جستجوی استاد راهنمای دانشگاهی

Thamo Sutharssanمشاهده پروفایل

مدرس ارشد

• Aeronautics
• Embedded systems
• System integration
• +۴ مورد دیگر

Dr Thamo Sutharssan is a Senior Lecturer at the University of East London within the Department of Engineering & Construction, School of Architecture Computing and Engineering. A Chartered Engineer and Chartered Manager, he leads both the BEng Aeronautical Engineering and BSc Railway Engineering programs, leveraging extensive prior experience at the University of Portsmouth, Havering College, Derby College, and Rolls-Royce Aerospace Academy. His academic qualifications include a BSc in Electrical & Electronic Engineering, an MSc in Aeronautical Engineering, and a PhD in Computing and Mathematics. Dr Sutharssan's research spans Aeronautics, Embedded systems, System integration, Machine learning, and Prognostics and Health Management (PHM). His work addresses critical reliability challenges in engineering systems, with significant contributions to fuel cell monitoring (2017 review cited 261 times) and data-driven PHM approaches (2015 review cited 145 times). Recent publications demonstrate expansion into AI-driven medical imaging and autonomous robotics. His publication trajectory reveals consistent expertise in prognostics evolving toward cutting-edge AI applications, with highly influential reviews establishing foundational methodologies now widely adopted in energy systems and electronics reliability engineering. Scientific recognition includes: Fellow of the Higher Education Academy Research leadership encompasses a £177,500 Knowledge Transfer Partnership project on fuel cell Balance of Plant systems, alongside academic service as Review Manager for the Prognostics and Health Management Society conferences since 2014. His industry collaborations include Scottish and Southern Energy and Sustainable Energy Technologies. Current academic partnerships involve Heriot-Watt University and New Mansoura University (Egypt), focusing on energy systems integration and engineering education development.

Shuyin Jiaoمشاهده پروفایل

دانشگاهی

• Computing Education
• Program Analysis
• Code Efficiency Analysis
• +۵ مورد دیگر

Shuyin Jiao is an active Associate Teaching Professor in the Department of Computer Science at North Carolina State University's College of Engineering. She serves as course coordinator for CSC 111 Introduction to Computing: Python and teaches multiple core undergraduate courses including CSC 116 (Java) and CSC 246 (Operating Systems), with extensive teaching experience spanning over 9 years at both NC State and the College of William & Mary. Her educational background includes a Ph.D. from the University of Houston (2015), a Diplôme d'Ingénieur from Ecole Central de Lyon, France, and a B.S. from Beihang University, China. She previously held positions as Assistant Teaching Professor (2020-2025) and Lecturer at NCSU, and taught as Lecturer and Adjunct Lecturer at William & Mary from 2015-2020. Dr. Jiao's research focuses on computing education and program analysis, with particular emphasis on developing tools to enhance student learning in computer science. Her work explores pedagogical methods to address diverse learning styles in increasingly large CS classrooms and investigates code inefficiencies through memory profiling and performance analysis. She leads the CERES lab at NCSU and serves as Publications Co-Chair for SIGCSE TS 2026. Her publication record shows consistent output in top venues including SIGCSE, CGO, MobiCom, and ICSE, with recent work spanning from 2025 back to 2012. Her research trajectory demonstrates a clear evolution from materials science (early career) to software engineering and computer science education, with current work heavily focused on educational technology and program analysis tools. Dr. Jiao has secured significant funding including as PI for NSF Grant DUE-#2417469 (2024-2027), NCSU Data Science Academy Seed Grant (2023-2024) as Co-PI, and NCSU GEARS Grants (2023-2024) as PI. She also serves as Co-PI for NSF Grant OAC-#2411136 (2025-2028) focused on computer system research. She actively involves undergraduate students in research through COE REU and CSC 498/499 projects, recruiting at CSC Undergraduate Research Lightning Talks each semester. Her course redesign projects include enhancing operating systems education through multimedia and redesigning CSC 111 with DELTA funding to address challenges in large-class instruction.

Long Wuمشاهده پروفایل

پژوهشگر ارشد

• Aeroacoustics
• Aerodynamics
• High-Order Numerical Methods
• +۱ مورد دیگر

Dr Long Wu is a Senior Research Fellow at the University of Southampton specializing in aeroacoustics and computational fluid dynamics, with research focused on high-order numerical methods and design optimization for noise reduction in aerospace systems including aeroengines and turbomachinery. Holding a PhD and BEng degree, his work centers on computational techniques for simulating and mitigating aircraft engine noise through high-fidelity simulations and adjoint-based optimization of fan and compressor components. Key research areas include boundary closure stability, tone noise propagation, and nonlinear acoustic effects in engine intakes. His 2018-2024 publications demonstrate consistent expertise in computational aeroacoustics, bridging theoretical numerical methods with practical noise control solutions for turbomachinery. Recent work emphasizes stable high-order schemes and optimization frameworks for reducing buzz-saw noise and improving aerodynamic-acoustic performance. As a member of the University's Acoustics Group, Dr Wu supervises PhD student Joseph Stephen Paul Binns on aeroengine noise propagation and accepts new PhD applicants. No scientific awards or research grants are documented in his current profile.

• Astronomy
• Astrophysics
• Machine Learning
• +۵ مورد دیگر

Robert Brunner serves as Professor of Astronomy at the University of Illinois at Urbana-Champaign, where he bridges astrophysical research with computational innovation. His work focuses on extracting knowledge from massive astronomical datasets through advanced statistical and machine learning techniques, while also extending methodologies to finance and agricultural applications. Research interests center on developing machine learning algorithms (random forests, deep neural networks, Bayesian estimation) for astronomical data analysis, cosmological parameter constraints via n-point clustering measurements, and hardware acceleration using GPUs/cloud systems. His interdisciplinary approach spans source classification, transient phenomena detection in surveys like SDSS and DES, and applications in financial time-series analysis and agricultural remote sensing. Recent publications (2019-2025) reveal strong cross-domain expertise: astronomical catalogs for Rubin Observatory and Spitzer surveys coexist with financial market analysis using community detection methods and agricultural computer vision systems. Key methodological threads include spatio-temporal forecasting, multimodal learning for earnings calls, and anomaly detection via extended isolation forests, demonstrating consistent innovation in handling petascale datasets across scientific boundaries.

• Artificial Intelligence
• Simulated and Digital Reality
• Computer Graphics
• +۵ مورد دیگر

Prof. Dr. Philipp Slusallek serves as Scientific Director and executive board member at the German Research Center for Artificial Intelligence (DFKI), where he leads the Agents and Simulated Reality research area. He holds a Full Professorship in Computer Graphics at Saarland University since 1999, co-founded the European AI initiative CAIRNE as Director of Strategy, and directs research at the Intel Visual Computing Institute. His career spans leadership roles in the Excellence Cluster on Multimodal Computing and Interaction and prior visiting positions at Stanford University and Nvidia Research. His academic foundation includes: 1983-1990: M.Sc. in Physics, University of Tübingen 1992-1995: Ph.D. in Computer Science, University of Erlangen Slusallek's research bridges Artificial Intelligence, Simulated Reality, and Computer Graphics with applications in high-performance computing, motion synthesis, and AI for science. His interdisciplinary work integrates real-time rendering, heterogeneous system programming (CPU/GPU/FPGA), and biomechanical modeling, driving innovations in digital reality frameworks and AI-driven simulation systems across medical, engineering, and autonomous vehicle domains. His 2025 publications reveal a strong focus on graphics compilation (Vulkan SPIR-V), rehabilitation biomechanics, and multi-agent motion simulation, demonstrating consistent integration of computer graphics foundations with emerging AI methodologies for practical real-world applications. Award highlights include the Eurographics Gold Medal (2023), acatech membership (2018), Land of Ideas Awards (2015, 2010), and Eurographics Fellowship (2013), recognizing his transformative contributions to computer graphics and AI. Eurographics Gold Medal (2023) acatech Membership (2018) Land of Ideas Award: Display as a Service (2015) Fellow of Eurographics Association (2013) CeBIT Innovation Award (2013) Land of Ideas Award: DFKI Visualization Center (2010) Slusallek leads major research initiatives including B5GCyberTestV2X (cybersecurity for autonomous driving), ENGAGE (AI computing environments), Carousel+ (digital character interaction), PRIME (predictive rendering), and TAILOR (trustworthy AI). His DFKI research group pioneers virtual environment frameworks while his academic mentorship has shaped generations of computer graphics researchers through Saarland University's programs. He co-founded the Intel Visual Computing Institute and established foundational visualization infrastructure at DFKI, maintaining active leadership in European AI strategy through CAIRNE and prior service on the European Commission's High-Level Expert Group on AI.

Tarik Grabaمشاهده پروفایل

مدرس

• Hardware Security
• Embedded Systems Design
• Integrated Circuit Reliability
• +۳ مورد دیگر

Tarik Graba is a Lecturer at Télécom Paris and a member of the Secure and Safe Hardware (SSH) research team within the Information Processing and Communication Laboratory (LTCI) department. His work focuses on hardware security, embedded systems, and cryptographic implementation. Education: Not explicitly mentioned Current Affiliations: Télécom Paris (Faculty), LTCI, SSH Team His research explores designing secure and reliable integrated systems , with emphasis on managing complexity, power consumption, and flexibility in embedded environments. He contributes to side-channel analysis , lightweight cryptographic algorithms , and hardware-based security mechanisms . The 15 most recent publications highlight his work in lightweight cryptography , hardware security , and embedded system optimization , particularly focusing on RISC-V extensions , side-channel leakage , TRNG circuits , and secure cryptographic processors . Scientific Awards Academic Palms award, Télécom Paris (2021) Tarik Graba collaborates extensively in hardware security research and has advised multiple projects on physical security countermeasures and embedded cryptographic systems . He works with teams like the SSH Laboratory to develop secure hardware architectures.

Jürgen Hesserمشاهده پروفایل

استاد

• Medical Imaging
• Computational Oncology
• Inverse Problems in Science
• +۷ مورد دیگر

Jürgen Hesser is a Professor at the Mannheim Medical Faculty , Heidelberg University, specializing in Experimental Radiotherapy and Medical Imaging . His research focuses on solving inverse problems in imaging, particularly for CT reconstruction , brachytherapy planning , and low-dose imaging . Current affiliations: Clinic for Radiotherapy and Radiooncology, Mannheim University Hospital Collaborative ties: Interdisciplinary Center for Scientific Computing (IWR) and Center for Bioinformatics (ZITI) at Heidelberg University Research interests center on anisotropic total variation techniques for medical and industrial applications, including MR-guided interventions and real-time radiation therapy . His work has led to a 1000x speed improvement in brachytherapy planning algorithms. Recent publications highlight expertise in image reconstruction (CT/X-ray), noise optimization , machine learning for cancer classification, and big data management solutions. His methods are applied to both clinical and industrial imaging challenges. Additional contributions include scientific data infrastructure development and variance stabilization techniques for medical sensors. The research group maintains strong interdisciplinary links with Physics, Mathematics, and Computer Science faculties.

• Software Engineering
• Software Testing
• Program Analysis
• +۴ مورد دیگر

Alessandro (Alex) Orso is the Dean and Professor at the University of Georgia College of Engineering , starting in 2025. Previously, he spent 25 years at Georgia Institute of Technology , where he served as a Professor in the College of Computing , Director of the Scientific Software Engineering Center , and Associate Dean for Off-Campus and Special Initiatives . Education: Ph.D. in Computer Science – Politecnico di Milano, Italy M.S. in Electrical Engineering – Politecnico di Milano, Italy B.S. in Electrical Engineering – Politecnico di Milano, Italy Research Focus: Alex Orso is a leading expert in software engineering , with a focus on software testing , program analysis , and debugging . His work aims to improve software reliability, security, and trustworthiness , and has been validated on real-world systems. He has led initiatives to integrate AI and large language models into testing workflows, particularly for REST APIs and mobile applications . Scientific Recognition: Distinguished Member of the Association for Computing Machinery (ACM) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) ISSTA Impact Paper Award (2021) Research Funding & Leadership: Orso has secured major funding from Google, IBM, Microsoft , and government agencies. In 2021, he led the acquisition of $11 million from Schmidt Futures to establish the Scientific Software Engineering Center at Georgia Tech, where he served as inaugural director. Teaching & Innovation: He was an early advocate of Georgia Tech’s Online Master of Science in Computer Science (OMSCS) and has taught a virtual software engineering course since its launch in 2014.

Julian Cooperمشاهده پروفایل

استادیار

• Chemical Reactivity in Materials
• Materials Science
• Mechanics of Materials
• +۳ مورد دیگر

Julian Cooper is an Assistant Professor at the University of Wisconsin–Madison Department of Chemistry, focusing on developing novel chemical reactivity in materials at the intersection of chemistry, materials science, and mechanics. His research addresses global challenges in sustainability, energy storage, and materials performance through interdisciplinary approaches. Education: B.S. in Chemistry from Rice University (2014), Ph.D. in Chemistry from MIT (2019), Beckman Institute Postdoctoral Fellow at the University of Illinois Urbana-Champaign (2019-2024). Research Interests: Combines chemistry and mechanics to create reprocessable thermosets, optimize polymerization reactions, and explore scalable solutions for material deconstruction. Scientific Awards: NSF Graduate Research Fellowship recipient. Students: Advises graduate student Katie Chism and undergraduate Ziang Zhao. The Cooper Group's recent publications focus on polymerization mechanisms, sustainable materials, and enzyme inhibition. Their work emphasizes practical applications in chemical recycling, predictive modeling for thermosets, and catalytic innovations. The lab actively recruits students and has established infrastructure with specialized glassware and instruments, supported by UW-Madison's master glassblower Tracy Drier.

• High-Performance Computing
• Computational Fluid Dynamics
• Numerical Analysis
• +۹ مورد دیگر

Niclas Jansson is a researcher at the PDC Center for High Performance Computing at KTH Royal Institute of Technology. He holds an M.S. in Computer Science (2008) and a Ph.D. in Numerical Analysis (2013) from KTH. His career spans roles such as postdoctoral researcher at RIKEN Advanced Institute for Computational Science (2013-2016) and visiting scientist at RIKEN (2018-2021), where he contributed to the Japanese exascale program Flagship 2020. A core focus of his research involves extreme-scale computing and numerical method development. He is a key developer of RIKEN's multiphysics framework CUBE , the HPC branch of FEniCS , and the spectral element flow solver Neko . His work is currently supported by a Swedish Research Council Starting Grant aimed at enhancing high-order spectral element methods for exascale fluid simulations. Niclas has published extensively on topics such as GPU acceleration , adaptive finite element methods , in situ visualization , and extreme-scale turbulence modeling . He also teaches Computational Fluid Dynamics (SG2212) at KTH.

Jignesh M. Patelمشاهده پروفایل

استاد

• Database Systems
• Query Optimization
• Data Analytics
• +۳ مورد دیگر

Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University, where he leads research on efficient data analysis methods. His work focuses on improving both system efficiency (e.g., high-performance data algorithms) and human efficiency (e.g., user productivity with data systems). Research Focus: Patel's group specializes in database systems, query optimization, hardware acceleration, and human-data interaction. Their interdisciplinary work spans: Transactional processing and real-time analytics Query optimization techniques Hardware-algorithm co-design Natural language interfaces for data systems Memory-efficient data processing Professional Activities: Co-founded four technology companies (Paradise, Locomatix, Quickstep, DataChat). Serves on program committees for premier conferences including SIGMOD and CIDR (as co-chair). Teaches database systems courses at CMU. Awards: Received Best Paper Award at DaMoN 2010 for work on cluster efficiency.

• Experimental High-Energy Particle Physics
• CP Violation Studies
• B-Physics and Strange Quark Physics
• +۴ مورد دیگر

Roger William Lewis Jones is a Professor and Head of the Department of Physics at Lancaster University . His career spans experimental high-energy particle physics, QCD research, and global computing infrastructure development for large-scale scientific projects like ATLAS and the Rubin Observatory. ATLAS experiment (LHC) for CP violation studies in B-physics NA62 experiment for strange quark physics LEGEND collaboration for neutrino research Leadership in GridPP and Worldwide LHC Computing Grid Research Focus : Experimental particle physics with emphasis on CP violation, QCD dynamics, and data-intensive computing systems. His work bridges hardware development (e.g., tracking detectors) and software innovation (e.g., Grid computing farms across UK institutions). Recent Publications highlight precision measurements in jet physics, Higgs decays, and searches for new particles like heavy neutral leptons. Articles span 2025 with collaborations on ATLAS and NA62, focusing on detector calibration, cross-section analysis, and beyond-Standard-Model signatures. Leadership Roles : Chair of NorthGrid Management Board Former Chair of STFC Particle Physics Advisory Panel (2019-2022) Chair of Institute of Physics Head of Department Forum Steering Committee (2019-2024) Key contributor to GridPP and Worldwide LHC Computing Grid

Ana Lucicمشاهده پروفایل

استادیار

• Interpretable Machine Learning
• Artificial Intelligence
• Earth System Science
• +۳ مورد دیگر

Ana Lucic is an Assistant Professor in Artificial Intelligence at the University of Amsterdam , with a joint appointment between the Institute for Logic, Language and Computation and the Informatics Institute . Her research focuses on interpretable machine learning applications for scientific discovery and societal impact. Formerly at Microsoft Research AI for Science and Partnership on AI PhD in Explainable Machine Learning from University of Amsterdam (2022) BSc/MSc in Mathematics from McMaster University Research Highlights: Develops mechanistic interpretability methods for deep learning architectures. Created Aurora , a foundation model for Earth system forecasting outperforming traditional operational models in air quality prediction and tropical cyclone tracking. Pioneers Clifford-Steerable CNNs for geophysical data analysis. Actively hiring PhD students for AI transparency research . Collaborative Networks: Contributions to ELLIS Summer School and ICML workshops . Collaborates with Microsoft Research AI for Science team on climate-related ML projects. Involved in organizing TerraBytes workshop at ICML 2025. Recent Advancements: Key role in publishing Aurora model in Nature (2025), demonstrating superior performance in Earth system forecasting. Supervises Ege Erdogan , new PhD student focused on mechanistic interpretability. Actively contributes to open-source AI development through GitHub repositories and technical discussions.

Stefan Böttcherمشاهده پروفایل

استاد

• Databases
• Data Compression
• Graph Algorithms
• +۳ مورد دیگر

Prof. Dr. Stefan Böttcher serves as Professor and Section Owner of Databases and Electronic Commerce within the Department of Computer Science at the University of Paderborn's Faculty of Computer Science, Electrical Engineering and Mathematics. His office (F2.217, Fürstenallee 11) operates by email appointment, and he teaches courses including Betriebssysteme (Operating Systems), maintaining active academic engagement through current publications and institutional roles. His research centers on advanced data compression methodologies for complex structures, specializing in grammar-based techniques for graphs, strings, and trees. Key focus areas include enabling efficient query processing directly on compressed representations—critical for semantic web triple stores and big data systems—while optimizing storage-performance tradeoffs through novel algorithmic approaches in Burrows-Wheeler transforms and tree recompression. Analysis of his 2018-2025 publications reveals consistent innovation in compressing non-linear data structures: from RECUT's tree recompression to 2025's string partitioning for BWTs, his work bridges theoretical algorithm design with practical database applications, particularly targeting high-performance querying in grammar-compressed graph frameworks for RDF triple stores. Scientific Awards No awards documented in source materials Prof. Böttcher mentors graduate researchers in database compression technologies through his Databases and Electronic Commerce section, with research outcomes published in premier venues like IEEE DCC and Big Data conferences. His projects demonstrate sustained funding through continuous high-impact outputs without explicit grant documentation in the provided texts. He leads the Databases and Electronic Commerce research group, driving innovations in compressed data structures that reduce storage overhead while maintaining query efficiency for semantic web and large-scale graph database implementations.

Lipeng Wanمشاهده پروفایل

استادیار

• Big Data Management
• High-Performance Computing
• Data-Intensive Computing
• +۳ مورد دیگر

Dr. Lipeng Wan is a tenure-track Assistant Professor of Computer Science at Georgia State University (GSU), located at 25 Park Place, room 733. He holds a B.Eng. in Communication Engineering from Nanjing University of Science and Technology (2008), an M.Eng. in Information and Communication Engineering from Southeast University (2011), and a Ph.D. in Computer Science from the University of Tennessee, Knoxville (2016). Prior to joining GSU, he served as a Computer Scientist at Oak Ridge National Laboratory (ORNL), first as a postdoctoral researcher (2016–2018) and later as a full-time research staff member (2018–202?). His research focuses on big data management and analytics , high-performance and data-intensive computing , and resilience and performance optimization for distributed systems . Key interests include scientific data workflows, I/O innovations for exascale systems, and error-controlled data compression frameworks like MGARD and HPDR. Dr. Wan’s recent work emphasizes adaptive data transmission (e.g., JANUS), load balancing in cloud environments (SciLance), and optimizing file access patterns on HPC systems. His publications address challenges in exascale computing, including I/O performance, geographically distributed data management, and feature-preserving compression for climate simulations. He leads research at GSU in collaboration with national labs like ORNL, focusing on advancing scalable data management techniques for high-performance computing applications.