جست‌وجوی استاد و پژوهشگر

• Computer Architecture
• Non-Volatile Memory Systems
• Deep Learning Systems
• +۳ مورد دیگر

Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.

Benjamin Scott Flavelمشاهده پروفایل

استاد پژوهشی

• Carbon Nanotubes
• Nanotechnology
• Photovoltaics
• +۷ مورد دیگر

Dr. Benjamin Scott Flavel is a Research Group Leader at the Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Germany. His research focuses on carbon nanotubes and their applications in photovoltaics and sensing technologies. He leads a dynamic research team conducting cutting-edge work on the separation, purification, and application of single- and double-walled carbon nanotubes. Education: Doctor of Philosophy (Chemistry), Flinders University, Australia, 2010 Bachelor of Science in Nanotechnology (Honors), Flinders University, Australia, 2005 Habilitation in Materials Science, Technische Universität Darmstadt, 2018 His research interests lie at the intersection of nanotechnology and energy materials, particularly in carbon nanotube-based solar cells , chirality-specific separation techniques , and nano-sensing devices . His group has pioneered methods such as ATPE for enantiomer sorting and developed innovative solar cell architectures with industrial relevance. The research spans fundamental charge transport mechanisms to applied device engineering. The publication record shows a strong trend in advancing carbon nanotube photovoltaics, with key contributions in interface engineering, scalable fabrication, and high-efficiency CNT:Si solar cells. Work also extends to spectroscopic characterization, alignment techniques, and software tools for data analysis. Scientific Awards: Heisenberg Program, DFG (2018) Emmy Noether Program, DFG (2013) Alexander von Humboldt Research Fellowship (2011) Australian Government Endeavor Research Fellowship (2009) Bloom-Gutmann Prize, Royal Australian Chemical Institute (2008) Hope Meeting, Japanese Society for the Promotion of Science (2008) Research Fellowship, Flinders University (2007) Dr. Flavel has successfully advised multiple PhD students, including Dr. Moritz Pfohl, Dr. Katherine Moore, and Dr. Daniel Tune. His research has been supported by significant grants from the German Research Foundation (DFG), including funding for a double-walled carbon nanotube project and an organic evaporation system (~320,000 EUR). Collaborations span institutions such as NIST, Freie Universität Berlin, University of Antwerp, McMaster University, and University of Sydney. The research group operates within the Institute of Nanotechnology at KIT, utilizing advanced infrastructure for nanomaterials synthesis, characterization, and device fabrication. The team is recognized internationally, with work featured in Open Access Government , Research Features , and Europhotonics , and covered by Nanotechweb.

• Data Visualization
• Scientometrics
• Science of Science
• +۴ مورد دیگر

Katy Börner is a Professor affiliated with Indiana University, Bloomington, USA. Her research focuses on data visualization, scientometrics, and the science of science, with contributions to tools like Network Workbench (NWB) and Sci2. She leads interdisciplinary projects such as the Human Reference Atlas and explores visualization literacy in education and public health. Her work spans virtual reality applications, biomedical knowledge networks, and AI-driven scientific discovery. Key research interests include mapping scientific collaboration networks, analyzing scholarly publications, and developing visualization frameworks for big data. She collaborates widely with institutions like NIH and VIVO, advancing open science and interdisciplinary research. Her projects often bridge computational methods with human-centric design, enhancing understanding of complex systems in health, technology, and social sciences. Publications highlight innovations in interactive visualization tools (e.g., opioid crisis research networks), data integration (Human Reference Atlas), and AI applications in biomedical research. She emphasizes translating data into actionable insights through visual analytics, impacting policy, education, and healthcare.

• Computer Science
• Software Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Dongwoo Kim is a researcher affiliated with Hanyang University, ERICA Campus (Department of Electronics and Communication Engineering) and has previously collaborated with institutions like POSTECH , Chungnam National University , and Microsoft . His work spans interdisciplinary domains in Computer Science and Engineering . Hanyang University, ERICA Campus - Department of Electronics and Communication Engineering POSTECH - Power Analog Electronics & Semiconductor Devices Lab Microsoft Chungnam National University Kim's research focuses on formal verification of automotive control software, deep learning applications in environmental monitoring, 3D modeling for indoor positioning, and machine learning for signal processing. His recent publications highlight advancements in graph neural networks (GNNs), including analyzing oversmoothing and gradient dynamics, as well as developing geometric vision-language models with domain-agnostic encoders. His 15 most recent articles (2023-2025) address topics like: Optimizing hybrid electric vehicle engine performance 3D modeling for indoor localization GNN training stability UAV-based environmental monitoring Algorithm difficulty prediction for programming problems Millimeter-wave antenna design Kim collaborates with researchers in software engineering , signal processing , and environmental science domains. His work intersects formal methods , applied machine learning , and embedded systems research.

Anett Hoppeمشاهده پروفایل

پژوهشگر

• Search as Learning
• Educational Technology
• Artificial Intelligence in Education
• +۵ مورد دیگر

Dr. Anett Hoppe is a research staff member at the Leibniz Information Centre for Science and Technology (TIB) in Hannover, Germany, where she works in the Visual Analytics research group. Her research focuses on the intersection of artificial intelligence, education technology, and information science, with particular emphasis on how people learn through search processes and educational video consumption. Dr. Hoppe completed her academic journey with: Ph.D. in Semantic Web technologies for online user profiles from the University of Burgundy, Dijon, France Her primary research interests span Search as Learning, software-based support for scientific reproducibility, and ethical considerations in computer-based decision making. She investigates how visual elements, reading sequences, and AI technologies impact knowledge acquisition during web search and educational video consumption. Her work bridges human-computer interaction, educational psychology, and information retrieval to create more effective learning experiences, with recent publications examining the role of large language models, vision-language models, and visual complexity in educational contexts. Analysis of her recent publications (2024-2025) reveals a strong interdisciplinary focus combining computer science, educational psychology, and information science. Her research examines video-based learning effectiveness, knowledge gain prediction, educational resource discovery, and the impact of visual elements on learning outcomes. She consistently explores how AI technologies can be leveraged to enhance educational experiences while maintaining attention to ethical considerations and scientific reproducibility. Dr. Hoppe maintains active collaborations with researchers across multiple institutions, with frequent co-authorship patterns indicating strong research partnerships, particularly with Ralph Ewerth and other members of the Visual Analytics group at TIB. Her work supports TIB's mission to advance knowledge infrastructure and scholarly communication through innovative technological solutions while directly addressing practical challenges in educational technology and information retrieval.

• Networked Systems
• Distributed Systems
• Social Computing
• +۵ مورد دیگر

Krishna Gummadi is a Scientific Director and Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Germany, where he leads the Networked Systems Research Group. He also holds a professorship at the University of Saarland, demonstrating his dual commitment to research and academic instruction in computer science. His educational background includes: Ph.D. in Computer Science and Engineering from the University of Washington (2005) B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Madras (2000) Gummadi's research spans networked and distributed computer systems with a current focus on social computing systems. His work addresses critical challenges in algorithmic fairness, privacy in social media, trustworthiness of online identities, and information dissemination in social networks. He approaches these problems through interdisciplinary methods combining user-centric studies, data-centric analysis, and systems-centric design to create practical solutions that enhance fairness, transparency, and user control in online platforms. His methodology integrates large-scale observational studies, computational modeling, and system implementation to tackle complex human-computer interaction challenges at societal scale. His recent publications reveal a strong emphasis on fairness in algorithmic decision making, with significant contributions to quantifying and addressing discrimination in machine learning systems. His work bridges computer science, social science, and ethics, creating frameworks for fair classification, understanding media bias, and developing privacy-preserving techniques that maintain functionality while protecting user data. The research demonstrates a progression from technical system design to addressing societal implications of computing systems. Among his notable scientific achievements: ERC Advanced Grant in 2017 for 'Foundations for Fair Social Computing' Test of Time Awards at ACM SIGCOMM and AAAI ICWSM Casper Bowden Privacy Enhancing Technologies (PET) and CNIL-INRIA Privacy Runners-Up Awards IW3C2 WWW Best Paper Honorable Mention Multiple Best Paper awards across prestigious conferences Gummadi has advised numerous PhD students and postdoctoral researchers who have gone on to prominent positions in academia and industry. His ERC Advanced Grant has supported extensive research into fair social computing, while his leadership in major conferences (including serving as General Chair for ICWSM 2016 and Program Chair for WWW 2015) has shaped research directions in the field. His teaching portfolio includes courses on Distributed Systems, Human-Centered Machine Learning, and Social Media Analysis. He leads the Networked Systems Research Group at MPI-SWS, which has developed several publicly available systems including tools for fair classification, privacy risk assessment, trust evaluation in social media, and information diet management. The group's work bridges theoretical advances with practical implementations that address real-world challenges in social computing, with numerous software releases and datasets made available to the research community.

• Geomechanics
• Geophysics
• Numerical Modeling
• +۵ مورد دیگر

Dr. Moritz Ziegler is a geomechanics researcher affiliated with the Technical University of Munich (TUM) and the Assistant Professorship of Geothermal Technologies . He previously worked at GFZ Potsdam (2017–2023) and earned his PhD in Geophysics from the University of Potsdam (2014–2017). His research focuses on geomechanical numerical modeling , uncertainty quantification , and 3D stress field analysis , with applications to geothermal energy, seismic hazard, and rock mechanics. Education: Bachelor of Science in Geosciences (2008–2011), Freie Universität Berlin Master of Science in Geophysics (2011–2014), University of Potsdam PhD in Geophysics (2014–2017), University of Potsdam & GFZ Potsdam His work integrates advanced computational methods ( Altair Hypermesh , Dassault Systèmes Abaqus , Python , Matlab ) to address complex geomechanical problems. Recent publications analyze stress gradients in the North Alpine Foreland Basin, fault-stress interactions, and physics-based machine learning in geomechanics. He has contributed to software development (DOuGLAS v1.0) and calibration tools (FAST Calibration v2.4). Scientific awards or honors are not explicitly mentioned in the provided text. However, his research has been published in leading journals such as Geophysical Journal International , Solid Earth , and Pure and Applied Geophysics .

Nils Ole Tippenhauerمشاهده پروفایل

استاد

• Cyber-Physical System Security
• Industrial Control Systems Security
• Physical-Layer Security
• +۷ مورد دیگر

Nils Ole Tippenhauer is a faculty member at the CISPA Helmholtz Center for Information Security , leading the SCy-Phy research group . His academic journey includes an Assistant Professorship at the Singapore University of Technology and Design (SUTD) (2014–2018), a PhD in Computer Science from ETH Zurich (2012), and a Diploma in Computer Engineering from Hamburg University of Technology (2007). He also holds an exchange study year at the University of Waterloo (2004–2005) supported by a DAAD scholarship. Research Interests focus on practical systems security , particularly Security of Cyber-Physical Systems Industrial Control Systems (ICS) and Industrial IoT (IIoT) Physical-Layer Security and Wireless Security Privacy-Preserving Technologies (e.g., DP3T project) Embedded Systems Security Threat Detection & Defenses His work integrates both theoretical and applied approaches, addressing vulnerabilities in critical infrastructures like water systems and power grids. Recent Publications highlight trends in Anomaly detection evasion in industrial networks Microarchitectural side-channel defenses Localization techniques in cellular networks Security analysis of ICS protocols Data-driven vulnerability assessments for robotics Cyber-physical simulation for infrastructure security Scientific Awards include Best paper at DIMVA'23 Distinguished Paper at ACSAC 2022 Best paper at CPSIOTSEC 2022 Best paper at CPSS 2017 K-H Ditze Award for diploma thesis (2007) SG Mark Institution award (2016) Runner-Up at smart energy hackathon (2013) Advising and Grants feature PhD defenses of Daniele Antonioli and Hamid Reza Ghaeini (2022), co-organizing conferences like WiSec and CPSS , and leading projects such as the National Science Experiment (NSE) with 50,000 mobile sensors in Singapore. He also contributed to the DP3T project for privacy-preserving contact tracing. Labs and Teams include the SCy-Phy research group at CISPA and the development of testbeds like SWaT (Secure Water Treatment), WADI (Water Distribution), and EPIC (Electric Power and Industrial Control) at SUTD. These platforms enable hands-on security research in realistic industrial environments.

Daniel J. Langمشاهده پروفایل

استاد

• Real-World Laboratory Research
• Transdisciplinarity
• Sustainability Research
• +۶ مورد دیگر

Prof. Dr. Daniel J. Lang is a leading academic at Karlsruhe Institute of Technology (KIT), where he serves as Professor for Designing Real-World Laboratory Research and Head of the Research Group "Designing Real-World Laboratory Research" at ITAS. He also holds the UNESCO Chair in Higher Education for Sustainable Development at Leuphana University of Lüneburg. His career spans key roles including Dean of the Faculty of Sustainability at Leuphana University (2012-2016) and leadership positions in multiple research projects. Current affiliations: KIT (2022-present), Leuphana University (2010-2022) Research focus: Real-world laboratories, transdisciplinary research, sustainability transformations, knowledge integration, and urban mobility systems His work connects academic research with practical sustainability challenges, particularly in mobility (TRAMIGO, ADMoS-Future), digitalization (LinkLab), and societal transformation. He has developed frameworks for evaluating socio-technical experiments and co-designed tools for policy implementation. Scientific contributions include establishing transdisciplinary methodologies and contextual analysis frameworks. Recent projects examine equity in automated mobility (CulturalRoad), knowledge transfer in real-world labs, and sustainability transitions in SMEs (TRANSFORM). His 15 most recent publications address topics like socio-technical experiments, equity metrics, and cross-case learning. Key awards: UNESCO Chair in Higher Education for Sustainable Development Advising: Supervised Beecroft, R.'s 2020 dissertation on real-world labs

Chanchal K. Royمشاهده پروفایل

استاد

• Code Clone Detection and Analysis
• Software Evolution and Maintenance
• Software Analytics Research
• +۳ مورد دیگر

Chanchal K. Roy is Professor of Software Engineering/Computer Science at the University of Saskatchewan and Co-Director of the Software Research Lab. He leads an NSERC CREATE graduate program on Software Analytics Research and co-leads the Data Management group for an NSERC CFREF project on Food Security, with over 170 publications cited 6,000+ times. His research centers on software clone detection using the widely adopted NICAD system, software evolution, empirical studies, and AI-driven software analytics. Recent work integrates large language models for code generation, clone detection in the AI era, and developer interactions with tools like ChatGPT, emphasizing practical applications in maintenance and analytics. Analysis of his 15 most recent publications reveals a strong trend toward AI/ML integration in software engineering: 12 of 15 articles (2025) explore LLMs, quantum computing, or deep learning for tasks like bug localization, code snippet generation, and feature-toggle analysis. Key themes include empirical validation of AI tools, Stack Overflow data mining, and cross-domain frameworks for Society 5.0. His scientific awards include: Most Influential Paper Awards (SANER 2018, ICPC 2018) Outstanding Young Computer Science Researcher Award (CS-Can/Info-Can, 2018) New Researcher Award (University of Saskatchewan, 2019) New Scientist Research Award (College of Arts and Science, 2019) As lead of the NSERC CREATE program and CFREF data group, he mentors graduate students in software analytics while securing major grants. He actively serves on program committees for ASE, ICSE, and FSE, reviewing journals and organizing workshops on clone detection and empirical methods. His lab focuses on real-world applications in food security data management and software evolution. The Software Research Lab, co-directed by Roy, drives projects like NICAD and the NSERC CREATE initiative, emphasizing open-source contributions and industry collaboration. Current efforts include quantum-SE integration and AI-augmented maintenance tools under the CFREF food security mandate.

Gert-Ludwig Ingoldمشاهده پروفایل

استاد

• Casimir effect
• Dissipative quantum systems
• Mesoscopic physics
• +۲ مورد دیگر

Prof. Dr. Gert-Ludwig Ingold is a Professor of Theoretical Physics at the Institute of Physics, Faculty of Mathematics, Natural Sciences, and Materials Engineering, University of Augsburg. His research spans multiple areas of theoretical physics with emphasis on quantum phenomena in nanoscale systems. His work on the Casimir effect explores interactions between various geometries including spheres, plates, and dielectric materials, with applications in nanotechnology and biophysics. His research on dissipative quantum systems investigates thermodynamic anomalies and quantum Brownian motion. In mesoscopic physics, he studies charge transport through nanoscale structures and quantum interference effects. His work on quantum systems in phase space connects classical and quantum dynamics, while his semiclassical research examines quantum revival patterns and phase-space trajectories. Prof. Ingold's publications reveal a strong focus on the Casimir effect, with numerous papers examining interactions between different geometries and materials. His work demonstrates expertise in both theoretical modeling and numerical methods, as evidenced by his development of the CaPS software for Casimir effect calculations. He frequently collaborates with international researchers, particularly with Paulo A. Maia Neto, Tanja Schoger, and Benjamin Spreng. Prof. Ingold has made significant contributions to physics education through textbooks and popular science books, including Quantentheorie: Grundlagen der modernen Physik and Die 101 wichtigsten Fragen: Moderne Physik . He has co-authored the Python-based educational resource Numerische Physik mit Python and maintains extensive online teaching materials including lecture notes and video tutorials.

Steffen Klamtمشاهده پروفایل

پژوهشگر ارشد

• Metabolic Engineering
• Systems Biology
• Computational Modeling
• +۲ مورد دیگر

Dr.-Ing. Steffen Klamt leads the Research Group 'Analysis and Redesign of Biological Networks' at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany, where he has been employed since 1998. He received his Diplom-Systemwissenschaftler degree from the University of Osnabrück in 1998 and his Dr.-Ing. from the University of Stuttgart in 2005. His research focuses on computational systems biology with emphasis on metabolic engineering, biochemical networks analysis, and bioprocess optimization. He develops computational tools like CellNetAnalyzer for network analysis and StrainDesign for metabolic engineering applications. Key research areas include constraint-based modeling, minimal cut sets analysis, and dynamic optimization of metabolic processes. His recent publications demonstrate strong focus on multi-stage bioprocess optimization, enzyme cascade engineering, and novel strain development strategies for chemical production. Common themes include ATP manipulation strategies, thermodynamic constraints in metabolism, and integration of experimental data with computational models. Scientific Awards: Ernst Dieter Gilles Lecture Award Ernst Dieter Gilles Fellowship He leads a research group developing computational methods for metabolic network analysis and maintains collaborations with experimental groups for model validation and application. The group develops open-source software tools widely used in systems biology research.

Ruben Portuguesمشاهده پروفایل

استاد

• Neuroscience
• Systems Neuroscience
• Behavioral Neuroscience
• +۷ مورد دیگر

Ruben Portugues is a Professor of Brain Circuit Function and Dysfunction at the Institute of Neuroscience, Technical University of Munich (TUM). He is a full member of the Graduate School of Systemic Neurosciences (GSN), an associate and advisory board member of the Munich Center for Neurosciences (MCN), and leads a research group focused on understanding the neural basis of behavior. His lab uses larval zebrafish as a model organism to investigate sensorimotor control, decision-making, and motor learning through whole-brain imaging and circuit analysis. His research interests lie at the intersection of systems neuroscience and behavior. He investigates how brain circuits process sensory information, integrate it with motor output, and enable adaptive and flexible behavior. Key areas include the function of the cerebellum, heading direction networks, sensorimotor transformations, and the neural mechanisms of decision-making. His lab employs cutting-edge techniques including custom-built microscopes, behavioral assays, and computational analysis. The recent publications and preprints from his lab demonstrate a strong trend in decoding distributed neural circuits underlying navigation and decision-making in zebrafish. There is a clear focus on identifying specific brain regions (e.g., interpeduncular nucleus, cerebellum) and cell types involved in processing visual, motor, and spatial information. The work increasingly emphasizes whole-brain functional imaging and the emergence of cognitive-like representations such as allocentric heading direction. FENS-Kavli Network of Excellence (FKNE) PhD Thesis Prize (awarded to student Luigi Petrucco) Ruben Portugues actively mentors PhD students, including current advisees Luigi Petrucco, Ot Prat, and Shuhong Huang, and has successfully graduated Dr. Elena Dragomir and Dr. Vilim Štih. His lab engages in extensive collaborations, hosts visiting researchers, participates in teaching (e.g., CSHL Imaging Course, Cajal Course), and secures resources for advanced research. The lab is known for building its own microscopes and software, fostering technical innovation. The Portugues Lab operates as a dynamic, interdisciplinary team that combines experimental neuroscience with computational and engineering approaches. They regularly hold retreats, participate in scientific events, and contribute to community initiatives like the Munich Brain Day. The lab is preparing to relocate to the Department of Neurobiology and Behavior at Cornell University, marking a new phase in its research trajectory.

• Probabilistic Machine Learning
• Bayesian Models
• Statistical Inference
• +۱۲ مورد دیگر

Luigi Acerbi is an Associate Professor in the Department of Computer Science at the University of Helsinki, where he leads the Machine and Human Intelligence research group. He is also an active member of the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning and computational neuroscience, particularly on developing efficient methods for statistical inference, Bayesian models of perception, and resource-constrained rationality. His work bridges machine learning and cognitive science, with applications in Bayesian optimization, simulation-based inference, and image completion. The recent publications highlight a strong trend toward unifying probabilistic conditioning across diverse tasks using transformer-based meta-learning frameworks like the Amortized Conditioning Engine (ACE). These works emphasize amortized inference, flexible latent variable modeling, and the integration of prior knowledge at runtime, enabling efficient and scalable Bayesian methods for complex problems. Scientific Affiliations: University of Helsinki, Department of Computer Science Finnish Center for Artificial Intelligence (FCAI) ELLIS (European Laboratory for Learning and Intelligent Systems) Education: PhD in Computational Neuroscience, Doctoral Training Centre, Edinburgh, UK Advisor: Sethu Vijayakumar and Daniel Wolpert Visiting work at Computational and Biological Learning Lab, Cambridge Postdoctoral Experience: Alex Pouget’s lab, University of Geneva, Switzerland Wei Ji Ma, New York University, USA Collaboration with the International Brain Laboratory Luigi Acerbi mentors PhD students including Daolang Huang and Nasrulloh Loka, and collaborates widely with researchers such as Samuel Kaski. He has contributed to open-source tools like PyVBMC and is involved in community initiatives such as the EurIPS conference. His work is supported by grants from the Research Council of Finland, Business Finland, and the UKRI Turing AI World-Leading Researcher Fellowship. He leads a research lab focused on amortized probabilistic inference, with ongoing projects including PriorGuide and Stacked VBMC, aiming to make Bayesian methods more practical and accessible for real-world scientific and engineering applications.

Thomas Hermannمشاهده پروفایل

پژوهشگر

• Auditory Data Science
• Interactive Sonification
• Smart Environments
• +۵ مورد دیگر

Dr. rer. nat. Thomas Hermann is a faculty member at Bielefeld University's Faculty of Engineering, leading the Ambient Intelligence Group and coordinating the Computer Science program. He specializes in sonification, auditory data science, and smart environments. Head of Ambient Intelligence Working Group Computer Science Program Coordinator Member of multiple academic advisory boards His research focuses on interactive sonification for biomedical applications, quantum systems, and smart environments. Key projects include ECG sonification for cardiac diagnosis, real-time auditory feedback in swimming, and sonic interfaces for AR cooperation. Recent publications span 2025 with Python-based sonification tools ( pya AGen ), quantum system sonification, and ST-elevation myocardial infarction monitoring. He contributes to open-access supplementary materials and interdisciplinary workshops. As a researcher , Hermann develops practical sonification frameworks like Panson for facial behavior analysis, CardioScope for portable ECG monitoring, and Base Cube One for smart environments. His work bridges academic research with industry applications.