جست‌وجوی استاد و پژوهشگر

• Computer Science
• Formal Methods
• Logic Programming
• +۵ مورد دیگر

Elvira Patricia Pino Blanco is a Professor in the Department of Computer Science at the Barcelona East School of Engineering, Polytechnic University of Catalonia (UPC). She is a member of the ALBCOM research group (Algorithms, Bioinformatics, Complexity and Formal Methods) and has an extensive publication record spanning over two decades. Her research interests focus on formal methods in computer science, particularly in graph theory, logic programming, and database systems. She has made significant contributions to navigational logics for graphical structures, graph databases, and model synchronization using triple graph grammars. Her work bridges theoretical foundations with practical applications in software engineering and data management. Her publication trends show a consistent focus on graph-based approaches to computational problems, with recent work emphasizing logical approaches to graph databases and navigational query languages. She has published in high-impact venues including Journal of Logical and Algebraic Methods in Programming, Theoretical Computer Science, and various international conferences. Professor Pino Blanco has been involved in numerous competitive R&D projects related to algorithmics, bioinformatics, and formal methods. Her work demonstrates interdisciplinary connections between theoretical computer science and practical applications in data management and software engineering. She is affiliated with the ALBCOM research group which focuses on algorithmics, bioinformatics, complexity theory, and formal methods. This group represents a vibrant research community working at the intersection of theoretical computer science and practical applications.

• Forensic Chemistry
• Analytical Chemistry
• Nanotechnology
• +۲ مورد دیگر

Christian Brinch Mollerup serves as a Forensic Chemist in the Section of Forensic Chemistry at the University of Copenhagen. His research is centered on nanomaterial development, particularly DNA-stabilized silver nanoclusters, for forensic analytical applications. He maintains an active publication record in high-impact journals spanning structural characterization, optical properties, and practical integration of nanomaterials into forensic workflows. His core research domains include Forensic Chemistry , Analytical Chemistry , and Nanotechnology , with specialized focus on: Synthesis and stabilization of DNA-silver nanoclusters Optical property analysis and stability testing Bioconjugation techniques for biomedical probes Analytical method development for forensic toxicology SQL-based data processing pipelines for metabolomics Analysis of his 2023-2025 publications reveals a cohesive research trajectory advancing nanomaterial-based forensic tools. His work bridges atomic-scale structural insights with practical forensic applications, demonstrating significant contributions to nanocluster characterization and metabolomics data analysis. This research directly impacts drug detection methodologies and toxicological screening protocols. As part of the University of Copenhagen's forensic chemistry infrastructure, Mollerup operates within collaborative networks that integrate chemical synthesis, spectroscopic analysis, and computational data science to address complex forensic challenges.

Shayan Shamsمشاهده پروفایل

استادیار

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

Dr. Shayan Shams is an Assistant Professor in the Department of Health Data Science and Artificial Intelligence at UTHealth Houston's McWilliams School of Biomedical Informatics, where he has been faculty since 2019. He holds a PhD in Computer Science from Louisiana State University and an MEng in Robotics from University of Malaya. His research group is affiliated with the Center for Secure Artificial Intelligence for Healthcare (SAFE). Dr. Shams' research integrates artificial intelligence, high-performance computing, and big data analytics to address healthcare challenges. Key focus areas include: Unsupervised/semi-supervised deep learning for medical computer vision High-performance computing infrastructure for genomic data Hardware acceleration (GPU/FPGA) for medical AI Knowledge transfer across clinical domains (radiology, pathology, neurology) Edge device implementation for personalized medicine His recent publications (2016-2019) demonstrate evolving work in deep learning frameworks for healthcare applications, spanning medical imaging diagnostics, genomics pipelines, disaster response analytics, and distributed computing infrastructure. Research consistently leverages convolutional networks and generative models for biomedical problem-solving. Dr. Shams actively seeks graduate students and postdoctoral researchers for projects at the intersection of AI and healthcare. He has served as reviewer for premier conferences including MICCAI, IEEE ICDCS, and ACM-BCB.

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

Michael Irwin Jordan is a Professor at the University of California, Berkeley, ranked #1 worldwide in Machine Learning and Distributed Computing, #36 in Computer Science, and #9 in Statistics according to Academic Influence rankings. His interdisciplinary research bridges theoretical foundations with practical applications across machine learning, statistics, and artificial intelligence. His educational background includes a PhD in Computer Science from the University of California, San Diego, a Master's in Mathematics from Arizona State University, and a Bachelor's in Psychology from Louisiana State University. This diverse foundation has shaped his approach to integrating perspectives from computer science, statistics, neuroscience, and psychology. Professor Jordan's research spans several decades, beginning with foundational work in neural networks and connectionist models. He made seminal contributions to probabilistic graphical models, variational inference, and Bayesian nonparametrics. His 2001 paper 'Latent Dirichlet Allocation' has received over 34,000 citations, becoming one of the most influential papers in machine learning. More recently, his work has focused on applications to biological data analysis, particularly single-cell transcriptomics, where he has developed probabilistic models and software tools like scvi-tools. Member of the National Academy of Engineering (2010) Ranked #1 worldwide in Machine Learning Ranked #1 worldwide in Distributed Computing Ranked #36 worldwide in Computer Science Ranked #9 worldwide in Statistics As an advisor, Professor Jordan has mentored numerous PhD students who have become leaders in academia and industry. His recent work through 2022 shows continued innovation across multiple domains, including uncertainty quantification, theoretical analysis of transfer learning, and scalable frameworks for reinforcement learning. He also contributes significantly to data science education, having helped develop Berkeley's undergraduate data science curriculum.