جست‌وجوی استاد و پژوهشگر

Livieris Ioannisمشاهده پروفایل

استادیار

• Machine Learning
• Optimization Methods
• Deep Learning
• +۷ مورد دیگر

Livieris Ioannis is an Assistant Professor in the Department of Statistics and Insurance Science at the University of Piraeus. He holds academic positions including Adjunct Professorships at the University of the Peloponnese and Technological Educational Institute of Western Greece. His research focuses on optimization methods for neural networks, machine learning, ensemble techniques, and their applications in healthcare, finance, education, and environmental science. Education: Ph.D. in Mathematics (2012), University of Patras M.Sc. in Computational Mathematics & Informatics in Education (2008), University of Patras B.Sc. in Mathematics (2006), University of Patras Research Interests: Dr. Livieris specializes in developing optimization algorithms for neural networks, semi-supervised learning, and ensemble methods. His work emphasizes practical applications such as time series forecasting (financial, environmental), medical image analysis (cancer detection, X-ray classification), and educational data mining (student performance prediction). He also explores explainable AI frameworks to enhance transparency in deep learning models. Key Contributions: He has contributed to over 50 peer-reviewed articles, including work on weight-constrained neural networks, gradient-based optimization, and CNN-LSTM models for cryptocurrency forecasting. His research has been recognized with inclusion in Stanford’s top 2% scientists (2020–2023) and a best paper award at HERCMA ’09. Awards & Roles: Associate Editor, Evolving Systems (Springer) Reviewer for 50+ journals including Neurocomputing and IEEE Transactions on Neural Networks Grants & Projects: Principal investigator in EU-funded projects like NEUROCLIMA (climate resilience via AI), ORBIS (democratic participation via AI), and PVAdapt (sustainable energy systems). He also leads initiatives in explainable AI for medical imaging and causal effect estimation in social science. Labs & Teams: Active in interdisciplinary teams at the University of Piraeus, focusing on AI-driven solutions in education, healthcare, and environmental monitoring. Collaborates with institutions like the IEEE and the Hellenic Association of ICT in Education.

George Tsekourasمشاهده پروفایل

استاد

• Artificial Intelligence
• Cultural Informatics
• Machine Learning
• +۶ مورد دیگر

Prof. George Tsekouras is a Professor in the Department of Cultural Technology and Communication at the University of the Aegean since 2002. His research focuses on artificial intelligence, cultural informatics, and expert systems, with over 100 publications in journals and conferences. He has contributed to projects involving machine learning fairness, blockchain sustainability, healthcare monitoring systems, and cultural heritage preservation. Research interests include AI applications in cultural heritage (e.g., 3D asset quality, art recoloring for color blindness), ethical AI in criminal justice systems, energy-efficient blockchain technologies, and environmental monitoring via machine learning. His work bridges computational methods with societal challenges, emphasizing interdisciplinary approaches. Notable trends in his publications include leveraging graph neural networks for healthcare (e.g., Parkinson’s disease monitoring), exploring ChatGPT for hospitality personalization, and developing hybrid machine learning models for disaster prediction. His contributions span technical innovation and social impact, such as improving accessibility through image recoloring algorithms and reducing blockchain’s environmental footprint. No scientific awards are listed. Advising and grants details are unavailable, but his extensive publication record indicates active research collaboration. His work often integrates cultural data with computational tools, reflecting his department’s mission at the University of the Aegean.

• Social Data Science
• Digital Humanities
• Quantitative and Computational Social Science
• +۴ مورد دیگر

Panagiotis Michailidis is a Professor at the Department of Balkan, Slavic and Oriental Studies, School of Economic and Regional Studies, University of Macedonia. His academic work focuses on Computational Methods and Informatics, integrating technological expertise with regional studies. BSc (1998) and PhD (2004) in Applied Informatics from University of Macedonia Academic career progression: Lecturer (2004-2013), Assistant Professor (2014-2019), Associate Professor (2019-2023), now Professor Research spans computational approaches to social science and humanities, with significant contributions to: Social Data Science Digital Humanities Quantitative Social Science Parallel/Distributed Computing String Matching Algorithms Scientific Computing Recent publications reveal interdisciplinary focus combining: Augmented Reality applications for cultural heritage Machine Learning for Greek language sentiment analysis High-performance computing implementations Metaheuristic optimization techniques Scientometric analyses of research fields Active in academic service through: IEEE memberships Conference program committee roles Guest editor for Algorithms special issue Research program participation Reviewing for multiple journals Teaching portfolio includes: DIGITAL SOCIAL RESEARCH METHODS INFORMATION, TECHNOLOGY AND SOCIETY QUANTITATIVE METHODS OF SOCIAL SCIENCES SPACE AND CULTURAL MANAGEMENT COMPUTER SCIENCE TOPICS

• Signal Processing
• Computer Vision
• Robotics
• +۵ مورد دیگر

Petros Maragos is a Professor at the National Technical University of Athens (NTUA) in the School of Electrical and Computer Engineering, where he directs the Division of Signals, Control and Robotics. He founded the Computer Vision, Speech Communication & Signal Processing Lab (1999) and the Hellenic Robotics Center of Excellence (2025). His research spans signal processing, computer vision, robotics, and machine learning, with 450+ publications and leadership in 50+ EU/Greek/US projects. Education includes a Dipl.Ing. from NTUA (1980), M.Sc./Ph.D. from Georgia Tech (1982/1985), and faculty positions at Harvard University (1985-1993) and Georgia Tech (1993-1998). Research Focus: Multimodal perception, nonlinear systems, assistive robotics, and deep learning. Recent work integrates tropical geometry with neural networks, robotic healthcare applications, and sign language technologies. Articles emphasize neural architectures, real-world robotics, and AI for social good. Awards: IEEE Fellow (1995), EURASIP Fellow (2010) IEEE W.R.G. Baker Prize (1995) NSF Presidential Young Investigator Award (1987-1992) CVPR/PETRA Best Paper Awards (2022-2025) Advising & Grants: Supervised 30+ PhDs and 130+ Master's students. Secured funding from EU Horizon 2020, NSF, and Greek national programs for projects like i-Walk (robotic mobility) and e-Prevention (mental health monitoring). Labs: Leads NTUA's Intelligent Robotics Lab and co-founded the Robotics Institute at Athena Research Center, focusing on human-robot interaction and perception systems.

Işıl Dilligمشاهده پروفایل

دانشیار

• Program analysis
• Program synthesis
• Program verification
• +۴ مورد دیگر

Işıl Dillig is an Associate Professor of Computer Science at the University of Texas at Austin, where she leads the UToPiA research group. Her academic career spans over a decade of significant contributions to programming languages research, particularly in program analysis, verification, and synthesis. Dr. Dillig received all her academic degrees (BS, MS, and PhD) from Stanford University before joining the faculty at UT Austin. Her educational background established the foundation for her innovative research approach that bridges theoretical computer science with practical applications. Her research focuses on developing techniques to make software systems more reliable, secure, and easier to build through advanced program analysis, verification, and synthesis methods. She has pioneered approaches that combine symbolic reasoning with machine learning to tackle complex software engineering challenges across multiple domains including security, databases, and programming language theory. Her work demonstrates exceptional depth in creating practical tools that address real-world software development problems while maintaining strong theoretical foundations. Analysis of Dr. Dillig's publication record reveals a consistent trajectory of innovation in program synthesis, with recent work expanding into neurosymbolic approaches that bridge neural networks with formal methods. Her research shows strong connections between theoretical foundations and practical applications, particularly in security-critical systems, database technologies, and blockchain applications. The evolution of her work demonstrates increasing sophistication in handling complex program structures while maintaining practical usability. Dr. Dillig has received prestigious recognition for her research contributions: Sloan Fellowship NSF CAREER award As a dedicated educator and research leader, Dr. Dillig has served in significant roles including Program Chair for PLDI 2022 and Steering Committee member for PLDI. She has mentored numerous students through her UToPiA research group, guiding research in program synthesis, verification, and analysis. Her work has been supported by substantial research grants that have enabled innovative projects at the intersection of programming languages and security. Dr. Dillig leads the UToPiA (UT Austin Programming, Languages, and Analysis) research group, which focuses on developing novel techniques for program analysis, verification, and synthesis. The group maintains strong collaborations with industry partners and academic institutions worldwide, translating theoretical advances into practical tools that address real software engineering challenges.

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.

Gilbert Bernsteinمشاهده پروفایل

استادیار

• Computer Graphics
• Programming Languages
• High-Performance DSLs
• +۷ مورد دیگر

Gilbert Bernstein is an Assistant Professor in the Computer Science & Engineering department within the College of Engineering at the University of Washington. His research bridges computer graphics and programming languages, with a focus on high-performance domain-specific languages. Previously, he was a post-doctoral scholar at UC Berkeley and MIT working with Jonathan Ragan-Kelley, and received his PhD from Stanford University under Pat Hanrahan. His research interests span Computer Graphics, Programming Languages, High-Performance DSLs, Physical Simulation, Geometry & Topology, Differentiable Programming, Hardware DSLs, Tools for Artists, Fabrication, and Human-Computer Interaction. Bernstein develops languages and compilers that enable efficient computation for creative applications, physical simulations, and graphics rendering systems. His recent publications reveal strong trends in differentiable programming for graphics applications, domain-specific languages for hardware acceleration, and computational approaches to traditional crafts like quilting and knitting. His work consistently combines formal language theory with practical applications in graphics and fabrication. Bernstein actively mentors students across multiple institutions including current advisees Felix Hahnlein (UW Postdoc), Ryan Zambrotta (UW PhD), Haoran Peng (UW PhD), and previous students including Alex Reinking (UC Berkeley PhD 2022, now at Qualcomm) and MacKenzie Leake (Stanford PhD 2021, now at Adobe Research). His lab works on diverse projects including debugging CAD programs, compilers for finite element methods, semantics for knitting machines, algebraic scheduling of tensor programs, and exocompilers for hardware accelerators. Bernstein also collaborates on DSLs for networking, Counterstrike bots, gradient-based optimization, memory management, hardware design, and garment design tools.

Sang-Hoon Kimمشاهده پروفایل

دانشیار

• Operating Systems
• Memory Systems
• Storage Systems
• +۴ مورد دیگر

Sang-Hoon Kim is an Associate Professor in the Department of Software and Computer Engineering and Department of Artificial Intelligence at Ajou University, South Korea. He leads the Systems Software Lab (Paldal Hall 1004-2) and maintains active collaborations with Virginia Tech as a Visiting Scholar since August 2024. His academic journey includes a Ph.D. in Computer Science from KAIST (2016) under advisors Seungryoul Maeng and Jin-Soo Kim, and a B.S. in Computer Science from KAIST (2002). His research spans operating systems, memory management, and storage systems with focus on mobile platforms, heterogeneous architectures, and SSD technologies. Key interests include memory fragmentation control , distributed thread execution , key-value storage optimization , and resource disaggregation . His work bridges theoretical innovation with practical system implementations, particularly for mobile and datacenter environments. Kim's publication portfolio shows consistent output in top-tier venues including USENIX FAST, VLDB, ICDCS, and ASPLOS. His research demonstrates evolution from mobile memory management (2015-2017) toward distributed systems and hardware-aware software (2019-present), with recent emphasis on resource-disaggregated environments and heterogeneous-ISA computing. The 2024 Best Paper Award at USENIX FAST highlights his impact in storage systems research. Best Paper Award at USENIX FAST'24 Multiple patents including US-9588912B2 for memory control He directs significant research projects funded by ETRI, NRF, and US ONR, including current work on memory-centric computing systems (2020-2023) and disaggregated non-volatile memory systems using RDMA (2018-2020). His Systems Software Lab maintains strong industry partnerships with Samsung Electronics and NHN, with prior projects improving Android memory management and developing SSD-based storage systems for large-scale internet services.

Chandrakana Nandiمشاهده پروفایل

استادیار

• Programming Languages
• Compilers
• Equality Saturation
• +۵ مورد دیگر

Chandrakana Nandi is the Director of US R&D at Certora and an affiliate assistant professor in the Department of Computer Science & Engineering at the University of Washington's College of Engineering. She completed her PhD at the University of Washington working with Zachary Tatlock and Dan Grossman in the PLSE research group. Her research focuses on building tools for scaling automated formal verification to real-world programs, particularly for DeFi applications. She works extensively with equality saturation techniques (egg project) and has made significant contributions to computational fabrication through projects like Carpentry Compiler, Szalinski, and LambdaCAD. Her work bridges programming languages, compilers, and digital fabrication, creating novel tools that transform how we design and manufacture physical objects. Nandi's publication record shows a strong trajectory in programming language techniques applied to verification and fabrication. Her work on equality saturation has become foundational in the field, with the egg library enabling state-of-the-art results in compiler optimization and program synthesis. Recent work has expanded into formal verification of smart contracts, demonstrating the versatility of her research approach across different domains. Distinguished Paper Award at OOPSLA 2021 Sigplan Research Highlight for POPL 2021 As Director of US R&D at Certora, she leads research efforts on verification tools for languages like WASM and techniques to help users write formal specifications more easily using mutation testing. She has served in numerous organizational roles for major programming languages conferences including as Workshops Co-Chair for ICFP 2025 and Committee Member for PLDI Review Committee. Nandi has established herself as a leader in the intersection of programming languages and computational fabrication, with her work on equality saturation becoming particularly influential across multiple subfields of programming languages research.

• Interaction in Intelligent Environments
• Novel HCI approaches and tools
• User Experience Design, Methods and Tools
• +۷ مورد دیگر

Partarakis Nikolaos serves as an Assistant Professor in the Department of Applied Informatics at the University of Macedonia. His teaching responsibilities include core courses such as Data Structures (AIC205), Human-Computer Interaction (AIC401), and Multimedia Technologies & Graphics (CSE706). His research program pioneers human-centered computing across intelligent environments and immersive technologies, with emphasis on inclusive design and real-world applicability. Key thematic areas include: Interaction in Intelligent Environments Novel HCI approaches and tools User Experience Design, Methods and Tools e-Accessibility, Design for All and Universal Access Representation of tangible/intangible Cultural Heritage X-Reality continuum interaction Adaptive User Interfaces With 11 books, 49 journal articles, and 39 conference publications, his scholarly output demonstrates sustained contributions to HCI theory and practice, particularly in accessibility frameworks and XR interface innovation. The publication trajectory reveals growing emphasis on cultural heritage digitization and motion-understanding systems since 2020.