جست‌وجوی استاد و پژوهشگر

• AI Accountability
• Socio-technical Systems
• Information Systems
• +۳ مورد دیگر

Long Hoang Nguyen serves as an Affiliated Researcher and PhD Candidate at the Technical University of Munich's Heilbronn campus within the Chair of Information Infrastructures (Prof. Ali Sunyaev). His work centers on AI accountability, integrating perspectives from Information Systems, Computer Science, and Law to examine socio-technical implications for stakeholders, particularly AI developers. He earned a Master’s degree in Information Systems from the University of Potsdam, specializing in Data Science and Human-Computer Interaction. His academic background is complemented by professional experience as a Research Assistant at Berlin's Weizenbaum Institute and as a Software Engineer at a Potsdam-based high-performance computing startup. Nguyen's research investigates how accountability mechanisms shape developer behavior in AI systems. He explores reward structures that encourage proactive accountability and synthesizes cross-disciplinary accountability frameworks, highlighting the interplay between technical design, legal requirements, and social dynamics in responsible AI development. His 2024 publications reveal a focus on practical accountability interventions for developers and interdisciplinary accountability dimensions. This work contributes to emerging trends in ethical AI, emphasizing actionable strategies for embedding accountability throughout the AI lifecycle. Scientific awards and major honors were not mentioned in the available information. He mentors students on thesis projects related to AI ethics, autonomous systems, and blockchain, with current topics including ethically aligned harm mitigation in autonomous driving and decentralized software applications. No specific research grants are detailed in the provided materials. Nguyen contributes to the AAIS research project (Accountable Artificial Intelligence-based Systems: A Multi-Perspective Analysis) within the Chair of Information Infrastructures, collaborating on multi-disciplinary investigations into AI accountability frameworks and developer practices.

• Side-channel attacks
• Post-Quantum Cryptography
• Cryptographic implementations
• +۳ مورد دیگر

Thomas Schamberger is a Researcher at the Chair of Information Security at Technical University of Munich, working under Prof. Dr.-Ing. Georg Sigl. He is based at Theresienstr. 90 in Munich and maintains an active research profile in cryptographic security with numerous publications spanning from 2018 to 2023. His primary research interests focus on side-channel attacks (particularly power analysis and electromagnetic analysis) targeting post-quantum cryptographic systems . He investigates efficient and secure implementations of post-quantum algorithms and develops countermeasures against side-channel vulnerabilities. His work bridges theoretical cryptography with practical hardware implementations. Analysis of his publication record from 2018-2023 reveals a strong focus on post-quantum cryptography security, with particular attention to NTRU, HQC, and Kyber cryptosystems. His research consistently addresses both attack methodologies (side-channel and fault attacks) and defensive countermeasures (masking techniques, DOMREP). The publications demonstrate expertise spanning hardware implementations, microcontroller security, and machine learning applications in side-channel analysis. While no specific scientific awards are mentioned in the available information, his work appears in prestigious venues including IEEE Transactions on Information Forensics and Security, IACR Transactions on Cryptographic Hardware and Embedded Systems, and major international conferences like CBCrypto and COSADE. Thomas actively supervises student research, offering opportunities for bachelor's and master's theses in his research areas. His position at the Chair of Information Security places him within TUM's broader research ecosystem focused on hardware security, cryptographic implementations, and countermeasures against physical attacks.

Vicente Yépezمشاهده پروفایل

مربی

• Computational Genomics
• RNA Sequencing
• Rare Disease Diagnostics
• +۳ مورد دیگر

Vicente Yépez is a Postdoctoral Researcher and Lab Scientific Manager at the Chair of Computational Molecular Medicine within the Department of Informatics at the Technical University of Munich (TUM), and has served as a Tutor for Data Analysis and Visualization and Statistical Methods for System Genetics courses at TUM since 2016. He currently holds dual roles as Chief Scientific Officer at OmicsDiscoveries GmbH and Clinical Research Network project manager at ERDERA. His educational background includes a Dr. rer. nat. in Computational Molecular Medicine from TUM (2021), an M.Sc in Mathematical Modeling in Engineering from the University of L'Aquila, Italy (2011-2013), and a B.Sc in Industrial Engineering from Tecnologico de Monterrey, Mexico (2006-2010). Additional training includes a Numerical Analysis semester at the University of Nice and an International Business Administration program in France. Yépez specializes in computational genomics with focus on RNA-seq analysis for rare disease diagnostics. His research develops methods for detecting aberrant splicing and gene expression events, integrating multi-omics data through machine learning to improve diagnostic rates for Mendelian disorders. Key contributions include FRASER and PROTRIDER computational frameworks applied in large consortia like Solve-RD. His 15 most recent publications demonstrate consistent innovation in genomic analysis pipelines, with 2025 works advancing organ development splicing prediction and multi-omics diagnostics. The publications reveal strong emphasis on clinical translation, particularly in neuromuscular and metabolic disorders, using HiFi sequencing and proteogenomics to solve previously undiagnosed cases. As a long-term Tutor at TUM, Yépez has shaped data science education in genetics while contributing to collaborative initiatives including his 2022-2024 leadership of Solve-RD's RNA-seq working group. His lab management role within the Gagneur research group supports high-impact translational work bridging academic research and clinical applications through ERDERA and OmicsDiscoveries.

• Stochastic Modeling
• Statistical Inference for Dynamic Processes
• Bayesian Statistics
• +۵ مورد دیگر

Prof. Dr. Christiane Fuchs is a full professor at the Faculty of Economics of Bielefeld University and heads the Data Science Group . She is also leading the Biostatistics Research Group and Core Facility Statistical Consulting at Helmholtz Munich . Her academic affiliations include the Bielefeld Graduate School of Economics and Management and the Bielefeld Center for Data Science (BiCDaS) . Education : MSc in Computational Modeling, Brunel University West London (2003) Diploma in Mathematics with Computer Science, University of Hanover (2005) PhD in Statistics, Ludwig Maximilian University of Munich (2010) Research interests span stochastic modeling , Bayesian inference , uncertainty quantification , and statistical applications in economics, medicine, and epidemiology. She specializes in diffusion processes , high-dimensional data analysis , and integrated statistical methods for cross-domain data (genomics, clinical, environmental). Recent publications focus on AI-driven clinical decision support systems , spatial epidemiology , fractional diffusion modeling , and statistical serology validation . Her work bridges methodological innovation in Bayesian statistics with real-world applications in infectious disease dynamics and hematological malignancies . Principal investigator in third-party funded projects from DFG , BMBF , NIH , and Helmholtz Association , including the UQ Consortium (2019-2024) and KoCo19 prospective COVID-19 cohort (2020-2024). She has developed statistical software packages like stochprofML and adaSC3 , and contributed to network-regularized regression methods. As Vice Rector for Research and Networking at Bielefeld University since 2023, she drives institutional research strategy while maintaining active roles in scientific societies including the International Society for Bayesian Analysis and Deutsche Statistische Gesellschaft .

Ulf Brackmannمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Enterprise Software
• +۶ مورد دیگر

Ulf Brackmann serves as an External Doctoral Researcher at the Ubiquitous Media Technology Lab (UMTL), with primary affiliations at the German Research Center for Artificial Intelligence (DFKI) and SAP. His work bridges artificial intelligence research and enterprise software innovation. His research spans foundational areas including Artificial Intelligence, Machine Learning, Enterprise Software, and Data Engineering, with specific expertise in: Future of enterprise software Business Foundation Models Foundation Models on structured data LLMs for Data Engineering Tasks Composable Business Software Brackmann investigates the integration of large language models and foundation models into structured enterprise data systems to develop adaptive business software architectures. No scientific awards were documented in the source material. Information regarding student supervision or research funding sources was not provided. He operates within the Ubiquitous Media Technology Lab, a research unit focused on AI-driven enterprise solutions and media technology convergence.

• Data Science
• Medical Informatics
• Scientific Collaboration Networks
• +۲ مورد دیگر

Hannah Marchi is a researcher at the Department of Empirical Methods within the Faculty of Economics at the University of Bielefeld . Contact: hannah.busen@helmholtz-muenchen.de . Research Interests: Data science applications in economics and medicine Scientific collaboration network analysis Clinical decision support systems Proteomics and respiratory disease MRI-based disease scoring systems Publication Trends: Her work bridges data science with medical research , focusing on antibiotic stewardship , lung disease modeling , and interdisciplinary collaboration using statistical and machine learning approaches. Key Collaborations: Active in hematology (MPN studies), neonatology (UNSEAL BPD scoring), and rheumatology (referral optimization).

• Computational Linguistics
• Natural Language Processing
• Unsupervised Learning
• +۵ مورد دیگر

Dr. Steffen Remus is a postdoctoral researcher at the University of Hamburg , affiliated with the Department of Informatics and the Language Technology Group (LT) . He holds a Ph.D. in 2023 from the University of Hamburg under Prof. Dr. Chris Biemann and earned his MA in 2012 at Technische Universität Darmstadt. Research Interests : Unsupervised methods, computational linguistics, distributional semantics, information extraction, knowledge induction, and focused web crawling. Projects : JOIN-T2, BIMDANUBE, WebAnno-MM, KDSL, AIPHES. Scientific Awards : KDSL scholarship holder (2017), Best Mentoring award (2015) for Algorithms of Language Technology course. Academic Contributions : Co-lecturer in Research Software Engineering at University of Hamburg (2024), teaching assistant in multiple NLP and software engineering courses since 2014. Supervised over 20 B.A./M.A. theses and independent studies. Active reviewer for major conferences (ACL, EMNLP, AAAI, EACL, COLING) and organizer for GermEval shared tasks.

Jannek Squarمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Parallel Computing
• Code Transformation
• +۵ مورد دیگر

Dr. Jannek Squar is a researcher affiliated with the University of Hamburg and the German Climate Computing Centre (DKRZ). His work focuses on high-performance computing (HPC) infrastructure, code transformation, and parallelization techniques. Academic Rank: Researcher Key Projects: i_SSS (Integrated Support System for Sustainability), HLRE-5 Supercomputer Procurement His research spans automatic code transformation using LLVM , parallelization via OpenMP/MPI , and high-performance I/O optimization . Recent publications highlight applications in sustainable agriculture and compiler-assisted HPC performance analysis. He has supervised multiple theses on topics including elastic cloud solutions, MPI communication modeling, and LLVM-based code verification. No scientific awards are explicitly mentioned in the provided text.

• Climate Modeling
• Snow Hydrology
• Vegetation Dynamics
• +۶ مورد دیگر

Dr. Evgenii Churiulin is a Postdoctoral Researcher at the Institute of Meteorology and Climate Research (IMKTRO), Karlsruhe Institute of Technology (KIT), specializing in climate modeling and numerical weather prediction. His work focuses on developing parameterization schemes for snow, vegetation, and wildfire dynamics within regional climate models, as well as creating pre- and post-processing tools for model evaluation. Education PhD in Meteorology, Lomonosov Moscow State University (2016–2019) M.Sc in Hydrometeorology, Russian State Hydrometeorological University (2014–2016) B.Sc in Hydrometeorology, Russian State Hydrometeorological University (2010–2014) Churiulin's research emphasizes improving climate models through novel algorithms and data assimilation techniques. His publications highlight expertise in snow water equivalent estimation, land surface interactions, and flood prediction using coupled hydrological-atmospheric models. Recent work includes software development for the SnoWE snow model and participation in convection-permitting climate simulations. His projects address regional climate adaptation, notably the UDAG initiative for Germany. Scientific Awards Certificate of registration program for PC №2018666844 (Preprocessing Tools) Certificate of registration program for PC №2018666845 (Postprocessing Tools) Certificate of registration program for PC №2018666846 (SnoWE Model) Churiulin collaborates across institutions, including the Max Planck Institute for Biogeochemistry and the University of Kassel, and contributes to high-resolution weather prediction systems like COSMO-Ru.

• Software Systems Engineering
• Self-Adaptive Systems
• Digitization and Big Data
• +۵ مورد دیگر

Klaus Pohl is a full Professor for Software Systems Engineering at the University of Duisburg-Essen. He serves as founding director of paluno – The Ruhr Institute for Software Technology – and was the scientific founding director of the Irish Software Engineering Research Centre (lero) from 2005 to 2007. His academic career spans roles as program and general chair for major conferences like CAiSE 2017, ICSE 2013, and SPLC 2005/2008. His research focuses on self-adaptive systems, cloud computing, cyber-physical systems, and requirements engineering. Key sub-disciplines include proactive adaptation, variability management, and data protection in distributed environments. His work bridges theoretical advancements with industrial applications, particularly in embedded and service-oriented systems. Recent publications (2024–2023) emphasize explainable reinforcement learning, decentralized coordination of adaptive systems, runtime privacy mechanisms, and predictive business process monitoring. These works span journals like Computing and conferences such as ACM SIGSAC , IEEE ACSOS , and Springer LNCS , reflecting interdisciplinary collaboration. Klaus Pohl has co-authored over 300 peer-reviewed papers and six textbooks, including foundational guides for requirements engineering. His leadership in research projects and advisory roles for enterprises underscores his impact on both academia and industry.

Alexander Bockمشاهده پروفایل

پژوهشگر

• Decision Support Systems
• Business Intelligence
• Management Support Systems
• +۳ مورد دیگر

Alexander Bock is a doctoral researcher at the University of Duisburg-Essen , affiliated with the Information Systems and Enterprise Modeling Research Group. He holds an M.Sc. (2013) and B.Sc. (2010) in Business Information Systems from the same institution, with a focus on decision support systems and conceptual modeling. His research explores how conceptual models shape decision and problem-solving processes in organizations, emphasizing semantic expressiveness, multi-perspective modeling, and the organizational conditions influencing decision-making. Education: B.Sc. and M.Sc. in Business Information Systems (with honors), University of Duisburg-Essen International semester at University of Jyväskylä, Finland Bock’s work bridges technical modeling approaches with organizational theory, investigating the Power/Generality Trade-Off in decision instruments and developing methods like MEMO GoalML for context-enriched modeling. His 21 publications since 2014 span topics like low-code platforms, ethics of information systems, and enterprise modeling frameworks. He was awarded the Best Research in-Progress Paper at ECIS 2021 and the Best Paper Award at EMMSAD 2018. His research trends highlight the integration of machine learning , design thinking , and low-code platforms into enterprise modeling, alongside ethical considerations in IS research. Bock’s teaching includes courses on business information systems, enterprise modeling, and scientific work, with supervision of 22 theses on topics like Big Data, AI ethics, and digital transformation in SMEs. Scientific Awards: Best Research in-Progress Paper (ECIS 2021) Best Paper Award (EMMSAD 2018)

Matthieu Jimenezمشاهده پروفایل

پژوهشگر ارشد

• Software Security
• Vulnerability Prediction
• Machine Learning
• +۳ مورد دیگر

Matthieu Jimenez is a Research Fellow at the University of Luxembourg's Faculty of Science, Technology and Medicine, working within the Serval team at the Interdisciplinary Center of Security and Trust under Professor Yves Le Traon. Since Summer 2020, he has focused on software vulnerability analysis and detection through machine learning approaches while teaching Introduction to Computer Science and contributing to Software Engineering II and Software Testing courses for Computer Science Bachelors. Education: PhD in Computer Science (2018) from University of Luxembourg with thesis 'Evaluating Vulnerability Prediction Models' (excellent grade) Engineer's degree (MSc equivalent) in Computer Science with major in Information Security from Polytech'Nice Sophia (2014) Exchange year at Polytechnique Montréal (2012-2013) Jimenez's research centers on applying machine learning to software security challenges, particularly vulnerability prediction modeling and source code analysis. His work bridges theoretical machine learning concepts with practical security applications, developing tools that help identify vulnerabilities in software systems through advanced data analysis techniques. He has made significant contributions to understanding how code naturalness metrics can reveal security flaws and how to properly evaluate prediction models in real-world scenarios. His publication record shows a consistent focus on vulnerability prediction methodology, with particular attention to practical implementation challenges like handling noisy historical data and proper model evaluation. The progression from his PhD work on evaluation frameworks to recent applications in real-world prediction demonstrates his commitment to solving practical security problems through rigorous research. Scientific Awards: Distinguished paper award at FSE'19 for 'The importance of accounting for real-world labelling when predicting software vulnerabilities' Jimenez serves on program committees for major software engineering conferences including ECOOP, ASE, and ICSE, demonstrating his standing in the research community. He has advised on artifact evaluation processes and contributed to research paper review committees. His work with the Serval team at SnT involves developing practical security tools while advancing theoretical understanding of vulnerability prediction. He leads development of the tuna project (Tuning Naturalness Analysis) for source code analysis and the data7 tool for automatically generating vulnerability datasets, both of which have become valuable resources for the software security research community. These tools enable more rigorous evaluation of vulnerability prediction models by providing standardized datasets and analysis frameworks.

Kristóf Marussyمشاهده پروفایل

استادیار

• Model-Driven Engineering
• Dependability Analysis
• Logic Solvers
• +۴ مورد دیگر

Kristóf Marussy is an Assistant Professor at the Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics. His work bridges model-driven engineering , dependability analysis , and logic solvers to address challenges in critical cyber-physical systems (railway, automotive, aerospace). He leads research in formal verification , automated reasoning , and graph generation , integrating AI with logic-based techniques for system reliability. His recent publications focus on stochastic analysis , abstraction algorithms , and distributed ledger architectures , often leveraging his tool Refinery for constraint-based model generation. Notable awards include the 2024 University Researcher Scholarship Programme (EKÖP) and the 2023 Josef Heim Award . He has served on program committees for OOPSLA, ECOOP, and MODELS. Education: PhD in Computer Science, Budapest University of Technology and Economics (2018–2023) MSc and BSc in Computer Science, Budapest University of Technology and Economics Professional Highlights: 2025–: Assistant Professor at BME, leading research in formal verification and graph-based logic reasoning 2020–2023: Assistant Research Fellow in Hungarian national projects (railway/automotive verification) 2018–2019: Assistant Research Fellow at MTA-BME Lendület Cyber-Physical Systems Research Group Scientific Contributions: Developed Refinery , an open-source graph solver for model generation Contributed to European Space Agency's VAMPIR project for positioning systems Created formal verification methods for critical subsystems in railway/automotive industries Awards: 2024 EKÖP Postdoctoral Scholarship Multiple ÚNKP Excellence Program grants (2016–2023) 2023 Josef Heim Award for innovation Conferences & Journals: Published 15+ papers in top venues (ICSE, ASE, MODELS, IEEETSE, etc.) Active in program committees for OOPSLA, ECOOP, and LLM4MDE Teaching: Course Coordinator for Critical Systems Laboratory since 2024 TA for 9 years, supervising 7 BSc and 4 MSc theses Advising Scientific Students' Association (5 Gold, 5 Silver awards)

Toni Mattisمشاهده پروفایل

پژوهشگر

• Software modularity
• Software metrics
• Exploratory programming environments
• +۵ مورد دیگر

Toni Mattis is a Ph.D. researcher and teaching assistant at the Software Architecture Group of the Hasso Plattner Institute (University of Potsdam, Germany). His work integrates artificial intelligence into software engineering practices, focusing on live programming environments , code repositories mining , and test prioritization . His research interests center around: Software modularity and architectural knowledge reification Exploratory programming and live programming environments Machine learning applications on code repositories Test prioritization techniques using AI and lexical analysis Recent publications analyze: AI-generated dynamic context for program comprehension (2024) Parallel version control for exploratory programming (2025) Game-based contexts for example-driven development (2024) Task complexity modeling in software maintenance (2022-2023) Scientific recognition includes: 2018 3rd Prize ACM Student Research Competition 2017 2nd Prize ACM Student Research Competition German Academic Scholarship Foundation (2010-2014) Multiple national computer science contest awards (2009) As educator, he has designed and supervised: Master's seminars on AI for Programming (2024) Code repository mining courses (2017-2020) Software testing modules covering TDD, BDD, and mutation testing

• Formal Verification
• Networks-on-Chips (NoC)
• Cache Coherence Protocols
• +۶ مورد دیگر

Freek Verbeek is an Assistant Professor at Virginia Polytechnic Institute and State University in the College of Engineering. He is affiliated with the Systems Software Research Group and the Open University of the Netherlands, with a research focus on formal verification and reverse engineering of binaries. His work bridges theoretical rigor with practical applications in networks-on-chips (NoC), cache coherence protocols, and x86-64 binary analysis. Research interests include formal verification of low-level systems, deadlock prevention in communication networks, and automated theorem proving using tools like Isabelle/HOL and ACL2. He has developed verification tools such as DCI2 and WickedXmas for deadlock detection in hardware designs and is actively working on bottom-up verification of assembly code and decompilation techniques. His publications span topics like binary decompilation, memory safety, and deadlock analysis in networks, with a notable best-paper award at SEFM 2020. His recent work explores cross-layer verification of cache coherence and communication fabrics, formal semantics of assembly code, and refinement relations between binaries and abstract models. Scientific awards include: Best-paper award at SEFM 2020 He is currently hiring PhD students and postdocs for projects on formal methods and reverse engineering of binaries, emphasizing decompilation with formally verified soundness and collaboration between Dutch and U.S. institutions.