جست‌وجوی استاد و پژوهشگر

Nilufar Akbariمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Biostatistics
• Clinical Epidemiology
• +۲ مورد دیگر

Nilufar Akbari is a Researcher at the Institute of Biometry and Clinical Epidemiology, Charité - University Medicine Berlin, specializing in bioinformatics and biostatistics. She has been employed at the institute since September 2019, contributing to biometric consulting, study planning, and methodical research in clinical epidemiology. Her educational background includes a Master's degree (2018-2021) and Bachelor's degree (2014-2018) in Bioinformatics from the Free University of Berlin. These qualifications underpin her expertise in statistical modeling and data analysis for medical research. Ms. Akbari's research focuses on applying R and Python programming to clinical trial methodology, epidemiological study design, and biometric evaluation. Her work bridges computational biology with practical healthcare applications, emphasizing robust statistical frameworks for medical data interpretation. She maintains active involvement in teaching and methodological consulting within clinical research contexts. Fluent in German (native), Persian (native), and English, she operates effectively in multinational research environments. No scientific awards or student advising activities are documented in available records, and her current position shows no indication of part-time status or retirement.

• Power Engineering
• Thermal-Fluid Processes Modeling
• Energy Systems Data Analysis
• +۱ مورد دیگر

Michalina Kurkus-Gruszecka is a researcher in the Division of Power Engineering at the Institute of Heat Engineering, Warsaw University of Technology, actively supervising student projects and theses in computational energy modeling. Her research focuses on: Mathematical and numerical modeling of thermal-fluid processes in power engineering systems Data analysis for optimization of energy systems On-shore and Off-shore wind energy project analysis She participates in multiple National Centre for Research and Development (NCBiR) projects including biomass cogeneration plants (BioCHP), innovative power equipment cleaning technologies (MVC), and waste-to-fuel conversion systems, with extensive industry collaboration spanning over 20 projects. Her scientific recognition includes: Rector of Warsaw University of Technology team award for scientific achievements (2020) Dr. Kurkus-Gruszecka maintains active industry partnerships through internships at Areva GmbH (Germany) and Zakłady Remontowe Energetyki, while supervising student computational projects using Aspen HYSYS, MATLAB, Python, and Ansys Fluent.

Isaac Kohaneمشاهده پروفایل

استاد

• Biomedical Informatics
• Computational Biology
• Genomics
• +۵ مورد دیگر

Isaac Kohane is the inaugural Chair of the Department of Biomedical Informatics and the Marion V. Nelson Professor of Biomedical Informatics at Harvard Medical School. He is also a Professor of Pediatrics at Boston Children's Hospital and an Associate Professor of Medicine at Brigham and Women's Hospital. His educational background includes an MD/PhD from Boston University (1987) and post-doctoral work at Boston Children's Hospital, where he practices as a pediatric endocrinologist. Research focuses on converting data to knowledge for medical innovation Trans-omics integration for disease reclassification AI applications in clinical decision-making Functional genomics of neurodevelopmental disorders Living laboratories in healthcare systems Recent publications highlight diverse applications of AI in medicine, genomic analysis of rare diseases like autism and cancer, and healthcare data infrastructure. His work spans 2025-2024 with key themes in Medical Informatics (12) , Genomics (9) , and Artificial Intelligence (15) . Scientific Recognitions : Institute of Medicine American Society for Clinical Investigation Kohane leads NIH-funded projects including TOPMED integration (OT3HL142480), PIC-FAIR (OT3OD025466), and N-GRID (P50MH106933). He has authored Microarrays for Integrative Genomics (2003) and The AI Revolution in Medicine (2023), and serves as Editor-in-Chief of NEJM AI .

Eric Bruningمشاهده پروفایل

دانشیار

• Atmospheric Electricity
• Lightning
• Cloud Physics
• +۴ مورد دیگر

Dr. Eric Bruning serves as an Associate Professor in the Department of Geosciences at Texas Tech University, where he has been a faculty member since 2010. His research program integrates atmospheric electricity, cloud physics, and advanced computational methods to investigate fundamental storm processes and lightning phenomena. His academic foundation includes a complete sequence of degrees in Meteorology from the University of Oklahoma (B.S., M.S., Ph.D., 2008), followed by postdoctoral research at the University of Maryland's Earth System Science Interdisciplinary Center. This training established his expertise in severe storms and satellite meteorology through collaboration with NOAA's National Severe Storms Laboratory. Dr. Bruning's research spans multiple interconnected domains: Atmospheric electricity and lightning physics, including storm electrification mechanisms Cloud microphysics and dynamics in diverse convective systems Scientific computing applications using Python for atmospheric analysis History and philosophy of science perspectives on meteorological research Interdisciplinary connections between scientific visualization and artistic design His publication record (2022-2025) reveals intensive focus on lightning observation systems, aerosol-cloud-precipitation interactions, and field campaign operations. Key themes include the development of novel lightning mapping techniques, analysis of lake-effect electrification events, and investigation of microphysical drivers of storm electrification through projects like ESCAPE, TRACER, and LEE. Professional engagement highlights include: Active membership on NOAA's GOES-R Geostationary Lightning Mapper (GLM) Science Team Principal Investigator for NSF-funded research on lightning flash size distributions and turbulence Former Chair of the American Meteorological Society Committee on Atmospheric Electricity Dr. Bruning contributes significantly to major collaborative field campaigns and instrumentation development, particularly through his leadership in the Lake-Effect Electrification (LEE) project and the Tracking Aerosols and Convection Experiment (TRACER). His work bridges theoretical modeling, observational analysis, and practical applications for improving severe weather understanding and forecasting capabilities.

• Fuzzy Logic
• Machine Learning
• Time Series Analysis
• +۴ مورد دیگر

Luis Rodríguez Benitez is an Associate Professor at the University of Castilla-La Mancha, affiliated with the Department of Information Technologies and Systems. His academic work bridges theoretical computer science and applied domains, with sustained contributions to computational intelligence methodologies. His research integrates: Fuzzy systems for linguistic data description Machine learning in transportation and environmental contexts Time-series analysis using evolutionary and prototyping approaches Software development for scientific computing (e.g., EEG analysis tools) Recent publications (2014–2025) demonstrate: Advancements in kernel methods for discrete choice modeling Novel applications of fuzzy logic in sustainability science Efficiency-focused algorithms for large datasets Interdisciplinary work spanning neurocomputing, transportation, and IoT

Samuel Shenمشاهده پروفایل

عضو هیئت علمی

• Data Science
• Machine Learning
• Climate Science
• +۳ مورد دیگر

Dr. Samuel Shen is a Distinguished Professor at the Department of Mathematics and Statistics , San Diego State University (SDSU) and a Visiting Research Mathematician at Scripps Institution of Oceanography, UCSD. He co-founded the SDSU Big Data Analytics (BDA) MS Program and currently serves as Co-Director of the Center for Climate and Sustainability Studies . Previously, he was McCalla Professor at the University of Alberta and President of CAIMS . B.Sc. in Engineering Mechanics (1982) from East China Engineering Institute M.A. (1985) and Ph.D. (1987) in Applied Mathematics from University of Wisconsin-Madison His research spans three major areas: Climate Informatics : Developed Spectral Optimal Averaging (SOA) and Ensemble Canonical Correlation Analysis (ECCA) for climate uncertainty quantification Data Science : Created US Climate Prediction Center's operational prediction tools and filed a US patent on spectral optimal gridding Nonlinear Wave Dynamics : Advanced theoretical understanding of stochastic differential equations for climate modeling Recent publications focus on 4D climate visualization , heat stress mitigation , AI democratization in climate science , and cloud-aerosol interactions . His work has secured major grants including: $2.7M NSF AI Institute grant (2023) $1.9M California Climate Action grant (2023) $2.1M NSF EaSM-3 grant (2014) Scientific accolades include: Arthur Beaumont Distinguished Service Award (CAIMS) US National Research Council Senior Fellowship Chinese Academy of Sciences Well-known Overseas Scholar He leads the SDSU Climate Informatics Lab (founded 2006) and has been featured in New York Times , NASA Top Story , and Nature Geoscience press releases.

Fabio Pellaciniمشاهده پروفایل

استاد

• Computer Graphics
• Computer Science
• 3D Design
• +۳ مورد دیگر

Full Professor of Computer Science at the University of Modena and Reggio Emilia. Previously held faculty positions at Sapienza Università di Roma (Associate and Full Professor), Dartmouth College (Assistant and Associate Professor), and Cornell University (Visiting Assistant Professor). Also worked in the R&D division of Pixar Animation Studios. Dr. Pellacini received an MS and Ph.D. in Computer Science from Cornell University and a Laurea degree in Physics from the University of Parma. His research focuses on using Computer Graphics methods to solve design problems, with emphasis on the design, creative and entertainment industries. His group combines algorithms and efficient systems to allow professional designers as well as novices to create 3D scenes with significantly less effort. They investigate algorithms based on numerical methods and machine learning to enable intuitive and interactive editing of complex environments. Key research areas include appearance design (patterns, materials, and lights), appearance fabrication, evaluation and visualization of designers' workflows, and cloud-based collaborative design. His recent publications demonstrate a strong focus on procedural modeling, appearance design, and collaborative 3D content creation. His work bridges the gap between theoretical computer graphics research and practical applications for designers and artists, with significant contributions to node-based editing systems, boolean operations on surfaces, and differentiable rendering techniques for appearance design. NSF CAREER Award Alfred P. Sloan Fellowship Nominated as Junior Faculty Fellow for Sapienza's Scuola Superiore di Studi Avanzi Dr. Pellacini has supervised numerous students and has been actively involved in research grants related to computer graphics and design systems. He teaches Computer Graphics, Introduction to Scientific Python, and Programming courses at the University of Modena and Reggio Emilia, continuing a long teaching career that spans multiple institutions including Sapienza University of Rome and Dartmouth College. He leads a research group focused on developing innovative tools for 3D design and content creation, with applications spanning from entertainment to industrial design. His work on collaborative design systems, appearance editing interfaces, and procedural modeling techniques continues to shape how designers interact with 3D content creation tools.

• Numerical Analysis
• Inverse Problems
• Machine Learning
• +۵ مورد دیگر

Marco Prato is an Associate Professor in Numerical Analysis (MATH-05/A) at the University of Modena and Reggio Emilia, specializing in computational mathematics with expertise in inverse problems and image reconstruction. His academic work bridges theoretical mathematics with practical applications in data science and computational imaging. His primary research focuses on: Numerical optimization methods for ill-posed problems Regularization theory and algorithms (TSVD, Tikhonov, Landweber) Image and signal reconstruction techniques Machine learning algorithms and computational statistical methods Applications in computed tomography and space data analysis Dr. Prato maintains active research affiliations as a member of the OASIS research group, HIPERT laboratory, and NASA's RHESSI team, demonstrating his international research impact. His teaching portfolio spans multiple departments: Fundamentals of Analysis for Computer Engineering Computational and Statistical Learning for Computer Science and Mathematics Inverse Problems and Applications for Mathematics Mathematics courses for Chemistry and Law programs His pedagogical approach combines theoretical foundations with hands-on computational practice using MATLAB and Python environments, emphasizing problem-solving skills for real-world applications in data analysis and scientific computing.

• Algorithms
• Formal Verification
• Hardware Model Checking
• +۴ مورد دیگر

Paolo Pasini is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) within Politecnico di Torino. His academic roles span teaching and research, with a focus on algorithms, formal verification, and hardware optimization. Scientific Branch: IINF-01/A - Electronics (Area 0009 - Industrial and Information Engineering) ERC Sectors: Algorithms, Software Engineering, Theoretical Computer Science, Web Systems Research interests center on hardware model-checking algorithms, portfolio-based verification engines, pre-simplification steps, circuit manipulation, and interpolation-based techniques. His recent publications highlight applications in FPGA optimization, edge computing, and machine learning classification. 2025: Low-Power Subgraph Isomorphism at the Edge Using FPGAs 2025: NN2FPGA: Optimizing CNN Inference on FPGAs 2024: Bounded Model Checking with Interpolation Teaching roles include: PhD: Data Structures in Python (2022/23) Master’s: Edge Computing Systems for AI and ML (2023/24-2025/26), Modeling and Optimization of Embedded Systems (2023/24-2024/25) Bachelor’s: Digital Electronic Design (2024/25-2025/26), Algorithms and Data Structures (2019/20-2022/23)

Salvador Aguilar Ruizمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Salvador Aguilar Ruiz is a Full Professor of Data Analytics at Pablo de Olavide University's School of Engineering, where he teaches courses including Data Mining, Big Data, and IT Management in both undergraduate and postgraduate programs. His academic position is within the Department of Sports and Computer Science, where he maintains an active research profile with over 250 publications and more than 105,000 reads on ResearchGate. With over two decades of experience, Professor Aguilar Ruiz specializes in Data Mining and Machine Learning, with significant contributions to biclustering algorithms, protein structure prediction, and genomic data analysis. His research bridges computer science with bioinformatics, developing methods for analyzing complex biological data while advancing fundamental machine learning techniques for high-dimensional datasets. His recent work focuses on addressing challenges in imbalanced multiclass data classification, explainable AI, and novel evaluation metrics for machine learning models. The professor has developed several innovative approaches including the IMCP Curve for performance visualization and class-specific feature selection techniques that enhance model interpretability while maintaining predictive power. Outstanding Ph. D. Award, Computer Science, University of Seville Founding Editor-in-Chief of Biodata Mining Journal (2008-2014) Professor Aguilar Ruiz previously served as Founding Dean of the School of Engineering at Pablo de Olavide University from 2005 to 2015 and has held research positions at the University of Massachusetts Boston. His international collaborations span institutions in Japan (National Institute of Genetics) and Italy (University of Bologna), reflecting his global research impact in data analytics and bioinformatics.

Roxana Radulescuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Roxana Radulescu is an Assistant Professor in AI and Data Science at the Department of Information and Computing Sciences, Utrecht University. She previously held an FWO Postdoctoral Fellowship at Vrije Universiteit Brussel. Her work bridges multi-agent systems, reinforcement learning, and multi-objective decision-making, focusing on hybrid human-agent collectives. Her research themes include: Multi-Objective Reinforcement Learning: Integrating optimal transport and utility-based frameworks for complex environments. Cooperative AI: Studying emergent cooperation in uncertain incentive settings and public goods games. Benchmark Development: Creating the MOMAland open-source Python library for standardized multi-agent evaluation. Recent publications analyze Pareto front dynamics, ethical AI alignment, and non-linear utility optimization. She actively contributes to conference tutorials at AAMAS 2024 and ESSAI 2023, emphasizing practical MORL applications. Scientific recognition includes: FWO Postdoctoral Fellowship Co-organizing ECAI 2023 workshops She teaches courses on Data Science and Society and Multi-Agent Systems , with office hours on weekdays. Contact: r.t.radulescu@uu.nl | contact@roxanaradulescu.com

Yair Shachar-Hillمشاهده پروفایل

استاد

• Arbuscular Mycorrhizal Symbiosis
• Non-Photosynthetic One Carbon Metabolism
• Metabolic Engineering
• +۳ مورد دیگر

Yair Shachar-Hill is a Professor in the Department of Plant Biology at Michigan State University , affiliated with the Molecular Plant Sciences Program and BioMolecular Science Gateway . His research focuses on metabolic networks and energy flows in plants and microbes. Education: Ph.D., Yale University His work addresses metabolic engineering for biofuels, arbuscular mycorrhizal symbiosis for sustainable agriculture, and non-photosynthetic one carbon metabolism . He employs 13C isotopic labeling , NMR spectroscopy , and computational modeling to quantify metabolic fluxes. Recent publications highlight advancements in condensed TCA cycles , carbon-concentrating mechanisms , and flux analysis methodologies across plants and algae. His lab trains researchers in interdisciplinary approaches combining experimental and computational techniques. Students in his lab include: Joshua Kaste (Ph.D. Candidate) Anne Steensma (Ph.D. Student) Antwan Green (Undergraduate Researcher) His team also includes postdocs like Dr. Yuan Xu and Dr. Danielle Hoffmann , who study carbon fixation and lipid metabolism in Camelina and Chlamydomonas.

Ryan LaRoseمشاهده پروفایل

استادیار

• Computational Physics
• Quantum Information Science
• Quantum Computing
• +۴ مورد دیگر

Ryan LaRose is an Assistant Professor at Michigan State University (MSU) with appointments in the Department of Computational Mathematics, Science and Engineering and the Department of Physics & Astronomy within the College of Natural Science. He also holds affiliations with the Department of Electrical and Computer Engineering and MSU's quantum initiative (MSU-Q), focusing on quantum computing research and development. His educational background includes a PhD in Computational Mathematics, Science and Engineering from Michigan State University (2022) and a BS with Distinction in Mathematics and Physics from the University of Michigan, Ann Arbor (2017). LaRose's research bridges computational physics and quantum information science through two interconnected themes: (1) the physics of computation, examining how quantum principles inform information theory and computer science, and (2) the computation of physics, leveraging quantum computers to solve complex problems in physical sciences. His work emphasizes practical quantum algorithm development, error mitigation techniques for noisy hardware, and open-source software tools for quantum computing. Analysis of his recent publications (2022-2024) reveals strong emphasis on quantum error mitigation strategies—particularly zero-noise extrapolation—and variational quantum algorithms for optimization and linear algebra problems. His contributions span hardware solutions, software frameworks like Mitiq and BGLS, and theoretical advances in quantum volume measurement. His notable scientific awards include: Engineering Distinguished Fellowship Fitch H. Beach Award for Outstanding Graduate Research NASA Space Technology Graduate Research Fellowship As an active contributor to the quantum community, LaRose co-organizes the QuIC Seminar series at MSU and develops open-source quantum software. He leads research efforts within MSU-Q, collaborating on quantum algorithm implementation and error mitigation techniques for near-term quantum devices.

William Bellمشاهده پروفایل

مدرس

• Experimental particle physics
• Data analysis
• Simulation of physical systems
• +۴ مورد دیگر

William Bell serves as a Teaching Fellow in the Department of Computer and Information Sciences at the University of Strathclyde, United Kingdom, focusing on computer science education and interdisciplinary research bridging particle physics and software engineering. He actively contributes to the Wellcome Trust-funded project "Cultures of Collaborative Research in a Socially Progressive Technological University" (2024-2025). His academic foundation includes a PhD in Searching for Bs -> J/psi eta with the Collider Detector at Fermilab from the University of Glasgow (1998-2001). Professional trajectory encompasses Maître-assistant roles at the University of Geneva (2008-2014), research assistantship at Glasgow (2001-2008), and senior consultancy at Frazer-Nash Consultancy (2015-2020). Research spans experimental particle physics—specializing in top quark physics, detector development, and Standard Model validation through ATLAS collaboration—and industrial software engineering, with emphases on agile development, embedded systems, and DevOps. Teaching expertise includes Python programming, cloud computing, containerization, and object-relational mapping, informed by decades of commercial sector experience. Recent publications analyze top-antitop cross-sections and tt production using LHC data, revealing trends in jet multiplicity and transverse momentum within proton-proton collisions. These works demonstrate methodological innovation in handling complex detector outputs and validating theoretical predictions. Breakthrough Prize in Fundamental Physics (2025) for contributions to Large Hadron Collider research As Research Co-investigator on the active Wellcome Trust project, he develops collaborative research frameworks while mentoring through public engagement initiatives across France, Switzerland, and the UK. His software development leadership spans editorial roles, international collaborations, and commercial applications. Technical capabilities encompass 15+ programming languages (C, Python, SQL, etc.), database systems (Oracle, PostgreSQL), and legacy/embedded architectures, reflecting 30+ years of hands-on development across scientific and industrial contexts.

Aman Husbandsمشاهده پروفایل

استادیار

• Plant Biology
• Cell and Developmental Biology
• Genetics
• +۶ مورد دیگر

Aman Husbands is the Mitchell J. Blutt and Margo Krody Blutt Presidential Assistant Professor of Biology at the University of Pennsylvania, with appointments in the School of Arts and Sciences and Department of Biology. His laboratory is located in the Caroline Lynch Building at 433 S University Ave, Philadelphia, PA 19104. Dr. Husbands' research focuses on understanding the mechanisms enabling reproducible developmental patterning, particularly in leaf morphogenesis. His lab investigates how plants solve the biological problem of producing flat leaf architecture from initially radially symmetric structures, with special emphasis on dorsoventral (top-bottom) patterning. A key research area involves the CLASS III HOMEODOMAIN LEUCINE ZIPPER (HD-ZIPIII) proteins, ancient transcription factors that play crucial roles in plant development, stem cell maintenance, and vascular patterning across 700 million years of evolution. His laboratory employs molecular, biochemical, and single-molecule techniques to study transcription factor regulation, guided by the philosophy that "evolution has done the experiments for you." Recent publications reveal a strong integration of computational approaches, particularly topological data analysis, to understand developmental robustness and leaf morphology across plant species. The work bridges molecular biology, genetics, and evolutionary biology to uncover fundamental principles of developmental patterning. Scientific Awards: Mitchell J. Blutt and Margo Krody Blutt Presidential Professorship Dr. Husbands leads an active research laboratory with multiple graduate students, postdocs, and undergraduate researchers including Nicole Callery (Lab Manager), Sarah Choudury and Ricardo Urquidi Camacho (Postdocs), Courtney Dresden and Isabella Higgins (Graduate Students), and Sarah Ortiz-Polanco (Undergraduate). Recent lab news includes a publication from Isabella and Sarah in New Phytologist. The lab maintains a collaborative environment focused on understanding developmental patterning through genetics, molecular biology, and computational analysis, with affiliations to the Penn Epigenetics Institute.