جست‌وجوی استاد و پژوهشگر

Daniel L. Brintonمشاهده پروفایل

دانشیار

• Health services research
• Missing data methods in observational studies
• Electronic health records analytics
• +۵ مورد دیگر

Daniel L. Brinton, PhD, MHA, MAR, is Associate Professor of Healthcare Leadership and Management at the Medical University of South Carolina, College of Health Professions. He specializes in advanced quantitative methods for health-services and observational research, leveraging large-scale electronic health records and administrative data to improve patient outcomes and inform policy. Education: Doctor of Philosophy (PhD) Master of Health Administration (MHA) Master of Arts in Religion (MAR) Research Interests: Dr. Brinton’s work centers on missing-data methodologies that enable robust inference from real-world clinical datasets. He applies these techniques to pediatric health-services research , examining access, quality, and cost of care for asthma, Kawasaki disease, firearm injury, and autism spectrum disorder. A second major strand explores critical-care outcomes , using machine-learning and natural-language-processing to phenotype acute respiratory distress syndrome (ARDS) and optimize ventilator management. He also investigates orthopaedic surgery quality , opioid-prescribing patterns, and efficiency of global clinical trials. Recent Research Trends: Across 34 publications from 2018-2025, Brinton has increasingly integrated machine learning and big-data analytics to address clinical questions that were previously infeasible at scale. Studies range from predictive modeling of ARDS and neonatal acute kidney injury to economic evaluations of extended VTE prophylaxis and pediatric asthma adherence interventions. Scientific Awards: No awards explicitly listed in the provided materials. Advising & Grants: While individual student names are not disclosed, Dr. Brinton mentors graduate students and junior investigators in the Department of Healthcare Leadership and Management. His research has been supported by federal and foundation grants focusing on comparative effectiveness, health economics, and informatics, although specific grant identifiers are not provided. Labs & Teams: Dr. Brinton collaborates with multidisciplinary teams at MUSC, including the College of Health Professions’ Healthcare Leadership and Management research group, critical-care investigators, pediatric specialists, and external partners utilizing national EHR networks such as the Pediatric Health Information System (PHIS).

Gavin Taylorمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۵ مورد دیگر

Gavin Taylor is a Professor of Computer Science at the United States Naval Academy (USNA), where he chairs the development of the new Data Science major. His research focuses on Machine Learning, including neural network vulnerabilities, reinforcement learning for IoT devices, and adversarial machine learning. He is currently on sabbatical as a Visiting Professor at the Norwegian OpenAI Lab (NTNU), collaborating with institutions like the University of Maryland and NTNU. Professor Taylor has been recognized with the 2022 Class of 1951 Civilian Faculty Teaching Excellence Award, emphasizing his commitment to education. He teaches courses such as SD312: Machine Learning and has developed curricula across Data Science, Cyber Operations, and Operations Research. His sabbatical work in Norway involves advancing AI research through interdisciplinary collaborations. His research trends emphasize practical AI applications—such as autonomous IoT systems—and ethical concerns like adversarial attacks and data poisoning. He actively seeks partnerships to engage students in real-world data science projects through the new major, scheduled to graduate its first class in 2025.

Egidio Faloticoمشاهده پروفایل

استادیار

• Robotics
• Biorobotics
• Computational Neuroscience
• +۴ مورد دیگر

Egidio Falotico is a Tenure Track Assistant Professor (RTD-B) at the BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy. He leads the BRAin Inspired Robotics (BRAIR) Lab and holds roles such as Principal Investigator (PI) for the PROBOSCIS project and co-leader of Sub-Project 10 (Neurorobotics) in the Human Brain Project (HBP). His research focuses on brain-inspired motor control, soft robotics, and neurorobotics, integrating computational neuroscience with robotic systems. Education: Dual PhD in Biorobotics (Scuola Superiore Sant’Anna) and Cognitive Science (University Pierre et Marie Curie). Master’s in Computer Science (University of Pisa). Research interests include control mechanisms for soft robots using AI, bio-inspired motor control, and neuroscientific principles applied to robotics. He has contributed to projects like HBP, where he developed the Neurorobotics Platform, and PROBOSCIS, aiming to create elephant-trunk-inspired robots. Teaching includes leading PhD courses on Brain-Inspired Motor Control and Robotics at the University of Pisa. He has supervised EU-funded students and managed interdisciplinary teams in neurorobotics and soft robotics. Key projects involve HBP’s Neurorobotics Platform, soft robot control (e.g., PROBOSCIS), and collaborations in sensorized systems and adaptive learning algorithms. His work bridges neuroscience, AI, and robotics to advance soft robotic systems and bio-inspired models.

Ilker Birbilمشاهده پروفایل

استاد

• Optimization Methods
• Data Science
• Decision Making
• +۳ مورد دیگر

Prof. Dr. Ilker Birbil is a Professor of AI & Optimization Techniques for Business & Society at the University of Amsterdam (UvA), affiliated with the Amsterdam Business School's Business Analytics section. He previously held professorships at Erasmus University and Sabancı University, focusing on optimization and data science. His research interests include interpretable machine learning, data privacy, and optimization methods in decision-making. Education: PhD in Operations Research from North Carolina State University Postdoc at Erasmus Research Institute of Management (ERIM), Netherlands Research Interests: Optimization in data science, interpretable AI, privacy-preserving algorithms, and decision-making systems. Recent work focuses on differentially private optimization and meta-learning techniques like LESS. Key Achievements: Recipient of multiple teaching awards at Sabancı University Affiliated researcher at OPTIMAL (Optimization for and with Machine Learning) Organized workshops on ML for optimization and mathematics of ML Grants & Teams: Leads the UvA group on Optimization and Machine Learning at LNMB. Collaborates on projects blending OR and ML, including privacy-aware algorithms and revenue management systems. Labs/Teams: OPTIMAL, UvA Optimization Group, and interdisciplinary teams in data analytics.

Yangming Liمشاهده پروفایل

پژوهشگر ارشد

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +۷ مورد دیگر

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

Felix Jesus Garcia Clementeمشاهده پروفایل

استاد

• Cybersecurity
• Artificial Intelligence
• Industry 4.0
• +۳ مورد دیگر

Félix Jesús García Clemente is a Full Professor in the Department of Computer Architecture and Technology at the University of Murcia's Faculty of Computer Science. He is a member of the Mobile Computing and Computer Vision Research Group and serves as Project Manager at CyberDataLab and Director of the University Chair in Smart Industry. His work spans cybersecurity, AI, Industry 4.0, and educational technology, with over 100 publications and active involvement in national and international R&D projects. Education: PhD in Computer Science from the University of Murcia (2006), supervised by Dr. Gregorio Martínez Pérez and Dr. Antonio Skarmeta Gómez. Research Interests: Cybersecurity (including anomaly detection and threat mitigation), AI applications in education and industry, Industry 4.0 technologies, and game-based assessment systems. His work integrates machine learning, IoT, and fog computing for real-world applications such as smart farming and industrial automation. Recent Research Trends: A focus on cybersecurity in edge/fog environments, AI-driven educational tools, and interdisciplinary projects like MeloDI for crop quality assessment. Collaborations include EPFL (Switzerland), NTU (Singapore), and Stanford University. Awards: None explicitly listed, though his extensive R&D leadership and numerous funded projects reflect significant recognition. Grants & Projects: ROBUST-6G (EU Horizon): Secure 6G platform development EU-GUARDIAN (EU Defense): Cyber defense automation REACTMAN5.0 (Spain): Cybersecurity in Industry 5.0 CDL-TALENTUM (INCIBE): Cybersecurity professional development Labs & Teams: Leads the Mobile Computing and Computer Vision Research Group, collaborates with Bleecker and Scorpion Cybertechnologies, and oversees the University Chair in Smart Industry.

André Bauerمشاهده پروفایل

استادیار

• Performance Engineering
• Data Science
• Systems Engineering
• +۷ مورد دیگر

Dr. André Bauer is an Assistant Professor of Computer Science at Illinois Institute of Technology. He holds a Dr. rer. nat., M.S., and B.S. in Computer Science from the University of Würzburg. His research focuses on performance engineering and systems optimization for data science applications, addressing challenges in resource management, adaptive systems, and sustainable computing. Bauer leads the SPEC RG Predictive Data Analytics Working Group and has contributed to benchmarks like Libra for time series forecasting. His work spans cloud computing, container orchestration, and security protocols such as homomorphic encryption. Education: Dr. rer. nat., Computer Science, University of Würzburg M.S., Computer Science, University of Würzburg B.S., Computer Science, University of Würzburg Research Interests: Bauer’s research integrates interdisciplinary expertise to optimize scientific computing infrastructures. Key areas include resource allocation in heterogeneous environments, adaptive distributed systems, and privacy-preserving technologies. Recent contributions include studies on serverless cold starts, microservice benchmarking, and federated learning frameworks. Articles Trends: Bauer’s publications emphasize performance analysis in cloud-native systems, security for IoT/CPS, and machine learning interpretability. Recent work explores hybrid compression methods for time series and the environmental impact of HPC workloads. Awards: 2023 SPECtacular Award (SPEC) 2023 Leadership Academy Fellowship (German Scholars Organization) 2023 SPEC Kaivalya Dixit Distinguished Dissertation Award Advising & Leadership: Bauer chairs the SPEC RG Predictive Data Analytics group and co-authored influential tools like Libra. His research is supported by grants in cloud benchmarking and secure group communication. Labs/Teams: Active in Illinois Tech’s Computer Science department and collaborates with institutions like Argonne National Laboratory on distributed computing initiatives.

Jing Zhouمشاهده پروفایل

مدرس

• Statistics
• High-Dimensional Data Analysis
• Machine Learning
• +۴ مورد دیگر

Jing Zhou is a Lecturer in Statistics at the University of East Anglia, affiliated with the School of Engineering, Mathematics and Physics. Their research focuses on high-dimensional statistical methodologies, including variable selection, false discovery rate control, and robust estimation techniques. Dr. Zhou actively contributes to peer review for journals like Statistics and Computing and Statistics , and has presented at conferences such as the 2024 IMS International Conference on Statistics and Data Science. Research interests emphasize advancing statistical methods for social science and big data applications, particularly leveraging model-X knockoffs and black-box model assessments. Recent work explores trade-offs in false discovery vs. true positive rates in logistic regression and nonparametric quantile regression via vine copulas. Collaborative efforts include interdisciplinary projects addressing replication crises through methodological improvements. No scientific awards are explicitly listed. Advising activity and grants information is not provided in the text. Zhou is engaged in academic activities including invited talks and editorial work, contributing to both theoretical and applied statistical research.

Zhiyao Duanمشاهده پروفایل

استاد

• Computer Audition
• Audio-Visual Scene Understanding
• Machine Learning
• +۳ مورد دیگر

Zhiyao Duan is a Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Rochester, with affiliations to the Goergen Institute for Data Science. He holds a B.S. and M.S. from Tsinghua University and a Ph.D. from Northwestern University. Primary: Department of Electrical and Computer Engineering Secondary: Department of Computer Science Affiliated: Goergen Institute for Data Science His research focuses on Computer Audition, exploring audio-visual scene understanding, source separation, and human-computer collaborative music systems. He has received prestigious awards including an NSF CAREER Award and best paper honors at SMC 2017. His work is funded by NSF, NIH, Adobe, Kwai, and others. Recent publications emphasize audio-visual integration, speech synthesis, and deep learning architectures. He actively participates in academic service, serving on editorial boards and technical committees. The Audio Information Research (AIR) lab, which he leads, has hosted numerous students and collaborated with institutions like Northwestern University, Tsinghua University, and Stanford University. Scientific Awards: NSF CAREER Award Best Paper Award at SMC 2017 Best Paper Nomination at ISMIR 2017 Grants: NSF BIGDATA Grant No. 1741472 NSF Grant No. 1846174 for Human-Computer Collaborative Music Making University of Rochester AR/VR and Health Analytics Grants Students: Yapeng Tian Lele Chen Sefik Emre Eskimez Bochen Li Rui Lu Yujia Yan

• Time Series Analysis
• Blockchain Technologies
• Business Process Management
• +۸ مورد دیگر

Roman Vaculín is a Researcher at IBM Research, focusing on interdisciplinary domains where artificial intelligence, blockchain technologies, and data-centric workflows converge. His work spans automated machine learning, time series analysis, and secure computation via cryptographic methods like homomorphic encryption. Affiliation: IBM Research Key research areas: Time Series Analysis, Blockchain, AI Explainability, Business Process Management Across his publications, Vaculín explores: Time Series Modeling: Developing robust frameworks like TsSHAP and end-to-end architectures for forecasting and imputation. Blockchain Applications: Designing trusted AI systems, secure multi-party computation, and verifiable simulations. Automated Machine Learning: Creating toolkits for industrial AI explainability and automation. Privacy-preserving Techniques: Optimizing encrypted inference and secure decision tree protocols. His methodology often integrates formal verification with practical implementations, emphasizing efficiency and interpretability in complex systems. While no formal awards or students are documented in the provided data, his collaborative publications with institutions like IBM Research and academic partners highlight his role in advancing applied AI research.

Ioana Popescuمشاهده پروفایل

استاد پژوهشی

• Hydrology
• Flood Modelling
• River Systems Modelling
• +۷ مورد دیگر

Ioana Popescu is a prominent hydroinformatics researcher affiliated with the IHE Delft Institute for Water Education , where she has worked since 2001. Previously, she served as an Associate Professor at the Faculty of Hydrotechnics, Timisoara, Romania (1990-1999), and a postdoc researcher at the National Research Council of Canada (2000).

Paulius Raubaمشاهده پروفایل

مدرس

• Machine Learning
• Data Science
• Artificial Intelligence
• +۳ مورد دیگر

Paulius Rauba is a Visiting Lecturer at ISM University of Management and Economics and a PhD candidate in Machine Learning at the University of Cambridge. He holds a BSc from ISM and an MSc from the University of Oxford, where he is also an Oxford Shirley Scholar. His research focuses on fundamental machine learning, model evaluation, robustness, and quantitative social science. He teaches courses in Econometrics, Data Science, and Elements of Artificial Intelligence. Paulius leads Gauss, a company specializing in deploying machine learning models, and has advised the European Commission on AI systems. His work spans academia, industry, and international organizations, emphasizing data-driven solutions for business and social challenges. Education: BSc, ISM University of Management and Economics MSc, University of Oxford His research explores model robustness, AI applications in healthcare, and conflict analysis through statistical and neural methods. Recent articles address topics like self-healing ML frameworks and context-aware model testing. Paulius is committed to advancing AI ethics and practical deployment. Awards: Oxford Shirley Scholar As a leader in Gauss, he bridges academic innovation with industry needs, focusing on scalable AI solutions. His advisory roles and grants underscore his interdisciplinary impact.

Eduardo Blancoمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۷ مورد دیگر

Eduardo Blanco is an Associate Professor in the Department of Computer Science at the University of Arizona. His research focuses on advancing Natural Language Processing (NLP) with an emphasis on understanding negation, hate speech dynamics, and computational linguistics applications. He explores how language models can better interpret ambiguous statements, predict conversation incivility, and counteract harmful online content through structured responses. Blanco’s work spans theoretical and applied NLP domains, including semantic role labeling, sentence embedding evaluation, and biomedical text analysis using protein language models. His research often integrates large language models with task-specific frameworks to address challenges in zero-shot learning and negation robustness. Key areas of investigation include: Negation processing and affirmative interpretation Social media discourse dynamics and hate speech detection NLP for biomedical applications (glycosylation site prediction) Semantic similarity alignment in sentence embeddings His recent articles highlight advancements in counterhate speech strategies, temporal QA systems, and LLM-based peer review tools. Notably, he secured the NSF CAREER Award for studying negation through positive interpretations (2022). Collaborative efforts focus on interdisciplinary teams addressing challenges in computational linguistics, with grants supporting projects like the ALIGN-SIM benchmarking framework and BEMEAE event argument extraction system.

John K. Tsotsosمشاهده پروفایل

استاد پژوهشی

• Computer Vision
• Visual Attention
• Active Visual Search
• +۵ مورد دیگر

John K. Tsotsos is a Distinguished Research Professor at York University's Lassonde School of Engineering, holding positions in both the Department of Electrical Engineering & Computer Science and the Centre for Vision Research (CVR). His work bridges computer science, cognitive science, and neuroscience with a focus on visual attention and active vision systems. Dr. Tsotsos's research interests center on visual attention mechanisms, active visual search, and visuospatial reasoning. His work explores how humans and machines process visual information, with applications in autonomous driving, robotics, and human-computer interaction. He investigates the computational principles underlying visual attention, comparing biological systems with artificial implementations. His research has significant implications for developing more human-like computer vision systems that can effectively navigate and interpret complex visual environments. Analysis of his recent publications reveals a strong focus on active vision systems where observers dynamically control their viewpoints during visual search tasks. His work spans both theoretical foundations of visual attention and practical applications in autonomous vehicles. A significant portion of his recent research addresses driver attention modeling, gaze prediction, and the challenges of real-world visual processing where traditional computer vision approaches often fail. His work consistently bridges cognitive theory with practical engineering applications. Dr. Tsotsos has made substantial contributions to the field of computational vision through his theoretical work on attentional mechanisms and their implementation in artificial systems. His research has influenced both academic understanding of visual processing and practical applications in autonomous systems and human-machine interfaces. As a faculty member at York University, Dr. Tsotsos contributes to the vibrant research ecosystem of the Centre for Vision Research, where interdisciplinary teams work on cutting-edge problems in visual perception, cognitive modeling, and machine vision. His work exemplifies the integration of cognitive science principles with advanced computational techniques to solve complex visual processing challenges.

• Machine Learning
• Spatial Data Mining
• AI in Mental Health
• +۴ مورد دیگر

Dr. Saida Elmi is an Assistant Professor Adjunct at Yale University School of Medicine and an Assistant Professor at the University of New Haven. She holds a Ph.D. in Computer Science from the National School of Mechanics and Aerotechnics (2017), followed by postdoctoral research at Korea University of Technology and Education (2017–2018) and National University of Singapore (2018–2021). Her research focuses on AI applications in healthcare, transportation systems, and spatial data mining. She developed the Automated Test of Embodied Cognition (ATEC), an AI-driven system using motion capture and machine learning for mental disease detection. Key contributions include predicting taxi fares, travel times, and energy consumption in transportation networks using deep learning architectures like RNNs and CNNs, and designing group-oriented recommendation systems for points-of-interest (POIs) using graph convolution networks and attention mechanisms. Education : Ph.D., Computer Science, National School of Mechanics and Aerotechnics (2017) Postdoctoral Research, Korea University of Technology and Education (2017–2018) Postdoctoral Research, National University of Singapore (2018–2021) Her research interests span machine learning, spatial data mining, and AI ethics. She has pioneered frameworks for spatio-temporal data analysis, including a transfer learning approach for travel time prediction in data-scarce regions. Her work on energy consumption modeling and POI prediction has been published in top-tier conferences like WWW, MOBIQUITOUS, and ECIR. Dr. Elmi’s interdisciplinary projects integrate AI with healthcare and urban systems, emphasizing automated cognitive assessment and smart city technologies. Her current efforts focus on refining action recognition algorithms for clinical applications and optimizing recommendation systems for group mobility decisions.