جست‌وجوی استاد و پژوهشگر

Roman Belavkinمشاهده پروفایل

دانشیار

• Information Theory
• Optimization
• Machine Learning
• +۳ مورد دیگر

Dr. Roman Belavkin is an Associate Professor in Informatics at the Department of Computer Science, Middlesex University, affiliated with the School of Science and Technology. He holds a PhD in Computer Science from the University of Nottingham (1998-2002) and an MSc in Physics from Moscow State University (1988-1994). His research focuses on information theory, optimization, and their applications to machine learning, evolutionary algorithms, and computational biology. He is an affiliated faculty member of the Center for Applied Optimization at the University of Florida and serves on the editorial board of Optimization Letters. His work spans theoretical foundations of information dynamics, mutation rate control in evolutionary systems, and applications to finance and cognitive architectures. Notable contributions include studies on entropy computation, value of information analysis, and neural modeling. He has presented at international conferences such as the Advanced Course on Data Science & Machine Learning (Siena, 2018) and published extensively in top-tier journals. Collaborations include interdisciplinary projects linking information theory with biology, finance, and artificial intelligence. Dr. Belavkin teaches courses in computer science and informatics at both undergraduate and postgraduate levels. His research interests also extend to cybersecurity, including detection of application layer DDoS attacks using clustering and Bayesian methods. He maintains an active personal website detailing his publications, software tools, and academic activities.

Terrence Triccoمشاهده پروفایل

استادیار

• Synthetic Data Generation
• Smoothed Particle Hydrodynamics (SPH)
• High-Performance Computing
• +۶ مورد دیگر

Terrence Tricco is an Assistant Professor in the Department of Computer Science at Memorial University of Newfoundland, cross-appointed with the Department of Physics and Physical Oceanography. He chairs the Master's of Data Science program and leads the Simulation, Modeling and Synthetic Data Lab. His research focuses on synthetic data generation using deep learning, astrophysical smoothed particle hydrodynamics (SPH), and high-performance computing. Tricco is a development lead for the Phantom SPH code and creator of the Sarracen Python analysis package. He is Principal Investigator of the Centre for Analytics, Informatics and Research (CAIR), managing a high-performance computing cluster. Collaborations include Verafin (financial synthetic data) and VBHC-NL (healthcare analytics). Key research areas include multi-physics SPH algorithms, parallel computing optimization, and numerical analysis of SPH convergence. Tricco's work bridges computational science with applications in astrophysics, healthcare, and finance. Notable software contributions include Phantom (astrophysical SPH simulations), Sarracen (SPH visualization), and Framalytics (FRAM modeling integration). His lab emphasizes synthetic data generation for sensitive domains like healthcare and finance. Recent work explores generative AI for data synthesis and improving SPH method scalability on modern architectures.

• Health Informatics
• Public Health
• Health Information Systems
• +۳ مورد دیگر

Sue Feldman is a Professor and Director of Graduate Programs in Health Informatics at the University of Alabama at Birmingham (UAB), School of Health Professions. She holds dual appointments in the School of Medicine as a Senior Scientist in the Informatics Institute and the Center for the Study of Community Health. With over 30 years as a Registered Nurse, her work integrates clinical practice with informatics, focusing on health information systems for social protections. She chairs accreditation site visits for CAHIIM and serves as 2023-2024 Chair of AMIA's Academic Forum. Dr. Feldman earned dual PhDs in Information Technology and Higher Education from Claremont Graduate University, alongside master's and bachelor's degrees in related fields. Her research spans telehealth implementation, health information exchange, and the impact of reimbursement models on healthcare quality. She actively publishes in top-tier journals and contributes to national forums as a program chair. Her teaching spans Clinical Informatics, MS Health Informatics, and PhD programs. She has pioneered cybersecurity simulations for students and explored pandemic-era communication strategies. Key collaborations include developing a substance use disorder web application and analyzing opioid use disorder case detection via NLP. Dr. Feldman's work bridges policy and practice, addressing challenges like nursing home staffing economics and emergency department crowding. She advocates for interprofessional collaboration, leading initiatives such as the 'Forge' student-alumni mentoring network. Her efforts aim to advance learning health system competencies and address social determinants of health through technology.

Jonathan Iworisoمشاهده پروفایل

مدرس ارشد

• Econometrics
• Financial Analysis
• Machine Learning Applications
• +۴ مورد دیگر

Dr. Jonathan Iworiso is a Senior Lecturer in Mathematics and Statistics at the School of Computing and Digital Media, London Metropolitan University. His expertise spans econometrics, financial analysis, machine learning applications, and statistical modeling with a focus on sustainability and economic development. His research interests include economic forecasting using advanced statistical models like GAMLSS, the impact of digital financial services on financial inclusion, and the application of machine/deep learning in stock market prediction. He has also explored Nigeria's economic challenges, taxation policies, and green energy solutions. His work bridges theoretical statistical methods with real-world economic and financial problems. Key publication themes include: (1) economic forecasting through advanced statistical techniques, (2) financial inclusion via digital innovation, (3) stock market predictability using ML/deep learning, and (4) Nigerian economic sustainability analysis. Recent work emphasizes out-of-sample forecasting accuracy and policy-relevant economic indicators. No specific grants, awards, or advising roles are documented in the provided materials. His teaching likely focuses on quantitative methods within the mathematics and statistics discipline at the university level.

Steven X. Wangمشاهده پروفایل

استاد

• AI/Machine Learning
• Healthcare Applications
• Biomedical Signal Processing
• +۳ مورد دیگر

Dr. Steven X. Wang is a full professor in the Department of Statistics at York University and currently serves as the Director of the undergraduate Data Science program. He holds a U.S. patent for Machine Learning and has been recognized with prestigious awards including the Queen Elizabeth II Platinum Jubilee Award (2023) and the Markham-Unionville Achievement Award and Medal (2025) for his contributions to AI in healthcare. Education: Postdoc in Data Science, Pacific Institute of Mathematical Sciences (2002) Ph.D. in Statistics, University of British Columbia (2001) M.Sc. in Applied Statistics, University of California, Riverside B.Sc. in Computational Mathematics, Beijing University of Technology Research Interests: Dr. Wang focuses on applying AI/machine learning to healthcare, particularly in pain analysis using EEG signals and financial time series modeling. His work bridges statistical theory with practical applications in biomedical engineering and medical data analysis. Publications: His recent articles emphasize advancements in neural networks for EEG analysis, financial portfolio strategies, and biomedical signal processing. His work consistently integrates interdisciplinary methods to solve real-world health and financial challenges. Awards: Queen Elizabeth II Platinum Jubilee Award (2023) Markham-Unionville Achievement Award and Medal (2025) U.S. Patent for Machine Learning Innovations Experience: With 30 years of consulting experience and 25 years in scientific computing, Dr. Wang leads research projects addressing critical challenges in healthcare and data-driven decision-making.

Leila Kosseimمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics
• +۳ مورد دیگر

Leila Kosseim is a Professor in the Department of Computer Science and Software Engineering at Concordia University, co-directing the CLaC (Computational Linguistics @Concordia) lab. She holds a PhD from the University of Montreal (1995) and has conducted postdoctoral research at Druide informatique inc. and the RALI group. Her academic leadership roles include Vice-President (2017-2019), President (2019-2021), and Past-President (2021-2023) of the Canadian AI Association (CAIAC), and current Graduate Program Director for the PhD Programs in CSSE. Her research focuses on Natural Language Processing (NLP) and Artificial Intelligence , with particular emphasis on discourse analysis, computational linguistics, and text generation. Notable projects include automated analysis of argumentative essays, clinical trial inference, and meme persuasion detection. Her work integrates deep learning, neural networks, and transformer models to address challenges in text coherence, discourse parsing, and multilingual NLP. Leila has supervised 8 PhD and over 20 Master’s students, contributing to the next generation of computational linguists. Her CLaC lab has participated in multiple international challenges (SemEval, TREC, DEFT) and produced influential tools like TIMBERT and the Concordia NLG Surface Realizer.

Martin Craneمشاهده پروفایل

استاد

• Computational Finance
• Complex Systems
• Time Series Analysis
• +۱ مورد دیگر

Martin Crane is Professor and Head of School at Dublin City University School of Computing. Funded Investigator in ADAPT Centre, with PhD in Mechanical Engineering from Trinity College Dublin. Research spans computational finance, complex systems modeling, and high-performance computing applications. Key research areas: Time series analysis in finance/biology Complex systems in finance, immunology, and lifelogging Wavelet methods for market analysis Parallel computing for large-scale simulations Current projects examine cryptocurrency dynamics, healthcare analytics, and brain tumor segmentation using deep learning. Leads FinTech Fusion initiative exploring financial technology applications. Authored significant works on random matrix theory filters for portfolio optimization and agent-based HIV modeling.

Frédéric Godinمشاهده پروفایل

دانشیار

• Quantitative Finance
• Risk Management
• Actuarial Science
• +۳ مورد دیگر

Frédéric Godin is an Associate Professor in the Department of Mathematics and Statistics at Concordia University, where he supervises graduate students in Mathematics and Statistics (MA, MSc, PhD) and actively accepts research inquiries. He earned his Ph.D. from HEC Montréal in 2014. Research Focus His work integrates advanced computational methods with financial and actuarial theory, spanning: Quantitative Finance : Option pricing, volatility modeling, and hedging strategies using machine learning Risk Management : Catastrophic risk assessment, portfolio optimization, and derivatives pricing Actuarial Science : Life insurance contract valuation and embedded guarantees Energy Markets : Electricity price forecasting and commodity derivatives Recent publications (2023-2025) demonstrate a strong emphasis on reinforcement learning applications in finance, particularly deep hedging frameworks and volatility surface modeling. Additional themes include climate-aware portfolio design and statistical methods for insurance risk. Academic Contributions As a thesis supervisor, he guides graduate research in mathematical finance and statistical modeling. No awards or grants were documented in available sources.

Ilias Chronopoulosمشاهده پروفایل

مدرس

• Financial Econometrics
• High-Dimensional Statistics
• Time Series Analysis
• +۱ مورد دیگر

Ilias Chronopoulos is a Lecturer in Finance at the Essex Business School , University of Essex, specializing in financial econometrics and machine learning. He joined the university in April 2022 and holds a PhD in Econometrics from King's College London (2022), an MRes in Economics (2017), and an MSc in Economics (2016) from Queen Mary University of London. His research focuses on: High-dimensional statistics for financial data Machine learning applications in econometric modeling Time series analysis and volatility forecasting Deep neural networks for risk management Key publications include work in the Annals of Statistics and Journal of Financial Econometrics , with recent studies on deep learning for Value-at-Risk forecasting and penalized estimation in high-dimensional settings. His teaching includes courses in Financial Modelling, Financial Time Series, and Research Methods in Financial Econometrics.

Emiris Ioannisمشاهده پروفایل

استاد

• Computer Algebra
• Computational Geometry
• Computer-Aided Geometric Design
• +۹ مورد دیگر

Emiris Ioannis is a Professor in the Department of Informatics & Telecommunications at the University of Athens. He holds a BScEng from Princeton University, MSc and PhD from UC Berkeley, and a Habilitation from the University of Nice Sophia Antipolis. His research focuses on computer algebra, computational geometry, robotics, bioinformatics, and fairness in AI. He has held tenured positions at INRIA, France, and has collaborated with institutions like ENS-Ulm, IHES, and the Ontario Research Center for Computer Algebra. His work bridges theoretical and applied areas, including structural bioinformatics, algorithm development, and machine learning. Notable contributions include tools for protein structure analysis (e.g., Hydraprot), explainable AI frameworks (GLANCE), and predictive models for renewable energy (wind speed forecasting). His research often intersects with interdisciplinary challenges in robotics, finance, and environmental sustainability. Emiris has organized international workshops and contributed to conferences like ISSAC. His publications span high-impact journals in computer science, mathematics, and bioinformatics. Despite no explicitly listed awards, his extensive academic contributions and leadership roles reflect his impact in theoretical informatics. His advising and grants focus on collaborative projects involving geometric algorithms, AI ethics, and data-driven decision systems. Research teams under his leadership often involve cross-disciplinary collaborations, addressing both fundamental and applied problems in computational science.

• Data Science
• Machine Learning
• Mobility Data Analysis
• +۶ مورد دیگر

Gunopulos Dimitrios is a Professor at the National and Kapodistrian University of Athens, Department of Informatics and Telecommunications. His research focuses on data science, machine learning, mobility data analysis, and interdisciplinary applications in urban systems, finance, and high-energy physics. He has contributed to frameworks like INSIGHT for urban traffic management and REMI for heterogeneous data mining. His work spans cloud computing, explainable AI, and spatiotemporal analysis, addressing challenges in real-world systems. Education: Education details not explicitly provided in the text. Research Interests: Dimitrios explores cutting-edge topics such as mobility data science, serverless computing, deep learning for financial forecasting, and causal reasoning. His work bridges theoretical computer science with practical applications in urban infrastructure, healthcare, and sensor networks. Recent efforts include developing algorithms for sparse data handling, fault detection in traffic systems, and counterfactual explainability in AI. Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: No listed advisees or grant details available. His contributions are primarily through collaborative frameworks and conference engagements. Labs/Teams: Involved in projects like INSIGHT for urban data integration and Dione for big data application profiling. Collaborates on heterogeneous data analysis and cloud resource optimization initiatives.

Nikola Nikolovمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• Graph Drawing
• +۵ مورد دیگر

Nikola Nikolov is an Associate Professor in the Department of Computer Science & Information Systems at the University of Limerick. He holds memberships in the Centre for Research Training in Foundations of Data Science, the Data-Driven Computer Engineering Research Centre, and Lero – the Irish Research Centre for Software, reflecting his deep integration into Ireland's national research infrastructure. His research spans Machine Learning, Natural Language Processing, and Graph Drawing, with specialized expertise in deep learning architectures, matrix factorization techniques, and feature extraction methodologies. Current work focuses on multilingual NLP applications for social media analysis, convolutional neural network optimizations, and collaborative filtering systems, demonstrating consistent innovation across computational linguistics and graph theory domains. Analysis of his 2023-2025 publications reveals a dominant focus on combating online toxicity through advanced NLP systems for racism and hate speech detection across multiple languages, alongside significant contributions to computer vision efficiency and recommendation system architectures. His work bridges theoretical advancements with real-world applications in healthcare analytics and autonomous systems. Prof. Nikolov actively contributes to UN Sustainable Development Goals through data science applications while maintaining strong industry connections via research centres focused on software innovation and data-driven engineering solutions.

Yuefan Dengمشاهده پروفایل

استاد

• Parallel Computing
• Molecular Dynamics
• Computational Biology
• +۵ مورد دیگر

Yuefan Deng is a Professor of Applied Mathematics and Statistics at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. He holds additional roles such as Associate Director of the Stony Brook Institute of Engineering-Driven Medicine. With a BA from Nankai University (1983) and a PhD in Theoretical Physics from Columbia University (1989), his career spans academic and research roles at institutions like NYU’s Courant Institute and IBM’s Watson Lab. His research focuses on parallel computing, molecular dynamics, and biomedical engineering, particularly multiscale modeling of platelet activation and supercomputer network optimization. He has advised nearly 30 PhD students and received prestigious teaching awards, including the 2016 SUNY Chancellor’s Award for Excellence in Teaching. Education: BA in Physics (Nankai University, 1983); PhD in Theoretical Physics (Columbia University, 1989). Key affiliations include Stony Brook, NYU Abu Dhabi, and Singapore’s National University. His work combines algorithm development with applications in engineering, medicine, and finance. Major contributions include the Galaxy Beowulf supercomputer (1997) and NankaiStars (China’s fastest in 2004). Current research emphasizes machine learning integration into biomedical modeling and optimizing supercomputer performance. Scientific Awards: SUNY Chancellor’s Award (2016), Stony Brook Dean’s Teaching Award (2015–16), and recognition for contributions to supercomputing architectures. His teaching spans courses like Parallel Computing and Numerical Analysis, reaching over 20,000 students. Active in conferences, he co-chaired Supercomputing Frontiers (2015) and chairs engineering-driven medicine events. Labs/Teams: Leads multiscale modeling projects at the IEDM, collaborating on thrombosis research and AI-driven drug discovery. His work bridges computational methods with biomedical applications, aiming to address challenges in hemostasis and cardiovascular health.

Jim Buckleyمشاهده پروفایل

استاد

• Software Reengineering
• Empirical Software Engineering
• Information Seeking in Software Engineering
• +۷ مورد دیگر

Jim Buckley is a Professor in the Department of Computer Science & Information Systems at the University of Limerick, and a Principal Investigator at Lero – the Irish Software Research Centre. He holds a BSc in Biochemistry from the National University of Ireland, Galway (1989), followed by an MSc (1994) and PhD in Computer Science (2002) from the University of Limerick. His research focuses on software reengineering, empirical software engineering, and source code analysis, with notable contributions to clone detection, software architecture recovery, and AI-driven code generation. He led the TREES Centre, a €6M industry-funded initiative, and currently oversees three research teams at Lero investigating large-scale clone detection, deep learning architecture recovery, and AI-generated source code. His work intersects technical innovation with societal impact, such as ethical software engineering and pandemic response technologies like contact tracing apps. Key research themes include software architecture consistency, feature location, and developer experience optimization. He has published over 118 peer-reviewed articles and received a Research Impact award from Lero. His contributions span open-source tool development, empirical studies in software practices, and interdisciplinary projects addressing global challenges like public health and cryptocurrency equity. Education: BSc Biochemistry, National University of Ireland, Galway (1989) MSc Computer Science, University of Limerick (1994) PhD Computer Science, University of Limerick (2002) Awards: Research Impact Award, Lero (2021) Labs/Teams: TREES Centre (Trustworthy, Responsible, Efficient Engineering of Software) Three core Lero teams: Clone Detection, Architecture Recovery, and AI-Code Generation

Yanlai Chenمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Machine Learning
• +۳ مورد دیگر

Yanlai Chen is a Professor in the Department of Mathematics at the University of Massachusetts Dartmouth and serves as Chief Research Officer. He holds a PhD (2007) and MS (2007) from the University of Minnesota Twin Cities, and a BS (2002) from the University of Science and Technology of China. His research integrates numerical analysis, scientific computing, and machine learning. Research interests focus on numerical PDEs, model reduction, machine learning applications in scientific computing, uncertainty quantification, and high-performance computing algorithms. His work bridges theoretical mathematics with practical computational challenges. His publications demonstrate consistent focus on physics-informed neural networks (2021-2024), model reduction techniques (2019-2024), and computational methods for differential equations. Recent work shows increased emphasis on machine learning integration with traditional numerical methods. He has supervised 6 doctoral dissertations and leads the NSF-funded ACCOMPLISH program supporting STEM education through contextualized computing curricula.