جستجوی استاد راهنمای دانشگاهی

Athanasios Rontogiannisمشاهده پروفایل

دانشیار

• Signal Processing
• Machine Learning
• Computational Signal and Image Processing
• +۴ مورد دیگر

Athanasios Rontogiannis is an Associate Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA). He holds a PhD in Signal Processing from the National University of Athens (1997) and has held roles including Research Director at the National Observatory of Athens (2017–2021). His research focuses on signal processing, machine learning, and hyperspectral image analysis. Education: MEng (Electrical Engineering, NTUA, 1991), M.A.Sc. (University of Victoria, Canada, 1993), PhD (Signal Processing, National University of Athens, 1997). Research interests include adaptive algorithms, sparse representations, and tensor models. He has served on editorial boards of IEEE Transactions on Signal Processing and EURASIP journals, receiving an honorary distinction in 2020. He is a Senior Member of IEEE and affiliated with EURASIP and the Technical Chamber of Greece. Key contributions span hyperspectral unmixing, Bayesian algorithms, and space data exploitation. His work integrates machine learning for applications in space science and signal processing.

Athanasios Liavasمشاهده پروفایل

استاد

• Signal Processing for Telecommunications
• Information Theory
• Tensor Decomposition
• +۳ مورد دیگر

Athanasios Liavas is a Professor at the Technical University of Crete in the School of Electrical and Computer Engineering , specializing in Signal Processing for Telecommunications and Information Theory . He has held administrative roles as Department Chair (2009-2011), Vice Chair (2011-2013), and Dean of the ECE School (2017-2021). Education: Diploma (1989) and PhD (1993) in Computer Engineering and Informatics from the University of Patras. Professional Background: Postdoctoral Marie Curie Fellow at INT, Evry (1996-1998); Lecturer at University of Ioannina (1999-2001); Assistant/Associate Professor at University of the Aegean (2001-2004) and Technical University of Crete (2004-present). His research focuses on Signal Processing for Telecommunications , Information Theory , and Tensor Decomposition . Recent work involves nonnegative tensor factorization , parallel algorithms , and fMRI data analysis , with applications in wireless communications and medical imaging . Articles show trends in optimization algorithms , LDPC code design , and MIMO system robustness . Scientific Awards include: Marie Curie Fellowship (1996-1998) Associate Editor, IEEE Transactions on Signal Processing (2005-2009) Elected Member, IEEE Signal Processing for Communications and Networking Technical Committee (2006-2011) He has taught courses like Telecommunications Systems II , Wireless Communications , and Information Theory , and supervised students such as Despoina Tsipouridou (PhD) and Alex Balatsoukas-Stimming (Graduate). He leads projects like Partensor (Parallel Tensor Toolbox) and COOPCOM (Cooperative Communications), and contributes to labs including the Telecommunications Laboratory .

Sriram Krishnamoorthyمشاهده پروفایل

استاد پژوهش

• Parallel Programming Models
• Fault Tolerance
• High-Performance Computing
• +۶ مورد دیگر

Sriram Krishnamoorthy is a Research Professor at Washington State University's School of Electrical Engineering & Computer Science and a research scientist at Pacific Northwest National Laboratory (PNNL), where he serves as the System Software and Applications Team Leader in PNNL's High Performance Computing group. Dr. Krishnamoorthy earned his B.E. from the College of Engineering, Guindy in Chennai, India, and his M.S. and Ph.D. degrees from The Ohio State University. He is a senior member of the Institute of Electrical and Electronics Engineers. His research focuses on parallel programming models, fault tolerance, and compile-time/runtime optimizations for high-performance computing. He has made significant contributions in areas including: Fault tolerance techniques that minimize rollback during failures Dynamic load balancing for irregular parallel applications Compiler and runtime optimizations for HPC applications GPU programming and heterogeneous computing Quantum chemistry simulations and quantum computing Dr. Krishnamoorthy's publications span computational science, high-performance computing, and quantum chemistry. His recent work shows strong trends toward quantum computing applications, fault tolerance in large-scale systems, and optimization of computational chemistry methods. He has developed techniques for density matrix quantum circuit simulation, floating-point error analysis, and scalable execution of coupled-cluster models. His scientific achievements have been recognized with several prestigious awards: Best Paper Award at International Conference on High Performance Computing (HiPC'03) Best Paper Award at International Parallel and Distributed Processing Symposium (IPDPS'04) U.S. Department of Energy Early Career award (2013) PNNL's Ronald L. Brodzinski Award for Early Career Exceptional Achievement (2013) The Ohio State University's Outstanding Researcher award (2008) Dr. Krishnamoorthy has advised numerous graduate students and collaborated extensively with researchers across computational science domains. His work on the NWChem project demonstrates significant grant funding and large-scale collaborative research efforts in computational chemistry. He leads research efforts in PNNL's High Performance Computing group, focusing on system software and applications development for next-generation supercomputing platforms.

Saday Sadayappanمشاهده پروفایل

استاد

• Compiler Optimization
• High-Performance Computing
• Sparse/Dense Matrix Computations
• +۳ مورد دیگر

P. (Saday) Sadayappan is a Professor at the University of Utah's School of Computing, specializing in high-performance computing and compiler optimizations. His research focuses on performance optimization for parallel systems, particularly for tensor computations, sparse matrix operations, and machine learning workloads. He leads multiple NSF and DARPA-funded projects focused on GPU optimization, tensor computations, and scalable machine learning frameworks. Research Interests: Dr. Sadayappan's work spans compiler optimizations for high-performance systems, optimization of sparse/dense matrix/tensor computations, scalable machine learning, and algorithm-architecture co-design. His recent projects include developing performance-portable frameworks for tensor applications and optimizing data locality for scientific computing. Publication Trends: His recent publications (2020-2022) predominantly focus on GPU acceleration of machine learning workloads (especially CNNs), automated I/O complexity analysis, and optimization techniques for sparse matrix/tensor operations. Earlier work (2018-2019) established foundations in GPU code generation for tensor contractions and cache optimization. Awards and Honors: ACM SIGPLAN Most Influential PLDI Paper Award (2018) for A Practical Automatic Polyhedral Parallelizer and Locality Optimizer Active Grants and Projects: NSF: A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications (2022-2027) NIH SBIR: Enabling next generation machine learning for large scale image analysis (2023-2025) NSF: AI Institute for Intelligent CyberInfrastructure (ICICLE) (2021-2026) NSF: Data Locality Optimization for Sparse Matrix/Tensor Computations (2020-2024) DARPA SBIR: Performance Portable Framework for Developing Graph Applications (2017-2022) Teaching: He currently teaches CS 4230/6230 (Parallel and High-Performance Computing) at the University of Utah.

Konstantinos Tyrosمشاهده پروفایل

دانشیار

• Ramsey Theory
• Density Combinatorics
• Banach Space Geometry
• +۲ مورد دیگر

Konstantinos Tyros is Associate Professor in Mathematics at the University of Athens, specializing in combinatorial analysis and Ramsey theory. His research connects density theorems in combinatorics with problems in Banach space geometry and probabilistic methods. Key contributions include density versions of combinatorial theorems (Carlson-Simpson, Hales-Jewett), structure theorems for stochastic processes on discrete cubes, and concentration inequalities for high-dimensional random arrays. His work on spreading models in Banach spaces reveals new structures in functional analysis. He has developed novel approaches to nonlinear spectral gaps and dual Ramsey theory for trees, while advancing the Moser-Tardos algorithmic framework. Honors include technical excellence awards from Greek academic institutions.

Yufei Dingمشاهده پروفایل

دانشیار

• Compiler Technology
• Machine Learning
• Quantum Computing
• +۳ مورد دیگر

Yufei Ding is an Associate Professor in the Computer Science & Engineering Department at the University of California, San Diego (UCSD), where she leads the PICASSO Lab. Her research spans domain-specific language design, architecture and compiler optimization, and hardware acceleration, with current focus on developing high-performance, energy-efficient, and high-fidelity programming frameworks for quantum computing and machine learning. Dr. Ding received her Ph.D. in Computer Science from North Carolina State University and a B.S. in Physics from the University of Science and Technology of China. Her interdisciplinary background bridges physics and computer science, enabling her to tackle challenges in emerging computing paradigms. Her research interests focus on Compiler Technology, Machine Learning, and Quantum Computing , with specific expertise in domain-specific language design, architecture and compiler optimization, and hardware acceleration. Dr. Ding's work addresses critical challenges in programming frameworks for emerging technologies, particularly in making quantum computing more accessible and efficient through innovative compiler techniques and runtime systems. Dr. Ding's scientific contributions have been recognized with prestigious awards including the NSF CAREER Award (2020) and the IEEE Computer Society TCHPC Early Career Researchers Award for Excellence in High-Performance Computing (2019) . As an active researcher and educator, Dr. Ding serves on program committees for major conferences including PLDI, PPoPP, and SPLASH. She currently has Ph.D. openings in quantum computing and machine learning systems research, as well as a postdoc position in quantum computing for physics Ph.D. candidates with relevant background. Dr. Ding founded and leads the PICASSO Lab at UCSD, which focuses on developing innovative solutions for programming emerging computing technologies. The lab's work bridges theoretical foundations with practical implementations to address real-world challenges in high-performance computing.