جستجوی استاد راهنمای دانشگاهی

Nils Fleischhackerمشاهده پروفایل

استادیار

• Cryptography
• Theoretical Computer Science
• Zero-Knowledge Proofs
• +۵ مورد دیگر

Nils Fleischhacker is an Assistant Professor for Cryptography at Ruhr University Bochum, where he leads the Theoretical Cryptography group within the Faculty of Computer Science. His research focuses on foundational aspects of cryptography with particular emphasis on zero-knowledge proofs, digital signatures, and secure computation. Prior to joining Ruhr University, he held postdoctoral positions at Johns Hopkins University and Carnegie Mellon University. Dr. Fleischhacker received his PhD in Computer Science from Saarland University in February 2017, advised by Dominique Schröder. During his doctoral studies, he was a research intern at Microsoft Research with Chris Brzuska and a research visitor at the University of Maryland, College Park with Jonathan Katz and Dana Dachman-Soled. His research spans theoretical cryptography with significant contributions to zero-knowledge proofs, multi-signature schemes, property-preserving hash functions, and security proofs for cryptographic primitives. Fleischhacker's work often addresses fundamental questions about the limits and possibilities of cryptographic constructions, with particular attention to black-box separations and lower bounds. His recent publications demonstrate a strong focus on lattice-based cryptography and post-quantum secure protocols. Fleischhacker's publications over the past five years reveal a consistent research trajectory focused on improving the efficiency and security of cryptographic primitives, with particular emphasis on signature schemes, zero-knowledge protocols, and secure computation frameworks. His work frequently appears in top-tier cryptography venues including CRYPTO, EUROCRYPT, and CCS. Dr. Fleischhacker actively mentors PhD students, with numerous successful graduations spanning from 2010 to 2024. His former PhD students include Önder Askin (2024), Floyd Zweydinger (2023), and Lars Schlieper (2022), among others. His research is supported through various academic channels and collaborations. He is affiliated with several prominent research organizations including the Horst Görtz Institute (HGI) for Information Security, the DFG Excellence Cluster CASA, the EU Marie Curie Network QSI, and the International Association for Cryptologic Research (IACR). These affiliations provide a strong institutional framework for his theoretical cryptography research.

Nate Fosterمشاهده پروفایل

استاد

• Programming Languages
• Networking
• Databases
• +۵ مورد دیگر

Nate Foster is a Professor of Computer Science at Cornell University and currently serves as the Associate Dean for Research in the Ann S. Bowers College of Computing and Information Science. He is also a Visiting Researcher at Jane Street and served as a Visiting Professor at École Polytechnique Fédérale de Lausanne during the 2023-24 academic year. His research uses ideas from programming languages to solve problems in networking, databases, and security. BA in Computer Science, Williams College (2001) MPhil in History and Philosophy of Science, University of Cambridge (2008, all work completed in 2003) PhD in Computer and Information Science, University of Pennsylvania (2009) Foster's research focuses on developing languages and tools that make it easy for programmers to build secure and reliable systems. His current work centers on the design and implementation of languages and tools for programmable networks, particularly using the P4 language. His past work includes bidirectional languages (also known as 'lenses'), database query languages, data provenance, type systems, mechanized proof, and formal semantics. His research group at Cornell has made significant contributions to network verification, software-defined networking, and formal foundations for programmable data planes. Analysis of Foster's recent publications reveals a strong focus on network verification and programming language foundations for networking. His work consistently applies formal methods to practical networking problems, with a particular emphasis on the NetKAT and P4 languages. Over the past five years, his research has evolved toward more complex network verification techniques, including infinite state verification, active learning of network models, and dependently-typed approaches to network programming. His work bridges theoretical computer science with practical networking systems, making formal methods accessible to network engineers. ACM Fellow (2025) ACM SIGPLAN Robin Milner Award (2023) ACM SIGCOMM Rising Star Award (2018) NSF CAREER Award (2013) Alfred P. Sloan Fellowship (2012) Multiple distinguished paper awards across top conferences including POPL, PLDI, and SIGCOMM Foster has advised numerous PhD and Master's students who have gone on to prominent positions in both industry and academia, with many continuing work in programming languages and networking. He has led multiple significant research grants including an NSF CAREER Award and has been involved in the P4 Language Consortium, serving as Chair of the P4 Language Governing Board. His work has been supported by various organizations including NSF, DARPA, and industry partners like Intel and Jane Street. Foster is also active in the programming languages research community, serving on numerous program committees and as Vice Chair of DARPA's Information Science and Technology (ISAT) study group. Foster leads a vibrant research group at Cornell focused on programming languages for networks, with collaborators from academia and industry. His group has developed several influential tools and frameworks including NetKAT, Petr4, and KATch. They maintain strong connections with the P4 community and work closely with industry partners to ensure their research has practical impact on real-world networking systems.

Yin Tat Leeمشاهده پروفایل

دانشیار

• Convex Optimization
• Convex Geometry
• Graph Algorithms
• +۲ مورد دیگر

Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington, and a Senior Principal Researcher in Microsoft AI. His research spans convex optimization , convex geometry , graph algorithms , online algorithms , and differential privacy , with applications in machine learning and theoretical computer science.

Rainer Böhmeمشاهده پروفایل

استاد

• Steganography and steganalysis
• Digital forensics
• Privacy-enhancing technologies
• +۴ مورد دیگر

Professor Rainer Böhme is a prominent academic in the field of IT security and privacy at the University of Münster's School of Business and Economics, Department of Information Systems. He also holds a professorship for Security and Privacy at the University of Innsbruck's Institute of Computer Science. His research spans multiple dimensions of digital security, with particular expertise in steganography, digital forensics, privacy-enhancing technologies, and the economic aspects of cybersecurity. Dr. Böhme earned his Doctoral Degree from TU Dresden's Faculty of Computer Science in 2008, following studies in Communications, Economics and Computer Science at Technische Universität Dresden and Université Paris XII. His academic journey includes positions as Assistant Professor at the University of Münster (2010-2015), Postdoctoral Fellow at the International Computer Science Institute in Berkeley (2009-2010), and Scientific Staff at TU Dresden's Chair of Privacy and Data Security. His research interests focus on steganography and steganalysis, digital forensics, privacy-enhancing technologies, security and usability, economic and behavioral aspects of information privacy, virtual currencies, and the economics of information security. His work bridges technical security mechanisms with economic and behavioral considerations, creating a unique interdisciplinary approach to cybersecurity challenges. Analysis of his recent publications reveals a strong trend toward the economics of security, particularly regarding blockchain technologies, cryptocurrency, and privacy valuation. His work increasingly examines the intersection of technical security mechanisms with market dynamics, regulatory frameworks, and user behavior. The research spans theoretical game-theoretic models, empirical studies of user behavior, and practical applications in digital forensics and privacy protection. Program Co-Chair, Financial Cryptography and Data Security (2015) Program Co-Chair, New Security Paradigms Workshop (NSPW) (2015) General Chair, 8th International Conference on IT Security Incident Management & IT Forensics (2014) Organizer and Program Co-Chair, 1st Workshop on Bitcoin Research (2014) Associate Editor, Electronic Commerce Research (since 2013) Associate Editor, EURASIP Journal on Information Security (since 2013) Professor Böhme has supervised multiple Master's theses in Information Systems, including work on static code analysis for smart contracts, remote maintenance protocol security, and application protection requirements in retail banking. His research is supported by significant grants from the German Federal Ministry of Research, Technology and Space, the DFG, and the EU FP7 program, with projects focusing on digital forensics, privacy-enhancing technologies, and cryptocurrency crime prevention. He leads the IT Security Research Group at the University of Münster, which investigates theoretical and applied aspects of information security with particular emphasis on the economic dimensions of security decisions.

Barbara Krausمشاهده پروفایل

استاد

• Quantum Information Theory
• Quantum Computing
• Entanglement and Quantum Correlations
• +۴ مورد دیگر

Prof. Dr. Barbara Kraus is the Chair of Quantum Algorithms and Applications at the Technical University of Munich (TUM), affiliated with the TUM School of Natural Sciences. She previously held academic positions at the University of Innsbruck, where she founded her research group in 2010. Education : Physics and Mathematics at the University of Innsbruck; Post-doctoral work at MPI for Quantum Optics and University of Geneva. Her research focuses on foundational problems in quantum information theory, particularly entanglement in multipartite systems, quantum simulation, and verification of quantum processors. She develops theoretical tools for quantum many-body systems and explores applications in quantum computing, emphasizing error characterization and experimental validation. Recent publications highlight advancements in Hamiltonian learning, symmetry-resolved entanglement detection, and multipartite state transformations. Her work bridges theoretical quantum physics with practical implementations, including Rydberg platforms and quantum metrology. Key Awards : START Prize (2010), Ignaz L. Lieben Award (2013), Boltzmann Prize (2011), Südtiroler Sparkasse Research Prize (2019). She supervises doctoral students and postdocs in quantum information theory, with a focus on stabilizer states, quantum networks, and entanglement measures. Her courses at TUM include Quantum Information , Quantum Algorithms , and workshops on entanglement manipulation.

Sebastian Steinhorstمشاهده پروفایل

دانشیار

• Embedded Systems
• Internet of Things (IoT)
• Cyber-Physical Systems
• +۵ مورد دیگر

Prof. Dr. Sebastian Steinhorst is an Associate Professor (W3-level with tenure) at the Technical University of Munich (TUM) within the Embedded Systems and Internet of Things group at the TUM School of Computation, Information and Technology . His research focuses on advancing the security, predictability, reliability, and interoperability of smart connected and autonomous systems, particularly for applications in Internet of Things (IoT) , Industry 4.0 , and automotive systems . PhD in Computer Science (2011) from Goethe University Frankfurt Postdoctoral roles at TUMCREATE Singapore (2011-2016) and Aarhus University (2016) Joined TUM in 2016 as Rudolf Moessbauer Tenure Track Professor His research areas include decentralized embedded systems, hardware/software co-design, modeling and verification of cyber-physical systems, security protocols for automotive networks, and time-sensitive networking (TSN) for industrial applications. Recent work explores blockchain-based data sovereignty, zero-knowledge proofs for vehicle authentication, and resilient architectures for autonomous systems. Key scientific contributions include the 2019 ACM TODAES Best Paper Award and pioneering work on CyberSecDome , LeapChain , and Simutack frameworks. He serves on editorial boards and conference committees, including co-organizing the Autonomous Systems Design initiative at DATE. His teaching portfolio spans lectures on System Design for IoT , Software Architecture for Distributed Systems , and IoT Security across multiple semesters. He also leads advanced seminars on embedded systems and IoT.

• Optimization for Machine Learning
• Collaborative Learning
• Distributed Learning
• +۷ مورد دیگر

Sebastian U. Stich is a tenured faculty member at the CISPA Helmholtz Center for Information Security , where he has been since December 2021. He is also a member of the European Lab for Learning and Intelligent Systems (ELLIS) since June 2020. His research focuses on optimization methods for machine learning, collaborative learning algorithms, privacy and security in distributed systems, and theoretical foundations of deep learning. Stich received his PhD in Theoretical Computer Science from ETH Zurich (2014), following a Master's in Mathematics at the same institution (2010-2014). Prior to CISPA, he worked as a research scientist at EPFL (2016-2021) and held positions at ETH Zurich and ICTEAM/CORE. He has been awarded the ERC Consolidator Grant 2024 , Google Research Scholar Award (2023), and Meta Privacy-Enhancing Technologies Research Award (2022). His team includes Dr. Anton Rodomanov (since 2023), Dr. Rotem Mulayoff (since 2024), Xiaowen Jiang (2023), Yuan Gao (2023), and notable alumni like Anastasia Koloskova (defended 2023). Stich actively organizes workshops (e.g., NeurIPS OPT 2024) and serves on editorial boards ( Journal of Optimization Theory and Applications , Transactions on Machine Learning Research ). He teaches advanced courses in optimization at Saarland University and has held visiting positions at MIT. Key scientific contributions include: Developing ProgFed for progressive federated learning (2021) Creating ProxSkip to accelerate communication in federated settings (2022) Formalizing SCAFFOLD with control variates for FL (2020) Introducing RelaySum mechanism for decentralized learning (2021) Proposing Lookahead-Minmax for GAN training (2021) His work addresses fundamental challenges in: Decentralized optimization theory Communication-efficient algorithms Privacy-preserving model training Handling heterogeneous data distributions Stochastic gradient dynamics Second-order optimization methods

Michael Ulbrichمشاهده پروفایل

استاد

• Nonlinear Optimization
• Optimal Control
• Numerical Analysis
• +۳ مورد دیگر

Prof. Dr. Michael Ulbrich is a full professor and Chair of Mathematical Optimization at the Technical University of Munich (TUM), within the School of Computation, Information and Technology. He has held this position since 2006 and previously served as Dean of Studies (2007–2010) and Vice Dean of the Faculty of Mathematics (2012–2015). His research focuses on nonlinear optimization, optimal control, and numerical analysis, with applications in fluid dynamics, shape optimization, and PDE-constrained systems. He leads projects in the DFG SPP 1962 and IGDK 1754, and has received prestigious awards including the Howard Rosenbrock Prize (2015) and the Doctoral Award from the TUM Association of Friends (1996). Ulbrich is Editor-in-Chief of Optimization and Engineering and contributes to multiple journals. His work bridges theoretical foundations and practical applications, including CO2 sequestration, fluid-structure interaction, and distributed optimization algorithms. Education: PhD (1996), Habilitation (2002) in Mathematics at TUM. Research stays at Rice University (USA) under DFG funding. Research Areas: Semismooth Newton methods, PDE-constrained optimization, optimal control of Navier-Stokes equations, and distributed parameter systems. Awards: Rosenbrock Prize, Teaching Excellence Awards, and recognition for doctoral work. Leadership Roles: Department Head of Mathematics (2022–), Member of TUM Senate (2019–2022), and Co-Chair of GAMM 2018. Ulbrich has authored influential textbooks like Semismooth Newton Methods for Variational Inequalities and Nichtlineare Optimierung . His recent projects include OptiGeoS (2024–2026) and collaborations on nonsmooth optimization and stochastic algorithms. His academic contributions span over 100 publications, emphasizing both algorithmic innovation and rigorous mathematical analysis.

Stephen Meisenbacherمشاهده پروفایل

پژوهشگر

• Privacy-preserving Natural Language Processing (NLP)
• Differential Privacy
• Hybrid, Expert-Driven Classification Systems
• +۵ مورد دیگر

Stephen Meisenbacher is a Research Associate at the Technical University of Munich (TUM) , affiliated with the School of Computation, Information and Technology and the Department of Computer Science, I19 . He has been part of the Software Engineering for Business Information Systems (SEBIS) chair since March 2022. His research focuses on Privacy-Preserving Natural Language Processing (NLP) , Differential Privacy , and Privacy-Enhancing Technologies (PETs) , with a particular interest in their integration into software development and business applications. His work also explores Hybrid, Expert-Driven Classification Systems and Usable Privacy solutions. Stephen’s recent publications address trends in AI Privacy Risks , Text Rewriting with DP , Legal AI Use Cases , and Data Protection Compliance . He has contributed to GDPR-related research and PETs adoption in small enterprises. His teaching includes Natural Language Processing seminars and Software Engineering lecture courses for Master’s and Bachelor’s students at TUM. He also organizes Entrepreneurship for Small Software-Oriented Enterprises seminars. Stephen holds a Master’s in Informatics from TUM (DAAD Graduate Scholarship) and a Bachelor’s in Computer Science from the University of Notre Dame, with additional studies in German Language and Literature. Contact: stephen.meisenbacher@tum.de | LinkedIn

• Medical Informatics
• Artificial Intelligence in Healthcare
• Healthcare Data Integration
• +۷ مورد دیگر

Prof. Martin Boeker is a Professor of Medical Informatics at the Technical University of Munich (TUM), affiliated with the TUM School of Medicine and Health. His work focuses on advancing healthcare through AI-driven solutions, interoperability frameworks, and precision medicine initiatives. Key projects include the German Medical Text Corpus (GeMTeX) and the MIRACUM DIFUTURE Alignment Hub. Expertise: Medical Informatics, AI in Healthcare, Federated Learning, Health Data Integration Key Contributions: FHIR-based systems, clinical decision support, patient-centered outcomes research Leadership: Director of the Institute for AI and Informatics in Medicine at TUM Hospital Right of the Isar Research emphasizes bridging clinical practice and data science through projects like modular health crawlers, automated guideline adherence monitoring, and cross-institutional medical NLP solutions. His work spans oncology informatics, rare disease management, and pandemic response data ecosystems. Recent articles highlight innovations in digital twins for precision oncology, federated analysis in oncology, and German-language medical NLP challenges. He collaborates internationally on EHR standardization and healthcare interoperability, contributing to the Medical Informatics Initiative (MII) and pandemic evidence ecosystems. Grants and collaborations involve the German Federal Ministry of Education and Research, European initiatives, and industry partnerships. Educational efforts focus on training future medical informatics professionals through MII competency programs.

Feng Chengمشاهده پروفایل

پژوهشگر

• Data-Driven Security Engineering
• Security Analytics
• Network Security
• +۱۹ مورد دیگر

Dr. Feng Cheng is a Senior Researcher and head of the IT Security Engineering (Sec-Eng) Team at the Hasso Plattner Institute (HPI), Germany, and serves as the Representative of the Chair "Internet Technologies and Systems" at the Digital Engineering Faculty, University of Potsdam. He holds a PhD from the University of Potsdam, an MEng from Beijing University of Technology, and a BEng from Beijing University of Aeronautics and Astronautics. His research is centered on data-driven security engineering, security analytics, network security, cloud security, firewall, IDS/IPS, attack modeling, and penetration testing. Dr. Cheng's research interests span a broad range of cybersecurity domains, with a strong emphasis on security analytics , data-driven threat detection , cloud and network security , and authentication . His work integrates advanced data engineering and machine learning techniques to enhance security operations, including intrusion detection, threat intelligence, and security information and event management (SIEM). Key application areas include IoT security, mobile security, and web security. The recent publications of Dr. Cheng and his team reflect a significant trend towards leveraging big data analytics , machine learning , and graph-based methods for cybersecurity. Their work focuses on developing frameworks for blockchain-based public key infrastructures, advanced SIEM analytics, malware detection using NLP, and chaos engineering for cloud security. This demonstrates a strong commitment to creating practical, scalable, and intelligent solutions for modern security challenges in complex, distributed environments. Dr. Cheng is actively involved in the academic community as a member of ACM and IEEE, a program committee member for numerous international conferences, and an organizer of workshops on cloud computing and cybersecurity. He has also served on editorial boards for international journals. He supervises a large group of PhD, master's, and bachelor's students, guiding research on topics such as security analytics, cyber threat intelligence, digital credentials, and behavioral authentication. He leads the Sec-Eng team, which conducts research on a diverse portfolio including the Security Analytics Lab, HPI-VDB (vulnerability database), Lock-Keeper (physical separation technology), and the Security Lab Generator for scenario-driven security training.

• Machine Learning
• Dynamical Systems
• Optimization
• +۳ مورد دیگر

Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.

• Information Systems
• Healthcare IT
• Knowledge Management
• +۵ مورد دیگر

Atreyi Kankanhalli is a Professor at the National University of Singapore, specializing in Information Systems with a focus on knowledge management, healthcare IT, and digital innovation. Their research spans over three decades, with prolific contributions in top journals like MIS Quarterly, Journal of AIS, and Information & Management. They have co-authored over 150 papers addressing topics such as crowdsourcing, online communities, and the impact of AI on scholarly practices. Notable work includes studies on user adherence to health apps, innovation in public sector data utilization, and the ethical challenges of generative AI in peer review. Kankanhalli has also led research on global virtual teams and digital technologies' role in social justice, reflecting a commitment to both technical and societal dimensions of information systems. Education & Background: While specific degree details are not provided, their extensive publication history and academic roles imply advanced qualifications in Information Systems or related fields. They have collaborated with global researchers across institutions like NUS, University of Illinois, and Singapore Management University. Research Themes: Core areas include digital health interventions (e.g., fitness app adherence), organizational innovation via open data and crowdsourcing, and the socio-technical challenges of AI in academia. Their work often bridges theoretical frameworks with practical applications, such as healthcare decision support systems and policy-driven technology adoption. Impact & Influence: As an editorial board member and frequent conference contributor (e.g., ICIS, PACIS), Kankanhalli shapes the field's research agenda. Their recent focus on generative AI's implications highlights proactive engagement with emerging technologies' ethical and methodological challenges.

Alexander Pretschnerمشاهده پروفایل

استاد

• Software Engineering
• Testing
• Information Security
• +۵ مورد دیگر

Prof. Alexander Pretschner is a Professor of Software & Systems Engineering at the Technical University of Munich (TUM) and Founding Director of the Bavarian Research Institute for Digital Transformation (bidt). He also serves as Scientific Director of fortiss, a Bavarian research institute for software-intensive systems. His research focuses on software engineering, testing, information security, and ethical software development. Pretschner holds a PhD from TUM and has held academic positions at Karlsruhe Institute of Technology (KIT) and TU Kaiserslautern. He is a co-editor of several prestigious journals, including IEEE Transactions on Reliability and the Journal of Software Testing, Verification and Reliability. Education: PhD in Computer Science, Technical University of Munich MSc in Computer Science, University of Kansas (on Fulbright Scholarship) Diplom in Computer Science, RWTH Aachen University Research Interests: His work spans testing methodologies, secure software design, and ethical considerations in agile development. Notable contributions include frameworks for metamorphic testing, distributed data usage control, and accountability mechanisms for cyber-physical systems. Awards: IBM Faculty Award (2012, 2013) Google Focused Research Award (2011, 2012) EARTO Innovation Prize (2014) 2nd Platz Supervisory Award (2020) Advising & Grants: Pretschner has supervised numerous PhD and Master’s students, contributing to over 200 publications. He leads projects like EDAP (Ethical Deliberation in Agile Processes) and collaborates with industry partners on cybersecurity and AI ethics initiatives. Labs & Teams: His work is anchored in bidt, fortiss, and TUM’s Chair of Software & Systems Engineering, focusing on societal impacts of digitalization and trustworthy AI systems.

Leo Schwinnمشاهده پروفایل

مدرس

• Robust Machine Learning
• Large Language Models Security
• Data-Efficient Learning
• +۵ مورد دیگر

Leo Schwinn is a Lecturer at the Technical University of Munich (TUM) within the Department of Computer Science (I26), working in the Data Analytics and Machine Learning group supervised by Prof. Stephan Günnemann at the TUM School of Computation, Information and Technology. His research focuses on robust machine learning with particular emphasis on data-efficient learning and robustness vulnerabilities of Large Language Models (LLMs). Dr. Schwinn's research interests span multiple critical areas in contemporary machine learning including: Robustness against adversarial attacks in LLMs Embedding space vulnerabilities and defenses Model unlearning and privacy preservation Efficient training methodologies for large models Time-series forecasting with probabilistic frameworks Graph-based machine learning approaches His work bridges theoretical understanding with practical security implications of modern AI systems. Analysis of his recent publications (2023-2025) reveals a strong focus on LLM security, with multiple papers accepted at premier conferences including ICML, CVPR, ICLR, and NeurIPS. His research demonstrates consistent innovation in identifying novel attack vectors while developing practical defense mechanisms, particularly through embedding space manipulation techniques. The work shows increasing sophistication in handling both theoretical aspects of model robustness and practical deployment concerns. His notable scientific achievements include: Receiving the ATE dissertation price for his PhD work at FAU Securing an oral presentation at ICLR 2025 Organizing the ICLR BlogPost Track Becoming a member of ELLIS (European Laboratory for Learning and Intelligent Systems) Dr. Schwinn has served as review process chair for the 2024 Conference on Lifelong Learning Agents (CoLLAs) and actively collaborates with researchers at Mila Quebec AI Institute. His research group at TUM focuses on addressing fundamental challenges in machine learning robustness, particularly as they apply to real-world deployment scenarios where security and reliability are paramount. He maintains active GitHub repositories related to LLM security research, including circuit-breakers-eval and LLM_Embedding_Attack, demonstrating his commitment to open science and reproducible research in the field of AI security.