasound, MRI, and CT imaging. Education Master’s in Biomedical Engineering, Università Politecnica delle Marche (Honors) Ph.D. in Information Engineering, Università Politecnica delle Marche (Laude) Research Interests: Dr. Fiorentino specializes in deep learning for medical imaging, with applications in diagnosing neurodegenerative diseases like Parkinson’s, cardiovascular conditions, and musculoskeletal disorders. Her recent work includes federated learning for fetal ultrasound analysis, AI-driven vocal fold pose estimation, and domain adaptation in MRI segmentation. Scientific Awards: Paolo Marziali Thesis Prize for her Master’s research Gruppo Nazionale di Bioingegneria award for her Ph.D. thesis Publications: Dr. Fiorentino’s work spans fetal brain image synthesis, zero-shot learning robustness, and machine learning for catheterization management and stenosis detection.

Aaron MaxwellView profile

Associate Professor

• Geospatial Science
• Remote Sensing
• Machine Learning
• +6 more

Aaron Maxwell is an Associate Professor in the Department of Geology and Geography at West Virginia University (WVU). He serves as the principal investigator of West Virginia View, a member of AmericaView, and director of the WV GIS Technical Center. His research focuses on spatial predictive modeling, geohazard mapping, machine/deep learning applications in remote sensing, and thematic map accuracy assessment. He holds degrees from Alderson Broaddus University (B.S. in Biology, Chemistry, Environmental Science) and WVU (M.S. and Ph.D. in Geology), with a GIS Professional (GISP) certification. Education: Bachelor of Science in Biology, Chemistry, and Environmental Science – Alderson Broaddus University Master of Science in Geology – West Virginia University Doctor of Philosophy in Geology – West Virginia University Research Interests: Dr. Maxwell’s work emphasizes computational methods to extract insights from geospatial data. Key areas include: Deep learning for geomorphic feature extraction (e.g., LIDAR-based semantic segmentation) Machine learning applications in forest fuel load estimation and slope failure modeling Best practices for assessing deep learning outputs in remote sensing Synthetic data generation for predictive modeling Community flood resiliency and participatory GIS Publications: His recent work highlights advancements in geospatial deep learning (e.g., the geodl R package), accuracy assessment metrics for imbalanced datasets, and modeling post-mining landscape evolution. Articles often blend theoretical frameworks with applied case studies across environmental and geotechnical domains. Grants & Funding: Supported by NSF (CAREER Award) and AmericaView, his work trains students and develops open-source geospatial tools. Current projects include synthetic forest stand modeling and flood resiliency initiatives. Labs & Teams: Leads WV View, fostering remote sensing education and data sharing. Collaborates on geospatial workforce development and open-source software initiatives (e.g., GIScience courses, ArcGIS Pro labs).

• Machine Learning
• Bioinformatics
• Medical Imaging
• +5 more

Paul J. Kennedy is a Professor at the University of Technology Sydney's Centre for Artificial Intelligence. He holds a PhD from the same institution (1999). His research focuses on machine learning applications in healthcare, bioinformatics, medical imaging, and data mining. Key areas include developing algorithms for genomic data analysis, healthcare pathway modeling, and edge-cloud frameworks for omics data. Education: PhD in Artificial Intelligence (1999, UTS). Research interests span machine learning, health informatics, and data compression. Notable work includes studies on administrative health records, lung nodule detection, and virtual reality-based cancer cohort analysis. He has co-authored over 100 publications across journals like BMC Bioinformatics, IEEE Transactions, and Artificial Intelligence in Medicine. Advising: Collaborates extensively with students/researchers but no explicit student list provided. Grants and labs: Active in interdisciplinary projects involving medical and computational teams, though specific grants are not detailed here.

Dana BrooksView profile

Research Professor

• Biomedical Signal Processing
• Medical Imaging
• Statistical Signal Processing
• +8 more

Dana Brooks is a Research Professor in the Department of Electrical and Computer Engineering at Northeastern University, with affiliations in Bioengineering. He holds a PhD from Northeastern University (1991) and has received the Søren Buus Outstanding Research Award (2006). His primary research focuses on biomedical signal and image processing, medical imaging techniques (including MRI and electrocardiography), and neuromodulation technologies such as transcranial magnetic stimulation (TMS). He is also involved in protein conformation estimation using X-ray scattering and optimization algorithms for medical applications. Dr. Brooks leads the Biomedical Signals Processing Lab and collaborates with the Center for Integrative Biomedical Computing . His work bridges engineering and medicine, with recent grants including a $400K NSF MRI grant for advanced TMS systems and a $600K NSF grant for motor cortical organization studies. He has advised students like Setareh Ariafar (PhD’20) and contributed to innovations in image mosaicking for confocal microscopy and machine learning applications in dermatology. His publications span computational neuroscience, cardiac imaging, and uncertainty quantification in biomedical simulations. Notable achievements include developing algorithms for ECG imaging, optimizing TMS protocols, and creating tools like UncertainSCI for simulation reliability assessment.

Jacquelyn HumphreyView profile

Associate Professor

• Sustainable Finance
• Responsible Investment
• Environmental, Social, and Governance (ESG) Factors
• +6 more

Associate Professor Jacquelyn Humphrey is the Discipline Convenor (Finance) at the University of Queensland's School of Business. She specializes in sustainable finance, ESG factors, and responsible investment, with interdisciplinary work in broader sustainability and funds management. Her research has influenced international entities like the UN Environment Programme and Norway's Government Pension Fund. She holds a PhD in Finance from UQ (2008) and an Honours degree in Business (2004). Her research focuses on how environmental, social, and governance considerations impact investment decisions, corporate behavior, and climate goals. Key areas include green bonds, fossil fuel extraction benchmarks, and the financial implications of corporate sustainability commitments. Her work appears in top journals like Nature Climate Change and Journal of Banking and Finance . Her 2022 study on climate skepticism's link to temperature trends and political shifts highlights her interdisciplinary impact. Recent articles analyze plastic pollution reduction strategies in the food sector and reproducibility in management science. She co-authored the open textbook Sustainable Finance (2024), addressing industry needs. Awards: Senior Fellow of Advance HE, BEL Faculty Teaching & Leadership Awards Industry Impact: Advised Queensland Treasury Corporation on social benefit bonds and DNR Capital on board performance Research Groups: Leads sustainability-focused projects with global industry partners

Marianne BaxterView profile

Professor

• Macroeconomics
• International Finance
• Asset Pricing
• +1 more

Marianne Baxter is Professor of Economics at Boston University, specializing in macroeconomics, international finance, and business cycle analysis. Her research examines global economic interactions including risk-sharing mechanisms, consumption-investment dynamics, and pricing strategies in international markets. Major research streams investigate household consumption patterns across different wealth groups, exchange rate pass-through in retail pricing (notably IKEA case studies), and determinants of bilateral trade flows. Her work integrates theoretical modeling with empirical analysis using firm-level and household datasets. Professor Baxter teaches international macroeconomics and finance courses. She has contributed methodological frameworks for analyzing saving-investment correlations and terms of trade fluctuations.

Yongwan ChunView profile

Professor

• Geographic Information Science (GIS)
• Spatial Statistics
• Urban Geography
• +5 more

Professor Yongwan Chun is a faculty member in the Geospatial Information Sciences program at the University of Texas at Dallas , affiliated with the School of Economic, Political and Policy Sciences. His academic journey includes a PhD in Geography (2007) and MS in Applied Statistics (2006) from the Ohio State University. Research Interests: Methodological advancements in GIScience, spatial statistics, environmental justice, urban crime analysis, population migration, and commodity flow modeling. Awards: Emerging Scholar Award (2017) from the Spatial Analysis and Modeling Specialty Group of the Association of American Geographers. Grants: Multiple awards from National Science Foundation and National Institute of Health totaling $1.4 million. Article Trends reflect expertise in spatial autocorrelation (Moran's I, Geary's C), location-allocation modeling (p-median, p-dispersion), and applications to healthcare accessibility, urban crime patterns, housing market segmentation, and climate impacts. His work integrates GIScience with Bayesian methods, eigenvector filtering, and big data uncertainty. Collaborators: Daniel A. Griffith, Hyun Kim, and students like Dongeun Kim and Changho Lee. Recent Topics: Trauma center accessibility, snowpack variation modeling, and spatiotemporal crime analysis. Contact: ywchun@utdallas.edu | Office: GR 3.208, UT Dallas

Roziana RamliView profile

Researcher

• Computer Science
• Medical Imaging
• Machine Learning
• +3 more

Roziana Ramli is an academic affiliated with Northumbria University, holding a PhD in Computer Science. Her research focuses on medical imaging techniques, cybersecurity in healthcare systems, and bio-inspired optimization algorithms. She has contributed to advancements in retinal fundus image registration and IoT security protocols. Her work integrates computer vision with biomedical applications, addressing challenges in healthcare monitoring and assistive technologies. Key research areas include federated learning in healthcare networks, prosodic feature analysis for language recognition, and secure communication for drone networks. Her systematic literature reviews and algorithmic innovations highlight her interdisciplinary approach to solving technical and clinical problems. Though no formal awards are listed, her active publication record from 1999 to 2024 demonstrates sustained academic engagement.

David E. BreenView profile

Professor

• Biomedical Image Informatics
• Geometric Modeling
• Textile Modeling and Design
• +12 more

David E. Breen is a Professor in the Department of Computer Science within the College of Computing & Informatics (CCI) at Drexel University. He leads the Geometric Biomedical Computing Group and is affiliated with the Metadata Research Center and the Center for Biological Discovery from Big Data. His research spans interdisciplinary domains including biomedical image informatics, geometric modeling, textile modeling, and bio-inspired self-organization algorithms. Education: PhD, Computer and Systems Engineering, Rensselaer Polytechnic Institute MS, Computer and Systems Engineering, Rensselaer Polytechnic Institute BA, Physics, Colgate University His research interests focus on computational methods for biomedical applications, including shape and image analysis for cancer diagnosis, 3D reconstruction of biological tissues, and video analysis of animal behavior. He also investigates geometric modeling techniques for textiles and self-organizing systems. His work integrates computer science with biology, medicine, and engineering to solve complex problems in biomedical computing. The recent publications highlight a strong trend in computational modeling of textiles, biomedical image informatics, and AI-driven data analysis. Key themes include geometric modeling of knitted fabrics, deep learning for medical image classification, agent-based modeling of cancer metastasis, and metadata generation for biological image collections. His work bridges fundamental geometric algorithms with practical applications in healthcare and digital archives. Scientific Awards: No specific awards mentioned in the provided text. Breen has advised numerous students and collaborators across multiple domains, particularly in biomedical computing and textile modeling. His research has been supported through affiliations with major centers and collaborations with institutions such as Johns Hopkins University and the Max Planck Institute. He has been involved in projects related to NSF Center for Visual & Decision Informatics and has contributed to over 100 technical publications. He leads the Geometric Biomedical Computing Group , which conducts research at the intersection of biology, medicine, engineering, and computer science. The group develops algorithms and software for geometry-related computing problems in biomedical applications. Collaborations include the Drexel Integrated Laboratory for Cellular Tissue Engineering, Dr. Dan Marenda's Lab, and Dr. Aleister Saunder's Lab in Drexel's Biology Department.

Maozhen LiView profile

Professor

• High Performance Computing
• Big Data Analytics
• Artificial Intelligence
• +13 more

Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University of London , within the College of Engineering, Design and Physical Sciences . He serves as the Vice-Dean of the NCUT Transnational Education (TNE) programme, overseeing a joint school with North China University of Technology. He has been at Brunel since 2002, progressing from Lecturer to Professor in 2013. Education: PhD, Institute of Software, Chinese Academy of Sciences (1997) Postdoctoral Research, School of Computer Science and Informatics, Cardiff University (1999–2002) His primary research interests lie in high performance computing, big data analytics, and artificial intelligence, with applications in smart grids, smart manufacturing, and cybersecurity. He focuses on developing interpretable, robust, and lightweight AI models, including work in causal AI, parallel machine learning, and edge computing. His research integrates advanced techniques such as deep learning, reinforcement learning, and blockchain for real-world system optimization. An analysis of his recent publications reveals a strong and consistent research trajectory in AI-driven solutions for environmental monitoring (e.g., PM2.5 prediction), industrial defect detection, IoT security, and intelligent transportation. His work frequently combines deep learning with graph-based modeling and federated or reinforcement learning, emphasizing scalability, efficiency, and robustness in distributed and edge environments. Scientific Awards and Recognition: Fellow of the Institution of Engineering and Technology (IET) Fellow of the British Computer Society (BCS) Shortlisted for the Computing UK BIG DATA EXCELLENCE AWARDS 2018 in the category of Most Innovative Big Data Solution Maozhen Li has successfully supervised 25 PhD students and examined over 30 PhD theses externally. He has secured significant research funding from EPSRC, the European Union (Horizon 2020), Innovate UK, and the Royal Society , with projects including Z-BRE4K, IoRL, and TDX-ASSIST. He serves as an Associate Editor for journals such as the Journal of Cloud Computing and the International Journal of Grid and High Performance Computing . Research Groups and Teams: He is affiliated with the Intelligent Engineering Frameworks (IEF) research group at Brunel, contributing to collaborative efforts in AI, IoT, and smart systems. His leadership in transnational education also fosters international research collaboration between Brunel and Chinese institutions.

Per-Arne AndersenView profile

Associate Professor

• Artificial Intelligence
• Reinforcement Learning
• Tsetlin Machines
• +7 more

Per-Arne Andersen is an Associate Professor at the Department of Information and Communication Technology within the University of Agder . His research focuses on artificial intelligence , reinforcement learning , Tsetlin machines , and deep learning , with applications in real-time strategy games , industrial environments , and IoT systems . Projects: RESTORE Research Groups: CAIR - Center for Artificial Intelligence Research, CIEM - Center for Integrated Crisis Management, Intelligent Mechatronics (iTron) His work explores safe and sustainable reinforcement learning , interpretable AI , and generative environment modeling . He has developed frameworks like CaiRL and CostNet for high-performance RL environments and goal-directed learning. Recent publications include advancements in Tsetlin automaton analysis , GNSS jamming classification , and road quality detection . Articles from 2025-2016 span machine learning , computer vision , and environmental modeling . He contributes to IEEE , Springer , and LNCS publications, with a focus on interdisciplinary AI applications in crisis management , cybersecurity , and industrial optimization .

• Geomorphology
• Geomorphometry
• Geoinformatics
• +6 more

Leszek Ludwik Gawrysiak is a Professor at the Department of Geology, Soil Science, and Geoinformatics , affiliated with the Faculty of Earth Sciences and Spatial Management at Maria Curie-Skłodowska University in Lublin, Poland. His academic career includes habilitation in Earth and Environmental Sciences (2019), a PhD in Geomorphology (2003), and a master’s in Physical Geography (1993), all from UMCS. Education: Master’s in Physical Geography (1993), UMCS PhD in Geomorphology (2003), UMCS Habilitation in Earth and Environmental Sciences (2019), UMCS His research focuses on Geomorphology , Geomorphometry , and Geoinformatics , with applications in Loess Erosion , Gully Dynamics , and Environmental Risk Assessment . He employs GIS, LiDAR, and remote sensing techniques to analyze terrain segmentation, forest cover changes, and geomorphological impacts on land use planning. Projects include studies on extreme meteorological events, geopark development, and Polish-Belarusian-Ukrainian water policy in the Bug River basin. Recent publications emphasize Loess Landscapes , Proglacial Valley Morphology , and Gully Erosion , with methodological advancements in Digital Terrain Modeling and Geomorphon Analysis . His work bridges computational methods (e.g., GPU-accelerated topographic parameter evaluation) with field-based geomorphological studies in Svalbard and Eastern Europe. Scientific Awards: Award from the Polish Geomorphological Society for best PhD thesis (2002) Team Award from the Rector of UMCS (2010) Individual Award from the Rector of UMCS (2013) He has contributed to understanding Loess Area Geomorphodiversity , Forest Cover Dynamics , and Climate Change Impacts on landscapes. His collaborations span interdisciplinary studies in gully erosion, geotourism, and Holocene soil erosion rates. Contact: leszek.gawrysiak@mail.umcs.pl | Phone: +48 81 537 68 71