جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Data Science
• Healthcare Informatics
• +۳ مورد دیگر

Lan Wang is a Research Associate in the Department of Computing at Imperial College London's Faculty of Engineering, specializing in data science and machine learning applications for healthcare and cloud systems. Her work focuses on transformer-based modeling of electronic health records to enable early cancer diagnosis and optimize primary care referrals. Her academic credentials include: Ph.D. in Cognitive cloud computing systems and networks (2018) M.Sc. in Communications and Signal Processing from Imperial College London B.Sc. from Shandong University, China Dr. Wang's research spans Machine Learning , Healthcare Informatics , and Cloud Computing , with emphasis on deep learning for cancer diagnosis , NLP/LLM methods , and autonomic network optimization . She develops AI tools to analyze patient health trajectories and treatment responses using EHR data, while maintaining expertise in cognitive packet networks for cloud resource management. Analysis of her 15 most recent publications reveals dominant themes in healthcare AI (particularly cancer diagnosis using transformer models) and autonomic cloud systems (task allocation, cognitive networking). Her work bridges clinical applications with networked computing infrastructure, showing increasing focus on healthcare since 2019. She actively contributes to major research initiatives: AI-DIP: Cancer early diagnosis in general practice (2024-present) LOCOMOTION: Long COVID Study (2022-2024) Cancer Pathway project (2019-2023) H2020 KONFIDO: Secure eHealth services (2017-2019) EU FP7 PANACEA: Cloud resource management (2014-2016) As part of Imperial's Intelligent Systems and Networks group, she collaborates across the Academic Women in Mathematics and Applied Mathematics networks, integrating theoretical computing with real-world healthcare challenges.

Ibraheem Al-Dhamariمشاهده پروفایل

پژوهشگر

• Trustworthy AI
• Deep Learning
• Computer Vision
• +۳ مورد دیگر

Ibraheem Al-Dhamari is a postdoctoral researcher in the Software Engineering Institute at the University of Koblenz, Germany. He holds a PhD in Computer Science supported by DAAD scholarships, alongside MSc and research grants from DAAD. His primary research focuses on Trustworthy AI, Deep Learning, Medical Image Analysis, and Cellular Automata, with applications in healthcare imaging and data protection. He has contributed to projects like medical image registration algorithms and cochlear implant analysis. Al-Dhamari's academic journey includes postdoctoral work at the Institute of Software Engineering (Department 4), and he previously belonged to the Arbeitsgruppe Paulus (Active Vision Group). His work bridges computer science and medicine, emphasizing practical tools for clinical imaging challenges. He actively mentors students seeking thesis opportunities in AI and medical imaging through DAAD-funded programs. His achievements include developing ultra-fast algorithms for medical image analysis and pioneering multi-modal imaging fusion techniques. He maintains an active GitHub profile showcasing repositories like medical-image-registration-course and tutorials on deep learning and recursion. Al-Dhamari is affiliated with organizations such as the International Data Spaces Association and contributes to open-source software for medical research. Key awards include DAAD scholarships for MSc (2007), short research (2008), and PhD (2012). His research has led to innovations in cochlear duct measurement, cervical spine analysis, and cellular automata-based data mining. He remains a sought-after collaborator in interdisciplinary projects at the intersection of AI and healthcare.

• Systems and Control
• Signal Processing
• Electrical Engineering
• +۳ مورد دیگر

João Pedro Castilho Pereira Santos Gomes is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (University of Lisbon). He is affiliated with the Institute of Systems and Robotics (ISR) and specializes in Systems, Decision, and Control. His teaching includes courses such as Signals and Systems , Big Data Processing , and Systems and Signals at both undergraduate and master's levels. Research interests focus on systems engineering, decision-making frameworks, and control theory, with applications in robotics and data-driven systems. His academic contributions are integrated into curricula emphasizing practical and theoretical aspects of electrical and computer engineering. No recent publications or scientific awards are explicitly listed. He advises students in his department and is involved in interdisciplinary projects through ISR. His work aligns with current trends in systems engineering and automation.

Wasim Ahmadمشاهده پروفایل

مدرس ارشد

• Signal Processing
• Machine Learning
• Data Analytics
• +۹ مورد دیگر

Wasim Ahmad is a Senior Lecturer (Associate Professor) in Signal Processing and Machine Learning at the School of Engineering, University of Glasgow. He earned his PhD from the University of Surrey (2011) and held prior academic positions at the University of Hull (2013–2015) and the University of Surrey (2011–2013). He is part of the Communication, Sensing and Imaging (CSI) research group at Glasgow, focusing on applications of signal processing and machine learning in sound modeling, robot audition, healthcare technologies, and IoT-based systems. Education: PhD in Engineering from the University of Surrey (2011), followed by postdoctoral roles at Hull and Surrey before joining Glasgow in 2015. Research Interests: His work spans signal processing for assistive technologies, teleoperation systems, activity recognition, and IoT-enabled healthcare monitoring. He has pioneered projects like radar-based indoor navigation for visually impaired individuals and emotion recognition for human-robot interaction. Publications: Over 40 peer-reviewed articles, with recent focus on edge computing optimization, immersive VR education, and teleoperation interfaces. His work bridges theoretical advancements with practical applications in robotics, healthcare, and education. Grants: Recipient of the University of Glasgow Learning and Teaching Development Fund (2017–2018) for student-staff partnership in assessment innovation. Teaching: Leads courses on real-time systems, microelectronics, and programming, emphasizing hands-on engineering projects. Supervised MSc/PhD students in telerobotics and edge computing. Labs/Teams: Active contributor to the CSI group, collaborating on projects involving sensor networks and AI-driven systems.

• Machine Learning
• Medical Imaging
• Healthcare Analytics
• +۵ مورد دیگر

Dr. Santosh Tirunagari is a Research Fellow in Machine Learning at Middlesex University , focusing on Computer Science with applications in medical imaging , healthcare analytics , and biometric security . His research bridges machine learning with clinical data to improve diagnostic accuracy and patient outcomes. His work spans medical imaging analysis , explainable AI , and biometric anti-spoofing . Recent publications highlight latent diffusion models for visual attribution and convex segmentation algorithms in biomedical imaging, alongside systematic reviews on pediatric AI applications and chronic disease prediction . Key technical contributions include dynamic mode decomposition for motion correction in MRI, adversarial latent transformations for medical image analysis, and self-organizing maps for patient clustering. His collaborations with researchers like David Windridge and Noriko Poh demonstrate interdisciplinary teamwork.

Roland Wuمشاهده پروفایل

استادیار بالینی

• Cardiology
• Echocardiography
• Cardiovascular Genetics
• +۵ مورد دیگر

Roland Wu is an Assistant Clinical Professor (Voluntary) at the University of California, San Francisco (UCSF) School of Medicine, specializing in cardiology. He is a board-certified cardiologist and physician-scientist with expertise in general cardiology and echocardiography. Dr. Wu practices inpatient cardiology at UCSF Medical Center and conducts research through the UCSF Cardiovascular Research Institute (CVRI). Dr. Wu's educational background includes: Bachelor's degree in Electrical and Computer Engineering from Cornell University Medical degree (M.D.) from University of Texas Houston Internship and Residency in Internal Medicine at University of Texas Southwestern Research Fellowship at Columbia University Clinical Fellowship in Cardiology at University of California San Francisco Dr. Wu's research focuses on understanding human cardiovascular disease by studying the genetics and gene regulation underlying these disorders. His work utilizes advanced technologies such as in vivo genetic and enhancer screens coupled with computational biology. With his background in electrical and computer engineering, Dr. Wu brings a unique perspective to the analysis of Big Data in cardiovascular research, often doing his own computer programming to process genomic data. His laboratory work at the CVRI investigates which genes are associated with beneficial and harmful responses to cardiovascular injury, assessing how different tissues respond to such injury. Dr. Wu also uses genome editing technologies in animal models to promote healing and minimize heart damage, with future plans to conduct large-scale drug screens for new cardiovascular therapies. Analysis of Dr. Wu's publication record reveals a strong focus on cardiovascular genetics and molecular mechanisms of heart disease. His research spans from basic science investigations of ion channels and gene regulation to clinical studies of cardiac interventions and transplantation. A notable trend is his progression from molecular and cellular studies toward more translational research with clinical applications, particularly in understanding genetic determinants of heart development, function, and disease. At UCSF, Dr. Wu works in the lab of Dr. Shaun Coughlin, director of the UCSF Cardiovascular Research Institute. The CVRI at Mission Bay provides an exceptional research community dedicated to advanced cardiovascular research, which Dr. Wu describes as "one of the most exciting places to work."

Hamidreza Ahady Dolatsaraمشاهده پروفایل

استادیار

• Health Care Analytics
• Finance
• Transportation
• +۷ مورد دیگر

Hamidreza Ahady Dolatsara is an Assistant Professor at Clark University's School of Business, specializing in data science applications across health care analytics, finance, and transportation. His research focuses on machine learning-based decision-support systems, blockchain adoption, and optimization techniques. Ph.D. in Industrial & Systems Engineering, Auburn University (2019) M.S. in Information Systems Management, Auburn University (2019) M.Eng. in Industrial & Systems Engineering, Auburn University (2015) M.S. in Civil-Transportation Engineering, West Michigan University (2014) B.S. in Railroad Operation and Management Engineering, IUST (2008) His work bridges cutting-edge technologies with societal impact, including applications of generative AI for fraud detection, blockchain in financial health analysis, and eco-efficiency evaluation using hybrid optimization models. Recent publications (2017-2026) demonstrate expertise in cryptocurrency trading algorithms, heart transplant survival modeling, and transportation safety. Scientific recognition includes Clark University Faculty Development Fund grants, Academic Innovation Fund awards, and the Business Edge Technologies Lab. His research spans healthcare analytics, financial technology, and transportation systems, with a strong focus on interdisciplinary collaboration.

• Neuroscience
• Cognitive Neuroscience
• Neural Signal Processing
• +۲ مورد دیگر

Dr. Davide Albertini is a Researcher at the Department of Medicine and Surgery, University of Parma. He teaches courses on Computer Skills , Bibliographic Research , and Neural Signal Analysis for both first and second cycle degree programs in Psychological Sciences. Neuroscience research focused on sensorimotor and sociocognitive neural mechanisms Develops visualization software (HiBoP) for intracranial EEG datasets Specializes in neural decoding of motor actions using computational methods His work combines neuroethology , machine learning , and neural signal analysis to study motor cortex functions in freely moving primates. Current projects examine: Neural coding differences between action execution and observation Clustering algorithms for premotor cortex activity patterns Shared neural representations for biological vs. nonbiological movements

• Machine Learning
• Cloud Computing
• Fault Diagnosis
• +۳ مورد دیگر

Chao Yin is a researcher at Shanghai University , Department of Computer Engineering and Science. His work spans multiple domains including Machine Learning, Cloud Computing, Fault Diagnosis, and Supply Chain Optimization. Key research areas: Machine Learning , Cloud Computing , Quantum Computing , Supply Chain Systems , Network Security Scientific Contributions (2024-2025): Developed heterogeneous graph neural networks for automotive supply chain analysis Created MSDF-VAE cloud-edge fault diagnosis framework using transfer learning Proposed quantum metrology methods with Heisenberg-limited precision Designed LARP pseudonym protocol for V2X communication Optimized fog computing resource scheduling with hybrid metaheuristics Prior Work (2012-2023): Contributed to fluid animation feature preservation from single images Developed label distribution learning for facial age estimation Designed erasure coding storage systems for big data Created multi-agent manufacturing networks in cloud environments

Cindy Chenمشاهده پروفایل

دانشیار

• Spatio-temporal Databases
• Data Mining
• Cloud Computing
• +۴ مورد دیگر

Cindy Chen serves as Department Chair and Associate Professor at the Miner School of Computer and Information Sciences within the Kennedy College of Sciences at the University of Massachusetts Lowell. Her academic journey includes a Ph.D. in Computer Science with specialization in Database and Knowledge Base Management from UCLA (2001), an MS in Computer Science from UCLA, and a BS in Space Physics from Peking University. Ph.D.: Computer Science (Database and Knowledge Base Management), University of California at Los Angeles (2001) MS: Computer Science, University of California at Los Angeles BS: Space Physics, Peking University - Beijing, China Her research spans spatio-temporal databases, data mining, cloud computing, and social network analysis. Early work focused on spatio-temporal data models and query languages , evolving into cloud data warehouse optimization and social network mining (particularly online dating behavior), then advancing to high-dimensional indexing , knowledge graph querying , and recent innovations in temporal database granulation using Allan Variance techniques for CAV friction modeling. Current work integrates database systems with autonomous vehicle infrastructure and real-time urban analytics. Her publication timeline reveals a transition from foundational spatio-temporal database systems (1998-2008) to cloud/social applications (2008-2015), then to granulation techniques and CAV systems (2015-present). Key domains include XML databases, skyline queries, social trend discovery, and friction data modeling for autonomous vehicles. Dean's Fellowship (1998) Dean's Fellowship (1999) Dr. Chen has secured significant research funding including the UMASS Transportation Center grant for an Interactive TMC Decision Support Tool (2008) and a UML Healey Grant Award for Spatio-Temporal Knowledge Discovery in Nuclear Physics (2008). Her collaborative projects span transportation systems, nuclear physics applications, and autonomous vehicle infrastructure. She actively advises on database curriculum development and serves as department chair overseeing academic operations.

• theoretical computer science
• theoretical foundations of big data analysis
• sublinear algorithms
• +۶ مورد دیگر

Michael Kapralov is an Associate Professor in the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he is part of the Theory Group. His research focuses on theoretical computer science with emphasis on the theoretical foundations of big data analysis. Dr. Kapralov completed his PhD at Stanford iCME under the supervision of Ashish Goel. Following his doctoral studies, he spent two years as a postdoc at the Theory of Computation Group at MIT CSAIL working with Piotr Indyk, and then a year at the IBM T. J. Watson Research Center as a Herman Goldstine Postdoctoral Fellow. His research primarily centers on sublinear algorithms, with specific directions including streaming algorithms, sketching techniques, sparse recovery, and Fourier sampling. Kapralov's work addresses fundamental questions in the theoretical computer science of big data, developing algorithms that can process massive datasets efficiently with limited computational resources. His approach often combines deep theoretical insights with practical considerations for real-world applications of theoretical computer science principles. Analysis of Kapralov's recent publications reveals a strong focus on advancing the state of the art in sublinear-time algorithms, particularly for graph problems and kernel methods. His work demonstrates a consistent trajectory toward developing more efficient algorithms for fundamental computational problems while establishing tight theoretical bounds on what's possible in streaming and sublinear settings. There's a notable emphasis on bridging theoretical computer science with practical machine learning applications, particularly through kernel methods and Fourier analysis techniques. ERC Starting Grant SUBLINEAR (2018-2023) Gene H. Golub Dissertation Award Best paper award in CT at Fully3D 2007 Institute of Physics (IoP) Select article Professor Kapralov has advised numerous PhD students and postdocs who have gone on to successful careers in both academia and industry, including several who now hold faculty positions at prestigious institutions worldwide. He has also received significant research funding, most notably the European Research Council Starting Grant that supported his SUBLINEAR project from 2018 to 2023. Within the EPFL academic community, Kapralov is actively involved in several initiatives including organizing the Turing Course for high school students, leading a reading group on the Foundations of Deep Learning, and participating in the EPFL Theory Seminar series. He also contributes to the broader theoretical computer science community through program committee service for major conferences including SOSA 2023 and STOC 2022.

• Big Data
• Social Network Mining
• Stream Processing
• +۱۲ مورد دیگر

Dr. Thanh Tam Nguyen is a Lecturer at the School of Information and Communication Technology, Griffith University, Gold Coast Campus. He holds a PhD in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, and is a Fellow of the Higher Education Academy (FHEA). His academic and research profile is centered on advancing Big Data and Smart technologies through efficient and trustworthy AI systems. His educational background includes a Doctor of Science and a Master of Science, both from EPFL. He is actively involved in high-impact research and has published over 65 papers in top-tier venues such as SIGMOD, VLDB, SIGIR, ICDE, IJCAI, VLDBJ, TKDE, and Pattern Recognition, with over 35 in CORE A* journals and conferences. His work has attracted more than 4,200 citations (h-index 35+). Dr. Nguyen's research focuses on Big Data Analytics, Social Network Mining, Stream Processing, Privacy-Preserving Machine Learning, Recommender Systems, Explainable AI, and Graph Neural Networks . He aims to bridge human insights with data models to ensure transparency and trust in data-driven decisions. His recent work explores misinformation management, machine unlearning, and federated learning, with applications in social networks, healthcare, and sustainable agriculture. The 15 most recent publications reflect a strong trend in privacy-preserving AI, explainability, federated learning, and graph-based modeling . Key themes include machine unlearning, adversarial robustness in recommender systems, heterogeneous graph representations, and trustworthy AI deployment. His work is frequently published in ACM and IEEE venues, indicating sustained excellence in computer science and AI research. Fellow of the Higher Education Academy (FHEA) Dr. Nguyen has secured over $1 million in research funding from government (DFAT, A4I, AKF), industry (CSIRO, Ubitech, Johnson & Johnson), and international bodies (NAFOSTED, ETRI, KARI). He serves as a guest editor for IEEE Journal of Biomedical and Health Informatics, area chair for ACL and EMNLP, and reviewer for top journals like TKDE, JVLDB, and CSUR. He supervises multiple PhD students and collaborates with leading international researchers such as Prof. Karl Aberer (EPFL), Prof. Björn Schuller (Imperial College), and Prof. Hongzhi Yin (UQ). He is a key contributor to the Responsible Big Data Lab at Griffith University and leads projects on misinformation management, AI safety, and sustainable agriculture through AI-powered traceability. His impact extends to policy and industry, particularly in cybersecurity and trustworthy AI adoption.

Mohammad Pourhomayounمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Mohammad Pourhomayoun is an Associate Professor in the Department of Computer Science at California State University, Los Angeles, and founding director of the Artificial Intelligence & Data Science Research Lab. His research focuses on AI and machine learning for healthcare, environmental monitoring, and urban sustainability. Education: PhD in Electrical and Computer Engineering (SUNY Binghamton, 2013), Postdoc in Computer Science (UCLA, 2015) His work spans predictive analytics in healthcare (e.g., stroke detection, diabetes monitoring), air quality modeling, and digital twin development for climate applications. He has published extensively in venues like IEEE Transactions and CSCI conferences. Recent publications highlight deep learning for wildfire progression analysis, PM2.5 forecasting, and medical imaging diagnostics. He has served on technical program committees for conferences including BSN, EMBC, and PerCom.

• Public Health
• Metabolomics
• Stress and Mental Health
• +۴ مورد دیگر

Pierpaolo CAVALLO is a Researcher at the Department of Physics 'ERCaianiello' of the University of Salerno . He holds an ORCID (0000-0003-3111-5241) and can be contacted at pcavallo@unisa.it. His office is located at Fisciano Campus, Building F, Fourth Floor, Room 011, with reception hours on Tuesdays 9:00-11:00 or via TEAMS appointments. Research Focus : Metabolomics, public health, stress analysis, pediatric obesity, cardiovascular diseases, and food safety. Key Contributions : Development of metabolomic signatures for cancer and liver disease, studies on stress biomarkers, and innovations in agricultural and clinical data analysis. Publication Trends : Recent work emphasizes metabolomic profiling (2019-2023), with applications in endometrial cancer screening, coeliac disease diagnostics, and maritime food safety. Collaborations span clinical research (2016-2020) and plant-microbe interactions (2014).

• Biostatistics
• Neuroimaging
• Functional Connectivity
• +۷ مورد دیگر

Dustin Pluta is an Assistant Professor in the Department of Biostatistics, Data Science, & Epidemiology at the School of Public Health, Augusta University. His academic work bridges biostatistics, neuroimaging, and data science, with a focus on developing advanced statistical methods for neuroscience and public health applications. Education: Ph.D. in Statistics, University of California, Irvine, 2020 M.S. in Applied Statistics, California State University, Long Beach, 2015 M.A. in Mathematics, University of California, Davis, 2008 His research interests lie at the intersection of biostatistics , functional brain connectivity , Bayesian modeling , and imaging genetics . He develops and applies statistical techniques to improve the quality and interpretability of neuroimaging data, particularly in the context of mental health and addiction behaviors. His work often involves Gaussian processes, penalized regression, and multivariate association testing. The recent publications reflect a strong trend in neuroimaging statistics , with emphasis on enhancing trial-level ERP analysis, modeling dynamic functional connectivity, and integrating genetic and brain imaging data. These works span disciplines including neuroscience, psychiatry, and biostatistics, demonstrating methodological innovation and application to real-world health problems such as e-cigarette use vulnerability. Scientific Awards and Honors: Distinguished Student Paper Competition Award, ENAR, International Biometric Society, 2018 Student Paper Competition Award, Joint Statistical Meetings, Statistics in Imaging, 2018 Robert L. Newcomb Memorial Endowed Graduate Student Award, UC Irvine, 2016 Summer Fellowship, UC Irvine, 2016 Chair’s Award, UC Irvine Department of Statistics, 2015 Dustin Pluta is actively involved in academic service, including serving as Committee Chair of the Journal Club and participating in faculty hiring and departmental meetings. He is also a reviewer for the journal Psychophysiology . He teaches graduate courses such as Applied Linear Models , Modern Methods of Multivariate Analysis , and the Data Science Capstone . While specific grant funding is not listed, his research output suggests active engagement in sponsored scholarly work. He contributes to a collaborative research environment within the Department of Biostatistics, Data Science, & Epidemiology, where interdisciplinary projects involving neuroscience, public health, and data science are central to the academic mission.