جست‌وجوی استاد و پژوهشگر

Dimitrios Soudrisمشاهده پروفایل

استاد

• Computer Engineering
• Embedded Systems
• Reconfigurable Architectures
• +۵ مورد دیگر

Dimitrios Soudris is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), leading the Microprocessor and Digital Systems Lab (MicroLab). Previously, he served as Lecturer, Assistant, and Associate Professor at Democritus University of Thrace from 1995 to 2008. Diploma in Electrical Engineering (1987), University of Patras PhD in Electrical Engineering (1992), University of Patras His research focuses on Embedded Systems , Reconfigurable Architectures (FPGAs) , Hardware Accelerators for data centers/space/cloud, Edge Computing , and Low Power VLSI Design . Recent publications highlight trends in: AI/ML acceleration for edge and space applications Secure FPGA architectures for 6G networks Energy-efficient heterogeneous memory systems Approximate computing techniques Transformer optimization for low-power contexts Scientific Awards: INTEL and IBM awards (project LPGD #25256) HiPEAC, DAC, and ISCA awards (2010–2024) XILINX Open Hardware Design Contest (2017, 2019, 2021) He has coordinated >70 R&D projects funded by the European Commission, ENIAC-JU, ESA, and industry partners. As Associate Editor of ACM TODAES and conference chair (PATMOS, VLSI-SOC), he contributes to academic leadership. His lab, MicroLab, specializes in hardware-software co-design for emerging computing paradigms.

• Deep Learning
• Multimodal AI
• Explainable AI
• +۶ مورد دیگر

Giorgos Stamou is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), and a Visiting Professor at the MIT Sloan School of Management and MIT Open Learning. He directs the Artificial Intelligence and Machine Learning Systems Laboratory (AILS Lab) and has been a senior researcher at the Institute of Communications and Computer Systems (2000-2008) and an academic visitor at Oxford University (2011-2012). His research spans Deep Learning , Explainable AI , and Knowledge Representation , with a focus on Large Language Models and Multimodal Learning . He has coordinated over 60 funded projects and published 150+ papers. Recent work highlights trends in LLM Evaluation , Gender Bias Mitigation , and Multimodal Music Analysis , reflecting his interdisciplinary approach to AI challenges. Stamou has served on steering committees for W3C working groups (Rule Interchange Format, Web Ontology Language) and contributed to cultural heritage metadata enrichment through the CrowdHeritage projects. He founded NTUA's MSc program in Data Science and Machine Learning (2018-2022) and has organized conference tracks on riddle-solving frameworks and hallucination detection.

• Mathematical Biology
• Biophysics
• Cochlear Mechanics
• +۳ مورد دیگر

Daphne Manoussaki is an Assistant Professor at the University of Crete. Her primary research area is Mathematical Biology, with a focus on biophysics and computational modeling. Education: PhD in Mathematics, University of Washington (1996) Research Interests: She investigates biomechanical processes in cellular and auditory systems, combining mathematical modeling with experimental techniques like atomic force microscopy. Key areas include cochlear mechanics, vascular network formation, and cytoskeletal dynamics. Publication Trends: Her recent work (2015-2002) spans biophysical modeling of cell migration, cochlear geometry effects on hearing, and vascular morphogenesis. Keywords include Cell Biology, Biophysics, and Computational Modeling. Labs & Teams: No explicit lab or team affiliations are mentioned in the provided texts.

Vangelis Charmandarisمشاهده پروفایل

استاد

• Mathematical modeling
• Computational modeling
• Complex molecular systems
• +۸ مورد دیگر

Vangelis Charmandaris is a Professor at the Department of Applied Mathematics within the School of Science and Technology at the University of Crete. He earned his Ph.D. from the University of Patras in 2002. His research focuses on mathematical and computational modeling of complex molecular systems, with expertise in big data analysis methods, inverse problems, dimensionality reduction techniques, multiscale hierarchical modeling, and the development of coarse-grained models. His work also involves statistical mechanics, molecular simulations, and studies of non-equilibrium systems. Contact: Office B-322, Phone 39-3735

Vissarion Papadopoulosمشاهده پروفایل

استاد

• Structural Engineering
• Computational Mechanics
• Stochastic Analysis
• +۳ مورد دیگر

Vissarion Papadopoulos is a Professor in the Department of Structural Engineering at the School of Civil Engineering, National Technical University of Athens (NTUA). His office is located at the Statics and Aseismic Research Laboratory, with contact details including email vpapado@central.ntua.gr and phone 210 772 4158. He has maintained an active research profile since at least 2016, focusing on advanced computational methods in structural engineering. His primary research domains include: Structural Engineering and Computational Mechanics Stochastic Analysis and Uncertainty Quantification Multiscale Modeling of Composite Materials Machine Learning for Engineering Simulations Optimization of Structural Systems Thermomechanical Behavior of Advanced Materials Analysis of his recent publications reveals a decisive shift toward integrating deep learning frameworks with traditional computational mechanics. His work demonstrates consistent innovation in accelerating solutions for parametric and transient structural problems through transformer networks, Bayesian inference, and physics-informed neural networks. Key application areas include carbon nanotube reinforced composites, sustainable automotive design, and seismic-resistant structures, with emphasis on enhancing computational efficiency while maintaining accuracy. He is affiliated with NTUA's Statics and Aseismic Research Laboratory, which specializes in structural dynamics, earthquake engineering, and advanced computational methodologies for civil infrastructure analysis and design.

Zeris Christosمشاهده پروفایل

استاد

• Structural Engineering
• Reinforced Concrete
• Seismic Performance Analysis
• +۳ مورد دیگر

Zeris Christos is a Professor in the Department of Structural Engineering at the National Technical University of Athens (NTUA), where he also directs the Reinforced Concrete Laboratory. His work focuses on seismic performance evaluation, fire-resistant concrete, and composite material applications. Academic Rank: Professor Department: Structural Engineering Institution: NTUA Research interests include: Seismic behavior of reinforced concrete (RC) structures Advanced cementitious composites with carbon nanotubes Corrosion resistance in concrete materials Second-order effects in structural analysis Rocking oscillators and soft story dynamics Blast mitigation strategies for structural systems His recent publications emphasize numerical modeling of seismic performance, chloride penetration in CNT-reinforced concrete, and cyclic load testing of RC interfaces. Key trends include fire-resistant concrete innovation and seismic retrofitting methodologies.

• Physical Chemistry
• Spectroscopy
• Condensed Matter Physics
• +۷ مورد دیگر

Angelos G. Kalambounias is a Professor in the Department of Chemistry at the University of Ioannina, Greece, where he has progressed from Assistant Professor (2015-2020) to Associate Professor (2020-2024) and finally to full Professor (November 2024-present). He maintains additional affiliations as a Collaborating faculty member at the Institute of Materials Science and Computation and the Institute of Environment and Sustainable Development, and serves on the Board of Directors of the Scientific and Technological Park of Epirus. His research spans multiple domains of physical chemistry with emphasis on spectroscopic techniques. His primary interests include condensed matter structure and dynamics, development of contactless high-temperature spectroscopic measurement techniques, Raman spectroscopy applications in thermodynamics, inorganic coordination complexes, glass science (particularly tellurite and oxide glasses), nanomaterials, atmospheric pollution analysis, and ultrasonic spectroscopy. His work bridges fundamental physical chemistry with practical applications in materials science and environmental monitoring. His publication record shows consistent output since 1999, with recent work focusing on molecular dynamics, peptide structure, drug delivery systems, microplastic analysis, and advanced spectroscopic techniques. His research group comprises numerous students across physics and chemistry disciplines, reflecting the interdisciplinary nature of his work. Among his professional roles, he serves as a Review Editor on the Editorial Board of 'Battery Systems and Applications' and has acted as a reviewer for over 30 international scientific journals spanning physical chemistry, materials science, and spectroscopy. His teaching portfolio includes undergraduate and graduate courses in physical chemistry, statistical mechanics, quantum chemistry applications, spectroscopic methods, and materials characterization at both the University of Ioannina and previously at the University of Patras.

Ioannis Skarmoutsosمشاهده پروفایل

استادیار

• Theoretical Physical Chemistry
• Computational Chemistry
• Supercritical Fluids
• +۴ مورد دیگر

Ioannis Skarmoutsos is an Assistant Professor at the Department of Chemistry, University of Ioannina , specializing in Theoretical-Computational Physical Chemistry . His research employs multi-scale molecular simulations and statistical engineering theories to investigate complex fluid systems. Focus on supercritical fluids, ionic liquids, and nanoporous materials Applications in green chemistry, energy storage, and environmental technology Expertise in molecular dynamics and computational modeling Recent publications highlight his work on electrolyte design for batteries, nanoporous material simulations for gas separation, and anomalous properties of water under extreme conditions. His computational approaches address challenges in solvation dynamics, dielectric properties, and structural transitions across diverse thermodynamic states.

Evaggelos Kaselourisمشاهده پروفایل

استادیار

• Applied Physics
• Applied Mathematics
• Vibro-acoustics
• +۵ مورد دیگر

Evaggelos Kaselouris is an Assistant Professor at the Hellenic Mediterranean University's Department of Music Technology and Acoustics and a Researcher at the Institute of Plasma Physics and Lasers (IPPL). His affiliations include prior roles as a PostDoc at IPPL (2016–2023) and a Scientific Collaborator at the Technological Educational Institute of Crete (2011–2016). He holds a PhD from the Technical University of Crete and a degree in Applied Physics and Mathematics from the National Technical University of Athens. Dr. Kaselouris's research integrates multiphysics simulations across applied physics, plasma dynamics, and vibro-acoustics. Key areas include: Laser-generated acoustic waves and their applications in material characterization and musical instrument analysis. Finite element modeling of thermo-mechanical processes in laser machining and plasma instabilities. Development of crystalline undulators for narrowband gamma-ray radiation and optically shaped gas targets for ion acceleration. His computational work employs FEM/BEM to simulate phenomena ranging from nanostructured surface modifications to violin bridge dynamics. Recent studies emphasize laser-based interferometry for instrument diagnostics and ultrafast photoacoustic transduction in thin films. He actively collaborates on European projects (e.g., TECHNO-CLS) and contributes to the ESFRI infrastructure HiPER. No awards or supervised students are documented, but his lab leverages IPPL's high-power laser systems and advanced simulation tools for experimental validation.

Nikolaos Papadakisمشاهده پروفایل

استادیار

• Composite Materials
• Strength of materials and structures
• Computational Engineering
• +۷ مورد دیگر

Nikolaos Papadakis is an Assistant Professor in the Department of Mechanical Engineering at Hellenic Mediterranean University, elected in 2019. His research spans computational engineering, composite materials, renewable energy systems, and statistical data analysis. With prior experience at Warwick Manufacturing Group and TEI of Crete's Wind Energy Lab, he brings expertise in both academic and industrial R&D contexts. Education: PhD: University of Warwick (Strain rate effects in thermoplastic composites) MSc: University of Warwick (Manufacturing Systems Engineering) BS: Aristotle University of Thessaloniki (Mechanical Engineering) Research Focus: His work integrates computational modeling with experimental techniques across: Composite material behavior under dynamic loads Statistical analysis of large engineering datasets Renewable energy system optimization Optical deformation measurement methods Automotive and biomechanical applications Projects & Publications: Actively participates in European/national R&D initiatives. Authored 10+ journal papers and 14+ conference publications (h-index 10), covering material science, energy systems, and automation technologies.

Mattia Fazziniمشاهده پروفایل

استادیار

• Software Engineering
• Software Testing
• Software Maintenance
• +۵ مورد دیگر

Mattia Fazzini is an Assistant Professor in the Department of Computer Science & Engineering at the University of Minnesota's College of Science and Engineering. His primary academic appointment focuses on software engineering research and teaching, with active involvement in major conferences including ASE, ISSTA, ICSE, and MOBILESoft where he has served in leadership roles such as General Co-chair (MOBILESoft 2023) and Program Committee Co-chair. His research centers on software testing, maintenance, and security , with particular emphasis on mobile applications. Key research themes include: Developing techniques for automated Android testing and maintenance Addressing API compatibility issues across Android versions Creating tools for test oracle generation and bug reproduction Investigating security vulnerabilities in mobile ecosystems Optimizing test suites through test double analysis His recent publications (2021-2025) reveal strong focus on Android-specific challenges, with recurring themes in compatibility testing, automated test generation, and security analysis. Over 60% of his work involves tool development for practical testing scenarios, particularly targeting mobile platforms. Notable recognitions include: IEEE TCSE Distinguished Paper Award (2024) for work on test suite optimization ACM Distinguished Paper Award (2022) for COVID-19 app analysis As an educator, he advises multiple PhD and Master's students while teaching undergraduate and graduate courses including CSCI 3081W (Program Design) and CSCI 5802 (Software Engineering II). His service contributions span conference organization (MOBILESoft, ISSTA, ICSE) and extensive program committee work across top software engineering venues. He leads research projects focused on practical testing solutions with real-world applicability in mobile software development.

Chao Pengمشاهده پروفایل

مدرس پاره‌وقت

• Software Testing
• Program Analysis
• Programming Languages
• +۵ مورد دیگر

Chao Peng is a Principal Research Scientist at ByteDance where he leads the Trae Research team (ByteDance Software Engineering Lab), conducting cutting-edge research on AI agents for software engineering. He also serves as a Part-time Postgraduate Student Mentor at Fudan University's School of Computer Science, bridging industry research with academic mentorship. PhD in Informatics (2021), University of Edinburgh, UK MSc in High Performance Computing and Data Science (2017), University of Edinburgh, UK BEng in Computer Science and Technology (2016), Xuzhou University of Technology, China Dr. Peng's research focuses on the intersection of software testing, program analysis, and large language models. His work explores how AI agents can revolutionize software engineering practices, with particular emphasis on automated bug detection, code generation, and testing frameworks. He has pioneered approaches for evaluating LLM performance in software engineering contexts and developing agent-based systems that enhance developer productivity while maintaining code quality and security. His recent publications demonstrate a clear trend toward integrating large language models with traditional software engineering practices. The research spans code generation evaluation, security vulnerability detection, automated bug reproduction, and issue localization. These works collectively advance the field of AI-assisted software development by addressing practical challenges in reliability, security, and efficiency of AI-generated code. Distinguished Reviewer for FSE'25 School of Informatics Scholarship (fully-funded PhD scholarship) Outstanding Graduate Scholarship at Xuzhou University of Technology Multiple China National Scholarships Honours Spot Bonus at ByteDance Certificate of Achievement for HPCAC Student Cluster Competition Dr. Peng actively mentors students through his role at Fudan University and previously at the University of Edinburgh, where he served as sub-supervisor for MSc projects and teaching assistant for software testing courses. His research has attracted significant industry attention, leading to multiple collaborations between ByteDance and academic institutions. He frequently serves on program committees for major software engineering conferences including ASE, FSE, and ICSE, demonstrating his leadership in the field. As leader of the Trae Research team at ByteDance Software Engineering Lab, Dr. Peng oversees research on AI agents for software engineering, including the application and evaluation of AI agents and training LLMs for agent-based systems. The lab's work focuses on practical systems that predict, detect, diagnose, and fix bugs across various software systems, with particular emphasis on real-world applications and measurable impact on developer productivity.

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.

Minki Choمشاهده پروفایل

پژوهشگر ارشد

• Verified Compilation
• Relaxed Memory Concurrency
• Program Logic
• +۲ مورد دیگر

Minki Cho is a Research Fellow in the Department of Computer Science and Engineering at Seoul National University, South Korea. Having completed their Ph.D. in August 2023 with a thesis titled "Simplifying Reasoning under Weak Memory Concurrency," they continue active research while maintaining institutional affiliation. Cho's educational background includes: Sep. 2017 - Aug. 2023: Ph.D. Computer Science and Engineering, Seoul National University Mar. 2014 - Feb. 2017: B.S. Computer Science and Engineering, B.S. Philosophy of Mathematics and Logics (double major), Seoul National University Mar. 2011 - Feb. 2014: Seoul Science High School Cho's research focuses on Verified Compilation , Relaxed Memory Concurrency , and Program Logic , bridging theoretical foundations with practical applications in programming language design and verification. Their work addresses fundamental challenges in weak memory models, compiler optimizations, and formal verification frameworks, with particular emphasis on developing rigorous semantic models that enable practical compiler optimizations while maintaining program correctness. Cho's publication record demonstrates consistent contributions to top-tier venues including PLDI, POPL, and OOPSLA from 2020-2025, showing evolution from foundational work on relaxed memory concurrency toward more comprehensive verification frameworks supporting complex language features like integer-pointer casting and liveness properties. Recognition for Cho's work includes: PhD Dissertation Award, Department of Computer Science and Engineering, Seoul National University (2023) Gold medal in University Students Contest of Mathematics, Korean Mathematical Society (2014) Prior to completing their Ph.D., Cho gained teaching experience as a TA for: Computational Civilization (2020 Spring) Programming Languages (2019 Fall) Principles and Practices of Software Development (2019 Spring/2018 Spring) Principles of Programming (2018 Fall)