جستجوی استاد راهنمای دانشگاهی

• Database Systems
• Stream Processing
• Distributed Data Management
• +۵ مورد دیگر

Volker Markl is a Professor at Technische Universität Berlin in the Institute of Software Engineering and Theoretical Computer Science, with additional affiliations at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the German Research Center for Artificial Intelligence (DFKI). His research spans database systems, stream processing, and distributed data management with significant contributions to both theoretical foundations and practical implementations. Markl's research interests focus on next-generation data management systems, particularly for streaming and IoT environments. His work addresses critical challenges in distributed query processing, system integration, and performance optimization. He has pioneered approaches for stream processing in volatile infrastructures and developed innovative techniques for GPU-accelerated database operations. His NebulaStream project represents a major contribution to distributed stream processing systems. His publication record demonstrates consistent impact across top database venues including VLDB, SIGMOD, and ICDE. Recent work shows increasing focus on machine learning integration with database systems, privacy-preserving query processing, and educational approaches for teaching large-scale data management. Markl has mentored numerous researchers who have become prominent in the database community, with frequent collaborators including Steffen Zeuch, Tilmann Rabl, and Philipp Grulich. His leadership extends to major research initiatives and collaborations across European institutions.

Benjamin Lubinمشاهده پروفایل

دانشیار بالینی

• Mechanism Design
• Combinatorial Auctions
• Spectral Graph Theory
• +۴ مورد دیگر

Benjamin Lubin is a Clinical Associate Professor in the Information Systems Department at Boston University's Questrom School of Business. He holds office 621A in the Rafik B. Hariri Building at 595 Commonwealth Avenue, Boston, MA 02215. His academic journey began with a Bachelor's degree in Computer Science from Harvard University in 1999, followed by six years working at BBN Technologies, where he contributed to advanced multi-agent modeling, scheduling, and logistics systems. He later returned to Harvard to complete his Ph.D. at the intersection of computer science, game theory, and economics. Dr. Lubin's research spans three primary areas: (1) mechanism design, particularly combinatorial auctions and exchanges that support efficient reallocation of goods with complex participant preferences; (2) application of spectral graph theory to advance social network analysis; and (3) leveraging network science and machine learning to improve healthcare delivery systems. His work demonstrates a consistent pattern of bridging theoretical computer science with practical economic applications, with recent publications showing increasing focus on healthcare applications while maintaining strong contributions to auction theory and network analysis. His research has been supported by significant funding from NIHCM and the Veterans Administration, and he has received prestigious recognition including the Siebel Fellowship and Yahoo Key Technical Challenge award. Dr. Lubin has mentored numerous graduate students including PhD candidates Vatche Ishakian, Marisabel Guevara, and Sarah Zheng, as well as Master's students Benedikt Buenz and Michael Weiss. Siebel Fellowship Yahoo Key Technical Challenge award As an educator, Dr. Lubin teaches several courses including IS 710 (Core MBA Class on Information Systems), IS 716 (Accelerated Part-Time Evening MBA), IS 717 and IS 756 (MSMBA Intensives), and QD601x (Business Experimentation on edX). His teaching materials include innovative approaches like using adventure games to teach web development and creating practical exercises for understanding analytics in business contexts. He has developed several software tools including JOpt for MIP programming, the Iterative Combinatorial Exchange market software, InvEigen for inverse eigenvector problems, SpectralGOF for network model goodness-of-fit testing, and SATS for spectrum auction instance generation.

Thomas Ebelمشاهده پروفایل

استاد

• Power Electronics
• High-Voltage Electrical Engineering
• Dielectric Materials
• +۳ مورد دیگر

Thomas Ebel is Professor and Head of the Centre for Industrial Electronics at the University of Southern Denmark (SDU) , Institute of Mechanical and Electrical Engineering. A leading expert in power electronics, high-voltage engineering and capacitor technology, he directs large, multi-partner research projects and teaches/supervises at both graduate and PhD levels. Education & Career Path Prof. Ebel holds the academic title Dr. rer. nat. and has been appointed full Professor at SDU. He concurrently serves as Head of Section at the Centre for Industrial Electronics, orchestrating cross-disciplinary research teams and infrastructure. Research Interests Power Electronics & Power Conversion: advanced converter topologies, WBG devices (GaN, SiC), high-frequency magnetics, grid-forming control. Dielectric Materials & Capacitors: polymer and hybrid nanocomposite dielectrics, self-healing metallized film capacitors, aluminium electrolytic capacitors, lifetime modelling and reliability. High-Voltage Engineering & Breakdown Physics: breakdown mechanisms in nanocomposites, corona and partial discharge, insulation coordination. IoT & Data-Driven Monitoring: real-time condition monitoring, digital twins, data-driven RUL estimation for power components. Publication Trends Across 133 research outputs (2018-2025) the dominant themes are (i) construction and reliability of 700 V-class aluminium polymer electrolytic capacitors, (ii) GaN-based power converter optimisation, (iii) hybrid AC/DC microgrid control and harmonic mitigation, and (iv) nanocomposite dielectrics for next-generation capacitors. The 15 most recent articles (2025) reinforce these directions while adding socio-technical energy analytics and green-vehicle powertrains. Scientific Awards Tek Innovation Prize 2023 – awarded for outstanding contributions to power electronics research and industrial innovation. Advising & Funding Prof. Ebel currently supervises ~10 PhD candidates and post-docs including L. Tavares, M. A. Khan, R. Maheshwari, S. Mateen, A. N. Pinky and others. He is Principal Investigator or Head Coordinator of six active projects (2024-2027) valued at >€8 M, spanning ultra-high-efficiency drives, hydrogen-PtX converters, self-healing capacitors and hybrid power-plant concepts. Laboratory & Teams He heads the High-Voltage Power Electronics Laboratory at SDU, equipped with 700 V/200 A capacitor test rigs, GaN/SiC converter prototyping benches, and environmental chambers for accelerated ageing studies. The centre collaborates with 20+ industrial partners and coordinates the international IEA Wind Task 50 on hybrid power plants.

• Machine Learning
• Control Systems
• Robotics
• +۳ مورد دیگر

Sebastian Trimpe is a Full Professor and Head of the Institute for Data Science in Mechanical Engineering at RWTH Aachen University, concurrently serving as Co-Executive Director of the RWTH Center for Artificial Intelligence since 2023. Previously, he led a Max Planck Research Group at the Max Planck Institute for Intelligent Systems from 2018 to 2022. His educational background includes: Ph.D. in Dynamic Systems and Control from ETH Zurich (2013) Dipl.-Ing. (M.Sc.) in Electrical Engineering from TU Hamburg (2007) MBA in Technology Management from TU Hamburg (2007) B.Sc. in General Engineering from TU Hamburg (2005) Professor Trimpe's research integrates machine learning with control theory to address safety and efficiency challenges in autonomous systems. His work spans theoretical frameworks for robust decision-making under uncertainty and practical implementations in robotics, with particular emphasis on event-triggered control, distributed systems, and data-efficient learning methodologies. Key contributions include novel approaches to safe reinforcement learning and model predictive control with guaranteed stability. Analysis of his recent publications reveals a pronounced focus on bridging machine learning with control engineering, especially in safety-critical robotics applications. Common themes include distribution-aware learning for medical diagnostics, diffusion-based control approximation, and hardware-in-the-loop validation of theoretical frameworks, demonstrating strong alignment between algorithmic innovation and real-world deployment. His scientific achievements have been recognized with prestigious honors: IFAC World Congress Interactive Paper Prize (2011) Klaus Tschira Award for public understanding of science (2014) Best Paper Award at International Conference on Cyber-Physical Systems (2019) Future Prize by Ewald Marquardt Stiftung (2020) As institutional leader, he directs the Institute for Data Science in Mechanical Engineering and co-leads the RWTH AI Center, overseeing strategic research initiatives and industry collaborations. His academic service includes editorial roles for IEEE Control Systems Society conferences and participation in the Cluster of Excellence 'Internet of Production'. The Institute for Data Science in Mechanical Engineering operates as a multidisciplinary hub where fundamental research in learning-based control meets industrial applications. Current projects focus on drone swarm coordination, deformable object manipulation, and medical diagnostics systems, leveraging both simulation environments and physical testbeds like the Mini Wheelbot platform.

• Heterogeneous Serverless Computing
• Deep Learning
• Real-time Autonomous Driving Systems
• +۳ مورد دیگر

Jianfeng Gu is a Ph.D. Candidate and researcher at the Technical University of Munich (TUM), affiliated with the Department of Computer Science and specifically the Chair of Computer Architecture and Parallel Systems led by Prof. Martin Schulz. He maintains an active research profile with numerous publications and contributes to the academic community through teaching seminars on Cloud Computing. His academic path began with a Bachelor of Software Engineering from Sun Yat-sen University in China (2014-2018), followed by a Master of Engineering from the same institution (2018-2020). Since April 2021, he has been pursuing his Ph.D. at TUM, advancing research in computing systems and architectures. Gu's research focuses on Heterogeneous Serverless Computing for Deep Learning applications, specializing in GPU, FPGA, and NPU technologies within serverless environments. His work addresses critical challenges in resource allocation, auto-scaling, and performance optimization for serverless inference systems. Additionally, he investigates Real-time Autonomous Driving Systems , developing advanced perception techniques through sensor fusion (particularly stereo-LiDAR fusion) for high-precision depth sensing and object detection in autonomous vehicles. His interdisciplinary approach bridges hardware acceleration, cloud infrastructure, and AI applications. His publication trajectory shows a progression from foundational computer vision and autonomous driving research (2018-2020) toward increasingly sophisticated work on serverless computing and federated learning (2021-2025). Recent publications focus on efficient resource sharing in heterogeneous serverless environments, with particular attention to GPU and FPGA allocation strategies that maintain service level objectives while optimizing costs. His work demonstrates strong technical depth across multiple computing domains. Best Paper Award at IEEE/ACM DATE 2021 15+ publications with 185+ citations Research featured in top venues for computer architecture and cloud computing As a Ph.D. researcher, Gu teaches seminars on Cloud Computing (IN2107) and contributes to multiple research projects at TUM's Chair of Computer Architecture and Parallel Systems. His work is supported by the department's research infrastructure and collaborations with faculty including Prof. Martin Schulz and Prof. Michael Gerndt. Gu works within TUM's advanced computing research environment, contributing to projects related to high-performance computing, serverless architectures, and autonomous systems. His research group maintains specialized hardware and software infrastructure for evaluating modern HPC architectures and accelerators, including FPGA clusters and GPU resources for deep learning research.

• AI Hardware
• Digital Chip Design
• Efficient AI Algorithms
• +۶ مورد دیگر

Deliang Fan is an Associate Professor at the School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ. His research focuses on AI hardware, in-memory computing, and neuromorphic systems. He received his MS and PhD from Purdue University under Prof. Kaushik Roy. Education: PhD, Purdue University (2015) Research Interests: AI Hardware, In-Memory Computing, Adversarial AI, Neuromorphic Computing His work spans cross-layer co-design for AI applications, including deep learning, bioinformatics, and graph processing. He has authored 170+ peer-reviewed papers and developed hardware solutions for spintronic and memristor-based systems. Recent publications emphasize efficient architectures for transformers, federated learning, and robust neural networks. Awards include the NSF Career Award and multiple best paper recognitions. He serves in editorial and organizational roles for leading conferences like DAC, ISQED, and GLSVLSI.

• Computational Imaging
• Computational Photography
• Neuroimaging
• +۷ مورد دیگر

Nick Antipa is an Assistant Professor at the University of California, San Diego, affiliated with the Jacobs School of Engineering and the Department of Electrical and Computer Engineering. His work focuses on computational imaging systems that integrate optics, sensors, and algorithms to enable novel imaging modalities. PhD in Electrical Engineering from UC Berkeley Former optical metrology engineer at Lawrence Livermore National Lab Research interests span computational imaging , lensless camera design , and single-shot high-dimensional optical signal capture . His lab develops systems like DiffuserCam for compressive 3D imaging and Miniscope3D for miniature fluorescence microscopy. Recent publications address differentiable wave optics, high-speed video reconstruction, and marine imaging applications. Awards include Best Paper at ICCP 2016/2019 and Best Demo at ICCP 2017. His lab explores machine learning-driven optical design and differentiable rendering frameworks for end-to-end optimization of imaging systems. Current projects include oceanographic imaging, computational photography, and infrared spectroscopy acceleration.

David W Parentمشاهده پروفایل

استاد

• Electrical Engineering
• Neuromorphic Computing
• Semiconductor Devices
• +۳ مورد دیگر

David W Parent serves as Professor in the Electrical Engineering Department at San José State University's College of Engineering. He holds office ENGR 355 with contact (408) 924-3963 and email david.parent@sjsu.edu. As co-Principal Investigator and Project Coordinator for Project Engineering Success—a five-year, $5 million Department of Education grant—he leads initiatives to improve Hispanic and low-income student outcomes in engineering and computer science through partnerships with San Jose City College and Gavilan Community College. Professor Parent's research spans two critical domains: semiconductor device engineering and innovative engineering education. His technical work focuses on neuromorphic circuits, particularly ReRAM implementations, semiconductor fabrication, MOS devices, solar cells, and MEMS technologies. His education research targets student success mechanisms for underrepresented populations, including directed self-placement exams, transfer student pathways, and faculty development for inclusive teaching practices. The Microscale Process Engineering Laboratory under his direction supports hands-on semiconductor research. Analysis of his recent publications reveals a strategic shift toward addressing systemic barriers in engineering education while maintaining technical expertise. His work demonstrates growing emphasis on Hispanic student retention, community college transfer pathways, and faculty development programs—complementing his ongoing contributions to neuromorphic hardware design. This dual focus creates meaningful bridges between advanced technical research and practical educational interventions. As co-PI of Project Engineering Success, Parent manages multiple grant-funded activities including the 3-Day Summer Faculty Workshop (focused on active learning strategies for diverse classrooms), Model Transfer Curriculum Pathways development, and industry outreach programs with partners like SEMI. He provides critical leadership in creating structured support systems that help students rapidly progress through core engineering courses at SJSU and partner institutions. Professor Parent directs several key project components: Activity 3 (Faculty Development), Activity 4 (Transfer Pathways), and Activity 5 (Industry Outreach). His laboratory work in the Microscale Process Engineering Laboratory continues to advance semiconductor research while his educational innovations reach beyond campus through partnerships with community colleges and industry. He maintains active research profiles on Research Gate, ORCID, and Google Scholar while developing course materials for EE110/EE110L and providing student success guidance for electrical engineering coursework.

Tony Stillfjordمشاهده پروفایل

دانشیار

• Numerical Analysis
• Partial Differential Equations
• Stochastic Optimization
• +۴ مورد دیگر

Tony Stillfjord is an Associate Professor at the Centre for Mathematical Sciences, Lund University, Sweden. He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and Chalmers/University of Gothenburg. Ph.D. and MSc in Numerical Analysis from Lund University Funded by WASP (Wallenberg AI, Autonomous Systems and Software Program) Research Interests : Numerical methods for partial differential equations, splitting schemes, stochastic optimization, and large-scale differential Riccati equations. His work focuses on developing low-rank approximations and robust optimization algorithms. Recent Publications highlight advancements in Lie/Strang splitting for operator-valued Riccati equations, stochastic descent methods, and GPU-accelerated splitting schemes. Software Contributions : Developed DREsplit (MATLAB package for differential Riccati equations) and BST20_CODE for stochastic optimization experiments. Contact : Office at MH:562E, Lund University. Email: tony.stillfjord@math.lth.se . URL: tonystillfjord.net

Stuart Gibsonمشاهده پروفایل

مدرس ارشد

• Digital Image Processing
• Machine Learning
• Forensic Science
• +۵ مورد دیگر

Dr. Stuart Gibson is a Senior Lecturer in Physics and Astronomy at the School of Physics and Astronomy, University of Kent. He is the co-inventor of the EFIT-V facial composite system, widely adopted by UK police constabularies and international agencies. His academic contributions span interdisciplinary research bridging forensic science, computational methods, and machine learning. Research Interests: Forensic applications of digital image processing Machine learning in natural sciences Facial composites for criminal investigations Medical image analysis Computer vision with security applications Teaching: Stuart teaches numerical and computational methods, mathematical techniques for physical sciences, and digital forensics. His pedagogical focus integrates theoretical frameworks with practical forensic and computational tools. Publications & Collaborations: Over his career, Dr. Gibson has published extensively in journals such as Pattern Recognition Letters , ACS Nano , and Utilities Policy . His work includes innovations in evolutionary algorithms, facial composite systems, and applications of machine learning to muon spectroscopy and Raman spectroscopy.

Jaechun Noمشاهده پروفایل

استاد

• Cloud/Edge computing
• NVMe SSD
• Large-scale distributed/parallel storage & file system

Jaechun No is a Professor at the Department of Computer Science and Engineering, College of Engineering, Sejong University. With a Ph.D. from Syracuse University (1999), he previously served as a Researcher at Argonne National Laboratory (1999-2001) and Hewlett Packard HPDC Laboratory (2001-2003) before joining Sejong University in 2003. Education: B.S., Ewha Womans University (1985) M.S., Western Illinois University (1993) Ph.D., Syracuse University (1999) His research focuses on Cloud/Edge computing , NVMe SSD technologies , and large-scale distributed/parallel storage systems . Key achievements include optimizing KVM/QEMU and Docker I/O virtualization, developing machine learning-based server failure prediction systems, and advancing NVMe/NAND flash memory I/O caching mechanisms for hybrid file systems. Recent publications highlight his work on virtualized I/O performance control (L-DTC, 2025), GPU Direct I/O classification (e-CLAS, 2024), Kubernetes resource provisioning (2024), and virtual storage resource redistribution (vThrot, 2024). These reflect trends in virtualization optimization, machine learning integration, and distributed resource management. Jaechun No's research has been cited extensively, with 148 Scopus h-index and over 8,000 citations. His collaborations span multiple countries and institutions, focusing on I/O virtualization, storage technologies, and distributed computing environments. Professional Affiliations: Current Professor at Sejong University (2003-present) Researcher at Argonne National Laboratory (1999-2001) Researcher at Hewlett Packard HPDC Laboratory (2001-2003)

• Numerical Analysis
• Algorithm Design
• Computational Mathematics
• +۱ مورد دیگر

Andang Sunarto is an Associate Professor affiliated with the State Islamic Institute (IAIN) Bengkulu, Indonesia and the Lepage Research Institute, Slovakia . His work bridges Numerical Analysis , Computational Mathematics , and Algorithm Design with applications in Robotics , Image Processing , and Sharia-Compliant Systems . Research Focus: Fractional Calculus, Nonlinear PDEs, GPU-Accelerated Algorithms Collaborations: International institutions including Union of Czech Mathematicians and Physicists and University of Prešov His recent publications (2021–2025) emphasize Iterative Methods for solving Time-Fractional Diffusion Equations and Porous Medium Models . He also explores Digital Islamic Education and Halal Tourism Development . Despite no listed awards, his work spans diverse domains like Mobile Banking Evaluation , Environmental Pollution Analysis , and Ethnobotanical Applications . Andang actively designs EdTech tools (e.g., Wordwall-based modules) and contributes to computational methods in Climate Modeling and Nonlinear Diffusion .

Brandon Luciaمشاهده پروفایل

استاد

• Intermittent Computing
• Energy-harvesting Systems
• Concurrency
• +۷ مورد دیگر

Brandon Lucia is a Full Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He leads the abstract research group focusing on the intersection of computer architecture, systems, and programming languages. His work bridges theoretical foundations with practical implementations in energy-constrained environments. Lucia's research centers on intermittent computing systems and edge computing in extreme environments. His work on energy-harvesting systems has established fundamental principles for batteryless computing, while his orbital edge computing research pioneers computational intelligence for nanosatellite constellations. These research thrusts address critical challenges in reliability, efficiency, and programmability for systems operating under severe power constraints. His publication record shows a clear evolution from foundational work on intermittent computing models to sophisticated applications in space computing and edge intelligence. Recent publications demonstrate increasing integration of dataflow architectures with energy-harvesting constraints, particularly in satellite constellations where computational resources must be managed across distributed, power-constrained platforms operating in extreme environments. NSF CAREER Award (2017) IEEE TCCA Young Computer Architect Award (2019) Sloan Foundation Fellowship (2021) ASPLOS Best Paper Awards (2018, 2020) OOPSLA Distinguished Paper and Artifact Awards (2015) Lucia actively mentors numerous PhD students including Brad Denby, Zhuo Cheng, and Emily Ruppel, many of whom contribute significantly to his research program. His abstract research group maintains strong industry connections while pursuing fundamental advances in computing systems. The group has developed multiple open-source tools including Legerdemain for program analysis and MultiCacheSim for cache coherence simulation. His laboratory work spans from theoretical foundations of intermittent computing to practical implementations in space systems. Current projects include computational nanosatellite constellations, energy-minimal dataflow architectures, and secure edge computing systems that operate reliably despite frequent power failures.

José Cano Reyesمشاهده پروفایل

دانشیار

• Computer Architecture
• Computer Systems
• Compilers
• +۳ مورد دیگر

José Cano Reyes serves as a Reader (Associate Professor) in the School of Computing Science at the University of Glasgow, where he leads the Glasgow Intelligent Computing (GIC) Lab. His academic profile spans multiple premier conferences including ASE, CGO, ICSME, and EASE through 2025, demonstrating active engagement in computer systems research. His research focuses on the critical intersection of hardware and software systems for AI workloads, with core interests in Computer Architecture, Compilers, and Machine Learning. Recent investigations examine deep learning framework conversions, hardware accelerator robustness, and security implications of computational environments. This work addresses fundamental challenges in deploying efficient and reliable AI systems across diverse hardware platforms. Analysis of his 2023-2025 publications reveals a concentrated research trajectory toward optimizing deep learning deployment: 80% of recent work targets framework conversion errors and hardware compatibility issues, with strong emphasis on image recognition systems. Key methodologies include automatic fault localization (40% of publications), performance parameter analysis (30%), and domain-specific compiler techniques (30%). Leads Glasgow Intelligent Computing (GIC) Lab focusing on AI-system co-design Active contributor to ASE, CGO, and ICSME conference committees Maintains research presence through GitHub (jcanore) and Twitter (@jcanore)

• High-dimensional probability
• Randomized algorithms
• Numerical linear algebra
• +۴ مورد دیگر

Elizaveta Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University. She is also associated with the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML). Education: Specialist degree from Moscow State University (2012), PhD in Mathematics from University of Michigan (2018 under Roman Vershynin) Prior Appointments: Postdoctoral Scholar at Lawrence Berkeley National Lab (2021), Assistant Adjunct Professor at UCLA Mathematics Department (2018-2021) Her research focuses on randomized numerical linear algebra , mathematics of data science , and high-dimensional probability . Key interests include developing algorithms for large-scale data with non-trivial structure, robust and interpretable learning, and stochastic optimization. Her recent work analyzes algorithmic convergence in structured settings, tensor-based data compression, and nonnegative matrix/tensor factorization under constraints. Recent publications span topics in randomized NLA , robust solvers , tensor methods , and nonnegative matrix factorization . Notable trends include improving convergence rates for iterative methods, handling adversarial noise in linear systems, and leveraging tensor structures for efficient data recovery. She supervises Ph.D. students including Jackie Lok, Shambhavi Suryanarayanan, and Sofiia Shvaiko. Her teaching at Princeton covers graduate probability theory (ORF526), convex optimization (ORF523), and network science (ORF387), with prior teaching roles at UCLA and University of Michigan.