جست‌وجوی استاد و پژوهشگر

Mahmud Hossainمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۳ مورد دیگر

Dr. Mahmud Shahriar Hossain is an Associate Professor in the Department of Computer Science at the University of Texas at El Paso. His research focuses on modern AI techniques including deep learning, machine learning, and data mining with interdisciplinary applications in biomedical sciences, mechanical engineering, sustainability, and software engineering. Academic Rank: Associate Professor Institution: University of Texas at El Paso Department: Computer Science Research Interests: Dr. Hossain develops algorithmic frameworks and practical prototypes to address domain-specific challenges. His work spans: Biomedical data analysis Intelligence analyst support systems Sustainable product redesign Temporal data modeling Network security algorithms Contextual information reuse Recent Publications focus on diffusion-based storytelling, student performance prediction, temporal embeddings, and cybersecurity frameworks. His work has been cited by institutions like the U.S. Army DEVCOM Army Research Laboratory. Awards: ProQuest/Pivot funding recipient Collaborative Networks: Regular collaborations with Naren Ramakrishnan, Monika Akbar, and others across cybersecurity, biomedical informatics, and software engineering domains. His work appears in journals like Knowledge and Information Systems , Springer publications, and conferences including IEEE BigData and ACM SIGKDD.

• Big Data Systems
• Distributed Systems
• Networking Systems
• +۳ مورد دیگر

Dr. Damiano Carra serves as an Associate Professor in the Department of Computer Science at the University of Verona, with a future promotion to Full Professor scheduled for October 1, 2025. His academic appointment falls under the IINF-05/A Information Processing Systems sector, with research aligned with ERC classifications PE6_2 (Distributed systems, parallel computing, sensor networks) and PE7_8 (Communication networks, IoT). His research interests span two primary domains: Data Intensive Scalable Computing - focusing on analysis, evaluation and design of large-scale parallel systems with emphasis on Big Data processing and Cloud Computing Networking Systems - specializing in protocol design, performance evaluation, network measurement, and overlay networks Dr. Carra maintains active research groups including the Big Data Analytics team working with spatio-temporal datasets and the INdAM Research Unit at the University of Verona. His work has been published in prestigious journals including IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Computers, and IEEE Transactions on Cloud Computing, as well as presented at major conferences such as IEEE Infocom, IEEE Cloud, and IEEE BigData. He currently leads multiple research projects including: Securing Decentralized Finance and Remote Healthcare Systems - SHIELD (starting 10/22/24) Novel Methodologies and Tools for Next Generation Cyber Ranges - NOMEN (starting 5/21/24) PRIN PNRR 2022 - Joint Communication and Sensing: CSI-Based Sensing for Future Wireless Networks (starting 11/30/23) Administratively, Dr. Carra serves as Chair of Esaminatori ECDL, Referent for Third Mission in Computer Science, and holds membership on multiple university committees including the Faculty Board of PhD in Computer Science, Computer Science Teaching Committee, and Mathematics and Data Science Teaching Committee. He also serves as Responsabile del Test Center for the ECDL Test Centre.

Maryam Rahnemoonfarمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Remote Sensing
• +۵ مورد دیگر

Dr. Maryam Rahnemoonfar is a Tenured Associate Professor of Computer Science and Engineering at Lehigh University’s P.C. Rossin College of Engineering and Applied Science, with a joint appointment in Civil and Environmental Engineering. She directs the Computer Vision and Remote Sensing Laboratory (Bina Lab) and leads interdisciplinary research in AI, sustainability, and environmental science. Previously, she held tenured positions at UMBC and Texas A&M University-Corpus Christi. Education: Ph.D. in Computer Science (University of Salford, UK, 2010), M.Sc. in Remote Sensing Engineering (University of Tehran, 2005), B.Sc. in Civil Engineering (University of Isfahan, 2002). Research focuses on AI-driven solutions for climate resilience, disaster management, and polar ice dynamics. Key areas include physics-informed machine learning, remote sensing algorithms for radar/sonar data, and UAV-based damage assessment. Her work has been funded by NSF, Amazon, Microsoft, and IBM grants. Scientific Awards: NSF HDR Institute Award, NSF BIGDATA Award, Amazon Academic Research Award Labs/Teams: Bina Lab (specializing in AI for environmental challenges) Grants: Over $1M from DOE Climate Resilience Center, NSF, and industry partnerships Recent projects include developing AI tools for ice sheet modeling, flood damage analysis using UAVs, and climate change monitoring via radar datasets. She actively collaborates with Antarctic research initiatives and advances AI for societal impact through workshops and interdisciplinary teams.

Leonardo Portes dos Santosمشاهده پروفایل

پژوهشگر ارشد

• Complex Systems
• Data Science
• Nonlinear Time Series Analysis
• +۴ مورد دیگر

Leonardo Portes dos Santos is an Adjunct Senior Research Fellow at The University of Western Australia (UWA), affiliated with the School of Earth and Oceans and the Department of Mathematics and Statistics. He holds a PhD in Sport Sciences from Universidade Federal de Minas Gerais (Brazil), complemented by a Master’s and Bachelor’s in Physics. His interdisciplinary research merges statistical physics, complex systems, and data analytics to address challenges in geosciences, sports, and biomedical fields. He leads data science initiatives for industries like Rio Tinto and Woodside, and develops geophysical visualization tools for decision-making. He mentors HDR students in Python and data science. Education: PhD in Sport Sciences (2014), UFMG MSc in Physics (1999), UFMG Bachelor’s in Physics (1996), UFMG Research Interests: Complex systems applied to data science, nonlinear time series analysis, network science, and multidisciplinary collaborations in geosciences (oil/gas reservoirs), human motor learning (EEG and behavioral data), and cancer immunotherapy. His work emphasizes bigdata visualization, risk assessment, and fusion of geophysical responses. Grants & Projects: Led the Subsurface Big Data Visualisation and Analysis: Oil/Gas Reservoirs project (2018–2019), funded by Woodside. His research contributes to UN SDGs related to sustainable resource management and innovation. Awards: 2016 Honorary Award at the 6th International Conference on Nonlinear Science and Complexity 1994 Junior Researcher Award in Exact Sciences (Earth Sciences and Engineering) Labs & Teams: Involved with UWA’s Centre for Exploration Targeting and the ITTC Data Analytics for Resources and Environments (DARE) group. His work integrates machine learning and network science for industry-relevant problems.

Hao Pengمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Big Data Analytics
• Computational Biology
• +۵ مورد دیگر

Hao Peng is a Senior Lecturer in the School of Computing at the University of Georgia, holding a Ph.D. in Computer Science (2019) and B.S. degrees in Computer Science and Statistics (2013). His research bridges Machine Learning and Computational Biology to address challenges in Traffic Flow Forecasting and Glycosylation Analysis . He was promoted to Senior Lecturer effective August 2025, following six years as a Lecturer. Doctor of Philosophy, Computer Science, University of Georgia (2019) Bachelor of Science, Statistics and Computer Science, University of Georgia (2013) Peng’s work spans two primary domains: Machine Learning for Transportation Systems and Bioinformatics for Glycan Analysis . His traffic forecasting models integrate Temporal-Spatial Data and Knowledge-Guided AI , while his computational biology research focuses on Glycan Microheterogeneity , Kinase Regulation , and Immune Response Modeling . He also contributes to Probabilistic Graphical Models for hierarchical data and Ontology-Based Big Data Analytics . Hao Peng’s publications highlight interdisciplinary trends, including Machine Learning for Traffic Simulation (2018-2020), Computational Glycobiology (2015-2022), and Neurodegenerative Disease Analysis (2025). His work often combines Algorithm Development and Data-Driven Biological Discovery , reflecting a dual focus on predictive modeling and biomedical applications. Scientific Awards : Student Career Success Influencer Award (2024) Best Student Paper Award (BigData Congress 2019, 2018) Outstanding Teaching Assistant Award (2018)

Farhana Zulkernineمشاهده پروفایل

استاد

• Big Data Analytics
• Artificial Intelligence
• Cognitive Computing
• +۵ مورد دیگر

Farhana Zulkernine is a Professor and Coordinator of the Cognitive Science program at Queen's University's School of Computing. She earned her Ph.D. in Computer Science from Queen's and is a licensed Professional Engineer (PEng). With over 15 years of international experience in software design and research, she collaborates with IBM Canada, SAP Germany, and medical institutions. Her research focuses on big data analytics, cognitive computing, and AI applications in healthcare, autonomous systems, and smart cities. She directs the Bigdata Analytics and Management (BAM) Lab and chairs major conferences like IEEE International Conference on Digital Health. Education: Ph.D. in Computer Science (Queen's University, 2009). Research Interests: Big data management, AI-driven decision systems, healthcare informatics, cloud computing, and autonomous vehicle technologies. She applies these to medical diagnostics, wearable health monitoring, and urban smart systems. Grants & Funding: Supported by NSERC, CFI, IBM, and others for projects like dementia detection via AI and remote triage systems. Recent grants include NSERC New Frontiers (2023-2026) and Medlior-Roche collaborations. Teaching: Teaches courses in cognitive science, machine learning, and neural networks. Recent courses include COGS 100 and CISC-874. Labs & Teams: Leads the BAM Lab, contributing to projects like Triage-Bot and Assist-Bot. Active in Queen's Conflicts Analytics Lab and SOSCIP Scientific Advisory Committee.

Christian Büdelمشاهده پروفایل

پژوهشگر

• Geomorphology
• Geoarchaeology
• Landscape Development
• +۴ مورد دیگر

Dr. Christian Büdel is a Researcher at the Chair of Geomorphology within the Institute of Geography and Geology at the University of Würzburg. He has been working at the university since November 2009 and is actively involved in multiple research projects including the INCREASE project (2021-2025) on geovisual analysis of geohazards and the BigData@Geo project on advanced environmental technologies using AI. Dr. Büdel completed his Geography studies at the University of Würzburg from 2001 to 2008, with a focus on Physical Geography and Geomorphology, and minors in History and Pre- and Protohistoric Archaeology. His diploma thesis focused on natural and quasi-natural morphological processes in the Spessart and Main Valley. Prior to his current position, he worked as a Geography Specialist at Luftbilddatenbank, Estenfeld from April 2008 to November 2009, where he prepared expert assessments regarding aerial image-based preliminary surveys for munitions contamination. His research interests span geomorphology, geoarchaeology, landscape and environmental development, and geomorphological systematization. He specializes in Quaternary stratigraphy, mapping techniques, and historical-geographical landscape inventory, with particular focus on arid regions, mid-mountain areas, river valleys, and specific regions including Iran and Southern Germany. His work often integrates remote sensing technologies and digital elevation models for landscape analysis. Current projects include GEO-XR, which uses extended reality techniques to visualize landscape development processes that occur too slowly for human perception. Analysis of Dr. Büdel's publications reveals a strong focus on geomorphological processes in arid regions, particularly in Iran, as well as landscape development in Central Europe. His research combines field work with advanced remote sensing techniques, including Sentinel-1 SAR data and TanDEM-X digital elevation models. He has made significant contributions to understanding alluvial fan formation, playa sediments, landslide dynamics in cuesta landscapes, and historical landscape changes. His collaborative work spans multiple institutions and international partners, particularly in Iran and within European research frameworks. Dr. Büdel is part of the Geomorphology research group at the University of Würzburg, working closely with Prof. Roland Baumhauer and other colleagues. His current work involves using extended reality (VR and AR) technologies to visualize landscape development processes that occur too slowly for human perception, such as river terrace formation and marshland drainage. He is also involved in projects related to smartphone-supported mapping of landforms and the characterization of surfaces in arid regions using Sentinel-1 radar data.

• Data Mining
• Machine Learning
• Geospatial Data
• +۷ مورد دیگر

Dr. Yanjie Fu is an Associate Professor in the School of Computing and AI at Arizona State University (ASU), part of the Ira A. Fulton Schools of Engineering. He holds a Ph.D. from Rutgers University (2016), a B.E. from the University of Science and Technology of China (2008), and an M.E. from the Chinese Academy of Sciences (2011). His research focuses on data mining, machine learning, geospatial data, and interdisciplinary applications, particularly in AI-driven urban planning and time series forecasting. He has received prestigious awards, including the NSF CAREER Award (2021), NAE Grainger Early Career Engineer Award (2023), and recognition as a Top 2% Global Scientist (2024). His work bridges theoretical advancements and practical applications, such as generative AI for urban design and reinforcement learning for feature transformation. Recent research emphasizes data-centric AI, including tabular data optimization, feature selection via generative models, and LLM-driven teamwork. He advises Ph.D. students who have transitioned to academic roles and collaborates on initiatives like the IEEE BIGDATA conference and the AAAI workshop on time series modeling.

• Monsoon Meteorology
• Indian Summer Monsoon
• Land-Atmosphere Interactions
• +۴ مورد دیگر

Dr. Praveen Kumar Rai is a Researcher at the Institute of Geography and Geology , University of Würzburg, focusing on climatology and environmental systems. He is based in the Climatology Working Group - Team Climate at Hubland Campus, Room 209, and holds an ORCID (0000-0001-8348-1150). His research spans monsoon meteorology, land-atmosphere interactions, ocean dynamics, and tropical climate variability, with particular emphasis on weather and climate extremes. Academic Background PhD in Environmental Sciences (2014–present), School of Environmental Sciences, Jawaharlal Nehru University (JNU), New Delhi, supervised by Prof. A. P. Dimri M.Sc. in Environmental Science (2014), JNU B.Sc. (Honours) in Chemistry (2009–2012), Banaras Hindu University Research Projects MOSAIC : Investigates irrigation impacts on South Asian hydrological cycles and monsoon dynamics using high-resolution (12 km) and convection-resolving (3 km) simulations with REMO model WE-ACT : Supports equitable transboundary water allocation through climate risk-aware planning BigData@Geo 2.0 : Enhances climate resilience for Bavarian agricultural SMEs (2023–2027, EFRE-funded) HYDRASIA (2019–2022): Analyzed Central Asian hydrology with novel irrigation and lake schemes in regional climate models Publication Trends show consistent contributions to understanding monsoon systems across South/Central Asia, with methodological advances in regional climate modeling, vegetation interaction analysis, and extreme event projections using CORDEX/RCM frameworks. Technical Expertise includes Google Earth Engine, REMO/REMO-iMOVE/RegCM4 modeling systems, EEMD-based time series analysis, and climate anomaly diagnostics. Active in international conferences like EGU General Assembly and ICRC-CORDEX.

Suvrit Sraمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Non-convex Optimization
• +۶ مورد دیگر

Suvrit Sra is the Esther and Harold E. Edgerton Career Development Associate Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). He is a core member of the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS). Currently, he is on leave at the Technical University of Munich (TU Munich). His research centers on the mathematics of artificial intelligence , with emphases on optimization for machine learning (especially non-convex, non-Euclidean, and geometric optimization), theory of deep learning, discrete probability, sampling methods, convex geometry, and polynomials. He also explores applications in operations research, supply chains, and large language models. His recent articles demonstrate a strong focus on Riemannian optimization, convergence analysis of gradient methods, minimax optimization in geometric spaces, and invariant representations in graph learning. Key trends include theoretical advances in non-convex optimization and applications to reinforcement learning and distributional robustness. Awards and honors include: Alexander von Humboldt Professorship for AI (2024) Criteo Faculty Research Award (2017) SIAM Outstanding Paper Prize (2011) He advises PhD students (e.g., Zelda Mariet, Chengtao Li, Hongyi Zhang) and has secured grants including an NSF BIGDATA award for interpretable learning via discrete probability. His lab affiliations include the MIT ML Group, Center for Statistics, and TILOS AI Research Institute. He co-founded Pendulum, serving as Chief Scientist.

Feng Luoمشاهده پروفایل

استاد

• Deep Learning
• Bioinformatics
• Computational Biology
• +۳ مورد دیگر

Feng Luo is a Professor at the School of Computing , Clemson University, and founding director of the Clemson AI Research Institute for Science and Engineering (AIRISE). He earned his Ph.D. in Computer Science from the University of Texas at Dallas in 2004 and held a postdoctoral position at the University of Texas Southwestern Medical Center before joining Clemson in 2006. Research Focus: Deep Learning, High-Throughput Biological Data Analysis, Computational Genomics Laboratory: BigData Analytics Research Group ( BigData@Clemson ) His research spans Deep Learning applications in bioinformatics, with emphasis on genomic data analysis and network biology . Recent work includes genome assembly algorithms, methylation detection, and AI-driven cybersecurity for IoT devices. His 15 most recent publications (2024-2025) focus on advanced AI/ML methods for 3D genome reconstruction , molecular structure generation , video denoising , and diffusion model security , reflecting interdisciplinary work bridging Computer Science with Genomics and Materials Science . Dr. Luo has advised numerous graduate students and postdocs, several of whom now hold positions at companies like Amazon , Meta , and LinkedIn . He actively serves on university and professional committees as a Senior IEEE Member and ACM Member . His funded projects include NSF , DOE , and NIFA grants addressing citrus disease resistance , off-road autonomy , and AI-enabled healthcare .

Doina Carageaمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Data Science
• +۶ مورد دیگر

Doina Caragea is a Professor in the Department of Computer Science at Kansas State University (KSU), part of the Carl R. Ice College of Engineering. She holds the Don and Linda Glaser Keystone Research Scholar appointment. Caragea earned her Ph.D. in Computer Science from Iowa State University (2004), with a focus on machine learning from distributed data sources. She has over 150 refereed publications and secured over $12M in extramural funding as PI/Co-PI. Education: Ph.D., Computer Science, Iowa State University, 2004 M.S., Computer Science, University of Bucharest, 1997 B.S., Computer Science, University of Bucharest, 1996 Research Interests: Machine Learning, Deep Learning, Data Science, Bioinformatics, Digital Agriculture, and Crisis Informatics. Her work focuses on semi-supervised learning, domain adaptation, and applications to plant phenotyping, social media analysis during disasters, and genomic sequence analysis. Notable Grants: NSF BIGDATA Award ($500,000): Domain adaptation for crisis-related social media analysis NSF PlantTransform ($1.9M): Genetic basis of maize regeneration USCP Sorghum Breeding Program ($1.6M): Climate-resilient crop development Awards: Deployed Application Award (AAAI IAAI 2021) Best Insight Paper (ISCRAM 2019) IBM Fellowship (2002–2004) Advising & Teaching: Supervised over 30 graduate students, including 10 Ph.D. graduates. Taught courses on Deep Learning, Data Mining, Bioinformatics, and Database Systems. Current research lab focuses on AI-driven solutions for agriculture, bioinformatics, and disaster response. Labs/Teams: Active collaborations with the Bioinformatics Center (KSU), USDA, and industry partners like Siena Analytics. Her lab develops tools for plant phenotyping, social media crisis analysis, and secure mobile applications.

Riccardo Cantiniمشاهده پروفایل

استادیار

• Deep Learning
• Large Language Models
• Sustainable AI
• +۳ مورد دیگر

Riccardo Cantini is an Assistant Professor (RTDA) at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria. He holds a European Ph.D. in Information and Communication Technologies (2023) and has been a visiting researcher at the Barcelona Supercomputing Center (BSC-CNS, 2021-2022). His research focuses on deep learning (Large Language Models, sustainable AI) and big social data analysis targeting politically polarized data and high-performance distributed systems. Education: B.Sc. (2016), M.Sc. (2019), and Ph.D. (2023) in Computer Engineering from the University of Calabria. Research interests include: Large Language Models and their ethical deployment Sustainable AI and energy-efficient edge computing Political polarization analysis using social media data Optimization of data-intensive workflows in distributed environments Key projects include the FAIR initiative (Green-Aware AI), eFlows4HPC (HPC workflows), and ASPIDE (Exascale data processing). He has authored/co-authored over 30 publications, including works on bias detection in LLMs and explainable AI in healthcare. Awards: 2024 Top 3 Best PhD Thesis in Big Data & Data Science (CINI), 2022 Editor's Choice article in Big Data and Cognitive Computing . Teaching roles include courses on Business Intelligence, High-Performance Computing, and Operating Systems. He has advised over 40 theses in AI, NLP, and big data. Professional services: Guest Editor for Big Data and Cognitive Computing , Program Chair of Green-Aware AI workshops, and reviewer for top journals/conferences (ICLR, IEEE BigData, etc.).

Mohammed Zakiمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Graph Learning
• +۵ مورد دیگر

Mohammed Zaki is a Professor and Department Head of Computer Science at Rensselaer Polytechnic Institute (RPI). He holds a Ph.D. in Computer Science from the University of Rochester (1998) and dual B.S./M.S. degrees from Angelo State University and the University of Rochester. His research focuses on data mining, machine learning, and graph-based methods, with applications in bioinformatics, financial analytics, and personal health. He has authored over 300 publications and 6 patents, including the textbook Data Mining and Machine Learning (2nd Ed., Cambridge University Press, 2020). Education: Ph.D. (1998) and M.S. (1995) from University of Rochester; B.S. (1993) in Computer Science and Mathematics from Angelo State University. Research Interests : Zaki's work spans novel data mining techniques for graph-structured/textual data, scalable graph algorithms (e.g., GRAIL, IBMGenerator), and applications in finance, healthcare, and bioinformatics. He has pioneered methods for frequent pattern mining, sequence analysis, and knowledge graph construction. Recognition : Recipient of NSF CAREER Award, DOE Early Career Award, and Fellowships from IEEE, ACM, AAAS, and SIAM. His research is supported by NSF, DARPA, NIH, DOE, and industry partners like IBM, Google, and NVIDIA. Grants & Leadership : Leads the CRAFT Center for Financial Technology research and serves on ACM SIGKDD's Board of Directors. Has chaired major conferences including SIGKDD, CIKM, and IEEE BigData. Active in open-source projects, maintaining tools like IBMGenerator for synthetic data creation and GRAIL for graph reachability queries. Labs/Teams : Directs research in RPI's Data Mining Lab, focusing on scalable algorithms and real-world applications. Collaborates with interdisciplinary teams on health informatics, financial systems analysis, and molecular graph learning.

Vipin Kumarمشاهده پروفایل

استاد

• Data Mining
• High-Performance Computing
• Climate Science
• +۴ مورد دیگر

Vipin Kumar is a Regents Professor and William Norris Endowed Chair in the Department of Computer Science and Engineering at the University of Minnesota . He leads the CSE Data Science Initiative and has held roles such as Department Head (2005–2015) and Director of the Army High Performance Computing Research Center (1998–2005). His research focuses on data mining, high-performance computing, climate science, and environmental applications . Education: B.E., Electronics & Communication Engineering, Indian Institute of Technology Roorkee (1977) M.E., Electronics Engineering, Philips International Institute, Netherlands (1979) Ph.D., Computer Science, University of Maryland, College Park (1982) Research Interests: Kumar pioneers Knowledge-Guided Machine Learning (KGML) for environmental science, climate change analysis, and global ecosystem monitoring. His work integrates computational methods with domain knowledge to address challenges in agriculture, hydrology, and climate modeling. Key contributions include scalable algorithms (e.g., METIS, PSPASES) and frameworks for big data-driven discovery. Publications: Over 300 articles and 10 books, including seminal works like Introduction to Parallel Computing and Introduction to Data Mining . Recent efforts focus on applying machine learning to monitor global change and predict environmental phenomena like floods, carbon cycles, and lake temperatures. Scientific Awards: ACM SIGKDD Innovation Award (2012) IEEE Sidney Fernbach Award (2016) NSF Expeditions in Computing Grant (Climate Change Project) Fellowships: ACM, IEEE, SIAM, AAAS Advising & Grants: Advisor to over 50 graduate students (listed in full description). Principal Investigator of major grants including NSF BIGDATA, NSF INFEWS, and DARPA World Modeler initiatives. Leads interdisciplinary teams on projects like the Global Observatory for Planetary Health and Ecosystem Resources (GOPHER) . Labs/Teams: Director of GOPHER, a multi-institutional effort to monitor Earth’s ecosystems using AI and satellite data. Active in initiatives like the Climate Change Data Expedition and the Data Science Leadership Summit .