Find Academic Supervisors

Leila De FlorianiView profile

Professor

• Geometric Modeling
• Data Visualization
• Spatial Data Representation and Processing
• +6 more

Leila De Floriani is a Professor at the University of Maryland, with appointments in the Department of Geographical Sciences and the University of Maryland Institute for Advanced Computer Studies (UMIACS). She previously served as a professor at the University of Genova (Italy) since 1990, where she developed Italy's first undergraduate and graduate curricula in computer graphics and directed the Ph.D. program in Computer Science for eight years. Her professional activities include serving as the 2020 President of the IEEE Computer Society and currently as IEEE Division VIII Director for 2023-24. Professor De Floriani's research spans geometric modeling, data visualization, spatial data representation and processing, computer graphics, shape analysis, and topological data analysis. Her work focuses on developing mathematical models and algorithms for representing, analyzing, and visualizing complex spatial data, particularly through hierarchical models, mesh-based representations, and topology-based approaches. Her research group, the GeoVis group, investigates applications in terrain modeling, environmental data analysis, and forest structure mapping using LiDAR technology. Analysis of her recent publications reveals a strong focus on terrain representation and processing, with increasing emphasis on topological data analysis, machine learning integration, and efficient algorithms for large-scale spatial data. Her work bridges theoretical foundations in computational topology with practical applications in geospatial sciences, demonstrating consistent innovation in data structures and visualization techniques. Scientific Awards & Recognitions Fellow of IEEE (2016) for contributions to geometric modeling and scientific visualization Fellow of International Association for Pattern Recognition (IAPR) (1998) for contributions to geometric modeling and image analysis Fellow of Eurographics Association (2020) for outstanding contributions to computer graphics and visualization Pioneer of Solid Modeling Association (2017) for seminal work in solid and feature-based modeling Inducted Member of IEEE Visualization Academy (2020) IEEE Computer Society Golden Medal Award (2018) Inducted Member of IEEE Honor Society Eta Kappa Nu (2019) Multiple best paper awards at major conferences including Shape Modeling International (2015), IEEE/EG Symposium on Volume and Point-Based Graphics (2008), and ACM SIGSPATIAL (2008) Professor De Floriani has successfully advised numerous PhD students including Xin Xu, Yunting Song, and Noel Dyer, whose recent dissertations focused on topology-based individual tree mapping, efficient terrain analysis, and bathymetric data visualization respectively. Her research has been funded by prestigious agencies including the National Science Foundation, NASA, and the European Commission. As the leader of the UMD GeoVis group, she oversees a research program that develops open-source tools for spatial data analysis available on GitHub, with current projects focusing on forest point cloud processing and topology-based geospatial data visualization. The GeoVis group, affiliated with the Department of Geographical Sciences, UMIACS, and the Center for Geospatial Information Sciences, maintains a strong collaborative environment with ongoing projects in geometric modeling, spatial data structures, topology-based machine learning, and mesh-based terrain modeling. The group has received recent funding from NASA's HPOSS program for developing an open-source library for forest point cloud processing based on topological data analysis.

• Internet of Things (IoT)
• Cybersecurity
• Sensor Networks
• +3 more

Shiwei Fang is an Assistant Professor in the Department of Computer & Cyber Sciences within the School of Computer and Cyber Sciences at the University of North Georgia. He holds a Ph.D. in Computer Science from the University of North Carolina (2021) and a B.E. in Computer Science from the State University of New York (2015). His research focuses on IoT systems, cybersecurity, sensor networks, and edge computing, with notable contributions to multimodal analytics, privacy visualization tools, and geospatial tracking datasets. He advises the Graduate Student Organization and serves on the SCCS Academic Web Oversight Committee. Education: Ph.D., Computer Science, University of North Carolina, 2021 B.E., Computer Science, State University of New York, 2015 Research interests include IoT security, context-aware systems, and sensor fusion. His work on IoBT-MAX and GDTM datasets highlights expertise in experimentation frameworks and geospatial tracking. Recent publications explore privacy risks in IoT, AR-based privacy visualization, and efficient inference models for edge computing. He has contributed to over 25 peer-reviewed articles since 2015, with a focus on real-world IoT applications and hardware-software co-design. Service roles include faculty advising and committee participation. He teaches courses like CSCI 3170/5170 on Computer Organization, bridging theoretical computer science with practical hardware concepts.

George BosilcaView profile

Research Professor

• Distributed Algorithms
• Parallel Programming Paradigms
• High Performance Computing
• +3 more

George Bosilca is a Research Professor at the University of Tennessee, Knoxville, affiliated with the Department of Electrical Engineering and Computer Science and the Innovative Computing Laboratory. He holds a PhD in Computer Science (University of Paris XI, 2004) and an MS in Math and Computer Science (University of Paris XI, 1999). His research focuses on distributed algorithms, parallel programming paradigms, performance modeling/optimization, and resilience in programming models. He contributes to exascale computing initiatives through projects like PaRSEC and Open MPI. Key research areas include task-based runtimes, MPI standardization for exascale systems, and fault-tolerant distributed computing. His work emphasizes scalable and portable constructs for high-performance applications. Bosilca is involved with the Innovative Computing Laboratory (ICL) and collaborates on projects like the EPEXA ecosystem and Argobots threading framework. Recent publications highlight advancements in asynchronous many-task systems, GPU-accelerated collective operations, and resilience strategies for HPC platforms. His contributions span theoretical frameworks and practical implementations, bridging algorithmic innovation with real-world HPC challenges.

Colleen BaileyView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +5 more

Colleen Bailey is an Assistant Professor in the Department of Electrical Engineering at the University of North Texas. Her research focuses on the intersection of machine learning, signal processing, and energy systems, with applications spanning biomedical imaging, environmental monitoring, and edge computing. Research Interests: Machine learning optimization for edge devices Entropy-based image compression techniques Attention mechanisms in vision transformers Urban air pollution prediction models Land surface temperature super-resolution Publication Trends: Recent works emphasize compact AI architectures (e.g., MHATT network, entropy bottleneck models) for efficient processing in resource-constrained scenarios. Applications include medical imaging (Chest X-ray analysis), environmental monitoring (air quality, Martian dust storms), and energy systems (household prediction, power quality classification). Contact: Email: Colleen.Bailey@unt.edu Office: Discovery Park B252 Phone: 940-891-6874

Karl WegmannView profile

Professor

• Geomorphology
• Active Tectonics
• Paleoseismology
• +5 more

Karl Wegmann is a Professor and Associate Head of the Marine, Earth and Atmospheric Sciences (MEAS) department at North Carolina State University. He holds a faculty fellowship at the Center for Geospatial Analytics. His research focuses on Earth surface processes, integrating geospatial analysis with field investigations to study landscape responses to tectonic and climatic forces. Key areas include geomorphology, active tectonics, paleoseismology, and geoarchaeology. Education: Ph.D. (2008, Lehigh University), M.S. (1999, University of New Mexico), B.A. (1996, Whitman College). Research emphasizes landslide dynamics, paleoclimatic reconstructions, and planetary geoarchaeology. Notable projects include studies in Mongolia, Greece, and the Pacific Northwest. He teaches courses in geology, natural hazards, and field geology. His work bridges disciplines, applying remote sensing and machine learning to geohazard assessment and cultural heritage preservation. Scientific achievements include the 2023 NCSU Outstanding Teaching Award. Ongoing research explores lunar anthropocene frameworks and Martian surface processes. Collaborative projects involve students in global fieldwork and innovative geospatial methodologies.

Mohamed AllaliView profile

Associate Professor

• Data Engineering
• Machine Learning
• Climate Data Analysis
• +3 more

Mohamed Allali is an Associate Professor at Chapman University, affiliated with both the Fowler School of Engineering (Department of Electrical Engineering and Computer Science) and Schmid College of Science and Technology (Department of Mathematics). His research spans data engineering, machine learning, climate informatics, and mathematical education. He has contributed to constraint-based intelligent tutoring systems, data drift detection using KL divergence, and geospatial analysis for environmental monitoring. Education: University of Oklahoma (BS, MA, PhD). His recent work focuses on data distribution divergence, climate modeling for sea turtle habitats, and neural network applications in medical imaging. He has collaborated extensively on drought indices, satellite data validation, and educational technologies. His publications highlight expertise in statistical learning, computational methods, and interdisciplinary environmental applications.

Sangmi Lee PallickaraView profile

Professor

• Big Data
• Data Science
• Data Engineering
• +13 more

Sangmi Lee Pallickara is a Professor of Computer Science and the Clare Booth Luce Professor at Colorado State University. She is affiliated with the Department of Computer Science in the College of Natural Sciences. Her research is supported by major agencies including the National Science Foundation, Department of Homeland Security, ARPA-E, and NIFA, with ongoing projects in AI Institutes, Cyberinfrastructure, and CyberPhysical Systems. Research Interests: Her work focuses on Big Data systems for scientific applications, including scalable storage, retrieval, metadata management, predictive analytics, and interactive visualization. She applies these to domains such as agriculture, atmospheric science, environmental monitoring, and epidemiology. Her research integrates data science, distributed systems, and deep learning to enable scalable knowledge extraction from high-velocity, voluminous datasets. Publication Trends: Her recent publications emphasize scalable solutions for geospatial and spatiotemporal data, including efficient storage (e.g., ATLAS), visualization (e.g., Glance, Iris), and deep learning (e.g., Argus, CloudNet). There is a strong focus on real-world applications such as wildfire prediction, satellite data imputation, and precision agriculture, leveraging generative models, embeddings, and ensemble methods. Scientific Awards: NSF CAREER Award IEEE TCSC Award for Excellence in Scalable Computing Best Paper Award at IEEE/ACM UCC 2019 Best Paper Award at IEEE CLUSTER 2019 Best Paper Award at IEEE/ACM UCC 2014 Finalist for Best Paper Award at IEEE BDCloud 2018 Advising and Grants: She advises numerous Ph.D. and Master’s students, many of whom have gone on to careers in industry and academia. Her research is funded by the NSF (AI Institutes, CPS), DHS, ARPA-E, NIFA, and the Environmental Defense Fund. She also leads the SWiFT outreach program for K–12 STEM education. Labs and Teams: She leads a vibrant research group focused on Big Data systems, with students working on distributed storage, deep learning for satellite imagery, spatiotemporal analytics, and interactive visualization. The team collaborates with domain scientists in agriculture, climate, and public health.

Amalia KokkinakiView profile

Associate Professor

• Groundwater Flow and Contaminant Transport
• Environmental Hydrogeology
• Contaminant Remediation
• +3 more

Amalia Kokkinaki is an Associate Professor in the Department of Environmental Science at the University of San Francisco (USF), where she has been a full-time faculty member since 2016. She also serves as the Program Director and is actively involved in the university community, including advising the Environmental Engineering and Science Club and serving on the curriculum committee for the new engineering program. Her educational background includes a PhD and MASc in Civil and Environmental Engineering from the University of Toronto (2013 and 2007, respectively) and a BS and MS in Environmental Engineering from the Technical University of Crete (2005). Dr. Kokkinaki's research focuses on the intersection of environmental engineering and mathematical modeling, with expertise in groundwater flow and contaminant transport , contaminant hydrogeology , and statistical modeling and data assimilation . Her work addresses critical challenges in groundwater remediation , particularly for dense non-aqueous phase liquids (DNAPLs), and extends to carbon dioxide storage monitoring and biological treatment of organic pollutants . She employs advanced techniques such as Bayesian inverse modeling and hydrogeophysical methods to characterize subsurface systems and improve remediation strategies. Her recent publications (2013-2023) demonstrate a consistent focus on developing and applying innovative modeling approaches for subsurface characterization and contaminant transport. Key trends include the integration of machine learning (e.g., deep learning-based inversion) with traditional hydrogeological methods, advancements in data assimilation techniques for real-time monitoring, and the application of these methods to complex problems such as DNAPL source zone remediation and CO2 storage. Dr. Kokkinaki has received recognition for her contributions, including: Best reviewer award, Journal of Environmental and Engineering Geophysics (JEEG) 26(3), 2021 University of San Francisco, Mentor of the Year, 2020 She is a dedicated mentor, having advised numerous undergraduate and graduate students on projects ranging from PFAS sorption kinetics to urban forest air quality analysis. Her students have worked on diverse topics including PFOA sorption, uranium-VI modeling, residential electrification, and graphical user interfaces for water resources management. Additionally, she teaches undergraduate courses in environmental science and graduate courses in the master’s program (MSEM) and the Geospatial Analysis Lab (GsAL). Dr. Kokkinaki is an active member of the Geospatial Analysis Lab (GsAL) and contributes to interdisciplinary initiatives such as the development of USF's new engineering program.

Nancy GlennView profile

Professor

• Remote Sensing
• Dryland Ecology
• Geological Engineering
• +2 more

Nancy Glenn is Professor in the Department of Geosciences at Boise State University and directs the Boise Center Aerospace Laboratory (BCAL). Her research specializes in remote sensing applications for environmental monitoring, particularly in dryland ecosystems and geological engineering contexts. Her research advances remote sensing methodologies including lidar, hyperspectral imaging, and UAV-based platforms for characterizing vegetation dynamics, snow properties, and land surface changes. Current projects focus on: quantifying rangeland fractional cover in sagebrush ecosystems, improving snow property retrievals through topographic modeling, and developing machine learning approaches for spoil pile characterization in mining environments. Professor Glenn maintains active research programs across diverse environments including peatlands, dryland shrub-steppe ecosystems, mountainous terrain, and post-mining landscapes. Her work integrates field measurements with advanced remote sensing techniques to address environmental challenges.

• Machine Learning
• Computer Vision
• Medical Imaging Analysis
• +5 more

Almabrok Essa is an Assistant Professor in the Department of Mathematics, Computer Science, and Data Science at John Carroll University. His research focuses on advanced machine learning techniques applied to medical imaging, computer vision, and signal processing. He holds a faculty position at Dolan Science Center E207 and can be reached via email at aessa@jcu.edu. His work bridges theoretical computer science with practical applications in healthcare, environmental monitoring, and security systems. Research interests include deep learning architectures for medical diagnostics (e.g., chest X-ray analysis and skin lesion segmentation), steganography for secure image transmission, and remote sensing technologies for geospatial analysis. He has pioneered methods like Multibit LSB matching for data hiding and developed frameworks like COVID-CLNet for pandemic response. His contributions span over 20 peer-reviewed articles from 2015 to 2025, showcasing innovations in face recognition algorithms, brain signal analysis, and environmental pattern recognition. Notable applied projects include real-time heart/respiration monitoring systems and automated machinery threat detection for infrastructure protection. His research demonstrates strong interdisciplinary potential, combining computational methods with domains like biomedical engineering, environmental science, and cybersecurity.

Cihan H. DagliView profile

Professor

• Systems Engineering
• Cyber-Physical Systems
• Machine Learning
• +7 more

Cihan H. Dagli is a Professor of Systems Engineering and Engineering Management at Missouri University of Science and Technology (Missouri S&T). He is the founder of the Systems Engineering Graduate Program at Missouri S&T and directs the Smart Engineering Systems Lab (SESL). He serves as a Senior Investigator in the DoD Systems Engineering Research Center-URAC. His academic journey includes a Ph.D. in Applied Operations Research from the University of Birmingham (UK), with early work on water resource systems and evolving into systems engineering, cyber-physical systems, and machine learning. Research interests span systems architecting, computational intelligence, and deep learning applications in complex systems such as transportation, energy, and healthcare. He has secured over $25 million in grants, including a $10M DoD grant for the Systems Engineering Research Center-UARC. With 450+ publications and an h-index of 31, he is a Fellow of INCOSE (2008), IIE (2009), and IFPR (2019). He chairs the Complex Adaptive Systems conference series and advises over 35 PhD students across Engineering Management, Computer Science, and Systems Engineering. Recent work focuses on AI-driven kidney transplant optimization, meta-architectures for cyber-physical systems, and system-of-systems resilience. His educational contributions include developing the SEEE portal for systems engineering education and pioneering courses like Advanced Neural Networks and Complex System Modeling. Education: Ph.D., Applied Operations Research, University of Birmingham, UK (1979) Key Roles: Director of SESL, INCOSE Fellow, Omega Alpha Society Board Chairman Scientific Awards: Fellow of INCOSE (2008) Fellow of IIE (2009) Fellow of IFPR (2019) Member of Omega Alpha Association (2013) Advising and Grants: Advised 26 Engineering Management, 1 Computer Science, and 9 Systems Engineering PhD students Total grants: $25M+ from federal, industrial, and academic sources Labs/Teams: Smart Engineering Systems Lab (SESL) DoD Systems Engineering Research Center-URAC Complex Adaptive Systems conference organizer

Brian BorchersView profile

Professor

• Optimization
• Inverse Problems
• Geophysical Inverse Problems
• +3 more

Roles and Affiliations : Professor of Mathematics at New Mexico Tech. Member of the Mathematics Department since at least 1992, actively involved in teaching and research. Maintains a research-focused website with links to publications, projects, and codes. Education : BS in Computer Science, MS and PhD in Mathematics from Rensselaer Polytechnic Institute. Research Interests : Specializes in optimization and inverse problems, including linear/nonlinear programming, geophysical inverse problems, and applications in hydrology and geophysics. Co-authored a widely used textbook Parameter Estimation and Inverse Problems (3rd ed., 2018). Active in developing computational tools like CSDP for semidefinite programming and CRONUScalc for cosmogenic nuclide dating. Publications : Over 50 peer-reviewed articles in journals such as Computational Optimization and Applications , Soil Science Society of America Journal , and International Journal of Remote Sensing . Focus areas include optimization algorithms, geophysical modeling, and remote sensing applications. Consulting and Grants : Available as a consultant for numerical analysis and inverse problems. Supervised projects involving data assimilation, convex optimization, and compressive sensing. Mentored students on funded research projects. Labs/Teams : Collaborates with interdisciplinary teams in geophysics (e.g., Rick Aster, Cliff Thurber) and soil science (Jan Hendrickx). Involved in sensor test facilities for landmine detection and environmental monitoring.

• Geological Carbon Storage
• Underground Hydrogen Storage
• Reservoir Engineering
• +4 more

Hongsheng Wang is a Research Fellow at the Bureau of Economic Geology within the Jackson School of Geosciences at The University of Texas at Austin. His research focuses on advancing subsurface energy technologies through interdisciplinary approaches combining geoscience, engineering, and machine learning. Key areas include geological carbon storage, underground hydrogen storage, reservoir simulation, and fracture mechanics. His work emphasizes innovative applications of machine learning for challenges such as CO2 plume migration forecasting, parameterization of 3D saturation data, and fracture conductivity analysis. He also investigates leakage mitigation strategies in hydrogen storage systems and the role of permeability heterogeneity in subsurface processes. Publications highlight contributions to microfluidic experiments, porous media dynamics, and AI-driven reservoir modeling. Current projects involve surrogate models for large-scale simulations and dimension reduction techniques to enhance computational efficiency in carbon storage assessments.

Suchendra M. BhandarkarView profile

Professor

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +4 more

Suchendra M. Bhandarkar is a Professor in the School of Computing at the University of Georgia, affiliated with the Franklin College of Arts & Sciences. He holds courtesy faculty positions in the College of Engineering and the School of Environmental, Civil, Agricultural, and Mechanical Engineering. His research focuses on Artificial Intelligence, Computer Vision, Pattern Recognition, and Computational Biology. He has advised PhD students such as Anirban Mukhopadhyay and has been actively involved in interdisciplinary projects spanning robotics, coral reef mapping, and food safety automation. Education: Ph.D. and M.S. in Computer Engineering from Syracuse University (1985-1989), and B.S. in Electrical and Electronics Engineering from Indian Institute of Technology Bombay (1983). His funded projects include advanced GeoAI terrain analytics, underwater robotics, and AI-driven food safety systems. Notable grants include support from USDA, NIH, and the US Army. Research highlights include 3D coral reef mapping via SLAM, deep learning for food safety, and zero-shot learning algorithms. Key collaborations involve the USDA Agricultural Research Service, Sony Corporation, and international networks like CapsNet for medical imaging. His labs and teams focus on applying AI to environmental, biomedical, and agricultural challenges.

Chau-Wai WongView profile

Associate Professor

• Machine Learning
• Multimedia Forensics
• Signal and Image Processing
• +5 more

Chau-Wai Wong is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University, with affiliations to the Forensic Sciences Cluster and Secure Computing Institute. He previously served as a Data Scientist at Origin Wireless, Research Assistant at University of Maryland, and Research Associate at Hong Kong Polytechnic University. His work bridges machine learning with applications in multimedia forensics, signal processing, and computational social science. Ph.D. , Electrical Engineering, University of Maryland (2017) M.Phil. , Electronic and Information Engineering, Hong Kong Polytechnic University (2010) B.Eng. , Electronic and Information Engineering, Hong Kong Polytechnic University (2008) Research spans federated learning (security vulnerabilities, communication efficiency), physically unclonable features (PUF-based authentication), generative models (GANs for hardware modeling), and computational social science (AI chatbots for disaster communication, TikTok behavioral analysis). Recent work explores neural tangent kernels and multi-LLM agent collaboration . Key publications focus on decentralized AI systems (ICML'25), deepfake detection (under review), and social media health analytics (Telematics and Informatics'25). His federated learning research has appeared at ICLR, IEEE T-NNLS, and USENIX Security. NSF CAREER Award IEEE Signal Processing Cup Organizer (2016) Technical Program Committee Chair (IH&MMSec'25) Area Chair (ICME'21–'24) Senior Member, IEEE Advises students in AI security , physiological sensing , and social science automation . Collaborates with teams at University of Maryland, Hong Kong Polytechnic University, and industry partners. Current projects include identity-privacy protection for smart health and UAV-assisted network optimization (IEEE DySPAN'25).