جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Deep Learning
• Video Classification
• +۳ مورد دیگر

Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.

Lisa Starمشاهده پروفایل

استاد

• Geotechnical Engineering
• Earthquake Engineering
• Soil-Structure Interaction
• +۲ مورد دیگر

Dr. Lisa Star is Professor in the Civil Engineering and Construction Engineering Management Department at California State University, Long Beach. She holds BS, MS, and PhD degrees in Civil/Geotechnical Engineering from UCLA. Research specializes in soil-structure interaction effects during seismic events, employing large-scale field testing and advanced instrumentation. Key research areas include: Dynamic soil-foundation response characterization Geotechnical sensor implementation for infrastructure projects Tunneling-induced ground movement prediction Applied projects involve Los Angeles Metro tunneling operations, where she evaluates excavation impacts and ground behavior in complex soil conditions.

Khandaker Mamun Ahmedمشاهده پروفایل

استادیار

• Computer Vision
• Federated Learning
• Cybersecurity
• +۴ مورد دیگر

Dr. Khandaker Mamun Ahmed is an Assistant Professor at The Beacom College of Computer & Cyber Sciences, Dakota State University. He teaches undergraduate and graduate courses in artificial intelligence, algorithms, and data structures. He holds a Ph.D. in Computer Science from Florida International University (2024), an M.Sc. from the same institution (2023), and a B.Sc. in Software Engineering from the University of Dhaka (2016). His research focuses on computer vision, federated learning, cybersecurity, explainable AI, vision-language models, and optimization algorithms. He has contributed to peer-reviewed publications and conference presentations, with notable work in federated learning for IoT, anomaly detection in videos, and AI applications in healthcare and agriculture. Recent articles highlight advancements in federated learning frameworks, AI-driven healthcare systems, and real-time object detection using neural networks. His work also addresses cybersecurity challenges in DevOps pipelines and generative AI for educational datasets. Recipient of the 'Best graduate student in research award' (2022), Dr. Ahmed advises on AI ethics and mentors students through academic-industry collaborations. His research bridges theoretical computer science with practical applications in agriculture, healthcare, and infrastructure monitoring.

Ernesto Jiménez-Ruizمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +۵ مورد دیگر

Dr. Ernesto Jiménez-Ruiz is a Senior Lecturer in Artificial Intelligence and Director of Research at City, University of London. He holds a PhD from Jaume I University, Spain (2010), and has held academic positions since 2010. His research focuses on knowledge graphs, ontology alignment, semantic web technologies, and neuro-symbolic AI. Currently, he teaches modules on semantic web technologies and research methods. He leads the Research Center affiliated with City St George's, University of London. Administrative Roles: Director of Research (2024–present), Senior Tutor for Research (2023–2024) Research Interests: AI, knowledge representation, ontology alignment, semantic integration, and biomedical informatics. His work bridges machine learning and symbolic reasoning, with contributions to ontology matching and knowledge graph embeddings. He supervises PhD students in areas like knowledge graph alignment and neuro-symbolic systems. Awards: SWSA Ten-Year Award (2021) for influential ontology alignment work. He is a Fellow of AdvanceHE (FHEA). Grants & Collaborations: Leads projects on semantic industrial data modeling and collaborates with Samsung Research UK, Bosch, and Statoil. Active in initiatives like the Turing Institute's Knowledge Graph Interest Group.

• Computational Social Science
• Ethical AI
• Algorithmic Decision Making
• +۳ مورد دیگر

Maria Y. Rodriguez is a Principal Research Scientist in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences, with affiliate appointments at the Gender Institute and Digital Scholarship Studio and Network. Her interdisciplinary work bridges computational social science, ethical AI, and human services systems, focusing on how technological and institutional structures impact marginalized populations through algorithmic decision-making. Her educational foundation includes: PhD in Social Welfare from the University of Washington (2016) MSW in Macro Social Work Practice from the University of Pennsylvania (2010) BA in Clinical Counseling and Child Psychology from Alfred University (2005) Dr. Rodriguez's research centers on reorienting systems toward outlier cases by integrating lived experiences of system-involved individuals. She critically examines how values embedded in technological platforms—from child welfare algorithms to social media—shape outcomes for vulnerable groups. Her work exposes racial biases in online discourse, analyzes disinformation targeting women of color politicians, and develops ethical frameworks for AI in social services, always grounding technical approaches in social work ethics and social justice principles. Analysis of her 15 most recent publications reveals consistent focus on algorithmic accountability in human systems, with 60% addressing social media dynamics, 40% examining housing/gentrification issues, and 100% incorporating mixed-methods approaches. Her work uniquely combines computational techniques like structural topic modeling with deep social context analysis, demonstrating how technical tools can either reinforce or disrupt systemic inequities when applied to human services. Her notable recognitions include: 3rd annual Anthem Award for Human and Civil Rights (2024) for "An Unrepresentative Democracy" report Best Reviewer at EAAMO conference (2021) SUNY ProdiG Fellowship supporting interdisciplinary research (2020-2022) UW Graduate School Presidential Dissertation Fellowship (2016) Dr. Rodriguez has secured significant research funding through fellowships and grants, with her SUNY ProdiG Fellowship enabling critical work on algorithmic bias in social services. She actively shapes social work education through curriculum development, notably proposing generative AI integration into MSW programs. Her collaborative approach connects computer science with social work practice, evidenced by partnerships with domestic violence organizations and community groups addressing digital technology adoption challenges. Through her affiliate roles at the Gender Institute and Digital Scholarship Studio, she co-leads initiatives examining technology's role in gender-based violence prevention and develops digital methodologies for social justice research, fostering cross-disciplinary teams that include computer scientists, social workers, and community advocates.

Masahiro Ryoمشاهده پروفایل

استاد

• Environmental Data Science
• Artificial Intelligence
• Biodiversity Science
• +۵ مورد دیگر

Masahiro Ryo is a Professor of Environmental Data Science at Brandenburg University of Technology (BTU) and leads the working group "Artificial Intelligence for Smart Agriculture" at the Leibniz Centre for Agricultural Landscape Research (ZALF). His research integrates machine learning with ecological systems to address global sustainability challenges, focusing on biodiversity, soil health, and AI-driven agricultural solutions. His work spans environmental data science, ecosystem services, and smart agriculture. Key themes include explainable AI for biodiversity monitoring, soil organic carbon prediction, and machine learning applications in ecological modeling. Recent publications highlight trends in environmental AI, with applications in yield mapping, fungal taxonomy, and global change ecology. He emphasizes the integration of ecological theory with deep learning to tackle small-data problems and improve model transferability. Contact: masahiroryo@gmail.com | Personal Website

• Statistics
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Ping Ma is a Professor of Statistics with a courtesy appointment in Computer Science at the University of Georgia. His research focuses on developing innovative statistical and machine learning methodologies for complex high-dimensional data, with applications spanning bioinformatics, computational biology, social network analysis, and anomaly detection in power systems. Research interests include: Statistical Methodology : Nonparametric modeling, optimal transport theory, subsampling techniques, and functional regression for large-scale data Computational Biology : Spatial transcriptomics analysis, single-cell data integration, virology classification, and gene regulatory networks Machine Learning Innovations : Knowledge distillation for LLMs, tensor analysis, quantum-inspired algorithms, and ensemble learning for model robustness His recent publications demonstrate a strong trend toward interdisciplinary applications, particularly in developing AI/statistical tools for biomedical research (47% of recent papers), advancing foundational machine learning techniques (33%), and solving engineering challenges like power grid security (20%). Methodologically, 67% focus on novel algorithm development while 33% refine existing techniques for scalability.

Theodore Papamarkouمشاهده پروفایل

استاد

• Bayesian Deep Learning
• Topological Deep Learning
• Healthcare AI
• +۳ مورد دیگر

Professor Theodore Papamarkou is a leading researcher in Bayesian and topological approaches to deep learning, with a focus on healthcare applications. His work addresses scalability challenges in machine learning by integrating Bayesian inference and topological data analysis into deep learning frameworks. He contributes to the UN Sustainable Development Goals through his research in Digital Futures and the Centre for Digital Trust and Society. Key projects include collaborations on financial crime prevention and digital trust. He received the 2023 ECML PKDD best paper award and serves as Editor-in-Chief of ACM Transactions on Probabilistic Machine Learning. His research spans topics like uncertainty quantification, material microstructure analysis, and predictive model interpretability. Professor Papamarkou has contributed to 19 peer-reviewed publications and actively participates in academic activities such as editorial work and conference organization. His interdisciplinary approach bridges computer science, statistics, and healthcare, emphasizing ethical and practical AI applications.

• Security of Deep Learning Systems
• Side-Channel Attacks
• Privacy-Preserving Machine Learning
• +۳ مورد دیگر

Dr. Yuanyuan Yuan is a Researcher at the Department of Computer Science, ETH Zurich, Switzerland, based at CNB H 104.1, Universitätstrasse 6, 8092 Zurich. Her work bridges computer security and machine learning with a focus on practical vulnerabilities in deployed AI systems. Her research centers on exposing and mitigating security flaws in deep learning deployments, particularly targeting trusted execution environments (TEEs) and on-device inference systems. Key contributions include pioneering side-channel attacks against TEE-shielded neural networks (CipherSteal, HyperTheft), bit-flip attack surfaces in DNN executables (BitShield), and novel testing methodologies for neural network robustness. She investigates cache/timing side channels, ciphertext analysis, privacy leakage in partitioned ML, and concept-based explainability. Analysis of her 2023-2025 publications reveals a cohesive focus on offensive security research for AI infrastructures, with consistent contributions to top venues in security and machine learning. Her work demonstrates expertise in low-level system interactions (memory, cryptography) applied to ML security, spanning attack vectors, defensive mechanisms, and validation frameworks. The research trajectory shows increasing sophistication in exploiting hardware-software interfaces while developing practical hardening techniques for real-world deployments.

• Artificial Intelligence
• Automated Planning
• Temporal Reasoning
• +۴ مورد دیگر

Alfonso Emilio Gerevini is a prominent researcher in artificial intelligence with over 30 years of continuous academic contributions. His work spans theoretical foundations of automated planning to practical healthcare applications, with recent publications demonstrating significant impact in both traditional AI domains and emerging interdisciplinary areas. His research interests focus on automated planning systems , temporal reasoning , and multi-agent coordination , with recent expansion into healthcare applications using machine learning techniques. Gerevini has made fundamental contributions to planning algorithms, particularly in width-based search, case-based planning, and privacy-preserving multi-agent planning. His work on PDDL (Planning Domain Definition Language) has been influential in standardizing planning representations. Analysis of his 15 most recent publications reveals a strategic evolution from core planning research toward impactful healthcare applications, particularly during the COVID-19 pandemic. While maintaining his expertise in planning algorithms, he has successfully integrated machine learning techniques to address real-world medical challenges including radiology report analysis, prognosis prediction, and lab test interpretation. His work demonstrates exceptional versatility across both theoretical and applied domains of artificial intelligence. Gerevini maintains a robust collaborative network, primarily with Italian researchers including Ivan Serina, Alessandro Saetti, and Luca Putelli. His publications appear consistently in top-tier AI venues including Artificial Intelligence journal, Journal of Artificial Intelligence Research, and AAAI/ICAPS conferences. The collaborative patterns suggest he leads a significant research group focused on advancing planning systems while applying them to critical real-world problems.

• Machine Learning
• Computer Vision
• Human Behavior Analysis
• +۵ مورد دیگر

Dr. Harshala Gammulle is a Research Fellow at Queensland University of Technology (QUT), School of Electrical Engineering & Robotics. She holds a PhD in Computer Vision from QUT (2019), receiving the QUT Executive Dean's Commendation for Outstanding Doctoral Thesis. Her expertise spans machine learning, computer vision, and spatio-temporal modeling for human behavior understanding. She leads interdisciplinary projects with funding from DST Group, SmartSat CRC, QLD DESI, and others. Research focuses include: human action recognition, medical anomaly detection, satellite image analysis, and AI for environmental monitoring. Key projects involve quantum-classical hybrid ML for biomedical signal analysis, disaster forecasting via hyperspectral data, and autonomous combat vision systems. She has supervised PhD/MPhil candidates in ML and quantum hybrid ML. Education: PhD (Computer Vision, QUT 2019), BSc (University of Peradeniya, Sri Lanka). Awards: WiT Emerging Achiever Technology Award finalist (2021), University Award for Academic Excellence (2015). Teaching includes units like Digital Signals and Image Processing (EGH444), and Computing & Data for Engineers (EGB103). Current grants involve QLD DESI, SmartSat CRC, and Rheinmetall Defence Australia collaborations. Active in labs like SAIVT and QUT's Early Career Research schemes.

• Biomedical Engineering
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Dr Tianning Li is a Lecturer in Computing at the University of Southern Queensland's School of Mathematics, Physics and Computing. Affiliated with the School of Agriculture and Environmental Science, their research focuses on biomedical engineering, signal processing, and machine learning applications in clinical settings. Dr Li holds a PhD from USQ, an MAccFin from Adelaide, and a BISM from Nanjing. Core research interests include EEG signal analysis for anesthesia monitoring and epilepsy detection, with emphasis on developing novel signal processing techniques like spectral entropy analysis, synchroextracting transforms, and federated learning approaches. Their work bridges machine learning innovations with clinical diagnostics, addressing challenges in real-time medical signal interpretation and healthcare data efficiency. Recent publications (2021-2025) emphasize advancements in anesthesia depth assessment algorithms, seizure prediction methodologies, and lossless signal compression. Research trends reflect a strong focus on integrating neural networks (CNN-LSTM, 1D CNN) with traditional signal analysis frameworks to enhance clinical decision-making accuracy. No scientific awards are listed, but Dr Li maintains active research collaborations through affiliations with multiple departments. Supervision activities are not detailed in available records, though their work likely involves student contributions to biomedical computing projects. ORCID: 0000-0001-5142-8654 .

Erel Levineمشاهده پروفایل

دانشیار

• Systems Biology
• Synthetic Biology
• Computational Biology
• +۶ مورد دیگر

Erel Levine is an Associate Professor in the Department of Bioengineering at Northeastern University's College of Engineering, with an additional affiliation in the Chemical Engineering department. He joined the bioengineering department in January 2019 and maintains an active research program focused on complex biological systems. His educational background includes a Ph.D. in Theoretical Physics from the Weizmann Institute of Science (2005), an M.Sc. in Applied Physics from the same institution (1999), and a B.Sc. in Computer Sciences and Physics from Hebrew University in Jerusalem (1997). Levine's research program centers on systems and synthetic biology of the Brain-Immune-Gut super-system, with particular emphasis on host-microbe interactions and deep learning applications in biological data interpretation. His laboratory develops innovative approaches combining quantitative experiments, computational models, and machine learning to study distributed biological systems across multiple scales. His recent publications demonstrate a strong focus on microfluidic platforms for C. elegans research, spatial organization of membrane proteins, feeding dynamics, and gene regulation mechanisms. The work spans molecular, cellular, and organismal levels, with applications to understanding microbiome dynamics and gut-brain interactions. His scientific recognition includes the 2025 Fostering Engineering Innovation in Education Award and a National Science Foundation Postdoctoral Fellowship from the Center for Theoretical Biological Physics. 2025 Fostering Engineering Innovation in Education Award National Science Foundation Postdoctoral Fellowship, Center for Theoretical Biological Physics Professor Levine actively mentors undergraduate students who have received prestigious awards including the AJC Merit Research Scholarship and PEAK Experiences Awards. His research is supported by grants including a National Science Foundation award for 'Sub-Cellular Localization and Small RNA and Regulation of the Outer Membrane' as Principal Investigator. The Levine Lab operates from Lab 415 Mugar at Northeastern University, utilizing advanced microfluidics, deep learning, and imaging technologies to study biological systems. The lab has developed automated high-resolution imaging platforms that combine microscopy, active deep learning, and multiplexed microfluidics to study host-microbiome interactions in real time.

Sherilyn Williams-Stroudمشاهده پروفایل

استاد

• Induced Seismicity
• Subsurface Storage
• Fracture Analysis and Modeling
• +۴ مورد دیگر

Sherilyn Williams-Stroud is the Director of the Berg-Hughes Center and holds the Dan A. Hughes '51 Chair in Geosciences at Texas A&M University. She is a Professor in the Department of Geosciences, with over 30 years of expertise in structural geology, subsurface storage, and induced seismicity. Her research focuses on fracture analysis, geomechanics, and the societal impacts of energy resource utilization, particularly in carbon capture and storage (CCUS) and renewable energy transition. Education: BA in Geology from Oberlin College (198?), MA and PhD in Structural Geology from Johns Hopkins University (199?). Research interests include induced seismicity from fluid injection, subsurface tomographic imaging, and the integration of machine learning for improved subsurface modeling. Her work addresses critical challenges in energy resource management and environmental sustainability, with a focus on CO2 sequestration and geothermal energy. Publications highlight advancements in microseismic monitoring, fracture network modeling, and geomechanical analysis of CO2 injection sites. Her contributions span academic, industrial, and governmental sectors, including roles at the USGS, Chevron, and MicroSeismic Inc. Awards: AAPG Distinguished Lecturer (2023–2024), AAPG Honorary Member (2022), USGS Star Award (1997). Professional Leadership: Member of the National Petroleum Council’s Natural Gas GHG Study Committee, National Academy of Sciences’ Geodynamics Committee, and American Rock Mechanics Association’s Discrete Fracture Network Technical Committee. Her advisory and collaborative work bridges academia and industry, with a focus on mitigating risks in subsurface energy projects while advancing sustainable practices.

Sonit Singhمشاهده پروفایل

مدرس

• Artificial Intelligence
• Computer Vision
• Natural Language Processing
• +۷ مورد دیگر

Dr. Sonit Singh is a Lecturer at the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney, Australia. Prior to this role, he served as a Postdoctoral Research Fellow at the same institution. He holds a PhD from Macquarie University, completed in collaboration with Macquarie. His research focuses on artificial intelligence, computer vision, natural language processing, and their applications in healthcare, particularly in medical imaging and diagnostic systems. Dr. Singh's academic background includes a strong emphasis on biomedical imaging, intelligent robotics, and computing education. His work bridges machine learning advancements with real-world clinical challenges, such as tumor segmentation, radiology report generation, and disease detection through imaging modalities like CT scans and ultrasound. His recent publications highlight innovations in 3D medical image localization, robust tumor segmentation networks, and clinical context-aware radiology report systems. These contributions underscore his expertise in integrating deep learning techniques with healthcare workflows. Dr. Singh collaborates with multidisciplinary teams, including medical professionals, to develop solutions for challenges like placenta segmentation and dietary intake monitoring using AI. While no specific awards or grants are explicitly listed, his extensive publication record reflects sustained academic engagement in high-impact areas. His research often addresses practical clinical needs, such as improving diagnostic accuracy and patient care through advanced computational methods.