جستجوی استاد راهنمای دانشگاهی

Pengfei Liمشاهده پروفایل

استادیار

• Online optimization
• Trustworthy machine learning
• Sustainable AI
• +۷ مورد دیگر

Pengfei Li is an Assistant Professor in the School of Information at the Rochester Institute of Technology (RIT), where he leads research at the intersection of machine learning, sustainability, and social equity. Previously, he completed his Ph.D. in Computer Science at the University of California, Riverside under Prof. Shaolei Ren, with additional collaborations at Caltech with Adam Wierman and an internship at Nokia Bell Labs. His educational background includes an M.S.E. in Robotics from Johns Hopkins University and a B.E. in Electrical Engineering from Zhejiang University. Dr. Li's research focuses on three interconnected pillars: developing trustworthy online algorithms with strict robustness guarantees, creating sustainable AI systems that minimize environmental impact, and addressing environmental and social inequities through algorithmic solutions. Analysis of his recent publications reveals a strong emphasis on the environmental consequences of AI systems, particularly water consumption ('Making AI Less 'Thirsty'') and geographical distribution of environmental burdens ('Towards Environmentally Equitable AI'). His work bridges theoretical computer science with practical sustainability challenges, often incorporating learning-augmented approaches to traditional online optimization problems. Scientific Recognition: Dissertation Completion Fellowship Award (DCFA) from the Graduate Program in Computer Science (February 2025) 'Making AI Less 'Thirsty'' in Communications of the ACM has received 15 citations and over 17,000 downloads Organizer of the workshop on learning-augmented algorithms at SIGMETRICS 2025 Dr. Li actively seeks to build a research group focused on societal fairness, reliable generative AI, and decision-focused learning. His work has established important connections between theoretical computer science and critical societal challenges, particularly around AI's environmental footprint and equitable resource distribution. Current research directions include developing algorithmic solutions for environmental and social fairness, with applications in water infrastructure, energy systems, and equitable AI deployment.

Patrick Westphalمشاهده پروفایل

پژوهشگر

• Structured Machine Learning
• Semantic Web
• Big Data Analytics
• +۲ مورد دیگر

Dr. Patrick Westphal is a Researcher at the University of Leipzig's AKSW (Agile Knowledge Engineering and Semantic Web) Research Group, where he specializes in structured machine learning for big data applications. As a core member of the MOLE (Machine Learning and Ontology Engineering) group, he develops scalable frameworks like DL-Learner for semantic web technologies and life science use cases. His research focuses on: Structured machine learning algorithms for knowledge graphs Parallelization of learning systems for distributed architectures Geospatial data inference and ontology engineering Hybrid human-AI workflows for smart city analytics He leads contributions to multiple EU-funded projects including SANSA-Stack (distributed semantic analytics), GeoKnow (geospatial linked data), and QROWD (crowd-sourced data integration).

Joan Lasenbyمشاهده پروفایل

استاد

• Image and Signal Analysis
• Motion Capture
• Geometric Algebra
• +۴ مورد دیگر

Joan Lasenby is a Professor of Image and Signal Analysis at the Department of Engineering , University of Cambridge. She is affiliated with the Cambridge Mathematics of Information in Healthcare Hub (CMIH) . Research focuses on advanced motion capture, geometric algebra applications, and healthcare technology Key contributions to 3D reconstruction, camera pose estimation, and biomedical sensor fusion Recent work spans neonatal monitoring, protein structure modeling, and fluid dynamics simulations Her publications demonstrate strong expertise in combining geometric algebra with deep learning for diverse applications in computer vision, biomedicine, and physics simulations. As part of the Cambridge Engineering faculty, she contributes to interdisciplinary research at the intersection of mathematics, computer science, and healthcare technology.

Andrew J. Connollyمشاهده پروفایل

استاد

• Data Science
• Cosmology
• Galaxy Evolution
• +۷ مورد دیگر

Andrew J. Connolly is Professor in the Department of Astronomy at the University of Washington, Director of the eScience Institute, Associate Vice Provost for Data Science, and William P. and Ruth Gerberding University Professor. He also serves as Adjunct Faculty in the Paul G. Allen School of Computer Science & Engineering. His research focuses on data-intensive astronomy, developing computational tools for massive surveys like the Sloan Digital Sky Survey and Legacy Survey of Space and Time (LSST). Key interests include cosmology through galaxy clustering and weak gravitational lensing, photometric redshift estimation, time-domain astronomy, and solar system studies. He pioneers machine learning applications in astrophysics, co-authoring the seminal textbook Statistics, Data Mining, and Machine Learning in Astronomy . William P. and Ruth Gerberding University Professor Connolly advises a large research group with current members Bryce Kalmbach, Chris Suberlak, Stephen Portillo, Kyle Boone, Hayden Smotherman, Dino Bektesevic, and John Franklin Crenshaw. His former students include Scott Daniel, Simon Krughoff, and Jake Vander Plas. His work receives significant support through initiatives like the Schmidt Futures Foundation-sponsored LINCC partnership for LSST data processing. He leads the eScience Institute as a hub for cross-disciplinary data science, founding the DiRAC Institute for astronomy-focused data science. Current projects involve cloud-native frameworks (Spark, Kubernetes, Kafka) for Rubin Observatory data and expanding data science education through Hackweeks, Bootcamps, and Data Science for Social Good programs.

Florian Marquardtمشاهده پروفایل

استاد

• Cloud Computing
• Computer Science

Prof. Dr.-Ing. Florian Marquardt serves as a Professor in the Department of Computer Science and Media at Brandenburg University of Technology, Brandenburg an der Havel, Germany. His office is located in Building C, Room C.0.15 at Magdeburger Straße 50. His primary research focus centers on Cloud Computing within the broader discipline of Computer Science. His expertise spans infrastructure design, distributed systems, and scalable computing solutions critical to modern data-intensive applications. Contact information includes telephone +49 3381 355-463 and professional email florian.marquardt@th-brandenburg.de.

• Computer Communications
• Fluid Modeling
• Parallel Computing
• +۵ مورد دیگر

Miloš Ivanović serves as an Assistant Professor at the Institute of Mathematics and Informatics within the Faculty of Natural Sciences and Mathematics at the University of Kragujevac. His academic career is deeply rooted in computer science and computational mathematics, with research spanning multiple interdisciplinary domains that bridge theoretical computer science with practical applications in engineering, physics, and biology. Dr. Ivanović earned his Doctorate in Computer Science from the Faculty of Natural Sciences and Mathematics (PMF) at the University of Kragujevac, establishing a foundation for his subsequent research career focused on computational methods and high-performance computing. His research interests represent a sophisticated convergence of computational theory and practical applications. Ivanović specializes in advanced computing paradigms including grid computing, GPGPU computing, and cloud-based high-performance computing systems. His work in fluid modeling employs meshless methods and particle dynamics approaches, particularly SPH and DPD techniques applied to microfluidic systems. The breadth of his research extends from theoretical mathematics (graph theory and chemical indices) to practical applications in hydrology, biomedical engineering, and nuclear physics. His interdisciplinary approach enables innovative solutions across diverse scientific domains that require intensive computational resources. The collection of his publications reveals a consistent trajectory of research focused on computational methods with applications spanning multiple scientific disciplines. His work demonstrates strong expertise in parallel computing architectures and their application to complex scientific problems. The publications show a progression from fundamental computational mathematics toward increasingly complex multi-domain applications, particularly in biomedical and environmental systems. His research consistently addresses the challenge of implementing computationally intensive methods on distributed and parallel architectures to solve real-world scientific problems. Throughout his career, Ivanović has participated in numerous national and European research and infrastructure projects, including FP6, FP7, and Tempus initiatives. His collaborative approach is evident in the diverse range of co-authors spanning multiple institutions and disciplines, reflecting his ability to bridge computational science with domain-specific applications. Dr. Ivanović is affiliated with the Group for Mathematical Modeling and Computer Simulations, which develops computational solutions across various domains including fluid dynamics, biomedical applications, and environmental modeling. The group's work encompasses software development for simulation purposes, as evidenced by projects like the SPH07 software package and various specialized simulation tools for engineering and biomedical applications.

Ebba Dellwikمشاهده پروفایل

پژوهشگر

• Wind Energy
• Meteorology
• Remote Sensing
• +۵ مورد دیگر

Ebba Dellwik is a Senior Researcher at the Department of Wind and Energy Systems, Technical University of Denmark (DTU), specializing in meteorology and remote sensing within the Wind Energy Systems Division. Her work focuses on understanding wind flow patterns, particularly in forested environments and around wind turbines, contributing significantly to wind energy research and development in Denmark and internationally. Dr. Dellwik's research expertise spans multiple critical areas in wind energy and atmospheric science. Her primary interests include: Forest meteorology and wind flow modeling in complex terrain Wind turbine aerodynamics and performance optimization Remote sensing applications using LiDAR and sodar technologies Tree-wind interactions and their implications for wind resource assessment Atmospheric boundary layer dynamics in forested landscapes Her recent publications reveal a strong focus on integrating advanced measurement techniques with computational modeling. Dellwik has pioneered methods for using point cloud data to create digital tree twins, improving our understanding of wind flow through forests. She has also made significant contributions to understanding rain erosion on wind turbine blades and developing more accurate wind resource assessment methods in complex terrain. Dr. Dellwik actively supervises PhD students and participates in numerous research projects. Current projects include: Wind Farm Modeling for Enhanced Performance (2024-2027) Dispersion and consequences of releases to the atmosphere in the urban environment (2023-2026) AIRE: Advanced study of the atmospheric flow Integrating REal climate conditions (2023-2026) Advanced numerical modeling of the interaction between trees and near-surface winds (2022-2025) Her research group utilizes state-of-the-art equipment including multiple WindScanners (coherent Doppler lidars) and conducts field measurements at DTU's test site in Høvsøre and the Østerild National Test Centre for Large Wind Turbines.

Achim Streitمشاهده پروفایل

استاد

• Data Management
• High-Performance Computing
• Artificial Intelligence
• +۴ مورد دیگر

Prof. Achim Streit is a Professor for distributed and parallel high-performance systems at the Karlsruhe Institute of Technology (KIT) and has served as one of the directors of the Steinbuch Centre for Computing (SCC) since 2010. He actively leads national and international initiatives including the Helmholtz program "Engineering Digital Futures", the National Research Data Infrastructure (NFDI), and the European Open Science Cloud (EOSC), with SCC operating GridKa—the German data hub for particle physics and a Tier 1 center of the Worldwide LHC Computing Grid. His research centers on secure, distributed management of large-scale scientific data, emphasizing metadata standards, AI-driven knowledge extraction, and quantum machine learning. He develops scalable solutions for data-intensive fields like climate research, materials science, and particle physics while prioritizing energy efficiency on heterogeneous computing systems. Streit champions open science, ensuring freely accessible software and datasets through rigorous research software engineering practices. The SCC under his direction implements federated IT services across Helmholtz platforms (HMC, Helmholtz.AI, HIFIS) and NFDI consortia (NFDI4Ing, NFDI-MatWerk, PUNCH4NFDI). His team collaborates extensively with disciplines ranging from energy research to humanities, focusing on distributed authentication infrastructures, data archiving, and resource optimization for global scientific communities.

• High Performance Computing
• HPC Interconnection Networks
• Enterprise Data Centre Networks
• +۶ مورد دیگر

Ernst Gunnar Gran is Associate Professor at the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU), where he heads the communication technology discipline. He also holds an adjunct research scientist position at Simula Research Laboratory, where he headed the Cloud department until December 2016. His research spans high performance computing (HPC), HPC interconnection networks, enterprise data centre networks, cloud computing, and data-intensive processing in multi-clouds. He serves as the Scientific Leader of Communication Technologies in the RCN-funded infrastructure project eX3 (Experimental Infrastructure for Exploration of Exascale Computing) and has significant experience with both RCN-funded and EU-funded research projects, including the H2020 project Melodic (Multi-cloud Execution-ware for Large-scale Optimised Data-Intensive Computing). Gran received his M.Sc. and Ph.D. degrees in computer science from the Department of Informatics, University of Oslo, in 2007 and 2014, respectively. Both theses focused on different aspects of resource management in high performance interconnection networks. He previously headed the RCN-funded project ERAC (Efficient and Robust Architecture for Big Data Clouds) and led the design, implementation, and deployment of the multi-homed IP-based research testbed NorNet Core. Gran also has several years of experience as a system administrator and scientific programmer. His research interests center on the intersection of high performance computing and networking, with particular focus on anomaly detection in time series data, HPC interconnection networks, network virtualization, and cloud computing infrastructure. His work demonstrates a consistent evolution from fundamental networking research to applied solutions for modern computing challenges, particularly in IoT security and smart home applications. His recent publications show a strong emphasis on developing lightweight, real-time anomaly detection systems using deep learning techniques. Analysis of his publication trends reveals a clear progression from traditional HPC networking research toward time series anomaly detection applications, particularly for IoT systems. His 15 most recent publications show dual focus areas: approximately 60% concentrate on anomaly detection methods for time series data (particularly for IoT applications), while the remaining 40% maintain his foundational work in HPC networking, virtualization, and cloud infrastructure. This evolution demonstrates his ability to adapt core networking expertise to emerging application domains while maintaining technical depth. While no specific scientific awards are mentioned in the provided text, Gran's leadership roles in significant research projects (eX3, Melodic, ERAC) indicate recognition of his research capabilities within the academic and research funding communities. His position as Scientific Leader of Communication Technologies in the RCN-funded eX3 project further demonstrates his standing in the Norwegian research community. Gran's teaching responsibilities include serving as course coordinator for DCSG1006 Data Communication and Networks, DCSG2001 Interconnected Networks and Network Security, and Networks: Administration, Programming and Security. His research leadership extends to significant grant-funded projects, including the RCN-funded eX3 infrastructure project and the EU H2020 Melodic project. His previous leadership of the ERAC project and the NorNet Core research testbed demonstrates sustained ability to secure and manage substantial research funding. His laboratory and team affiliations include the Department of Information Security and Communication Technology at NTNU, where he heads the communication technology discipline, and Simula Research Laboratory, where he maintains an adjunct position. The NorNet Core research testbed, which he led the development of, represents a significant infrastructure contribution to the networking research community. His current work with the eX3 project suggests ongoing involvement in experimental infrastructure for exascale computing exploration.

Tomislav Medićمشاهده پروفایل

مدرس

• Geomatics
• Remote Sensing
• Laser Scanning
• +۶ مورد دیگر

Dr. Tomislav Medić is a Lecturer and PostDoc at the Department of Civil, Environmental and Geomatic Engineering, ETH Zurich. His research focuses on advanced geospatial technologies, particularly terrestrial laser scanning (TLS) applications in deformation monitoring, sensor calibration, and precision agriculture. MSc in Geodesy and Geoinformation, University of Zagreb, Croatia PhD in Geodesy, Bonn University (IGG), Germany Dr. Medić’s research spans geomatics, remote sensing, and sensor engineering. Key areas include TLS radiometric calibration, point cloud processing for 3D displacement analysis, and multispectral LiDAR applications in agriculture. He contributes to improving geodetic measurement accuracy through innovative calibration strategies. His recent publications emphasize TLS integration with RGB data for geomonitoring (2025), hyperspectral scanning for fruit quality assessment (2024), and calibration field design for panoramic scanners (2023). Articles demonstrate expertise in error modeling, multi-sensor fusion, and environmental monitoring applications. At ETH Zurich’s Geosensors and Engineering Geodesy (GSEG) group, he leads the Alpine Measurement Lab collaboration project. He also participates in PhenoRob, a cluster of excellence in robotics and phenotyping for sustainable crop production at Bonn University.

• Knowledge Representation and Reasoning
• Ontology-Based Data Management
• Data Quality and Data Preparation
• +۳ مورد دیگر

Marco Console is a tenured Assistant Professor (Ricercatore a Tempo Determinato di cat. B) at the Department of Computer, Control and Management Engineering (DIAG) of Sapienza University of Rome. His research focuses on knowledge representation, ontology-based data management, and the quality of data preparation, with recent emphasis on querying incomplete and heterogeneous data. Education: PhD in Engineering in Computer Science, Sapienza University of Rome, 2017 Research Interests: Console investigates foundational and practical aspects of Knowledge Representation and Reasoning , especially ontology-mediated query answering and data quality . His work spans semantic technologies , incomplete data , and explainable AI , aiming to provide users with meaningful and informative answers over large, heterogeneous knowledge bases. Across his latest publications he explores informativeness measures for query results, disaggregated data architectures for scalable workflows, and semantic explanations of machine-learning classifiers through ontologies, demonstrating a cohesive agenda that bridges symbolic AI and data management. Scientific Awards: Best Paper Award, International Conference on Principles of Knowledge Representation and Reasoning (KR), 2018 Editorial Service: Guest Editor, ACM Journal of Data and Information Quality (JDIQ), Special Issue on Quality Aspects of Data Preparation, 2022.

Anne C. Elsterمشاهده پروفایل

استاد

• High-Performance Computing
• Machine Learning
• GPU Acceleration
• +۴ مورد دیگر

Anne C. Elster is a Professor and Director of the Heterogeneous and Parallel Computing Lab (HPC-Lab) at NTNU's Department of Computer Science, with additional roles as HPC Leader at the Center for Geophysical Forecasting and Senior Research Fellow at the Oden Institute. She holds board positions at NTNU and its Faculty of Information Technology. Her research spans: High-Performance Computing : GPU acceleration, auto-tuning, and heterogeneous systems Machine Learning : Applied to optimization and computational geosciences Parallel Algorithms : For scientific computing and real-time simulations Her recent publications (2021-2024) focus on GPU auto-tuning, quantum-HPC integration, distributed systems, and ML-driven geophysical data analysis, with strong emphasis on performance optimization across architectures. Awards and honors: IEEE Computer Society Distinguished Contributor (2021) IEEE Distinguished Speaker (2019-2022) IEEE Senior Member (2000) She has supervised 100+ master's students, 15+ PhDs, and secured major grants including EU H2020 projects. Current Post Docs focus on HPC acceleration and AI applications. Her HPC-Lab collaborates with CERN, Equinor, and international universities, specializing in GPU-accelerated scientific computing and tools for performance portability.

Scott Feisterمشاهده پروفایل

استادیار

• Scientific Computing
• High-Performance Computing
• Laser-Plasma Physics
• +۴ مورد دیگر

Dr. Scott Feister is an Assistant Professor in the Department of Computer Science at California State University Channel Islands (CSUCI), where he has held a tenure-track position since 2019. His academic journey includes a B.S. in Physics from the University of Notre Dame and M.S./Ph.D. in Physics from The Ohio State University, followed by postdoctoral work at the University of Chicago and UCLA. His research focuses on scientific computing at the intersection of computer science, physics, and engineering. Primary domains include: Control systems infrastructure for high-power laser facilities Machine learning applications in experimental science High-data-rate instrumentation and embedded systems High-performance computing for plasma physics simulations Publication analysis reveals consistent focus on laser-plasma interactions (12 papers), high-performance computing (4 papers), and educational innovation (3 papers), with recent work emphasizing machine learning integration and equity-focused STEM training. He leads multiple grants involving collaborations with: Lawrence Livermore National Laboratory Lawrence Berkeley National Laboratory NVIDIA Ohio State University Argonne National Laboratory Teaching innovations include cloud-based learning environments (AWS, Replit), open-source textbooks, and hardware prototyping. He has developed 12 distinct courses and holds certifications in AWS Cloud, Mental Health First Aid, and equity pedagogy.

Ramin Yahyapourمشاهده پروفایل

استاد

• Data-intensive applications
• Cloud computing
• Data science infrastructures
• +۵ مورد دیگر

Prof. Dr. Ramin Yahyapour is a Professor of Computer Science at the University of Göttingen with extensive leadership responsibilities: Managing Director of GWDG (Max Planck Society/University of Göttingen joint computing center) CIO of University and University Medical Center Göttingen Spokesperson for Göttingen Campus Institute for Data Science (CIDAS) Director of CAIMed (Lower Saxony Center for AI & Causal Methods in Medicine) His research focuses on data- and compute-intensive systems , cloud computing architectures , and data science infrastructures . Through CAIMed, he pioneers AI applications in biomedicine including clinical decision support, causal inference methods, multi-omics data analysis, and biomedical image recognition using high-quality patient care datasets. He oversees major computing resources as head of GWDG, a national HPC and AI service center, and drives interdisciplinary initiatives through CIDAS and CAIMed that bridge computer science, AI methodologies, and clinical research.

Konstantinos Kallasمشاهده پروفایل

استادیار

• Computer Systems
• Compilers
• Programming Languages
• +۱ مورد دیگر

Konstantinos Kallas serves as Assistant Professor of Computer Science at the University of California, Los Angeles (UCLA), commencing his appointment in January 2025. Previously affiliated with the University of Pennsylvania as evidenced by his 2020 PLDI contribution, his research bridges theoretical formal methods with practical systems engineering across multiple high-impact conferences including PLDI, POPL, and SPLASH. His research program centers on enhancing computational efficiency and correctness in systems software, with three flagship projects defining his trajectory: PaSh for automatic shell script parallelization, Durable Functions for stateful serverless computing semantics, and DiffStream for differential testing of stream processing. These efforts consistently target the intersection of programming language theory and real-world systems constraints, particularly in parallelism, concurrency, and cloud-native environments where correctness guarantees are challenging to implement. Analysis of his publication history since 2020 reveals a methodological pattern: developing formal semantic models to enable practical optimizations in distributed systems. His work increasingly focuses on serverless architectures and data-intensive pipelines, with recent contributions emphasizing automated verification techniques. The evolution from shell script optimization (2020-2021) to serverless state management (2021-2022) demonstrates strategic expansion into cloud computing's hardest problems. Dr. Kallas actively contributes to the academic community through program committee service for PLDI (2022, 2025), POPL (2021, 2022, 2023), and SPLASH (2020-2023), including leadership roles as Publicity Co-Chair for PLDI 2025 and 2026. His June 2024 announcement confirms recruitment for Fall 2025 students at UCLA, targeting researchers interested in systems, compilers, and programming languages who can advance his work on correctness-preserving parallelization and serverless computing.