Dr. Sebastian Brandstäter serves as Lecturer at the Institute for Mathematics and Computer-Based Simulation, Bundeswehr University Munich since 2022. His academic trajectory includes research associate positions at Hamburg University of Technology (2021) and Technical University of Munich (2016-2021). His research focuses on: Scientific Machine Learning for biomechanical systems Uncertainty Quantification and Bayesian Inference Global Sensitivity Analysis of complex models Multi-Physics & Multi-Scale Modeling of biological tissues Open-source scientific software development Dr. Brandstäter's work centers on gastrointestinal biomechanics, particularly computational modeling of gastric electromechanics and motility. He has pioneered applications of Gaussian-process metamodelling for sensitivity analysis in vascular and gastric systems, and develops open-source frameworks (QUEENS, 4C) that enable efficient multi-query analysis of large-scale models. He actively supervises student theses on patient-specific modeling and computational biomechanics, teaches advanced numerical methods courses, and contributes to the scientific community through conference organization, peer review, and international collaborations. His recent work demonstrates increasing emphasis on data-driven surrogate modeling and solver-independent computational frameworks for biomedical applications.
Professor Andreas Kronenburg serves as Institute Director and Dean of Studies at the Institute for Reactive Currents (WASTE) at the University of Stuttgart. With a background in mechanical engineering from RWTH Aachen and a PhD in Combustion Engineering from the University of Sydney, he has established himself as a leading researcher in combustion science. His career includes significant positions at Imperial College London where he served as Governor's Lecturer in Thermofluids (2000-2007) and Reader in Combustion (2007-2008) before joining the University of Stuttgart in 2009. Professor Kronenburg's educational background includes: RWTH Aachen, Mechanical Engineering (1989-1994) Universidad Politécnica de Madrid, Study Abroad (1992-1993) University of California at Davis, Study Abroad (1992-1993) University of Sydney, PhD in Combustion Engineering (1995-1998) His research focuses on advanced combustion modeling, particularly turbulent reactive flows, spray combustion, and nanoparticle dynamics. Kronenburg has made significant contributions to Large Eddy Simulation (LES) techniques, Conditional Moment Closure (CMC) methods, and particle-based modeling approaches. His work spans fundamental combustion science and practical applications in energy systems, with recent emphasis on sustainable fuels including hydrogen, ammonia, and biomass conversion. His research group develops sophisticated computational models that address challenges in predicting complex combustion phenomena with high accuracy. Analysis of his recent publications (2023-2026) reveals a strong focus on emerging energy technologies, particularly hydrogen and ammonia combustion for decarbonization, advanced particle dynamics in combustion systems, and computational methods for efficient simulation of complex reacting flows. His work demonstrates consistent innovation in modeling techniques while addressing practical engineering challenges in sustainable energy systems. Professor Kronenburg's scientific achievements have been recognized with numerous prestigious awards: Fellow of the Combustion Institute (2019) Distinguished Paper Award of the Combustion Institute (2013) Hinshelwood Prize for meritorious work of a young researcher (2006) Two Sudgen Awards for significant contributions to combustion science (2005, 2006) Best paper award at the Australian Symposium on Combustion (1997) Springorum Commemorative Medal for academic excellence (1994) With over 3,300 citations across 164 publications and an h-index of 33, Professor Kronenburg maintains an active research program with significant impact. His work has received support from organizations like the German Research Foundation (DFG), and he collaborates extensively with international institutions including Imperial College London and the University of Sydney. The computational resources available to his research group through bwGrid and HLRS enable large-scale simulations that advance the understanding of complex combustion phenomena. The Institute for Reactive Currents under Professor Kronenburg's leadership focuses on cutting-edge research in combustion science and engineering. The institute develops advanced computational models for predicting combustion behavior in various applications, from traditional energy systems to emerging sustainable technologies. With expertise in both fundamental combustion processes and practical engineering applications, the institute contributes significantly to addressing current challenges in energy conversion and environmental protection.
Dr. Thorsten Zirwes serves as Deputy Head of Institute at the Institute for Reactive Currents (IRST) at the University of Stuttgart, where he leads research in combustion engineering and reactive flows. With a strong background in chemical engineering from Karlsruhe Institute of Technology (KIT), where he completed his B.Sc., M.Sc., and PhD, Dr. Zirwes has established himself as a leading researcher in computational combustion. Deputy Head of Institute, Institute for Reactive Currents, University of Stuttgart (2023-present) DAAD PRIME Fellow & Visiting Postdoctoral Scholar, Stanford University (2022) PhD in Chemical Engineering and Process Engineering, Karlsruhe Institute of Technology (2016-2021) Dr. Zirwes' research focuses on advancing computational methods for understanding and modeling complex combustion processes, with particular emphasis on carbon-free fuels like hydrogen and ammonia. His work spans fundamental flame dynamics, turbulent combustion, and the development of efficient numerical algorithms for high-performance computing environments. He has made significant contributions to the understanding of thermodiffusion effects, flame instabilities, and porous media combustion for clean energy applications. His extensive publication record shows a clear trend toward sustainable energy solutions, with increasing focus on hydrogen and ammonia combustion as carbon-free alternatives. The research spans fundamental fluid dynamics, practical combustion applications, and computational method development, demonstrating both theoretical depth and practical relevance to energy transition challenges. Jürgen Warnatz Prize (German Section Combustion Institute, 2023) Distinguished Paper Award from the Combustion Institute (2023) Bernard-Lewis Fellowship of the Combustion Institute (2022) Most downloaded author of the Springer Journal FTaC (2022) Doctorate at KIT with summa cum laude (2021) Dr. Zirwes actively supervises research projects and collaborates with both academic and industrial partners. His group develops and maintains the EBI-DNS solver, an OpenFOAM extension for direct numerical simulation of combustion processes. Current research emphasizes carbon-free combustion technologies crucial for achieving climate goals, with strong connections to industry partners working on sustainable energy solutions. His research group maintains strong connections with international institutions, including Stanford University, and participates in major collaborative projects focused on advancing clean combustion technologies. The team combines expertise in fluid dynamics, numerical methods, and high-performance computing to tackle challenging problems in sustainable energy.
Prof. Alfred Kersch is a Professor leading research in computational physics and semiconductor materials at an institution in Munich. He directs the Technical Physics study program and serves on the Bachelor Engineering Physics and Data Science working group. As Master User of the Leibniz Supercomputing Centre (LRZ), he leverages high-performance computing for materials research. His research integrates: Fundamental physics and quantum phenomena Multiscale materials simulation Machine learning for semiconductor optimization Defect engineering in hafnium/zirconium oxides Ferroelectric material design He leads significant externally funded projects including: SIDFEM (2025-2028): Ferroelectric optimization in HfZrO₂ CHIPS of Europe (2024-2028): Semiconductor industry-academia partnerships KI SPEED (2024-2028): AI-driven SiC power electronics D3PO (2022-2026): Defect physics in oxide devices ALPHA (2025-2027): Physics education technology He directs the Laboratory for Modeling and Simulation, focusing on computational approaches to materials science challenges.
Junior Professor Dr. Julia Westermayr leads the Theoretical Chemistry of Materials Design group at the Wilhelm-Ostwald-Institute for Physical and Theoretical Chemistry (Leipzig University). Her interdisciplinary research bridges machine learning , quantum chemistry , and materials science to advance molecular simulations and reaction mechanism discovery. Academic rank: Assistant Professor (Junior Professor) Research focus: AI-driven excited-state dynamics, interatomic potentials, CO₂ conversion, and photocatalysis Key collaborators: Bell Flavors & Fragrances GmbH, ScaDS.AI, TU Berlin, University of Vienna Her team develops transferable ML models for nonadiabatic molecular dynamics , enabling long-timescale simulations of photodriven processes at metal surfaces and solvent environments . Recent work includes equivariant neural networks for UV absorption spectra and generative AI for molecular design . The group actively trains PhD students like Daniel Bitterlich, Peter Fichtelmann, and Robin Curth, while hosting international researchers from institutions like Bologna and Vienna. Research trends span Computational Chemistry (15/15 articles), with subfields including Excited-State Nonadiabatic Dynamics , Interatomic Potential Modeling , Photochemistry , Semiconductor Design , Reaction Mechanism Discovery , and ML-Augmented Quantum Simulations . The group participates in major scientific collaborations (DFG Cluster of Excellence, ScaDS.AI) and industry partnerships (Bell Flavors & Fragrances GmbH). They host regular research stays (e.g., Sascha Mausenberger from Vienna) and student internships , while maintaining active presence at conferences like PsiK2025 .
Dr. Robert Haase is a Lecturer and Training Coordinator at the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) under Leipzig University , with prior leadership roles at the DFG Cluster of Excellence 'Physics of Life' at TU Dresden . He specializes in Bioimage Analysis , GPU-Accelerated Image Processing , and Large Language Models (LLMs) for life sciences. His research focuses on democratizing bioimage analysis through open-source tools like CLIJ , clesperanto , and bia-bob , aiming to bridge microscopy with data science . Recent projects explore LLM-driven code generation for image analysis and interactive workflow design in platforms like napari . He leads initiatives such as the NFDI4BioImage consortium for research data management in Germany and GloBIAS , a global society for bioimage analysts. Funded by organizations including the Chan Zuckerberg Initiative (CZI) and DFG , his work emphasizes reproducibility , open science , and interdisciplinary collaboration in bioimaging. As an educator, he conducts training programs like "Large Language Models for Bioimage Analysis" and "Collaborative Working with Git" , and contributes to workshops at institutions such as EMBO , Institut Pasteur , and ScaDS.AI Summer Schools .
Chris Mundy is a Lab Fellow and Physicist at Pacific Northwest National Laboratory (PNNL), specializing in theoretical and computational approaches to complex interfacial systems. His research integrates statistical mechanics and molecular simulations to address fundamental challenges in electrolyte behavior, solvation phenomena, and energy-related materials science under the Department of Energy's Basic Energy Sciences portfolio. His educational background includes a PhD in Chemistry from the University of California, Berkeley (1992) and a BS in Chemistry from Montana State University (1988). Mundy has held significant leadership roles including Chair of the Gordon Research Conference on 'Chemistry and Physics of Liquids' (2025), Chair of the Theoretical Chemistry Subdivision of the American Chemical Society (2022), and Vice Chair (2020-2021). Mundy's research focuses on bridging molecular-scale phenomena to macroscopic outcomes in electrolytes and interfacial systems. His work spans computational modeling of ion hydration, solvation dynamics, and nanoscale assembly processes relevant to energy storage and environmental systems. Recent publications demonstrate strong emphasis on advanced simulation techniques applied to battery electrolytes, biomimetic materials, and aqueous interfaces. His 15 most recent publications reveal consistent focus on computational chemistry methods applied to interfacial phenomena, with growing integration of machine learning and advanced spectroscopy techniques. Key themes include ion-specific effects at interfaces, solvation structure characterization, and predictive modeling of electrolyte behavior across concentration regimes. American Physical Society Fellow (2014) Mundy actively contributes to professional service through leadership in Gordon Research Conferences and ACS subdivisions. His work at PNNL connects fundamental theoretical chemistry to Department of Energy mission areas including energy storage, environmental remediation, and materials science. Current research leverages high-performance computing resources to develop predictive frameworks for complex fluid systems. As a senior researcher at PNNL, Mundy collaborates extensively across national laboratory teams and academic institutions, focusing on theoretical development that informs experimental design in interfacial science and electrochemistry. His group utilizes advanced molecular simulation techniques to probe systems ranging from battery electrolytes to biological interfaces.
Lai-yung Ruby Leung is a Battelle Fellow at Pacific Northwest National Laboratory (PNNL) working in Earth Systems Analysis & Modeling. She serves as Chief Scientist of the Energy Exascale Earth System Model (E3SM) supported by the U.S. Department of Energy, leading major efforts to develop state-of-the-art capabilities for modeling human-Earth system processes on high-performance computers. Dr. Leung's research broadly spans climate and hydrological cycle modeling with expertise in land-atmosphere interactions, orographic processes, monsoon climate, and climate extremes. Dr. Leung earned her educational credentials from prestigious institutions: Ph.D., Atmospheric Science, Texas A&M University M.S., Atmospheric Science, Texas A&M University B.S. (Honors), Physics & Statistics, Chinese University of Hong Kong Her research interests focus on regional and global climate modeling , land-atmosphere interactions , and the regional hydrologic cycle . She investigates orographic precipitation mechanisms, climate extremes, climate variability and change, and aerosol-cloud interactions. Her work integrates advanced modeling techniques with observational data to understand complex Earth system processes, with research featured in Science , Popular Science , Wall Street Journal , and National Public Radio . Dr. Leung has published over 500 peer-reviewed papers and serves as an editor for the American Meteorological Society's Journal of Hydrometeorology . Analysis of Dr. Leung's recent publications reveals her leadership in developing and applying the Energy Exascale Earth System Model (E3SM), with significant contributions to understanding mesoscale convective systems, soil moisture dynamics, urban hydrology, and climate extremes. Her work demonstrates increasing integration of machine learning techniques with traditional climate modeling approaches, particularly in model evaluation frameworks and high-resolution simulations. She maintains strong focus on practical applications of climate science for understanding water resources, extreme weather events, and climate change impacts. Dr. Leung's scientific recognition includes: Election to the National Academy of Engineering (NAE) Election to the Washington State Academy of Sciences (WSAS) Fellow of the American Geophysical Union (AGU) Fellow of the American Meteorological Society (AMS) Fellow of the American Association for the Advancement of Science (AAAS) AMS Hydrologic Sciences Medal (2022) U.S. Department of Energy Office of Science Distinguished Scientist Fellow (2021) Reuter's Hot List of top 1,000 most influential climate scientists (2021) AGU Jacob Bjerknes Lecture (2020) AGU Bert Bolin Global Environmental Change Award (2019) As Chief Scientist of E3SM, Dr. Leung leads major research initiatives funded by the Department of Energy and has organized key workshops sponsored by DOE, NSF, NOAA, and NASA. She has served on numerous advisory panels and National Academies committees that define future priorities in Digital Twin, AI/ML, climate modeling, hydroclimate, and water cycle research. Her professional service includes membership on the Board on Atmospheric Sciences and Climate of the National Academies, council membership with the American Meteorological Society, and editorial roles for prominent journals. Dr. Leung directs research within PNNL's Earth Systems Analysis & Modeling group, collaborating with national and international climate research teams. She leads efforts to advance the Energy Exascale Earth System Model (E3SM), which represents cutting-edge capabilities in modeling human-Earth system processes. Her work connects with multiple PNNL research areas including atmospheric science, global change, and coastal science, contributing to the laboratory's mission of addressing complex environmental challenges through scientific innovation.
Muhammad Ali Babar is a Professor in the School of Computer Science at the University of Adelaide. He leads a theme on architecture and platform for security as service in the CyberSecurity Cooperative Research Centre (CSCRC), which has an estimated budget of A$140 Millions over 7 years with A$50 Millions provided by the Australian government. Prof Babar has established CREST (Centre for Research on Engineering Software Technologies), directing the research, education, and engineering activities of more than 25 researchers and engineers. He has attracted more than $12 Millions in funding from industry and government since 2017. Prof Babar's research focuses on Secure Software Systems and Services for emerging technologies including Cloud Computing, Edge Computing, Internet of Things (IoT), and Big Data. His work sits at the intersection of Software Engineering, Artificial Intelligence (ML/NLP), and Cyber Security, employing empirical research methods both qualitative and quantitative. His recent publications demonstrate a strong emphasis on vulnerability prediction, secure microservices management, and applying large language models to security challenges. With over 320 peer-reviewed scientific papers and 17,763 citations (h-index 66 as of January 2025), Prof Babar ranks among the leading Software Engineering researchers in Australia and New Zealand. His research has been recognized with the most influential paper award for the Australasian Software Engineering Conference in 2014 and multiple best paper awards at international conferences. Most influential paper for Australasian Software Engineering Conference (2014) Multiple best paper awards at international conferences Prof Babar obtained his Ph.D. in Computer Science and Engineering from the University of New South Wales, Australia. His research is conducted in collaboration with industry and government partners including DST, ActewAGL, TSS, ATO, Jemena, Cisco, DST Group, Health SA, and Defence SA, as well as key players in the Australian Cyber Security ecosystem such as AustCyber, Data61, DST Group, Oceania Cyber Security Centre, and Australian Cyber Collaboration Centre (AC3). Prof Babar is eligible to supervise Masters and PhD students and has led high-performing R&D teams that have successfully carried out high-quality research in close collaboration with industry. His leadership in the Cyber Security Cooperative Research Centre represents a significant contribution to Australia's cyber security research capabilities and industry engagement.
Xiang Gao is a Pre-tenure Associate Professor in the School of Software at Beihang University, China. His research focuses on applying program analysis, test generation, and formal methods to improve software quality through automated bug fixing and program synthesis. He has established significant collaborations with Fujitsu Laboratories of America, Microsoft Research, and other leading institutions in the software engineering field, demonstrating strong industry-academia connections. Dr. Gao received his Bachelor's degree in Computer Science (Elite Class) from Shandong University in 2016, followed by a Ph.D. from the School of Computing at the National University of Singapore, where he also served as a Postdoctoral Fellow until December 2021. His educational background spans both Chinese and Singaporean academic institutions, providing him with a global perspective on software engineering research. His primary research interests span multiple cutting-edge areas of software engineering: Program Analysis techniques for detecting and fixing software bugs with formal methods Software Security vulnerabilities with focus on automated repair methods Automated Program Repair systems that generate high-quality patches without overfitting Program Synthesis for creating transformation rules from examples Software Engineering for Artificial Intelligence (SE4AI) to improve AI model reliability and security Mobile Software Engineering with particular attention to UI testing and automation Deep Learning Security including model protection and obfuscation techniques Dr. Gao's recent publication trajectory shows a strategic evolution toward integrating large language models with traditional software engineering approaches, particularly in test generation and program repair. His work on DNN modularization (NeMo, CNNSpliter, SeaM) represents an innovative approach to enhancing model reusability and security in resource-constrained mobile environments, addressing critical challenges in deploying AI on edge devices. His scientific contributions have been recognized with multiple prestigious awards: ACM SIGSOFT Distinguished Paper Award for "ProveNFix: Temporal Property guided Program Repair" at FSE'24 IEEE TCSE Distinguished Paper Award for "Investigating and Detecting Silent Bugs in PyTorch Programs" at SANER'24 ACM SIGSOFT Distinguished Paper Award for "Modularizing while Training: A New Paradigm for Modularizing DNN Models" at ICSE'24 Distinguished Artifact Award for "Automated Patch Backporting in Linux (Experience Paper)" at ISSTA'21 Dr. Gao actively mentors students at various levels, seeking "self-motivated Ph.D, master, undergraduate students and interns with strong programming skills" for his research projects. He serves on numerous program committees for top software engineering conferences including ICSE, ASE, ISSTA, and FSE, demonstrating his growing influence in the academic community. His research has been supported through collaborations with industry partners including Microsoft Research and Fujitsu Laboratories of America, translating theoretical advances into practical applications. His laboratory focuses on several key research projects including Automated Software Vulnerability Repair (with techniques like Fix2Fit, VulnFix, and ExtractFix that address the overfitting problem in program repair), Program Synthesis for Program Transformation (including Semi-supervised synthesis and FixMorph for automated patch backporting in Linux), and Software Engineering for Artificial Intelligence (with projects like CNNSpliter, SeaM, and Sensei that apply software engineering principles to improve AI model usability and robustness). These projects represent cutting-edge work at the intersection of traditional software engineering and modern AI techniques, addressing critical challenges in software reliability and security.
Professor Jerry Blackford is a leading marine systems modeller with 32 years of expertise at Plymouth Marine Laboratory (PML), where he serves as Head of Science for the Marine Systems Modelling Group. He directs a 20-scientist team spanning postdoctoral researchers to professors, pioneering applications of biogeochemical models for climate policy and carbon management. His research centers on three interconnected domains: ocean acidification impacts (notably leading the NERC-Defra UKOA programme on shelf-sea acidification), carbon capture and storage risk assessment (including the world-first QICS CO 2 release experiment), and operational oceanography. A core innovation is his "balanced complexity" methodology—developing computationally efficient models that retain ecological fidelity for societal applications like IPCC reporting and CCS regulation. Recent publications reveal strong thematic clustering: 60% address carbon cycle dynamics (CCS leakage detection, microplastic interactions), 30% focus on model development (ERSEM framework), and 10% analyze climate feedbacks. High-impact journals dominate his output, with Nature Climate Change papers establishing methodologies now adopted by international monitoring programs. Professor Blackford maintains extensive funding through: CMEMS NOWMAPS (operational oceanography) NERC CLASS National Capability ACT/BEIS ACTOM (CCS monitoring toolboxes) UK Earth System Model program He advises UK government departments (BEIS, Defra), IPCC, and ICES while chairing the Advances in Marine Ecosystem Modelling conference series. His lab provides critical modelling infrastructure for national marine strategy assessments and emerging CCS regulation. The Marine Systems Modelling Group operates as PML's hub for policy-relevant oceanography, combining high-performance computing with field validation campaigns. Current work integrates machine learning into biogeochemical frameworks for real-time monitoring, positioning the lab at the forefront of climate mitigation science.
Colin Reiff serves as a Research Assistant at the Institute for Control Engineering of Machine Tools and Manufacturing Units at the University of Stuttgart, focusing on advanced manufacturing systems and process optimization. His work bridges theoretical research with industrial applications in automotive, aerospace, and general production contexts. His research interests center on process control and optimization in multi-stage production systems and Additive Manufacturing (Powder Bed Fusion Processes) . Reiff has developed innovative approaches for zero-defect manufacturing, particularly through smart centering methods for rotation-symmetric parts and automated vision data systems using collaborative robots. His work demonstrates how dimensional deviations can be compensated during production rather than detected at final inspection. Analysis of his publication record from 2018-2024 reveals consistent focus on manufacturing innovation, with increasing emphasis on data-driven approaches, software-defined manufacturing, and sustainable production. His research spans both theoretical frameworks and practical implementations, with several solutions transitioning to industrial applications. Reiff actively supervises student theses and practical experiments, including the "Simulation of a feed axis closed loop control with MATLAB/Simulink" laboratory course. His work has been supported through EU-funded projects like ForZDM under Horizon2020 and the High-Performance Center "Mass Personalization" in Stuttgart. His research group operates within the University of Stuttgart's manufacturing ecosystem, contributing to initiatives like the "Stuttgarter Maschinenfabrik" - a fully digitalized production environment for customer-individualized products. This environment leverages digital twins and new technological infrastructure to enable application development freedom and machine park flexibility.
Dr. Modesto Orozco is a Full Professor of Biochemistry and Molecular Biology at the University of Barcelona and Group Leader of the Molecular Modelling and Bioinformatics research group within the Mechanisms of Disease program at the Institute for Research in Biomedicine (IRB Barcelona). He also serves as Director of the Molecular Modelling and Bioinformatics Unit at IRB Barcelona and previously directed the Life Sciences Department at the Barcelona Supercomputing Center (2005-2015). Dr. Orozco earned his M.Sc. in Chemistry from the Universitat Autònoma de Barcelona in 1985 and his PhD in Biochemistry from the same university in 1990. His academic career includes positions as Assistant Professor of Biochemistry (1989-1990), Professor of Biochemistry and Molecular Biology (1991-2001), and Full Professor (2002-present). He was also an Invited Scientist at Yale University's Department of Chemistry (1991-1993). His research focuses on the theoretical study of biological systems through computational approaches, with particular emphasis on nucleic acid structures, protein dynamics, and drug discovery. Dr. Orozco has pioneered methods for molecular simulation that have significantly advanced our understanding of biomolecular systems, with close to 500 publications and over 35,000 citations resulting in an h-index of 93 - the highest for a computational chemist in Spain. Analysis of his recent publications (2021-2024) reveals a strong focus on DNA/RNA structural dynamics, particularly i-motif structures, molecular inhibitors for disease pathways, and development of computational tools for biomolecular simulation. His work spans multiple disciplines including computational chemistry, structural biology, and bioinformatics, with applications in drug discovery and understanding fundamental biological mechanisms. Diaz de Santos National Award for young scientist (1997) Distinción Investigadora de la Generalitat de Catalunya (2000) FEBS Anniversary Prize (2001) Fundación Marcelino Botín fellowship (2007) Brucker award for research in biophysics (2010) ICREA Academy award for excellence in research Advanced Grant of the European Research Council Dr. Orozco has served in editorial roles for prestigious journals including WIRES Computational Molecular Sciences, Theoretical Chemistry Accounts (2005-2014), Journal of Computational Chemistry, and Nucleic Acids Research. He is founder and president of Nostrum Biodiscovery, demonstrating his commitment to translating academic research into practical applications. His Molecular Modelling and Bioinformatics Unit at IRB Barcelona serves as a hub for interdisciplinary research combining computational approaches with biological questions to address disease mechanisms.
Prof. Dr. Alfred Höß is a Professor of Electrical Engineering at the Amberg-Weiden University of Applied Sciences, where he has served since 1995. He chairs the examination committee for multiple engineering programs including Electrical and Information Technology, Software Systems Technology, and Industrial IT. His academic leadership extends through over a decade of service on the university senate and various planning committees. Dr. Höß completed his electrical engineering studies at Friedrich-Alexander-Universität Erlangen-Nürnberg (1983-1987), earning his diploma with distinction in 1988. He earned his PhD from Ruhr-Universität Bochum in 1991 with distinction, followed by industry experience at Siemens AG in both medical and automotive divisions before joining academia. Friedrich-Alexander-Universität Erlangen-Nürnberg: Electrical Engineering (1983-1987) Ruhr-Universität Bochum: PhD in High-Frequency Technology (1988-1991) His research spans cutting-edge automotive technologies with particular focus on autonomous driving systems, electric mobility solutions, and wireless communication architectures. Dr. Höß leads multiple EU-funded research projects including Archimedes, AI4CSM, AUTBUS, and Powerized, with emphasis on practical implementations for real-world transportation challenges. His work integrates artificial intelligence with edge computing to solve complex problems in vehicle communication, battery management, and autonomous navigation systems. His publication portfolio demonstrates strong emphasis on practical applications of machine learning in automotive contexts, particularly in range prediction for electric vehicles, federated learning for battery management, and communication systems for autonomous vehicles operating in challenging environments. His research shows consistent progression from fundamental electrical engineering principles to advanced AI integration in transportation systems. Dr. Höß has received notable academic recognition including the Diplompreis Elektrotechnik in 1988 and the Gebrüder-Eickhoff-Preis in 1992 for his doctoral work. Diplompreis Elektrotechnik (1988) Gebrüder-Eickhoff-Preis (1992) He actively mentors numerous graduate students across multiple research projects, supervising master's theses and research assistantships. His laboratory for electrical measurement technology serves as the foundation for hands-on student research. Dr. Höß secures substantial research funding through EU projects and industry collaborations, focusing on practical implementations of advanced automotive technologies. His administrative leadership includes chairing examination committees for multiple engineering programs, demonstrating his commitment to academic excellence and curriculum development. Dr. Höß directs the Electrical Measurement Technology Laboratory at Amberg-Weiden UAS, which serves as the primary research facility for his automotive electronics work. His research teams collaborate across multiple EU-funded projects including ADACORSA for drone communications, PRYSTINE for programmable automotive intelligence systems, and AUTBUS for rural autonomous transportation solutions. These interdisciplinary teams combine expertise in electrical engineering, computer science, and applied mathematics to tackle complex challenges in modern mobility systems.
Jenny Schmalfuss is a Doctoral Researcher at the Institute for Visualization and Interactive Systems (VIS) within the Faculty of Computer Science, Electrical Engineering, and Information Technology at the University of Stuttgart. She is also a scholar of the International Max Planck Research School for Intelligent Systems (IMPRS-IS). Her research focuses on computer vision and machine learning, specifically investigating robustness of deep learning methods against distribution shifts and adversarial attacks. Her primary research interests include computer vision, machine learning, and the intersection of these fields with robustness analysis. She has made significant contributions to understanding weaknesses in vision language models and motion estimation techniques like optical flow. Her work explores how to quantify and improve model robustness through adversarial testing frameworks. Her publication record shows a strong focus on adversarial robustness in motion estimation, with multiple papers at top-tier conferences including CVPR, ICCV, and ECCV. Recent work includes the PARC framework for analyzing vision language models (CVPR 2025), Distracting Downpour for weather-based adversarial attacks (ICCV 2023), and foundational work on adversarial snow attacks (ECCV AROW 2022). Poster Award at ICVSS 2023 Best Paper Award at ECCV AROW Workshop 2022 Best SimTech Bachelor's thesis 2021 (supervised) Jenny actively supervises numerous Master's theses, Bachelor's theses, and research projects annually, focusing primarily on optical flow robustness, adversarial attacks, and motion estimation. She has also completed an internship with NVIDIA's Autonomous Vehicle Perception Research Group in Santa Clara, CA, USA from April to November 2024, and previously worked as a research intern at the National University of Singapore and University of Houston. She teaches regularly in the Computer Vision and Intelligent Systems program, organizing colloquia and supervising seminars on Recent Advances in Computer Vision since 2021. Her teaching portfolio includes coordinating tutorials for Computer Vision and Imaging Science courses.
