Find Academic Supervisors

Matthias RareyView profile

Professor

• Bioinformatics
• Cheminformatics
• Algorithmic Molecular Design
• +5 more

Prof. Dr. Matthias Rarey is a computer scientist and Professor at the University of Hamburg's Center for Bioinformatics. He holds a Ph.D. in Computer Science from the University of Bonn (1996) and has been leading the Algorithmic Molecular Design working group since 2002. His research focuses on molecular design algorithms, cheminformatics tools, and 3D bioinformatics. Co-founder of BioSolveIT GmbH Former cheminformatics group leader at Fraunhofer SCAI Former researcher at SmithKline Beecham and Roche Bioscience Head of Helmholtz Data Science Graduate School DASHH Director of Center for Data and Computing in Natural Science (CDCS) Research interests span algorithmic molecular design, cheminformatics, structure-based drug discovery, and machine learning applications in bioactivity prediction. His group developed widely used tools like FlexX, PoseView, and SpaceLight for molecular modeling and fragment space analysis. Recent publications focus on geometric pattern matching in protein-ligand interfaces, combinatorial fragment space encoding, adverse drug reaction network analysis, and efficient shape-based virtual screening. The work emphasizes scalable algorithms for billion-sized compound libraries and integration of machine learning with traditional cheminformatics approaches. Scientific awards include: GMD Award 1996 (Best Dissertation) GMD Award 2000 (Best Project) NRW Wissenschaftspreis 2002 Corwin Hansch Award 2005 Emerging Technologies Award 2011 Norddeutscher Wissenschaftspreis 2020 Academic leadership roles: Founding director of Center for Bioinformatics Co-founder of M.Sc. Bioinformatics and B.Sc. Computing in Science programs Chair of doctoral committee at Faculty of Computer Science Member of EMBL-EBI's Molecular and Cellular Structure advisory board Former Associate Editor of Journal of Chemical Information and Modeling

• Hyperdimensional Computing
• Neuromorphic Hardware
• In-Memory Computing
• +3 more

Paul R. Genssler is a Dr.-Ing. researcher at the Chair of AI Processor Design (AI-Pro) within the Technical University of Munich (TUM), actively advancing hardware solutions for artificial intelligence under Prof. Hussam Amrouch. His work bridges computer engineering and emerging technologies, focusing on overcoming fundamental limitations in conventional computing architectures through brain-inspired paradigms. His research spans critical domains in next-generation computing: Hyperdimensional Computing for robust pattern recognition and bioinformatics applications Neuromorphic and In-Memory Computing architectures for energy efficiency Reliability engineering for emerging memory technologies (FeFET, etc.) Quantum computing support systems including cryogenic embedded electronics Machine learning-driven transistor aging prediction and mitigation Analysis of his 15 most recent publications (2023-2024) reveals a dominant trend toward hyperdimensional computing as a unifying framework for addressing reliability challenges in emerging technologies. His work consistently integrates in-memory computing techniques to bypass von Neumann bottlenecks while targeting real-world applications like genome matching and unsupervised learning. A significant portion focuses on error-resilient implementations for unreliable nanoscale devices, demonstrating exceptional cross-stack expertise from transistor physics to algorithm design. As a core member of TUM's AI Processor Design group affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI), Genssler collaborates extensively on projects spanning cryogenic quantum control systems, FPGA-based AI resilience, and monolithic 3D integration. The team operates at the intersection of semiconductor physics, computer architecture, and machine learning, with strong industry connections evident through publications at DATE, ASP-DAC, and ICCAD.

Florian ZaussingerView profile

Professor

• Thermal Convection
• Fluid Dynamics
• Astrophysics
• +6 more

Prof. Florian Zaussinger is a faculty member at the Faculty of Applied Computer and Life Sciences at Mittweida University of Applied Sciences. His research focuses on thermal convection, fluid dynamics, and numerical simulations in both geophysical and astrophysical contexts. He has contributed extensively to studies on microgravity experiments, including the GeoFlow and AtmoFlow projects conducted on the International Space Station (ISS). University: Mittweida University of Applied Sciences Faculty: Applied Computer and Life Sciences Department: Mathematics Contact: +49 3727 58-1381 | florian.zaussinger@hs-mittweida.de | Building 6, Room 6-131 His research involves advanced numerical modeling of complex fluid systems, including spherical convection, dielectric heating, and double-diffusive processes. He has developed and applied computational tools like the ANTARES code to simulate convection in DA white dwarfs, planetary atmospheres, and Earth's mantle. His work bridges theoretical fluid mechanics with experimental validation in space-based microgravity environments. Recent publications highlight his expertise in thermo-electrohydrodynamic convection, planetary fluid flow analysis, and microgravity-induced instabilities. While the scraped data does not list scientific awards or students directly, his academic profile emphasizes interdisciplinary collaboration with engineering and life sciences, particularly in applied mathematics for fluid dynamics and experimental data processing.

Saman AmarasingheView profile

Professor

• Programming Languages
• Compilers
• Domain-Specific Languages
• +7 more

Professor Saman Amarasinghe is a full Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), and Principal Investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Commit compiler research group, which focuses on programming languages and compilers that maximize application performance on modern computing platforms. His work spans multiple academic departments and research centers, with strong affiliations to both MIT's School of Engineering and CSAIL. Professor Amarasinghe's research interests center around high-performance domain-specific languages and compiler technology . His work combines language design with sophisticated compilation techniques to deliver unprecedented performance for targeted application domains. His research spans multiple areas including image processing (Halide), sparse tensor algebra (TACO), graph analytics (GraphIt), stream computations (StreamIt), and bioinformatics (Seq). A significant thread throughout his work is the application of machine learning for compiler optimizations, from Meta optimization in 2003 to the OpenTuner autotuner framework. Analysis of Professor Amarasinghe's recent publications reveals a strong focus on sparse computing , compiler vectorization , and domain-specific language implementation . His work consistently bridges theoretical compiler concepts with practical performance gains across diverse application domains. The progression from earlier work on StreamIt and Halide to more recent projects like GraphIt and TACO shows an evolution toward more specialized, high-performance DSLs targeting specific computational patterns. His 2020-2025 publications particularly emphasize sparse tensor operations, GPU acceleration, and machine learning integration with compiler technology. ACM Fellow (2019) Professor Amarasinghe has made significant contributions to academic entrepreneurship and student development. He founded Determina, Inc. (acquired by VMware) based on security research from his MIT lab and co-founded Lanka Internet Services, Ltd., Sri Lanka's first ISP. As faculty director of MIT Global Startup Labs, his programs across 17 countries have helped create over 20 successful startups. His teaching includes the popular Performance Engineering of Software Systems (6.172) course with Professor Charles Leiserson, as well as innovative project-based courses like the Open Source Software Project Lab and Bring Your Own Software Project Lab. His educational approach emphasizes hands-on experience with compiler and language design concepts. Professor Amarasinghe leads the Commit compiler research group at MIT CSAIL, which has produced numerous influential domain-specific languages and compilers including Halide, TACO, Simit, StreamIt, and GraphIt. The lab maintains strong industry connections through projects like OpenTuner and Determina, and collaborates with researchers worldwide on compiler technology. The group's work spans both theoretical compiler research and practical implementation, with a consistent focus on bridging the performance gap between high-level programming abstractions and hardware capabilities.

• Explainable AI
• Human-AI Interaction
• Machine Learning
• +7 more

Andreas Holzinger is a Professor at Graz University of Technology, with additional affiliations at Medical University Graz and University of Natural Resources and Life Sciences Vienna in Austria. He is recognized as an IFIP Fellow (2021) for his significant contributions to information processing and computer science. His work spans multiple institutions across Europe, with notable collaborations extending to the University of Alberta in Canada. Professor Holzinger's research focuses on Human-Centered AI, Explainable AI (XAI), and their practical applications across diverse domains. His work bridges theoretical AI advancements with real-world implementations in healthcare, forestry, and human-robot interaction. He has pioneered approaches in counterfactual explanations, graph neural networks, and human-in-the-loop systems that emphasize transparency and trustworthiness in AI decision-making processes. His recent publications demonstrate a strong trend toward integrating large language models with traditional AI systems while maintaining explainability. Holzinger's work consistently emphasizes the human element in AI systems, ensuring that technological advancements serve human needs rather than obscuring decision processes. His research in medical AI, smart forestry, and agricultural applications shows a commitment to solving practical problems with human-centered technological solutions. Scientific Awards: IFIP Fellow (2021) Professor Holzinger has been instrumental in establishing design guidelines for explainable AI systems, particularly through his work on post-hoc versus ante-hoc explanations. His research on Kandinsky Patterns has provided valuable experimental frameworks for pattern analysis and machine intelligence. He has secured significant research funding for projects bridging AI with practical applications in healthcare and environmental monitoring. His leadership extends to the organization of major conferences and workshops, including the CD-MAKE conference series, where he has fostered interdisciplinary collaboration between AI researchers and domain experts. His work on the CLARUS platform demonstrates practical implementations of interactive explainable AI for medical applications.

Matthias KrauledatView profile

Professor

• Machine Learning
• Brain-Computer Interface
• Signal Processing
• +3 more

Prof. Dr. Matthias Krauledat is a faculty member at Hochschule Rhein-Waal , specifically within the Faculty of Technology and Bionics . His academic career spans both theoretical research and industrial application, with a focus on Machine Learning and Brain-Computer Interfaces . After completing his PhD in Electrical Engineering/Computer Science at Technische Universität Berlin , he has contributed significantly to the advancement of EEG-based communication systems and neural signal processing methodologies. Born in Essen, Germany Studied Mathematics with a minor in Computer Science at University of Münster/Oxford Doctoral research at TU Berlin on Brain-Computer Interfaces Industrial experience at Henkel AG & DMT GmbH Research Interests focus on Machine Learning applications in Neuroscience and Biomedical Engineering , specifically Brain-Computer Interfaces , EEG Signal Processing , and Adaptive Classification Systems . His work explores how algorithms can be developed to enable self-learning computers to solve complex tasks involving neural data interpretation and prediction for previously unseen data in clinical and technological contexts. Publications demonstrate a consistent contribution to Neuroscience and Machine Learning fields, with particular emphasis on Brain-Computer Interface systems from 2004 through 2009. His research has focused on reducing training requirements, improving signal processing accuracy, and developing novel interaction paradigms like the Hex-o-Spell mental typewriter while addressing statistical challenges like covariate shift in neural data analysis. Professional Experience includes academic research at TU Berlin's Intelligent Data Analysis group, industrial software development roles at Henkel AG's Scientific Computing department, and TÜV Nord Group's Optical Metrology and Machine Diagnostics divisions. He maintains active research connections through collaborative publications with leading experts in the field.

• Smart Products
• Service Systems
• Digital Platforms
• +5 more

Prof. Dr. Stefan Eicker is a Professor and Chairholder of Business Information Systems and Software Engineering at the Faculty of Computer Science, University of Duisburg-Essen, Germany. He has held this position since April 2004, following previous academic appointments at the Technical University of Clausthal, the University of Essen, and other German institutions. His research spans multiple domains within information systems and software engineering, with a particular focus on digital transformation and emerging technologies. Prof. Eicker's research interests center around Smart Products , Service Systems , Internet of Things , and Platform Economics . His work explores how digital technologies transform traditional business models and create new value propositions. He has developed taxonomies for smart services and investigated quality factors in self-tracking solutions, demonstrating his interdisciplinary approach that bridges technical and business perspectives. His research particularly emphasizes the integration of physical and digital components in modern products and services. His recent publications (2019-2024) reveal a strong focus on digital platform economies, smart services, and IoT applications. The research shows a clear trajectory toward understanding value creation mechanisms in digital ecosystems, with increasing attention to practical applications in energy systems, self-tracking technologies, and business model innovation. His work often involves collaboration with colleagues like Gero Strobel and Tobias Brogt, indicating an active research group focused on digital transformation. Prof. Eicker has contributed significantly to the academic community through his extensive publication record spanning nearly two decades, with work appearing in journals, conference proceedings, and edited volumes. His research bridges theoretical frameworks with practical applications in business contexts. He maintains an active role in academic administration and education at the University of Duisburg-Essen, where he has contributed to curriculum development and the implementation of systems for managing academic information. His work on the bolognaT3 system demonstrates his commitment to improving academic processes through technology.

Katharina EggenspergerView profile

Researcher

• Automated Machine Learning (AutoML)
• Hyperparameter Optimization (HPO)
• Tabular Machine Learning
• +1 more

Katharina Eggensperger is an Early Career Research Group Leader at the University of Tübingen , leading the AutoML for Science group within the Cluster of Excellence Machine Learning for Science . She previously completed her Ph.D. at the University of Freiburg under Frank Hutter and Marius Lindauer (2022), and actively contributes to the AutoML community through open-source tool development and competition leadership. Co-developer of AutoML.org tools Faculty member of IMPRS-IS Chair for multiple AutoML workshops/conferences (2019-2025) Her research focuses on automated machine learning (AutoML) with specific attention to: AutoML Systems Hyperparameter Optimization Tabular Machine Learning Scientific Applications of ML She has organized multiple AutoML schools and conferences, including serving as Program Chair for AutoML 2024 and Non-archival Track Chair for AutoML 2025. Her work emphasizes making machine learning accessible through automation while maintaining scientific rigor and interpretability, particularly for tabular data applications. Katharina actively recruits PhD students through IMPRS-IS and collaborates with institutions like the University of Freiburg and Cyber Valley .

• European Business Law
• International Commercial Law
• Intellectual Property Law
• +7 more

Andrea Wechsler is a Professor of Private Business Law at Pforzheim University and currently serves as a Member of the European Parliament (2024–2029) on extended leave from the university. She holds an M.A. from Oxford, LL.M. degrees from Columbia University and Ludwig-Maximilians-Universität München, and is a certified business mediator. Her research focuses on European and international business law, with specialties in commercial law, competition law, intellectual property, data protection, and start-up law. She leads research initiatives at the Design Factory Pforzheim, GründerWERK Center for Start-Ups, and the Competence Center for Consumer Research (vunk). Notable projects include studies on Asian legal systems (especially Chinese law), EU-China legal cooperation, and legal methodologies integrating economic analysis. She has received the 2013 Faculty Prize from LMU for her dissertation. Prof. Wechsler has authored/co-authored over 30 publications, including textbooks on business law and intellectual property. She serves on advisory boards for the Bundesverband Direktvertrieb Deutschland and the Baden-Württemberg Consumer Commission. Her work bridges academia and practice, with consulting roles at McKinsey & Company and contributions to EU legal reforms. Education: M.A. (Oxford University) LL.M. (Columbia University School of Law) LL.M. (Ludwig-Maximilians-Universität München) Research Themes: European and international commercial law harmonization IP rights enforcement in digital markets Competition law in tech industries Mediation mechanisms in cross-border disputes Asian legal systems analysis Sustainability in consumer law Awards: 2013 Fakultätspreis für die Dissertation Grants & Projects: Editorial leadership of the Schriftenreihe des Instituts für Europäisches Wirtschafts- und Verbraucherrecht Consultant for the "Junge Innovatoren" innovation fund PRME (Principles for Responsible Management Education) coordinator at Pforzheim University Labs/Teams: Design Factory Pforzheim (innovation ecosystem) GründerWERK Start-Up Center vunk Competence Center for Sustainable Consumption

• Database Systems
• Distributed Computing
• Cloud Computing
• +2 more

Bettina Kemme is a faculty member at McGill University in Montreal, Canada. Her research focuses on database systems , distributed computing , and cloud data management . She has made significant contributions to database replication, consistency models, and middleware frameworks for scalable applications. Research Themes : Database replication, distributed systems, cloud computing, and software engineering. Notable Collaborations : Jörg Kienzle, Joseph Vinish D'silva, Yunjia Zheng, and Marta Patiño-Martínez. Publications span critical areas such as graph database view management, transactional recovery in key-value stores, and latency-aware publish/subscribe systems. Her work is published in venues like VLDB , ICDE , Middleware , and SRDS .

Sebastian von MammenView profile

Professor

• Games Engineering
• Virtual Reality
• Interactive Simulations
• +3 more

Prof. Dr. Sebastian von Mammen is a tenured professor at the University of Würzburg's Institute for Computer Science, where he heads the Games Engineering research group and contributes to the Chair for Human-Computer Interaction. His group leads the Games Engineering academic program. Previously, he completed his habilitation (2012-2016) at the University of Augsburg's Chair of Organic Computing and was a postdoctoral fellow at the University of Calgary. His research spans: Real-Time Interactive Systems : Visual programming, immersion techniques, software engineering Interactive Simulations : Serious games for healthcare/logistics/construction Artificial Life : Self-organisation, adaptive systems, evolutionary computation Artificial Intelligence : Agent-based modeling, procedural content generation Recent publications (2023-2025) demonstrate strong focus on: Virtual reality applications in education (femtoPro optics simulator, BrainBuilder neuroanatomy) Healthcare technology platforms (VIA-VR for medical serious games) Game mechanics analysis (Match-3, Jump'n'Run flow) AI-driven emotion recognition and interactive systems Computational modeling of biological systems He leads the Games Engineering research group and previously participated in the Evolutionary and Swarm Design group (Calgary) and LINDSAY project. His lab develops VR simulations for scientific training and serious games applications.

Tien N. NguyenView profile

Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +9 more

Tien N. Nguyen is a Professor in the Computer Science Department at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He has been actively contributing to the software engineering research community since 2005, with significant publications and service to major conferences including ASE, ICSE, and ESEC/FSE. His extensive research portfolio spans multiple areas at the intersection of artificial intelligence and software engineering. Dr. Nguyen's research focuses on AI/ML4Code, encompassing Machine Learning, Natural Language Processing for Software Engineering and Software Security. His work specifically addresses Program Analysis, Software Evolution and Mining, Software Security, Software Maintenance, Mining Software Repositories, Version and Configuration Management, and Web Code Analysis and Security. His research has been consistently funded by multiple NSF grants including NSA NCAE-C-002-2021, CNS-2120386, CCF-1723215, CCF-1723432, CNS-1723198, and others dating back to CCLI-0737029. His recent publications demonstrate a strong trend toward leveraging large language models for various software engineering tasks including program analysis, bug detection, code completion, and automated program repair. The research spans both theoretical foundations and practical applications, with numerous papers accepted at top-tier conferences across multiple years. His scientific contributions have been recognized with several prestigious awards: ACM SIGSOFT Distinguished Paper Award at FSE 2024 IEEE Computer Society TCSE Distinguished Paper Award at SANER 2022 ACM SIGSOFT Distinguished Paper and ASE Best Paper Award at ASE 2014 ACM SIGSOFT Distinguished Paper Award at ASE 2012 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009 Dr. Nguyen has served in numerous leadership roles including Program Co-Chair for ICSE 2020 Demonstrations, Doctoral Symposium Co-Chair for ESEC/FSE 2021, NIER Track Chair for ASE 2020, and Tutorials Co-Chair for ASE 2024. He has received multiple NSF grants supporting his research in software analysis, mining, and security. His work with the Boa infrastructure for ultra-large-scale code mining has established significant infrastructure for the research community. His laboratory focuses on AI for software engineering, with particular emphasis on program analysis, software security, and mining software repositories. The research group develops techniques that bridge the gap between artificial intelligence and practical software engineering challenges, creating tools that are both theoretically sound and practically applicable to real-world software development.

Jan Christof ReckerView profile

Professor

• Digital Innovation
• Digital Transformation
• Digital Entrepreneurship
• +3 more

Jan Christof Recker is Nucleus Professor and holder of the chair for Information Systems and Digital Innovation at the University of Hamburg Business School, funded through the Excellence Strategy of the Federal and State Governments. He also holds adjunct professor positions at the University of Agder (Kristiansand, Norway) since 2022 and at the QUT Business School (Brisbane, Australia) since 2018, and has previously served as Professor for Information Systems and Systems Development at the University of Cologne (2018-2021) and Full Professor for Digital Innovation at QUT Business School. Dr. Recker holds bachelor's and master's degrees in information systems from the University of Münster and a PhD in Information Systems from the Queensland University of Technology. His educational background forms the foundation for his expertise in bridging technical and organizational aspects of digital transformation. His research focuses on how organizations deal with digital innovation, digital transformation, and digital entrepreneurship. As a field researcher, he has collaborated with large organizations including Woolworths, SAP, Hilti, Commonwealth Bank, Federal Police, Lufthansa, and Ubisoft, as well as various startups. Dr. Recker employs quantitative, qualitative, and mixed field methods in his research and is also competent in design research. His current research interests include technology analysis and design in the digital age, digital entrepreneurship and new venture creation, digital innovation and transformation in large organizations, digitalization of products, services, and processes, and digital solutions for sustainable development. His work aligns with multiple UN Sustainable Development Goals including Industry, Innovation and Infrastructure (SDG 9), Responsible Consumption and Production (SDG 12), and Reduced Inequalities (SDG 10). Dr. Recker's recent publications demonstrate a sophisticated exploration of the intersection between physical and digital experiences, generative AI development methodologies, societal impacts of crises on business growth patterns, and responsible digital innovation frameworks. His work spans multiple high-impact journals across information systems, management science, and entrepreneurship disciplines, consistently addressing practical organizational challenges with theoretically grounded approaches. His notable awards and honors include: AIS Fellow Award (2018) Outstanding Associate Editor Award by MIS Quarterly (2019) SIGGreen Best Paper Award (2021) Best Paper Award by the Journal of Information Technology Theory and Application (2014) Vice Chancellor's Award for Excellence (2014) Dr. Recker has secured significant research funding for projects related to digital innovation and transformation, including the HIVESOUND start-up scholarship (2024-2025) and research on how digital products are created through hardware-software interactions (2022-2024). As Editor-in-Chief for Communications of the Association for Information Systems (2015-2020) and current Senior Editor for the MIS Quarterly, he has shaped scholarly discourse in the field. His supervision of doctoral students has been recognized with multiple awards, reflecting his commitment to developing the next generation of information systems scholars. His Management Transfer Lab at the University of Hamburg serves as a nexus for academic-industry collaboration, focusing on translating theoretical insights into practical solutions for digital transformation challenges. The lab maintains strong partnerships with major corporations and startups alike, ensuring research relevance while providing students with real-world experience in digital innovation contexts.

Michael MöhringView profile

Professor

• Data Science
• Big Data Analytics
• Industry 4.0
• +4 more

Prof. Dr.-Ing. Michael Möhring is a Professor of Data Science at Reutlingen University's Faculty of Informatics. He serves as Prodekan for the Herman Hollerith Zentrum (HHZ) and leads research in data analytics, Industry 4.0, and process mining. Previously, he held roles as an IT consultant, project manager at Bosch Group/BSH, and academic researcher. Education: Dr.-Ing. (PhD) in Business Informatics M.Sc. in Business Informatics B.Sc. in Business Informatics Research Interests: Focuses on leveraging structured/unstructured data for industrial applications, enterprise architecture management, digital twins integration, and AI-driven decision support. Specializes in bridging technical systems with organizational processes in manufacturing and service industries. Lab Affiliations: AI-Real Lab AIDA Future Mobility Lab Internet of Things Lab Virtual Reality Lab Articles Trends: Recent work emphasizes practical implementations of AI in production failure analysis (language models), energy optimization systems (HollerithEnergyML), and technical debt management in SMEs. Consistently explores data integration challenges across manufacturing, service ecosystems, and digital twin frameworks. Grants & Collaborations: Active in EU-funded projects like 5G-PreCiSe and bwHealthApp. Collaborates with industry partners on digital transformation initiatives through HHZ's applied research programs.

Marc ReichenbachView profile

Professor

• Integrated Circuits
• Embedded Systems
• VLSI Design
• +7 more

Prof. Dr.-Ing. Marc Reichenbach serves as the Chair of Integrated Systems at the Institute for Applied Microelectronics and Data Technology at the University of Rostock. His office is located at Albert-Einstein-Straße 26, 18059 Rostock, Room 102 (1st floor), with contact information including telephone (0381) 498 7270 and email marc.reichenbach@uni-rostock.de. Professor Reichenbach's research focuses on the intersection of hardware design and artificial intelligence, with particular expertise in memory technologies and computing architectures. His work spans several key areas: Development of specialized computer architectures for deep learning applications Advanced VLSI design and CPU architecture Emerging memory technologies, particularly RRAM (Resistive Random-Access Memory) FPGA-based acceleration systems Hardware implementations for neural networks and AI applications Analysis of Professor Reichenbach's recent publications (2023-2025) reveals a strong focus on memory computing technologies, particularly RRAM-based systems. His work demonstrates expertise across multiple dimensions of computer architecture including ASIC design, FPGA acceleration, and novel memory systems. The publications show a clear trajectory toward implementing AI and machine learning capabilities directly in hardware, with applications ranging from edge computing to satellite systems. A significant portion of his recent work addresses the challenges of implementing neural networks using emerging memory technologies, focusing on efficiency, reliability, and performance optimization. Professor Reichenbach teaches several advanced courses including: Computer architectures for deep learning applications Project seminar Embedded Systems Advanced VLSI Design (Advanced CPU Design) His research group appears to be actively engaged in several cutting-edge projects related to hardware acceleration for AI applications, memory computing, and embedded systems design. The group collaborates on projects involving digital twins for hardware systems, real-time operating systems for heterogeneous architectures, and specialized computing systems for various applications from medical devices to drone technology.