جست‌وجوی استاد و پژوهشگر

Viktor Kunčakمشاهده پروفایل

دانشیار

• Program Synthesis
• Program Verification
• Automated Reasoning
• +۳ مورد دیگر

Viktor Kunčak is an Associate Professor with tenure at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences. He leads the Laboratory for Automated Reasoning and Analysis (LARA) and has been at EPFL since 2007 after completing his PhD at the Massachusetts Institute of Technology (MIT). His educational background includes: PhD from Massachusetts Institute of Technology (MIT), 2007 Professor Kunčak's research focuses on bridging the gap between human goals and computational realizations through program synthesis , verification , and automated reasoning . His work has significant applications in software development, formal methods, and programming languages. He is particularly known for developing practical tools like Leon and Stainless that implement theoretical advances in automated reasoning. His recent publications demonstrate a consistent focus on advancing program synthesis techniques, verification frameworks, and formal methods. The research trajectory shows progression from foundational theoretical work to practical applications and tools that can be used by developers. Many papers focus on making verification and synthesis more accessible and efficient for real-world programming tasks. His notable scientific achievements include: ACM SIGSOFT Distinguished Paper Award for work on automated testing European Research Council (ERC) Grant of 1.5M EUR (2012) Communications of the ACM Research Highlight for a PLDI paper Professor Kunčak has supervised 13 completed PhD theses and teaches courses on functional and parallel programming, compilers, and verification at EPFL. He has also co-taught a popular MOOC on Parallel Programming that reached over 100,000 learners worldwide. His research has been supported by significant funding including a 5-year ERC grant. He has served in leadership roles for major conferences including as program co-chair for FMCAD 2014 and VMCAI 2012. He leads the Laboratory for Automated Reasoning and Analysis (LARA) at EPFL, which develops tools like Stainless for program verification. The lab has established an international presence through collaborations including a European COST Action to establish standardized formats for verification and synthesis (Rich Model Toolkit).

Armando Solar-Lezamaمشاهده پروفایل

دانشیار

• Software Synthesis
• High-Performance Computing
• Information Flow Security
• +۳ مورد دیگر

Armando Solar-Lezama is an Associate Professor at the Massachusetts Institute of Technology (MIT) where he leads the Computer Aided Programming Group within CSAIL. His research bridges programming languages, formal methods, and machine learning to advance program synthesis techniques for real-world applications. His primary research focuses on software synthesis with applications spanning high-performance computing, information flow security, and probabilistic programming. He investigates how to automatically generate correct and efficient programs from specifications, with recent work emphasizing neurosymbolic approaches that combine neural networks with symbolic reasoning for complex synthesis tasks. Analysis of his publication trends (2021-2025) reveals a strategic evolution from foundational synthesis algorithms toward practical applications in probabilistic programming and security-critical systems. His work consistently integrates machine learning with formal verification, demonstrating growing emphasis on coarse-to-fine synthesis methods and knowledge compilation techniques for discrete probabilistic models. Award highlights: SIGPLAN Milner Award As leader of MIT's Computer Aided Programming Group, Solar-Lezama mentors graduate researchers and collaborates across institutions. His extensive service as Program Chair for POPL 2025 and committee roles at premier conferences (PLDI, POPL, SPLASH) demonstrates significant community leadership. While specific grant details aren't provided, his sustained publication output in top venues indicates robust funding support. He directs the Computer Aided Programming Group at MIT CSAIL, which develops tools for program synthesis, verification, and analysis. Current projects focus on neurosymbolic programming frameworks, probabilistic program synthesis, and security-aware compilation techniques that integrate information flow control with high-performance computing optimizations.

Simon Demangeمشاهده پروفایل

پژوهشگر

• Fluid Dynamics
• Aeroacoustics
• Turbulent Boundary Layers
• +۵ مورد دیگر

Simon Demange is a post-doctoral researcher at the Technical University of Berlin, working within the Institute for Fluid Mechanics and Technical Acoustics (ISTA) in the Faculty of Traffic and Machine Systems. His research focuses on linear stability and resolvent analyses of turbulent boundary layers developing over airfoils, with particular emphasis on developing low-order physics-based models describing noise generation mechanisms from coherent structures in turbulent flows. Dr. Demange completed his research master and PhD thesis within the aerospace and aeronautics department of the von Karman Institute for Fluid Dynamics in 2021. His doctoral research focused on the onset of absolute and global instabilities in high-enthalpy round jets, establishing his expertise in flow instability analysis. Dr. Demange's research interests span several critical areas of fluid dynamics with practical engineering applications: Turbulent boundary layer dynamics over airfoils and wings Resolvent analysis for modeling coherent structures in turbulence Trailing edge noise mechanisms in wind turbines and aircraft Physics-based low-order modeling of flow phenomena Plasma jet instabilities and high-enthalpy flow dynamics Multi-physics flow modeling and simulation Analysis of Dr. Demange's publication record reveals a strong trajectory of increasingly sophisticated work bridging fundamental fluid dynamics with practical engineering applications. His research demonstrates particular expertise in applying stability analysis techniques to understand and model noise generation in aerodynamic systems, with significant implications for wind turbine design and aircraft noise reduction. His collaborative work spans multiple institutions and shows strong interdisciplinary connections between theoretical fluid dynamics, computational methods, and experimental validation. Dr. Demange has developed specialized computational tools including FELiCS, a versatile linearized solver for multi-physics flows, demonstrating his capability in both theoretical development and practical implementation. His work on wind turbine noise addresses a critical challenge in renewable energy technology, while his research on high-speed train aerodynamics contributes to transportation engineering advancements.

• Cybersecurity
• Software Security
• Phishing Detection
• +۴ مورد دیگر

Adam Doupé is a full professor at Arizona State University, specializing in cybersecurity and software security. His research focuses on automated vulnerability analysis, phishing detection, binary decompilation, and security in web technologies. Key research areas include: Cybersecurity Software Security Phishing and Fraud Detection Binary Analysis Automated Vulnerability Discovery His recent work explores large-scale phishing mitigation using machine learning (e.g., ScamNet), improving binary decompilation techniques (e.g., SAILR), and analyzing fraud browser detection via fingerprints. Trends show a strong emphasis on practical security tools, human-centric security analysis, and firmware vulnerability discovery. While no specific awards or grants are listed in the DBLP data, Doupé actively contributes to security education through frameworks like SENSAI and CTF-as-a-service. He collaborates extensively with researchers such as Yan Shoshitaishvili, Ruoyu Wang, and Tiffany Bao.

Dirk Reiserمشاهده پروفایل

پژوهشگر

• Plasma Physics
• Numerical Methods
• Plasma Transport
• +۱۰ مورد دیگر

Dr. Dirk Reiser serves as Team Leader for "Theory and Numerical Simulations" at Forschungszentrum Jülich, specifically within the Plasma Physics (IFN-1) group of the Institute of Fusion Energy and Nuclear Waste Management. His research focuses on advancing computational methods for plasma physics and fusion energy applications, with particular emphasis on plasma edge physics and divertor operation in tokamak devices. Dr. Reiser's expertise spans multiple critical areas of computational plasma physics: Numerical Methods and Partial Differential Equations for plasma modeling Plasma Transport and Turbulence analysis using advanced computational techniques Statistical approaches including Bayesian Inference and Monte Carlo Methods Machine learning applications for plasma modeling and data analysis Plasma-material interactions and surface physics His research bridges theoretical plasma physics with practical computational approaches to solve complex fusion energy problems, particularly related to impurity transport, plasma edge physics, and divertor operation. He has made significant contributions to the EMC3-EIRENE simulation framework, which is widely used in the international fusion community for modeling plasma edge and divertor physics. Analysis of his recent publications (2021-2025) reveals a strong focus on applying advanced computational methods to fusion energy challenges, with increasing integration of artificial intelligence techniques alongside traditional physics-based modeling. His work demonstrates consistent contributions to major international fusion projects including ITER, ASDEX Upgrade, and JET, addressing critical challenges in plasma exhaust, impurity control, and plasma-material interactions essential for future fusion reactors. As a team leader at one of Europe's largest research institutions, Dr. Reiser oversees research efforts that contribute significantly to the international fusion energy research community. His work helps advance the physics basis for ITER operation and future fusion power plants, with particular emphasis on solving the complex plasma physics challenges at the boundary between hot plasma and material surfaces.

Christian Prauseمشاهده پروفایل

استاد

• Software Engineering
• Cybersecurity
• Software Quality Assurance
• +۵ مورد دیگر

Christian Prause is a Professor at the Institute for Information Systems (IEM) at Technische Hochschule Mittelhessen (THM). He supervises bachelor's and master's theses in software engineering and cybersecurity, emphasizing practical topics related to his research areas. Research Interests: Software Quality Assurance Cybersecurity Software Verification & Validation Agile and Hybrid Development Models Research Software Engineering Documentation & Knowledge Management His recent publications focus on software reliability in spaceflight, automated verification tools, gamification for coding standards, and hybrid development practices. He leads the Software Quality Engineering Laboratory and contributes to cybersecurity initiatives at THM. Professor Prause is actively involved in curriculum development, offering courses in cyber security, software engineering, IoT projects, and network security. He maintains regular office hours for student consultations in Friedberg.

Henrik Walterمشاهده پروفایل

استاد

• Psychiatry
• Neuroscience
• Psychology
• +۸ مورد دیگر

Henrik Walter is a Full Professor (W3) of Psychiatry with a focus on Psychiatric Neuroscience and Neurophilosophy at Charité – Universitätsmedizin Berlin. He serves as Director of the Mind and Brain Research Division and Deputy Medical Director (Research) at the Department of Psychiatry and Psychotherapy, Charité Campus Mitte. Walter is also a faculty member at the Berlin School of Mind and Brain , a faculty member of the Bernstein Computational Center Berlin , and a principal investigator at the Berlin Center for Advanced Neuroimaging . Clinical expertise: Schizophrenia and affective disorders Empirical research: Working memory, volition, reward mechanisms, emotion regulation, mentalization, imaging genetics, connectomics Philosophical research: Philosophy of mind, neurophilosophy, neuroethics, philosophy of psychiatry Research trends in his 15 most recent publications (2009-2024) emphasize connectome-based machine learning , dynamic network reconfiguration , self-control mechanisms , and predictive models for psychiatric relapse . His work explores the intersection of neural network organization , emotional processing , and philosophical frameworks in mental health. Walter oversees major projects including environMENTAL (data harmonization in large cohorts) and FOR5187 PREACT (personalized psychotherapy). His team actively trains students and researchers through internships, theses supervision, and doctoral programs.

Jan Vogesمشاهده پروفایل

پژوهشگر

• Genomic Data Compression
• DNA Data Storage
• Bioinformatics
• +۳ مورد دیگر

Dr. Jan Voges is a Research Group Leader at the Institute for Information Processing and the L3S Research Center at Leibniz University Hannover, where he leads the BioAI Lab. He has held previous research leadership positions at the same institution (2022-2024), CIMA University of Navarra (2024-2025), and maintains a visiting position at the Peter L. Reichertz Institute for Medical Informatics. Dr. Voges' research spans the intersection of computer science, genomics, and medical informatics, with a focus on: Developing AI methods for multi-omics data analysis DNA sequencing data compression and storage 3D genome reconstruction Single-cell RNA sequencing data modeling International standardization of genomic data His recent work demonstrates a strategic shift toward integrating artificial intelligence with genomic data analysis, particularly for medical applications. The development of Genie, the first open-source ISO/IEC encoder for genomic data, represents a significant contribution to genomic data standards. His research shows strong translational potential, bridging computational methods with clinical applications as evidenced by his P4D Dashboard project for depression cohort monitoring. Dr. Voges has received recognition for his work, most notably earning his Dr.-Ing. degree with distinction in 2022. His active participation in ISO/IEC standardization committees and collaborations with international institutions demonstrates the impact of his work beyond traditional academic boundaries. As a supervisor, Dr. Voges maintains a focused lab structure with 3 PhD students, emphasizing quality mentorship and opportunities for students to contribute to high-impact projects with real-world applications. His lab provides unique opportunities at the cutting edge of genomic data science, with exposure to international standardization processes and interdisciplinary collaborations.

Andreas Hertenمشاهده پروفایل

پژوهشگر

• High-Performance Computing (HPC)
• GPU Programming
• Parallel Computing
• +۶ مورد دیگر

Andreas Herten is a Researcher at the Jülich Supercomputing Centre (JSC) within Research Center Jülich GmbH. He serves as Co-Lead of the Novel System Architecture Design division and heads the ATML Accelerating Devices group, focusing on GPU programming, parallel computing, and high-performance computing (HPC) optimizations. Key Expertise: GPU programming, parallel algorithms, HPC systems, benchmarking, Python, LaTeX Research Focus: Accelerating scientific applications on GPUs, European exascale initiatives, AI workload evaluation, and heterogeneous computing architectures His recent publications highlight advancements in GPU-accelerated materials science, AI training on HPC systems, and exascale benchmarking. Herten contributes to projects like OpenGPT-X, JUPITER, and CARAML, with work spanning computational physics, environmental science, and machine learning applications.

Alexandre Strubeمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Parallel Computing
• Scientific Computing
• +۲ مورد دیگر

Dr. Alexandre Strube is a researcher at the Jülich Supercomputing Center (JSC) within Forschungszentrum Jülich, Germany's largest interdisciplinary research institution and a member of the Helmholtz Association. The JSC operates some of Europe's most powerful supercomputers, providing critical computational infrastructure for scientific research across physics, chemistry, biology, climate science, and engineering disciplines. His research focuses on advancing computational methodologies through: High-Performance Computing (HPC) system optimization Parallel algorithm development for exascale architectures Scientific simulation frameworks for multiscale modeling Big data analytics pipelines for research datasets AI/ML integration in computational workflows The Jülich Supercomputing Center serves as Germany's national supercomputing hub, supporting over 1,000 research projects annually through cutting-edge hardware like the JUWELS system and specialized expertise in code optimization, data management, and emerging computing paradigms. As part of Europe's PRACE infrastructure, JSC enables large-scale simulations for international scientific collaborations. While specific details about Dr. Strube's advising activities are not publicly documented, researchers at JSC typically supervise doctoral candidates through the Jülich Graduate Academy and collaborate with university partners like RWTH Aachen. Grant funding for JSC researchers primarily flows through Helmholtz Association programs, European HPC initiatives like EuroHPC, and national funding bodies such as the German Research Foundation (DFG).

Laurie C Murphyمشاهده پروفایل

پژوهشگر

• Computer Science Education
• Programming Pedagogy
• Novice Programming
• +۶ مورد دیگر

Laurie C Murphy is a researcher at Pacific Lutheran University, Tacoma, WA, USA, specializing in computer science education and pedagogy. Her work focuses on novice programming, debugging strategies, recursive algorithms, and student mindset theories. Her research explores student-centered learning , code explanation techniques , and assessment methodologies in programming education. Key topics include debugging strategies , self-theories in computing , and learning style comparisons between programming and mathematics. Recent publications (2017–2012) highlight trends in student-centered classroom practices debugging error analysis recursive/iterative comprehension code literacy assessments She collaborates with institutions like Grand Valley State University and College of Charleston on studies about instructional practices and programming education challenges.

Michael Teichmannمشاهده پروفایل

پژوهشگر

• Modeling intelligent systems
• Adaptive systems
• Learning mechanisms
• +۲ مورد دیگر

Michael Teichmann is a Researcher at the Professorship of Artificial Intelligence, Department of Computer Science, Chemnitz University of Technology. He has held this position since April 2015, following his role as a PhD student (2012–2015) in the interdisciplinary Research Training Group 'Crossworlds,' which explored connections between virtual and real social environments in communication, emotions, sensomotorics, and learning. Education: Dr. rer. nat. in Artificial Intelligence (2018), Chemnitz University of Technology. Dissertation: 'A plastic multilayer network of the early visual system inspired by the neocortical circuit.' Major in Computer Science with minors in Artificial Intelligence and Operation Research (2004–2010), Chemnitz University of Technology. Research Focus: His work centers on biologically inspired computational models, including: Adaptive learning mechanisms in neural networks Visual perception and invariance learning Emotion-driven perceptual processing Robustness in intelligent systems under information loss Publication Trends: His 15 most recent articles demonstrate a consistent focus on neural plasticity, bio-inspired vision systems, and computational neuroscience. Key thematic clusters include Hebbian learning stabilization, spiking neural networks for image processing, emotion-computer vision intersections, and competitive learning for enhanced robustness in feature detection. Awards: University award for best computer science thesis (2010) Affiliations: He operates within the Professorship of Artificial Intelligence lab, contributing to projects on neural modeling and vision systems. His prior involvement in the 'Crossworlds' research group emphasized interdisciplinary collaboration between computer science and social sciences.

Stephan Kruscheمشاهده پروفایل

استاد

• Educational Technologies
• Software Engineering
• Human-Computer Interaction
• +۷ مورد دیگر

Prof. Dr. Stephan Krusche is a faculty member at the Technical University of Munich (TUM) with the Professorship of Applied Education Technologies in the TUM School of Computation, Information and Technology . He conducts research at the intersection of educational technologies, software engineering, human-computer interaction, and artificial intelligence. Born: 1986 Email: krusche@tum.de Research Interests Education Technologies: Artemis platform, Iris chatbot Software Engineering: Agile development, DevOps, Continuous Delivery Human-Computer Interaction: User experience, Interactive systems AI Applications: Automated feedback, Learning analytics Recent Publications focus on AI-driven educational tools, interactive learning methodologies, and software engineering pedagogy. He organizes international summer schools and serves on the board of directors of the European Society for Engineering Education (SEFI). Scientific Honors Angela Molitoris Diversity Award (2024) Best practitioner report award (2023) Ernst Otto Fischer Teaching Award (2021) Ars Legendi Prize (2020) Pandemic teaching award (2020) Advising covers 60+ theses on topics including Artemis platform development, collaborative systems, and mobile education technologies. He actively promotes diversity through initiatives like ExploreTUM , she.codes , and HackerSchool .

Stefan Wagnerمشاهده پروفایل

استاد

• Software Engineering
• Software Quality
• Human Factors
• +۴ مورد دیگر

Prof. Dr. Stefan Wagner is a Full Professor of Software Engineering at the Technical University of Munich (TUM), where he leads the Chair of Software Engineering within the TUM School of Computation, Information and Technology. Based at the TUM Campus Heilbronn, Prof. Wagner joined TUM in 2024 after serving as Professor of Empirical Software Engineering at the University of Stuttgart since 2011. His research has significant practical relevance, often conducted in collaboration with industry partners, particularly in automotive and AI-based software domains. Prof. Wagner's educational background spans multiple disciplines: Computer Science studies at Augsburg and Edinburgh Psychology studies at Hagen Doctorate in Computer Science from TUM (2007) His primary research interests focus on software engineering with particular emphasis on software quality, human factors in development processes, AI-supported engineering methods, and empirical studies. Prof. Wagner's work bridges theoretical foundations with practical applications, especially in automotive software systems and AI-based domains. His interdisciplinary approach, combining computer science with psychology, enables unique insights into developer behavior and software quality assessment. The research conducted at his chair addresses critical challenges in modern software development, including quality assurance in complex systems and the integration of artificial intelligence into engineering processes. Prof. Wagner's extensive publication record demonstrates evolving research trends from traditional software quality models toward increasingly sophisticated integration of AI and human factors in software development. His recent work shows a growing emphasis on empirical studies of developer behavior, AI-assisted programming, virtual reality applications in software engineering, and the psychological aspects of technical debt. The publications reveal a consistent focus on practical applicability while maintaining scientific rigor, with strong industry collaboration evident throughout his career. Prof. Wagner has received numerous prestigious awards recognizing his contributions to software engineering: Class of IEEE Computer Society Distinguished Contributors (2022) Best Full Paper Award, ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (2020) IEEE Computer Society TCSE Distinguished Paper Award, IEEE International Conference on Software Maintenance and Evolution (2019) Most Influential Paper Award, IEEE International Conference on Software Maintenance and Evolution (2017) Google Research Award (2009) Prof. Wagner actively mentors students and researchers, with several PhD candidates and master's students working under his supervision. His research is supported by various grants, including industry collaborations and competitive research funding. He serves on multiple editorial boards including IEEE Software and Empirical Software Engineering, contributing significantly to the academic community. At TUM, he chairs the aptitude assessment committee for the M.Sc. Information Engineering program and serves on the scientific board for the TUM Global Postdoc Fellowship. The Chair of Software Engineering at TUM Heilbronn maintains strong connections with industry partners, particularly in the automotive sector. The research group actively participates in multiple collaborative projects focusing on AI in software development, software quality assessment, and empirical studies of development practices. The team combines expertise in software engineering, psychology, and artificial intelligence to address complex challenges in modern software systems.

• Generative AI
• Large Language Models
• Code Migration
• +۷ مورد دیگر

Sivajeet Chand is a Researcher at the Technical University of Munich , affiliated with the Chair of Software & Systems Engineering . He began his PhD in July 2024 under the supervision of Prof. Dr. Alexander Pretschner . His research focuses on Generative AI and Large Language Models (LLMs) for code migration and modernization. He holds a master's degree in Software Engineering and Technology from Chalmers University of Technology, Sweden , and has prior industry experience as a Data Engineer at Volvo Group and as a student engineer at Aptiv and Good Solutions. His research interests span Generative AI , LLMs , Software Engineering , and Code Migration . Recent publications highlight his work on design pattern recognition using LLMs (2023), automating requirements review in the automotive sector (2024), and empirical evaluations of LLMs in code migration (2025). His projects often intersect with automotive industry applications , code refactoring , and AI-assisted software development .