جست‌وجوی استاد و پژوهشگر

• Computer Graphics
• Visualization
• 3D Reconstruction
• +۳ مورد دیگر

Susanne Weitz is a Researcher at the Chair of Computer Graphics and Visualization, Technical University of Munich, working under Prof. Westermann. Her office is located in room 02.13.052 at Boltzmannstr. 3, 85748 Garching b. München, with contact email susanne.weitz@tum.de and office hours Monday–Thursday 9:00–15:00. Her research focuses on advancing computer graphics through 3D Gaussian Splatting, topology optimization, and neural rendering techniques. Key interests include efficient novel view synthesis for consumer devices, scientific visualization of meteorological data, and geometry processing for cinematic applications. She bridges theoretical innovation with practical implementation, emphasizing real-time performance and accessibility. Analysis of her 2022–2025 publications reveals a strong trajectory in deep learning-enhanced graphics, particularly for 3D reconstruction and visualization. Work spans Gaussian Splatting optimizations, turbulence analysis in fluid dynamics, and lattice structure design, demonstrating interdisciplinary applications from medical visualization to climate science. At TUM, she teaches core courses including Game Physics, Visual Data Analytics, Deep Learning in Computer Graphics, and Machine Learning for 3D Geometry across winter and summer terms. Her curriculum integrates cutting-edge research like neural rendering and topology optimization into practical student projects.

Ashish Goelمشاهده پروفایل

استاد

• Algorithms
• Optimization
• Probability
• +۱۱ مورد دیگر

Ashish Goel is a Professor at the Stanford University , with primary affiliations in the Management Science and Engineering department, and by courtesy, the Computer Science department. He is actively involved in the Social Algorithms Lab , the Stanford Crowdsourced Democracy Team , and the RAIN seminar . Goel's research centers on algorithm design, analysis, and applications , integrating optimization, probability, stochastics, and game theory to solve real-world problems. His current focus areas include Social Networks, Social Choice, Crowdsourced Democracy, Internet Commerce, Reputation Systems, and Large-Scale Data Processing . He emphasizes tangible impact in application domains, particularly through algorithmic innovation in networked systems. The research trends in his publications highlight Personalized PageRank, Social Network Analysis, Crowdsourced Decision-Making, and Randomized Algorithms . These works often intersect computational social science, distributed systems, and economic incentives , demonstrating his interdisciplinary approach. Scientific Awards: Best Paper Award, WWW 2009 Goel has mentored students in algorithmic research and collaborated on computational social science through the Stanford Crowdsourced Democracy Team. His industry experience, particularly at Twitter, Teapot, and Stripe , has significantly influenced his academic work, especially in recommendation systems, ad targeting, and search algorithms .

Douglas Cloweمشاهده پروفایل

استاد

• Observational Cosmology
• Gravitational Lensing
• Dark Matter
• +۷ مورد دیگر

Douglas Clowe is a Professor in the Department of Physics and Astronomy at Ohio University, affiliated with the College of Arts and Sciences and the Astrophysical Institute. His research focuses on observational cosmology, particularly gravitational lensing and the study of galaxy clusters to understand dark matter and dark energy. Ph.D., University of Hawaii Postdoctoral positions: Max Planck Institute for Astrophysics, University of Bonn, University of Arizona Joined Ohio University faculty in 2006 Clowe's research centers on observational cosmology, with a strong emphasis on gravitational lensing as a tool to probe the mass distribution in galaxy clusters. His work aims to determine the nature of dark matter and dark energy, measure cosmological parameters, and study the structural evolution of massive cosmic structures. He is particularly known for his groundbreaking research on the Bullet Cluster, which provided direct evidence for dark matter. His current projects include the European Distant Cluster Survey (EDisCS), the Local Volume Complete Cluster Survey (LoVoCCS), and contributions to major initiatives like LSST and DESTiny. His research integrates optical, infrared, and X-ray data to understand cluster mergers, shock heating, and star formation. His recent publications reflect a strong focus on weak lensing mass distributions, merging clusters, and cosmological surveys. Themes across his work include the alignment of mass and light in clusters, calibration of shear measurements, and the use of clusters as gravitational telescopes. He is deeply involved in large collaborations such as the LSST Dark Energy Science Collaboration. Notable scientific contributions and affiliations include: Lead investigator on the imaging side of the EDisCS survey Member of the LSST weak lensing science working group Proposed research for the Dark Energy Space Telescope (DESTiny) Key role in studies of the Bullet Cluster and Abell 2146/3827 mergers Clowe advises students and collaborators in advanced data analysis and observational techniques. He has secured collaborative research grants, including NSF funding for LoVoCCS. He is involved in major observational campaigns using Hubble, Chandra, and ground-based telescopes. His lab and team focus on weak lensing analysis, multi-wavelength data integration, and cosmological simulations. Future work includes expanding weak lensing surveys to lower redshifts and preparing for data from next-generation telescopes like LSST and JWST.

Giovanni Negliaمشاهده پروفایل

پژوهشگر

• Performance Evaluation of Distributed Systems
• Cache Networks
• Distributed Machine Learning
• +۷ مورد دیگر

Giovanni Neglia is a Research Director (DR2) at Inria, affiliated with the NEO team and the 3IA Côte d'Azur Chair, focusing on performance evaluation of distributed systems, cache networks, and large-scale machine learning. He is based in Sophia Antipolis, France. Inria, Sophia Antipolis NEO Team 3IA Côte d'Azur Chair His research interests center on distributed systems, leveraging mathematical tools such as Markov processes, control theory, fluid models, and game theory. Key areas include caching policies, distributed optimization for machine learning, and performance modeling of networks. He has previously worked on P2P networks, delay tolerant networks, smart grids, and complex networks. Recent publications demonstrate a strong focus on similarity caching, federated learning, and distributed optimization. Trends include developing online learning algorithms for caching, improving privacy and efficiency in federated systems, and optimizing content delivery in 5G and small cell networks. Best paper award at ITC-33 IEEE Infocom Distinguished TPC member (2017, 2018, 2022) Best paper award at ITC28 2016 Best paper award at IEEE Online Greencomm 2014 Best paper award at IEEE Infocom NetSciCom 2014 Best paper award at IEEE VTC2013-Spring Best student paper award at VALUETOOLS 2012 Best paper award at BIONETICS 2007 Giovanni Neglia leads the Fed-Malin Inria challenge on federated machine learning and has secured grants including a 3IA Côte d'Azur chair for PERUSALS (Pervasive Sustainable Learning Systems). He advises PhD students such as Othmane Marfoq and Chuan Xu, and collaborates with Accenture Labs, SAP, and Nokia Bell Labs. He has organized winter schools and tutorials on complex networks and similarity caching. He leads the NEO team and previously led the Maestro team. He is involved in the joint Brazilian-French research team Thanes on network science and has served on numerous technical program committees for major conferences including IEEE Infocom, INFOCOM, and SIGMETRICS.

• Information Systems
• Grid Computing
• Data Storage
• +۵ مورد دیگر

Fokke Dijkstra is a Researcher at the University of Groningen , specializing in Information Systems , Grid Computing , and Petabyte-scale Data Storage . His work focuses on enabling large-scale scientific research through infrastructure development. Key Collaborations: Astro-WISE, LOFAR (Low-Frequency Array) projects Research Themes: Long-term archiving, distributed computing, and grid technology for astrophysics Recent Activity: Contributed to a memorial article for Joop van Lenthe in 2022 Academic Output includes 15+ publications on scaling data systems for astronomy, with notable work on LOFAR and Astro-WISE infrastructure. His research bridges computational methods with astrophysical data challenges. Activities include participation in the BioSB 2020 conference, demonstrating his interdisciplinary engagement.

David Hung-Chang Duمشاهده پروفایل

استاد

• Intelligent Storage Systems
• Sensor/Vehicular Networks
• Cyber Physical Systems
• +۵ مورد دیگر

David Hung-Chang Du is a Professor and Qwest Chair Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities. He served as the Center Director of the NSF multi-university I/UCRC Center of Research in Intelligent Storage (CRIS) from 2009-2021, which was sponsored by 18 companies with $3.35M industrial membership funding. Prior to that, he organized an Intelligent Storage Consortium at Digital Technology Center with industrial funding from 2002-2009. He was also a Program Director at the National Science Foundation CISE/CNS Division from March 2006 to August 2008. Dr. Du received his B.S. degree in Mathematics from National Tsing-Hua University (Taiwan) in 1974 and M.S. and Ph.D. degrees from University of Washington (Seattle) in 1980 and 1981 respectively. He joined University of Minnesota as a faculty member in 1981 and has been there for over 40 years. He has also been a visiting professor in Germany, Korea, Singapore, Hong Kong and Taiwan. Dr. Du's research focuses on intelligent storage systems, sensor/vehicular networks, and cyber physical systems. His early career work included parallel processing and database design, followed by Computer-Aided Design for VLSI circuits in the 1980s and 1990s. From the late 1980s to present, he has worked on computer networking and its related applications including multimedia computing. Starting from 2000, his research has focused on new memory and storage technologies for handling extremely large volumes of available data and long-term data preservation. His current research focuses on hyper-converging infrastructure, recognizing that the Internet has become the largest existing computer system where data collection, storage, and networking must be integrated. His recent publications (2019-2022) demonstrate continued innovation across multiple domains including DNA storage technologies, hybrid storage systems, key-value store optimization, and edge computing. These works reflect his ability to adapt to emerging technological landscapes while maintaining focus on fundamental storage and systems challenges. The publications show strong industry collaboration, particularly with major technology companies like Facebook, where his team has characterized and optimized key database workloads. IEEE Fellow (since 1998) Fellow of the Minnesota Supercomputer Institute ACM Recognition of Service Award (2013) IEEE Certificate of Appreciation (2012, 2007) NSF Director's Award for Collaborative Integration (2008) Best Paper Award, International Conference on Internet of Vehicles (2015) Best Paper Award, International Conference on Computer Design (1998) Dr. Du has been highly active in professional service, serving as Editor of IEEE Transactions on Computers (1993-1998), member of several editorial boards, and holding leadership positions in numerous conferences including General Chair for IEEE Security and Privacy Symposium (2009), Program Committee Co-Chair for International Conference on Parallel Processing (2009), and General Chair for IEEE International Conference on Distributed Computing Systems (2010-2011). He has graduated 67 Ph.D. students (58 as single adviser and 9 jointly supervised) and 109 Master's students over his 40+ year career. His research has been supported by substantial grants from NSF, industry partners, and other funding sources totaling millions of dollars.

• Integer Linear Programming
• Operations Research
• Scheduling
• +۶ مورد دیگر

Onur Ozturk is an Associate Professor at the Telfer School of Management, University of Ottawa. Holding a PhD in Industrial Engineering from Grenoble Institute of Technology (2012), he previously worked as a postdoctoral fellow at Ivey Business School (2012-2014) and as an assistant professor at ESIEE Paris (2014-2017). Education: Ph.D. in Industrial Engineering, Grenoble INP His research focuses on large-scale optimization applied to healthcare and transportation systems, with methodological expertise in scheduling theory, simulation modeling, and integer linear programming. Current work includes pandemic-era staff scheduling and medical sterilization process optimization. Recent publications (2018-2024) demonstrate cross-sector applications of operations research in healthcare resource allocation, pandemic logistics, batch processing systems, and urban freight transport. These works appear in journals like Omega , European Journal of Operational Research , and Production and Operations Management . NSERC Grant (2019-2026): Exact and Approximate Solution Methods for Batch Scheduling Problems ($169,000) Telfer School Internal Grants (2018-2022): Projects on home health care routing, sterilization optimization, and pandemic response He contributes to decision-making tools for urban transportation through the Efficacity Institute and teaches in areas of business analytics and operations management.

Dikran Meliksetianمشاهده پروفایل

استاد مدعو

• Software Engineering
• Cloud Computing
• Machine Learning
• +۵ مورد دیگر

Dr. Dikran Meliksetian serves as Adjunct Faculty in the Computer Engineering and Computer Science Department at the University of New Haven's Tagliatela College of Engineering. An accomplished Senior IT Professional with extensive US and international experience, he leverages industry expertise from his tenure as an IBM Senior Technical Staff Member to enrich academic instruction. His educational foundation includes a Ph.D. in Computer Engineering from Syracuse University. Dr. Meliksetian's research spans critical domains including software engineering, cloud computing, machine learning, and artificial intelligence, with significant contributions to geospatial processing, enterprise IT services, grid computing, and quantum technology. His work bridges theoretical frameworks with practical industry applications, particularly in developing scalable architectures and service delivery models. Current involvement in initiatives like the QuantumUp! Challenge demonstrates his commitment to emerging technologies. His publication history reveals an evolution from foundational grid computing research (2004-2006) toward contemporary work in geospatial processing and cloud service transformation (2016-2019), reflecting industry shifts toward distributed systems and AI-driven solutions. Notable recognitions include: IBM Master Inventor status Senior Membership in the ACM Life Membership in IEEE and IEEE Computer Society Portfolio of over 50 patents While specific student names aren't documented, Dr. Meliksetian actively mentors through projects like the QuantumUp! Challenge where students develop quantum technology skills. His extensive patent portfolio and industry-academia transition exemplify significant research impact beyond traditional grant funding structures. As part of the Computer Engineering and Computer Science Department, he contributes to the academic ecosystem by integrating real-world IBM experience with theoretical instruction, particularly in emerging fields like quantum computing where student teams compete in national challenges.

Mher Safaryanمشاهده پروفایل

پژوهشگر

• Optimization theory and algorithms
• Machine learning
• Federated learning
• +۷ مورد دیگر

Dr. Mher Safaryan is a postdoctoral researcher at the Institute of Science and Technology Austria (ISTA), affiliated with Prof. Dan Alistarh's research group since 2022. Previously, he held postdoctoral positions at King Abdullah University of Science and Technology (KAUST) from 2019-2022 and served as a research technician there from 2016-2019. He earned his Ph.D. in Mathematics from Yerevan State University in 2018 under Prof. Grigori Karagulyan's supervision. Education: Ph.D. in Mathematics (2018, YSU) Past Affiliations: KAUST (2016-2022), Neural Magic/Red Hat (industrial secondment) His research focuses on optimization theory and algorithms for machine learning , particularly in developing communication/computation/memory-efficient methods for large-scale training and federated learning. Key contributions include LDAdam (low-dimensional gradient statistics optimization), GradSkip (accelerated local gradient methods), and Unified Scaling Laws for compressed representations. He has published in top venues like NeurIPS, ICML, ICLR, TMLR, and The Journal of Geometric Analysis. Scientific Awards: Marie Skłodowska-Curie Fellowship (MSCA COFUND IST-BRIDGE) His work bridges machine learning optimization with mathematical foundations from his earlier research in real harmonic analysis. Current collaborations include Prof. Dan Alistarh (ISTA), Dr. Alexandre Marques (Neural Magic), and Prof. Peter Richtárik (KAUST).

• Networks and Data
• Distributed Systems
• Peer-to-Peer Storage
• +۵ مورد دیگر

Gil UTARD is a University Professor at Université de Picardie Jules Verne (UPJV), specializing in Networks and Data. His research spans from foundational work in parallel computing and distributed systems to cutting-edge applications in medical IoT security and blockchain technology. Research Focus: Professor Utard's work has evolved significantly over three decades, beginning with fundamental research in parallel computing architectures and matrix operations. His early contributions include developing efficient out-of-core algorithms for large-scale matrix computations and pioneering work in data-parallel program validation. More recently, his research has pivoted toward critical applications in healthcare technology, specifically securing medical Internet of Things (IoT) devices through innovative blockchain implementations and advanced cryptographic protocols. Research Areas: Distributed Systems and Peer-to-Peer Networks Medical IoT Security and Privacy Blockchain Technology for Healthcare Parallel and High-Performance Computing Large-scale Data Storage and Management Cryptographic Protocols for Secure Data Sharing Research Evolution: His publication trajectory demonstrates a clear evolution from theoretical computer science foundations to practical security applications. The 2000-2010 period focused on parallel computing fundamentals, particularly matrix operations and storage systems. The 2011-2020 period saw expansion into peer-to-peer storage systems and distributed architectures. The 2021-present period represents a dramatic shift toward medical applications, with 70% of recent publications addressing healthcare-specific security challenges. Laboratory Affiliation: Professor Utard is associated with the Networks and Data domain within UPJV's research structure, though specific laboratory details are not provided in the source material.

Anne Trefethenمشاهده پروفایل

استاد

• Scientific Computing
• High-Performance Computing
• Numerical Algorithms
• +۳ مورد دیگر

Anne Trefethen is a Professor of Scientific Computing and Pro-Vice-Chancellor at the University of Oxford, responsible for the institution's Digital Strategy. As a Fellow of St Cross College, she has played a pivotal role in advancing high-performance numerical algorithms and large-scale scientific applications through her research. Pro-Vice-Chancellor for Digital Strategy, University of Oxford Director of the Oxford e-Research Centre (2005-2012) Chief Information Officer at University of Oxford (2012-present) Her research expertise spans: Parallel numerical algorithms Energy-aware algorithm design Scientific software engineering She has held leadership roles in the UK e-Science Core Programme and worked in the US with Thinking Machines Inc and Cornell University’s Theory Centre.

• Machine Learning
• Large-Scale Data Analysis
• Bioinformatics
• +۳ مورد دیگر

Inderjit Dhillon , Professor at the University of Texas at Austin , is a leading researcher in machine learning and big data analytics. As Director of the Center for Big Data Analytics, his work focuses on scalable algorithms for high-dimensional data, social network analysis, and gene-disease association prediction. ACM, IEEE, SIAM, and AAAS Fellow Gottesman Family Centennial Professor (2014) ICES Distinguished Research Award (2013) Research Interests: His contributions span machine learning , bioinformatics , and scientific computing , with recent work emphasizing deep learning for time series, adversarial robustness, and distributed optimization. Publications in top venues like NeurIPS and ICML highlight his expertise in inductive matrix completion and non-convex optimization . Scientific Awards: 2016: AAAS Fellow 2014: ACM, SIAM, IEEE Fellow 2013: ICES Distinguished Research Award 2011: SIAM Outstanding Paper Prize His students and collaborators frequently publish in areas like extreme classification , kernel methods , and multi-scale spectral decomposition . Labs and teams under his leadership have advanced nomadic computing and asynchronous optimization for massive datasets.

Dixin Tangمشاهده پروفایل

استادیار

• Databases
• Systems and Networking
• Vector Databases
• +۳ مورد دیگر

Dixin Tang is an Assistant Professor at the University of Texas at Austin Department of Computer Science. He previously served as a postdoc scholar at UC Berkeley under Prof. Aditya Parameswaran and earned his Ph.D. from the University of Chicago CS department under Prof. Aaron Elmore. Current research focuses on vector databases, large-scale unstructured data analysis, and data systems leveraging CXL memory technology. Key contributions include systems like Tigon (distributed CXL pod databases) and Pasha (scalable CXL pod architecture). His work emphasizes user-centered data systems, with projects like FormS (spreadsheet-to-SQL translation), Smash (string distance metrics), and Lux (visualization recommendations) demonstrating cross-decade innovation. Recent publications (2025) highlight trends in: CXL memory-optimized data architectures Homomorphism-based query frameworks Scalable unstructured data processing Scientific recognition includes: VLDB 2023 Best of Special Issue ICDE 2021 Short Paper Award Teaching roles include: CS 395T: Database Systems and LLMs (Fall 2025) CS 347: Data Management (Fall 2024) CS 395T: Advanced Query Optimizations (Spring 2024) Affiliated with the UT Data Systems research group, collaborating with Profs. Witchel and Chidambaram on CXL memory projects.

Jayesh Badwaikمشاهده پروفایل

پژوهشگر

• Computational Physics
• High Performance Computing
• Software Engineering
• +۴ مورد دیگر

Dr. Jayesh Badwaik is a Scientific Researcher at the Jülich Supercomputing Center (JSC) within Forschungszentrum Jülich, a leading interdisciplinary research center in Europe. His work is centered in the Accelerating Devices Lab, where he focuses on high-performance computing architectures and computational methods for scientific applications. Dr. Badwaik's research spans multiple disciplines at the intersection of computer science, physics, and mathematics. His primary research interests include: Computational Physics and numerical methods for scientific computing High Performance Computing (HPC) with focus on exascale systems GPU programming models and accelerated computing Software engineering for large-scale scientific applications Lattice Boltzmann Methods for fluid dynamics simulations Parallel numerical algorithms for conservation laws Analysis of Dr. Badwaik's publication record reveals a clear evolution from theoretical numerical methods toward practical implementation on cutting-edge computing architectures. His early work (2016-2020) focused on mathematical foundations of numerical schemes for conservation laws, while his recent publications (2023-2024) demonstrate increasing emphasis on exascale computing challenges. His contributions to the JUPITER benchmark suite represent significant work in evaluating next-generation supercomputing systems. A substantial portion of his research centers on scaling the Lattice Boltzmann Method to exascale platforms, addressing critical challenges in computational fluid dynamics at unprecedented scales. His expertise in GPU programming is evident from his practical overview of programming models, which provides valuable insights for the HPC community. Dr. Badwaik is actively involved in the Accelerating Devices Lab at JSC, contributing to Europe's high-performance computing ecosystem. His work bridges theoretical numerical analysis with practical implementation on advanced computing architectures, making significant contributions to scientific computing at scale. His research has implications for multiple scientific domains that rely on large-scale simulations, including climate modeling, materials science, and computational fluid dynamics.

Paul F. Baumeisterمشاهده پروفایل

پژوهشگر

• Parallel Programming
• Electronic Structure Calculations
• Simulation
• +۵ مورد دیگر

Dr. Paul F. Baumeister is a Researcher at the Forschungszentrum Jülich, leading the Simulation and Data Lab Electrons & Neutrons (SDLen) within the Large-Scale Data Science (LSDS) department. His work bridges computational simulations and experimental techniques, focusing on real-space density functional theory (DFT) methods for high-performance computing (HPC) applications. He contributes to advancing modeling of nanostructures and develops libraries for HPC platforms. His research spans interdisciplinary domains, including electron microscopy (Ernst Ruska-Centre), neutron scattering (JCNS), and X-ray scattering. These efforts aim to integrate theoretical models with empirical data for materials science and computational physics applications. Paul F. Baumeister is based at the Jülich Supercomputing Centre (JSC) in Building 14.14, Room 4015, Jülich, Germany. His ORCID identifier is 0000-0002-2005-4474.