جست‌وجوی استاد و پژوهشگر

• Geospatial Data Science
• GeoAI
• Remote Sensing
• +۴ مورد دیگر

Aiman S Soliman serves as a Research Assistant Professor in Urban and Regional Planning and Senior Research Scientist at the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign, with additional affiliation at the Center for Digital Agriculture. His interdisciplinary work integrates geospatial data science, machine learning, and high-performance computing to address complex challenges in environmental monitoring, public health, and urban systems. His research spans Geospatial Data Science and GeoAI with specific applications in Pan-Arctic permafrost monitoring using satellite imagery, machine learning-driven poverty assessment among displaced populations, and epidemiological modeling of neighborhood violence impacts on lung cancer progression. Key methodologies include feature detection algorithms, human mobility analysis, and habitat suitability modeling for vector-borne diseases, demonstrating strong technical expertise in handling large-scale geospatial datasets. Recent publications reveal a consistent trend in applying machine learning to interdisciplinary problems: from permafrost feature detection in extreme environments to modeling Zika virus epidemics through human mobility patterns and neighborhood violence effects on tumor biology. This work bridges computer science with environmental science and public health, emphasizing actionable solutions for sustainability and health equity. Dr. Soliman actively collaborates through NCSA and the Center for Digital Agriculture, leveraging supercomputing infrastructure for large-scale geospatial analysis. His dataset on US building coverage distribution supports urban sustainability research, while media coverage of his neighborhood violence and lung cancer study demonstrates real-world impact through 8 news outlets and social media engagement.

• Information Retrieval
• Natural Language Processing
• Social Media Analysis
• +۳ مورد دیگر

Marco Viviani is a researcher affiliated with University of Milano-Bicocca (Italy) and University of Torino (Candiolo, Italy), focusing on information retrieval , health informatics , and social media credibility . His work spans multi-dimensional relevance assessment generative AI applications misinformation detection privacy-aware systems Research highlights include ROMCIR workshops on reducing online misinformation, Web2Vec for structural content analysis, and LLM reasoning capabilities evaluation. Key collaborations with Gabriella Pasi, Rishabh Upadhyay, and Marinella Petrocchi appear across 103 publications from 2004-2025. His work integrates machine learning and knowledge graphs for healthcare applications, with recent focus on blockchain social media dynamics and privacy-utility trade-offs . He contributes to ECIR WI/IAT CLEF eHealth MDAI conferences and journals like IEEE Access and Frontiers in Artificial Intelligence .

• Inverse Problems
• Ill-Posed Problems
• Microwaves
• +۴ مورد دیگر

Mlkhail Klibanov is a Professor in the Department of Mathematics and Statistics at the University of North Carolina at Charlotte , where he has served since 1990. His research sits at the intersection of pure mathematics and applied science, focusing on inverse problems for PDEs , ill-posed problems , and interdisciplinary applications in microwaves and nano-science . Dr. Klibanov's work revolutionized the field through his introduction of Carleman estimates (1981), enabling global uniqueness and stability theorems for multidimensional inverse problems. He later developed globally convergent numerical methods —including the convexification method —which solve inverse problems without prior knowledge of solution neighborhoods , unlike conventional approaches. His 2014-2016 work on phaseless inverse scattering resolved a 40-year-old open problem from Chadan and Sabatier's quantum scattering theory textbook, with applications to live cell imaging and nanotechnology . His publications demonstrate remarkable disciplinary reach, spanning mathematical finance (Black-Scholes equation), control theory (1991 stability analysis), and computational methods verified on experimental data. With over 8,800 Google Scholar citations and $3.3 million in federal grants , he ranks among the most highly funded mathematicians in the US. Scientific Recognition: Laudation in Journal of Inverse and Ill-Posed Problems (2022) celebrating his 70th birthday Malta Conference Medal (May 2022) for distinguished contributions Klein College of Science Excellence in Research Award (May 2025) for ongoing impact He serves on editorial boards of Communications in Analysis and Computations , Numerical Methods and Programming , and Journal of Applied and Industrial Mathematics .

Amanda Starcمشاهده پروفایل

دانشیار

• Health Economics
• Industrial Organization
• Insurance Pricing
• +۷ مورد دیگر

Amanda Starc is an Associate Professor of Strategy at Kellogg School of Management , Northwestern University , and a Faculty Research Fellow at the National Bureau of Economic Research (NBER) . Her research sits at the intersection of industrial organization and health economics , focusing on health insurance pricing, consumer demand dynamics, and market mechanisms in healthcare. Education: BA in Economics (Case Western Reserve University), PhD in Business Economics (Harvard University) Dr. Starc's research explores: Health insurance coverage and pricing models Information asymmetry in insurance markets Effectiveness of competitive bidding in Medicare Impact of provider network restrictions on costs Regulatory effects on generic drug competition Her recent publications analyze anesthesia rollups, Medicare Part B reimbursement, and multidimensional screening in insurance menus. She has received grants for research on Medicare Advantage and provider network mortality effects. Scientific award: Faculty Research Fellow, NBER

• Information Retrieval
• XML Search Engines
• Text Search Engines
• +۸ مورد دیگر

Shlomo Geva is an Adjunct Professor in the School of Computer Science at Queensland University of Technology's Faculty of Science. His research focuses on information retrieval systems, particularly in specialized areas including XML search engines, text search engines, link discovery, and document computing. His academic work spans multiple disciplines within computer science, with particular emphasis on information retrieval technologies and their applications. Professor Geva's research interests include clustering algorithms, cross language information retrieval, focused information retrieval, information retrieval systems, link discovery mechanisms, search engine technologies, text indexing and retrieval methods, and XML indexing and retrieval techniques. His work demonstrates a consistent focus on improving the efficiency and effectiveness of information access systems across various data formats and domains. His recent publications reveal a trend toward applications of information retrieval techniques in diverse fields including remote sensing, bioinformatics, and data stream processing. The research shows an evolution from traditional information retrieval problems toward more specialized applications requiring advanced clustering algorithms and efficient data processing techniques for large-scale datasets. Professor Geva has successfully supervised numerous doctoral students whose research topics include indoor environment mapping by robots, cross-language information retrieval, natural language query interfaces for XML, evolvable hardware, and autonomous robot behavior systems.

Clare Coveneyمشاهده پروفایل

پژوهشگر

• Proteomics
• Biomarker Discovery
• Mass Spectrometry
• +۲ مورد دیگر

Dr Clare Coveney is a Research Associate in the Biological Mass Spectrometry & Clinical Proteomics group at the John van Geest Cancer Research Centre, part of Nottingham Trent University's School of Science & Technology. She holds a BSc in Pharmacology and Neuroscience (2007) and a part-time PhD focused on proteomic/transcriptomic biomarker discovery in ovarian cancer from the same institution. Her career includes roles as a Research Assistant and subsequent transition to the John van Geest Centre in 2010. Her research centers on proteomics and mass spectrometry , specializing in DIA/SWATH-based quantitative profiling for biomarker discovery. She maintains the centre's mass spectrometry facility (equipped with SCIEX TripleTOF 5600+/6600 systems) and develops workflows for multidimensional proteomic data analysis. Her work spans diverse diseases, including prostate/breast cancer, melanoma, leukemia, lymphangioleiomyomatosis (LAM), Alzheimer's, COPD, and diabetes. Publications emphasize cancer biomarkers, molecular mechanisms of disease, and computational proteomics , with applications in immunotherapy, metastasis, and metabolic pathways. Research integrates omics technologies (proteomics, transcriptomics) and machine learning for clinical insights.

Marina Chugunovaمشاهده پروفایل

استاد

• Surfactant-driven thin film flows
• Nonlinear parabolic equations
• Stability problems in fluid dynamics
• +۵ مورد دیگر

Marina Chugunova is the Ellis Cumberbatch Professor of Mathematics at Claremont Graduate University's Institute of Mathematical Sciences, where she has been since 2012. She also serves as Program Director of the PhD in Engineering and Computational Mathematics. Her research focuses on surfactant-driven thin film flows, nonlinear parabolic equations, stability in fluid dynamics, scientific computations, applied operator theory, and Sturm-Liouville problems with spectral parameter boundary conditions. She holds a PhD from McMaster University and additional degrees from Russian institutions, including Moscow Institute of Physics and Technology. Her work bridges mathematics and applications in biomedical engineering, microfluidics, and environmental systems. Notable projects include modeling alveolar surfactant dynamics and liquid film flows on fibers. She has received awards such as the Simons Collaboration Grant and Atkinson Teaching Award. Chugunova's computational expertise includes C/C++, MATLAB, and MPI/OpenMP, reflecting her interdisciplinary approach. Publications from 2021–2025 highlight advancements in radiotherapy modeling, equidistant grid algorithms, and ion behavior in biomaterials. Her collaborative work addresses industrial coating, biomedical systems, and data clustering challenges. She advises the PhD program in Engineering and Computational Mathematics and contributes to conferences like the Mathematical Problems in Industry Workshop. Education: PhD (McMaster University), Candidate of Math.Sc. (Ulyanovsk University) Affiliations: Institute of Mathematical Sciences, Claremont Graduate University Grants: Simons Foundation, MACSI Research

• Energy Poverty
• Residential Energy Demand
• Energy Efficiency
• +۷ مورد دیگر

Dr. Roberto Barrella is a Research Fellow at the Comillas Pontifical University’s Higher Technical School of Engineering (IIT-ICAI) , where he serves as Coordinator of the Chair of Energy and Poverty . His work bridges energy systems modeling with social sustainability, focusing on energy poverty metrics, climate change adaptation, and just energy transitions. Education : PhD in Energy Poverty (2022, Comillas Pontifical University); Master’s (2018) and Bachelor’s (2015) in Energy Engineering (Università di Roma La Sapienza) Research Expertise : Bottom-up energy demand modeling, techno-economic feasibility analysis, sustainable building design, and XAI applications in energy poverty studies Barrella’s research explores residential energy demand under crisis conditions, energy efficiency strategies, and policy frameworks for energy poverty eradication . His recent work analyzes the impact of shallow renovations on vulnerable households and develops hidden energy poverty indicators. He has contributed to 15+ peer-reviewed publications in journals like Sustainability , Energy Research & Social Science , and Energies , with emphasis on Southern Europe. Selected as an Outstanding Reviewer (2022) and recipient of COST Action STSM funding (2022). Scientific Leadership : Member of EU Energy Poverty Observatory, Vocal de la Junta Directiva MAUE, and contributor to EPAH indicator frameworks Outreach : Featured in media outlets like RTVE and El País, discussing energy poverty trends affecting 10+ million Spanish households

• Youth Athletic Development
• Talent Identification
• Strength and Conditioning
• +۷ مورد دیگر

Kevin Till is a Professor of Athletic Development at the Carnegie School of Sport, Leeds Beckett University . He holds leadership roles including co-director of the Centre for Child and Adolescent Physical Literacy and programme lead for the Doctor of Professional Practice in Sport (DProf) . His career at Leeds Beckett spans roles as Lecturer, Senior Lecturer, Reader, and Professor since 2012. Education : BSc, MSc, PhD from Leeds Beckett University His research focuses on youth athletic development , bridging health and performance through: Talent identification and development Strength and conditioning for youth populations Concussion and injury prevention in rugby Physical literacy and movement competence Coach education and applied practice Policy-driven knowledge exchange Recent publications emphasize head acceleration monitoring , tackle technique optimization , and school-based physical education interventions . Collaborations include the Rugby Football League, World Rugby, and English Institute of Sport. His applied PhD supervision model integrates research with practice in professional sport organizations.

Jorge Riveroمشاهده پروفایل

استادیار

• Applied Econometrics
• Causal Inference
• Panel Data Analysis
• +۹ مورد دیگر

Jorge Rivero is an Assistant Professor in the Department of Economics at Rensselaer Polytechnic Institute (RPI), affiliated with the School of Architecture, Geosciences, and Engineering (SAGE). His research lies at the intersection of econometrics, statistics, and machine learning, focusing on causal inference under unobserved heterogeneity and multidimensional inequality measurement. His research interests include: Applied Econometrics and Causal Inference Panel and Longitudinal Data Analysis Optimal Transport Theory for Economic Distributions Unsupervised and Reinforcement Learning in Economics Multidimensional Inequality Measurement His recent publications demonstrate a strong mathematical foundation, applying greedy approximation techniques to econometric and signal processing problems. These works explore waveform dictionaries for function approximation, with potential applications in economic modeling and data analysis. He teaches both undergraduate and graduate courses, including ECON 2010 Intermediate Microeconomic Theory and ECON 6730 Econometrics 1 for PhD students. His educational background includes a Ph.D. in Economics from the University of Washington, Seattle, an M.S. in Mathematical Sciences, and a B.A. in Economics from Florida International University. There are no scientific awards listed in the provided text. Dr. Rivero advises no students mentioned in the text and there is no information about grants, research labs, or collaborative teams. He can be contacted via email at riverj11@rpi.edu and maintains a professional website at https://www.jarivero.com/.

Vladimir Makarenkovمشاهده پروفایل

استاد

• Bioinformatics
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Roles and Affiliations: Full Professor of Computer Science at Université du Québec à Montréal (UQAM), Director of the DESS in Bioinformatics program. Regular member of LaCIM and affiliate member of Mila (Quebec AI Institute). Served as Assistant Professor (2001–2006) and Associate Professor (2006–2011) before becoming Full Professor in 2011. Education: B.Sc./M.Sc. in Applied Mathematics and Computer Science, Moscow State University (1987–1992) DEA in Computer Science, University Paris V (1993–1994) Ph.D. in Computer Science and Mathematics, École des Hautes Études en Sciences Sociales (EHESS), Paris (1994–1997) Research Interests: Focuses on machine learning applications in bioinformatics, including clustering algorithms, phylogenetic analysis, and genomics. Specializes in CRISPR/Cas9 off-target prediction, SARS-CoV-2 origin analysis, and high-throughput screening (HTS) data correction. Develops tools like T-REX web server for phylogenetic analysis and DUNEScan for skin cancer uncertainty estimation. Integrates deep learning and reinforcement learning for clustering and medical diagnostics. Publications and Grants: Over 100 peer-reviewed articles spanning bioinformatics, AI, and computational biology. Key grants include projects on HTS data correction, phylogenetic networks, and machine learning for biomedical applications. Collaborates with institutions like Mila and Université de Montréal. Labs and Teams: Leads research teams at UQAM’s Department of Computer Science, focusing on interdisciplinary projects combining AI and life sciences. Maintains collaborations with international groups in computational biology and data science.

Mathews Jacobمشاهده پروفایل

استاد

• Medical Imaging
• Machine Learning
• Image Reconstruction
• +۵ مورد دیگر

Mathews Jacob is a Professor of Electrical and Computer Engineering at the University of Virginia’s School of Engineering and Applied Science, leading the Computational Biomedical Imaging Group (CBIG). His research focuses on image reconstruction, analysis, and quantification in MRI, particularly in ultrahigh-resolution brain MRI, cardiac/pulmonary MRI, and metabolic imaging. He holds a B.Tech from National Institute of Technology, M.E. from Indian Institute of Science, and Ph.D. from Swiss Federal Institute of Technology. Research Contributions: Dr. Jacob pioneered model-based deep learning frameworks like MoDL and MOL, advancing accelerated MRI techniques. His work bridges machine learning and medical imaging, with applications in dynamic speech MRI, motion-compensated cardiac imaging, and compressed sensing. Professional Roles: He serves as Associate Editor for IEEE Transactions on Medical Imaging and was General Chair of IEEE International Symposium on Biomedical Imaging (2020). A Fellow of IEEE (2022), he has received NSF CAREER (2009), ACS Research Scholar (2011), and Eminent Researcher (2024) awards. Grants & Projects: His NIH-funded projects include ultra-high-resolution multi-contrast MRI, free-breathing dynamic MRI, and MR spectroscopic imaging for Alzheimer’s research. Current grants emphasize computational MRI innovations. Labs & Impact: The CBIG lab develops cutting-edge imaging algorithms, collaborating on clinical applications like pulmonary hypertension detection via cardiac MRI radiomics. His work impacts radiology, oncology, and neurology.

Mariusz Topolskiمشاهده پروفایل

استاد

• machine learning
• pattern classification
• feature extraction and selection
• +۱۲ مورد دیگر

Mariusz Topolski is a Professor at the Department of Computer Systems and Networks, Faculty of Computer Science and Telecommunications, Wrocław University of Science and Technology. He received his habilitated doctor degree in Computer Science in 2024 and has been actively contributing to research and teaching in machine learning, data analysis, and optimization. He is a member of multiple research teams including the Machine Learning Team (ZUM), Advanced Data Analysis Methods Team, and Metaheuristics Team. Education: Habilitated Doctor in Computer Science, Wrocław University of Science and Technology, 2024 PhD in Technical Sciences (Computer Science), Wrocław University of Science and Technology, 2008 Master's and Engineering Degree in Technical Physics (Biomedical Engineering), Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, 2003 His research interests include machine learning, pattern classification, feature extraction, explainable AI, multi-objective optimization, and data mining. His work applies advanced computational techniques to real-world problems in medicine, construction, pharmaceuticals, and logistics. He has developed novel methods in PCA modification, feature selection, and signal analysis, particularly for diagnostic applications in neurology and oncology. The recent publications reflect a strong trend in applying machine learning and optimization to medical diagnostics and industrial risk modeling. Key areas include PCA-based classification, multi-objective feature selection, and hybrid signal processing techniques. His interdisciplinary work bridges computer science with healthcare and civil engineering, demonstrating the versatility of AI in solving complex, multi-domain challenges. Scientific Awards: Degree of Habilitated Doctor in Computer Science (awarded February 28, 2024) Mariusz Topolski has led and participated in numerous national and international research projects, including collaborations with Jagiellonian University Collegium Medicum, Medical University of Warsaw, Gdańsk Medical University, and foreign institutions such as the National Mining University of Ukraine and Fundación ICIL in Spain. He supervises diploma theses and contributes to academic training through workshops and lectures. He is also engaged in industrial applications, developing optimization and machine learning methods for supply chain logistics. Research Teams: Machine Learning Team (ZUM) Advanced Data Analysis Methods Team Metaheuristics Team Computer Networks Team Teaching Team

• Customer Experience
• Brand Attachment and Loyalty
• Consumer Behavior
• +۱۲ مورد دیگر

Douglas B. Grisaffe is a Professor in the Marketing Department at the College of Business, University of Texas at Arlington (UTA), where he has been a faculty member since September 2022. He holds a PhD in Social Psychology with a concentration in Consumer Research from Vanderbilt University and has built a distinguished academic career centered on consumer behavior and marketing analytics. PhD, Social Psychology (Consumer Research), Vanderbilt University, 1989 MS, Social Psychology, Vanderbilt University, 1987 BS, Psychology (Science Minor), Bowling Green State University, 1984 His research focuses on customer experience outcomes, emotional attachment to brands and social media, frontline employee influences, and methodological developments in psychometric measurement and structural equation modeling. He explores how managerial practices affect sales teams and customer loyalty, and he contributes to theoretical advancements in servant leadership and customer value frameworks. The most recent publications reflect a strong interdisciplinary trend, combining marketing, psychology, and organizational behavior. Key themes include brand attachment, prosocial behavior in marketing contexts, servant leadership, and advanced modeling techniques for customer metrics. His work frequently appears in top-tier journals such as the Journal of Business Research , Journal of the Academy of Marketing Science , and Industrial Marketing Management . Dr. Grisaffe has received notable scientific awards, including: Recognition for Positive Impact on Students (UTA, 2024 and 2023) University of Texas System Regents’ Outstanding Teaching Award (2011) College of Business Outstanding Graduate Teaching Award (2013) Outstanding Undergraduate Teaching Award (2009) He has advised numerous graduate students across marketing, information systems, and management disciplines, serving on multiple dissertation committees. His service includes leadership roles such as Chairperson of the MSMR Graduate Studies Committee and active participation in the Faculty Senate, accreditation efforts, and faculty review processes. He also contributes as a reviewer and editorial board member for journals including Frontiers in Psychology and Journal of Consumer Satisfaction/Dissatisfaction and Complaining Behavior . Dr. Grisaffe leads funded research projects, such as the 'Business Deans Restricted Research Grant Award' (2020), and teaches advanced courses in marketing research, multivariate statistics, and business analytics, demonstrating a deep commitment to both research and pedagogy.

• Databases
• Big Data
• Data Storage
• +۱۲ مورد دیگر

Alberto Abello Gamazo is a full professor at the Facultat d'Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), where he is affiliated with the Department of Services and Information Systems Engineering. He coordinates the Erasmus Mundus Doctoral Program in Information Technologies for Business Intelligence and leads research within the inSSIDE and inLab FIB groups. His work bridges computer science and healthcare, focusing on data-intensive systems and their real-world applications. Research Interests: His expertise spans databases, big data, NoSQL, OLAP, data storage, and data science. He investigates automated data pipelines, federated data management, feature selection, and AI-driven analytics. His work increasingly integrates healthcare applications, particularly in automated diagnosis using AI and digital microscopy for diseases such as malaria and schistosomiasis. Publication Trends: His recent articles (2023–2025) emphasize automated data science pipelines (e.g., Hyppo), fairness and interpretability in machine learning, efficient data management in document stores, and the application of AI to medical diagnostics. A strong trend is the development of low-cost, open-source robotic microscopy systems for global health challenges. Scientific Awards: Premiada (recognized activity) XXV Congreso Nacional de la Sociedad Española de Enfermedades Infecciosas y Microbiologia Clínica Advising and Grants: He has advised numerous PhD students and leads multiple competitive R&D+i projects funded by national and European programs (e.g., HORIZON 2020, Plan Nacional). His projects include 'Graph-driven federated data management,' 'EXPeriment driven and user eXPerience oriented analytics,' and health-focused initiatives like 'For an improvement of Health through rapid and economic diagnosis (iH-red).' He collaborates extensively with institutions like the Hospital de la Vall d'Hebron and Fundació PROBITAS. Labs and Teams: He is a core member of the inSSIDE (integrated Software, Services, Information and Data Engineering) and inLab FIB research groups, both affiliated with CIT-UPC. These groups focus on cutting-edge data engineering, information systems, and their societal applications.