Johannes Soeding is a Research Group Leader in the Computational Biology department at the Max Planck Institute for Multidisciplinary Sciences in Göttingen, Germany. His work bridges physics, bioinformatics, and molecular biology, focusing on computational methods for biological data analysis. His research interests include computational biology, protein structure and function prediction, metagenomics, transcriptional regulation, and statistical genomics. He develops widely used software tools such as HH-suite, HHpred, MMseqs2, and Foldseek for protein sequence and structure analysis. The recent publications demonstrate a strong focus on high-throughput biological data, particularly in protein structure search (e.g., Foldseek), metagenomic gene discovery (e.g., MetaEuk), and regulatory genomics. His work combines algorithm development with deep biological insights, often published in top-tier journals like Nature Biotechnology , Science , and Nature Methods . He has been involved in significant methodological advances in sequence clustering, contact prediction, and eQTL analysis, showing a consistent trend toward scalable, data-driven approaches in genomics and proteomics. Soeding has contributed to major projects in gene regulatory networks and RNA biology, often in collaboration with experimental groups. His leadership in developing open, efficient bioinformatics tools has had a broad impact on the scientific community. He is affiliated with several graduate programs including IMPRS Physics of Biological and Complex Systems, Biomolecules: Structure - Function - Dynamics, and Genome Science, indicating active participation in training the next generation of scientists.
Boris Kaus is a Full Professor and Chair of Geophysics and Geodynamics at the Institute of Geosciences, Johannes Gutenberg University Mainz, Germany. His research focuses on understanding geological processes from grain scale to planetary scale using mathematical and numerical models. Funded by the German Research Foundation, European Research Council, and BMBF, his work spans lithospheric deformation, melt migration, fold-and-thrust belts, and high-performance computing applications in geosciences. His research interests center on geodynamic modeling of lithospheric processes, including subduction zones, mantle convection, and crustal deformation. Kaus develops novel numerical approaches to simulate complex geological phenomena, with emphasis on coupling between erosion, lithosphere dynamics, and mantle flow. His group creates specialized software for high-performance computing systems to tackle multi-scale geophysical problems. His scientific awards include the Paul Niggli Medal, EGU Arne Richter Award, multiple ERC grants (Starting, Proof-of-Concept, Consolidator), and the Carl Friedrich Gauss Lecturer honor. He has received recognition for editorial contributions including G-Cubed's Excellence in Refereeing award. ERC Consolidator Grant MAGMA (2018-2023) ERC Proof of Concept Grant SALTED (2016-2017) ERC Starting Grant MODEL (2010-2015) John von Neumann Excellence Project for HPC ETH Medal for Ph.D. thesis Kaus actively supervises graduate students and leads research projects funded by major European and German agencies. His group develops open-source software like GeophysicalModelGenerator.jl and maintains strong collaborations with international institutions including ETH Zürich and USC. Current projects focus on magma dynamics, lithospheric shear localization, and the development of advanced numerical methods for geodynamic simulations. The research group operates within the Geodynamics & Geophysics team at JGU Mainz, utilizing high-performance computing resources and collaborating with multiple European research initiatives including IMPRS and FORTHEM networks. Their laboratory specializes in numerical modeling of Earth systems with applications to tectonics, volcanology, and crustal evolution.
Dr. Pascal Reuss is a Researcher at the Intelligent Information Systems (IIS) Division within the Institute of Computer Science , University of Hildesheim . His work focuses on Case-Based Reasoning (CBR) systems, Multi-Agent Systems , and Knowledge Management applications. Active in CBR framework development and game-based AI research Teaching Computer Science III (Databases) for winter 2025/26 Participating in university sustainability initiatives like Stadtradeln 2024/25 Reuss contributes to AI education through practical implementations in gaming environments and has developed visualization tools for CBR agent behavior. His research spans multi-agent collaboration , dynamic case bases , and domain-specific language implementations for knowledge maintenance. Notable contributions include: Co-developing the FEATURE-TAK framework for knowledge extraction Designing case factories for distributed CBR systems Implementing finite state machines for tactical game agents Creating CBR-based fitness planning systems His work appears in various CBR and Game Development publications from 2011-2024. The research demonstrates practical applications of CBR in aircraft maintenance diagnostics , training plan generation , and educational technology contexts.
Professor Matthias Lederer serves as a faculty member at the Weiden Business School of Ostbayerische Technical University Amberg-Weiden, specializing in Business Informatics with a focus on Process Management. His academic position is complemented by extensive industry experience across multiple sectors including IT services, manufacturing, and consulting. Dr. Lederer's research spans four interconnected domains: process analysis and optimization, IT process management, agile process transformation, and didactics for process digitization. His work bridges theoretical frameworks with practical applications, particularly in business process management (BPM), digital transformation, and the integration of agile methodologies into organizational structures. His research demonstrates a clear trajectory toward increasingly sophisticated applications of data science and artificial intelligence in process optimization. Analysis of his recent publications reveals a strong focus on practical implementations of business process management, with particular emphasis on agile transformations, data-driven process design, and the application of AI in business contexts. His work consistently addresses the intersection of academic research and industry practice, with publications appearing in both academic journals and professional conference proceedings. The research demonstrates growing attention to digital platforms, smart manufacturing applications, and sustainable business practices. His scientific recognition includes: Best Program Director award from ISM International School of Management Project Award 'Innovative LernOrte' from OTH Award for Good Teaching from the Bavarian State Ministry Best Paper Award at the 2014 International Conference on Information Systems Multiple professional certifications including Lean Six Sigma Black Belt and OMG-Certified Expert in Business Process Management Professor Lederer has developed significant academic leadership through his role as Chairman of the Institute of Innovative Process Management. His teaching approach integrates practice-integrated methodologies, reflecting his belief in connecting academic concepts with real-world applications. His extensive consulting background with organizations like REHAU AG + Co. and the Bavarian Ministry of Justice informs his academic work and student mentorship.
Yu Jiang is an Associate Professor at the School of Software, Tsinghua University, China. His research focuses on software security with emphasis on fuzz testing, embedded systems, and database security. He leads the Software System Security Assurance Group which has discovered over 1,000 bugs in major system software with 300+ CVEs registered. Dr. Jiang's research interests include Software Engineering , Embedded Systems Security , and Cross-Layer Fuzzing . His work addresses vulnerabilities in operating systems, databases, communication protocols, and IoT firmware through innovative fuzzing frameworks. Key contributions include semantic-aware fuzzing for heterogeneous software stacks and learning-based vulnerability detection for embedded systems. His recent publications demonstrate strong trends in database security (Hulk, PUPPY, THANOS), ransomware defense (Fawkes, Preventing Disruption), and web security (JANUS). Research spans both theoretical advances in fuzzing techniques and practical industrial applications, with significant impact evidenced by numerous distinguished paper awards. Career Award, NSFC: 2026 Distinguished Paper Award, ISSTA: 2025 First Prize for Technical Invention, CCF: 2024 Distinguished Paper Award, USENIX Security: 2024 SIGSOFT Distinguished Paper Award, FSE: 2022 Dr. Jiang has advised over 50 graduate students including 20 PhD candidates. His research is supported by major grants including NSFC projects ($600,000 for Software Trustworthiness Construction and $350,000 for Distributed Database Reliability), Huawei ($80,000 for LLM-Powered Fuzzing), and Tencent ($120,000 for LLM-Powered Unit Testing). The Software System Security Assurance Group maintains strong industry partnerships with Huawei, Alibaba, Tencent, and Webank. The group operates cutting-edge infrastructure for fuzz testing across multiple domains including database systems, operating kernels, blockchain platforms, and industrial control systems. Current projects integrate LLM technologies with traditional fuzzing techniques to enhance vulnerability detection in complex software stacks.
Raffi Khatchadourian is an Associate Professor in the Department of Computer Science at Hunter College and the Graduate Center of the City University of New York (CUNY). His research focuses on techniques for automated software evolution, particularly automated refactoring and source code recommendation systems, with the goal of easing the burden associated with evolving large and complex software through automated tools. He also conducts research on the automated analysis of Object-Oriented programs. Ph.D., Computer Science & Engineering, Ohio State University (2011) MS, Computer Science & Engineering, Ohio State University (2010) BS, Computer Science, Monmouth University (2004) Khatchadourian's research spans multiple areas of software engineering and programming languages, with particular emphasis on automated software evolution techniques. His work addresses critical challenges in refactoring legacy systems to modern language constructs, optimizing parallel processing in Java 8 streams, and addressing technical debt in machine learning systems. His recent research has expanded into deep learning program transformation, where he develops techniques to convert imperative deep learning code to more efficient graph execution models while ensuring safety. His approach combines static analysis, program transformation, and empirical validation to create practical tools that developers can integrate into their workflows. Analysis of Khatchadourian's recent publications reveals a strong focus on bridging the gap between theoretical program analysis and practical software engineering challenges. His work increasingly intersects with machine learning systems, examining both how to improve ML code through refactoring and how to ensure safety in deep learning frameworks. The research demonstrates consistent evolution from foundational work on Java language features toward more complex systems involving concurrency, deep learning, and automated program transformation. Distinguished Paper Award at SCAM '18 for work on Java 8 stream optimization EAPLS Best Paper Award at FASE '20 for study on Java 8 stream usage EAPLS Distinguished Paper Award at FASE '25 for Deep Learning refactoring work Best Paper Award nominee at IJCAI '24 for AI safety framework Khatchadourian actively mentors graduate and undergraduate students, with several advisees going on to successful academic and industry positions. His former Ph.D. student Tatiana Castro Vélez accepted a tenure-track Assistant Professor position at the University of Puerto Rico. He has supervised numerous master's theses and undergraduate research projects, often resulting in co-authored publications at top software engineering venues. His research has been supported by various grants, though specific funding details are not prominently featured in the available information. Through his work on tools like Fraglight for aspect-oriented programming and Hybridize Functions for deep learning refactoring, Khatchadourian has established a research group focused on practical program analysis and transformation. His lab develops Eclipse plugins and other IDE-integrated tools that help developers with automated refactoring, bug detection, and code optimization. The group maintains active collaborations with researchers at other institutions and contributes to open-source projects on GitHub.
Alexandre Bartel is a Professor in the Department of Computing Science at Umeå University, Sweden, specializing in software security and software engineering. His research focuses on system security and analysis of permission-based software stacks, particularly Android. With numerous publications in top-tier security and software engineering conferences and journals, Bartel has established himself as a leading researcher in vulnerability analysis and software security. Bartel's research interests primarily center around software security, with a particular emphasis on Java and Android ecosystems. His work delves into vulnerability analysis, deserialization attacks, control flow integrity, and security mechanisms for complex software systems. He investigates how to verify security properties through efficient algorithms and examines existing software layers from a security perspective. His research bridges theoretical security concepts with practical implementation challenges in real-world systems. Analysis of Bartel's recent publications reveals a strong focus on Java deserialization vulnerabilities, control flow integrity mechanisms, and Android security. His work demonstrates a consistent trajectory from fundamental vulnerability analysis to developing practical security solutions and benchmarks. The research spans both theoretical frameworks and empirical evaluations, with significant contributions to understanding long-term security adoption patterns and developing tools for vulnerability detection. Scientific Awards: Most influential Paper ICSE N-10 Award for IccTA: Detecting Inter-Component Privacy Leaks in Android Apps Bartel actively contributes to the academic community through service roles, having served on program committees for major conferences including ASE, ESEC/FSE, ICSE, and FSE. His research has practical implications for software developers and security practitioners, particularly in the areas of vulnerability detection and security mechanism implementation. While specific grant information isn't detailed in the provided materials, his extensive publication record suggests successful funding for his research initiatives. Though not explicitly detailed in the provided information, Bartel's research likely involves collaboration with students and researchers on projects related to software security analysis. His work on benchmarks like Gleipner and CONFUZZION suggests involvement in developing tools and resources for the security research community.
Laura Becker is an Assistant Professor at the Department of General Linguistics, University of Freiburg. Her research focuses on linguistic typology, quantitative methodology, and the role of coding efficiency in grammatical structures. She holds a PhD exploring article systems across languages and has contributed to cross-linguistic studies on syntax, morphology, and sociolinguistics. Her work emphasizes statistical rigor and computational tools, such as the glottospace R package for geospatial linguistic analysis. Recent projects include investigating phoneme inventories in Polynesian languages and socio-linguistic influences on conditional constructions. Publications highlight her expertise in morphosyntax, language contact, and corpus-based approaches. No scientific awards are explicitly mentioned in the provided materials. Dr. Becker has advised no recorded students in the available data and has not listed specific grants or labs. Her current research trends prioritize quantitative frameworks to address typological and evolutionary questions in linguistics.
Simone Ferlin is an Adjunct Senior Lecturer at Karlstad University working with 5G and Internet evolution. She completed her PhD in computer science in 2017 at the Simula Research Lab and Universitetet i Oslo under the supervision of Dr. Ozgu Alay and Prof. Michael Welzl. Her PhD dissertation focused on increasing robustness in multipath transport with MPTCP. Dr. Ferlin's educational background includes a PhD in Computer Science from the Simula Research Lab and Universitetet i Oslo (2017). Her doctoral research centered on enhancing robustness in multipath transport protocols, specifically focusing on MPTCP (Multipath TCP). She also completed undergraduate work that contributed to a book project with Prof. Friedrich Oehme on electronics and circuit technology. Dr. Ferlin's research spans multiple domains at the intersection of networking, systems, and performance engineering. Her primary interests include network and system measurements, performance analysis, security, and congestion control. She investigates how networks like the Internet evolve, examining technology development, adoption patterns, and their impacts on various entities. Additionally, she explores ways to harmonize security and privacy while making them more usable and assessable. Her work particularly focuses on transport layer and multipath transport protocols, examining their performance and security aspects. She also investigates application and transport layer performance, automation, and monitoring. Her research extends to network programming in both Linux kernel and user space, mobile broadband networks from 2G to 5G, and their intersection with the Internet. She is deeply engaged in observability, distributed and system performance monitoring, and automation. Analysis of Dr. Ferlin's recent publications reveals a strong focus on next-generation networking technologies. Her work spans multiple domains including 5G/6G networks, transport protocols (particularly QUIC and MPTCP), network virtualization, container orchestration, and the application of machine learning to networking problems. She has increasingly incorporated large language models into network configuration and automation research. Her publications demonstrate a consistent emphasis on performance measurement, optimization, and security across diverse networking environments from the edge to the cloud. Dr. Ferlin has received notable recognition for her research contributions: Best paper award at IEEE ICIN'21 for 'Learning-based Incast Performance Inference in Software-Defined Data Centers' Applied Networking Research Prize (ANRP)'25 winner for 'NetConfEval: Can LLMs Facilitate Network Configuration?' Dr. Ferlin is actively involved in mentoring the next generation of networking researchers. She has co-supervised numerous Master's and PhD students across multiple institutions including Karlstad University, KTH, TU Berlin, University of Oslo, and universities in Brazil. Her students have worked on diverse topics including NAT64 performance comparison, system tracing visualization, network observability, ML applications to multipath transport, FEC integration with QUIC, high-performance networking for 5G, congestion control, shared bottleneck detection, multipath IoT applications, and container runtime performance. She is also involved in several significant research projects including Vinnova's SEMLA (Securing Enterprises via Machine-Learning-based Automation), Horizon Europe's CODECO (Cognitive Decentralised Edge Cloud Orchestration), and the Knowledge Foundation of Sweden's DRIVE (Data-driven Latency-Sensitive Mobile Services for a Digitized Society). Dr. Ferlin serves as Workshop Chair for ACM SIGCOMM '25, is a member of the ACM/IRTF Applied Networking Research Workshop (ANRW) steering committee, and co-chairs the Internet Congestion Control Research Group (ICCRG) at the IRTF. She previously served as Associate Technical Editor for IEEE Communications Magazine and has been active on numerous program committees for major networking conferences including SIGCOMM, CoNEXT, IMC, and PAM.
Dr. Kerstin Raudonat is a researcher at the Institute of Computer Science, Faculty of Mathematics and Natural Sciences, Humboldt University of Berlin. She has been working at the 'Computer Science and Society' chair since September 2022, focusing on the intersection of technology, gender, and society. Her research spans multiple domains including human-computer interaction, media education, and digital game culture, with particular emphasis on creating more inclusive technological environments. Dr. Raudonat's research interests center on gender equity in technology development, examining how societal biases manifest in IT systems and how to break cycles of discrimination in software design. Her work explores the complex relationships between technology and social structures, with special attention to how digital environments can be designed to promote diversity and inclusion. She investigates these issues through multiple lenses including practice theory, cyberfeminism, and participatory design methodologies, seeking practical approaches to make technology development more socially responsible. Her recent publications reveal a consistent trajectory examining gender dynamics in technology, with increasing focus on AI systems and their societal impacts. The research shows evolution from analyzing gaming cultures and communication patterns to addressing systemic biases in contemporary AI technologies. Her work demonstrates strong interdisciplinary connections between computer science, sociology, gender studies, and media education, reflecting a comprehensive approach to understanding technology's role in society. Dr. Raudonat has been actively involved in multiple significant research projects funded by the German Federal Ministry of Education and Research, including: KIRA (2021-2024): Developing AI-based matching for vocational training with gender sensitivity considerations IT&me (2017-2020): Creating multimedia knowledge resources for women in IT fields Gender-UseIT (2013-2014): Establishing a network for interdisciplinary research on gender perspectives in human-computer interaction VIT3A : International virtual teaching and collaboration initiatives Her research has been conducted in collaboration with multiple institutions including Fraunhofer IAO, Hochschule Heilbronn, and Vietnamese-German University, demonstrating strong national and international research partnerships. Dr. Raudonat's work contributes significantly to understanding how technology can be developed in more socially responsible ways that promote equity and inclusion.
Carolin Müller is a Juniorprofessor for the Theory of Electronically Excited States at the Friedrich-Alexander University Erlangen-Nuremberg since November 2023. Previously, she was a Feodor Lynen Postdoctoral Researcher at the University of Luxembourg (June 2022-October 2023) and a Postdoctoral Researcher at Friedrich Schiller University Jena (March 2021-May 2022). Dr. Müller received her B.Sc. (2016) and M.Sc. (2018) in Chemistry from Friedrich Schiller University Jena, followed by her Ph.D. (Dr. rer. nat) in 2021 from the same institution. Her doctoral research focused on "Towards Operando Spectroscopy of Supramolecular Photocatalysts – A Case Study on Ru-dppz-derived Systems" under the supervision of Prof. B. Dietzek-Ivanšić. Dr. Müller's research focuses on the theoretical understanding of photoinduced processes in molecules and materials. Her group (CPC Group) investigates electron transfer processes, isomerization reactions, and excited-state dynamics with the goal of controlling and optimizing light-driven processes for increased reactivity and efficiency. Her work combines computational chemistry, spectroscopy, and machine learning approaches, specifically utilizing methods like TD-DFT, CASSCF, molecular/quantum dynamics, and cheminformatics techniques including SVD, MCR, and global/target lifetime analysis. Her recent publications demonstrate a strong interdisciplinary approach spanning computational chemistry, spectroscopy, and machine learning. Key themes include nonadiabatic molecular dynamics, excited-state simulations, photoswitch design, photocatalysis, and the development of computational tools like KiMoPack for kinetic modeling. Her work often bridges theoretical predictions with experimental validation through close collaboration with spectroscopy research groups. Feodor Lynen Research Fellowship (Alexander von Humboldt Foundation) Thuringian Research Award 2023 for Applied Research Albert-Weller Award (German Chemical Society) Dissertation Award (Faculty of Chemistry and Earth Sciences) FCI Kekulé PhD fellowship As a Juniorprofessor, Dr. Müller leads the CPC Group at FAU, where she mentors students in computational chemistry research. She has developed expertise in combining spectroscopic techniques (resonance Raman, transient absorption, and time-resolved emission spectroscopy) with computational methods and cheminformatics approaches. She also actively contributes to the scientific community through service roles including co-organizing the ESTML 2023 Workshop and serving as an active member in the yPC organization of the German Bunsen Society. Dr. Müller is actively developing the CPC Group research program at the Computer Chemistry Center, focusing on light-induced physical processes and chemical reactions. Her group combines quantum chemistry, chemoinformatics, and experimental spectroscopy to reveal mechanisms behind photoinduced phenomena and optimize light-driven processes.
Prof. Dr. Klaus Schmid is a Professor in the Department of Software Systems Engineering at the University of Hildesheim, part of the Faculty of Mathematics, Natural Sciences, Economics, and Computer Science. His research focuses on Machine Learning Operations (MLOps), software product lines, adaptive systems, and variability modeling. He leads projects such as EXPLAIN and ReGaP, emphasizing explainable AI and industrial MLOps integration. His work addresses challenges in Cyber-Physical Production Systems (CPPS), including data management, model calibration, and domain knowledge integration. Key research interests include MLOps architecture design, variability modeling transformations (e.g., UVL to IVML), and incremental verification techniques for software product lines. He has published extensively in venues like IEEE ETFA, IEEE Software, and SPLC conferences. Notable achievements include a Best Paper Award for work on control patterns in self-adaptive systems. Collaborations with industry partners highlight his focus on bridging academic research and practical industrial applications. Prof. Schmid’s contributions extend to tool development, such as EASy-Producer for variability-aware software ecosystems, and frameworks for environment modeling in adaptive systems. His research addresses both foundational challenges (e.g., syntax-preserving slicing) and applied topics like MLOps platform comparisons and industrial case studies in Industry 4.0.
Karl Schmid is a W3 Professor of Crop Plant Biodiversity and Breeding Informatics at the University of Hohenheim's Institute of Plant Breeding, Seed Science and Population Genetics within the College of Agricultural Sciences. His research integrates evolutionary genetics, population genomics, and machine learning to address agricultural challenges. Ph.D. in Biology, University of Munich (1996) Postdoctoral Research, Cornell University (1997-1999) Emmy-Noether Research Group, Max Planck Institute of Chemical Ecology (2000-2006) Group Leader, Leibniz Institute of Plant Genetics (2006-2008) Professor of Genetics, Swedish Agricultural University (2008) His research focuses on crop biodiversity conservation, evolutionary genetics of plant pathogens, and breeding informatics applications. Current work leverages deep learning for phenotyping (quinoa panicles, barley genomics) and analyzes pathogen evolution (Exserohilum turcicum in maize). His team actively develops computational tools like GGoutlieR for geo-genetic pattern detection. Recent publications demonstrate strong trends in applying AI to agricultural genomics, particularly in quinoa improvement and pathogen surveillance. His group leads the EU H2020 INVITE project on molecular markers in plant variety protection and organizes international symposia like the 2024 Quinoa Symposium at Hohenheim. Head of Crop Biodiversity and Breeding Informatics Group Principal Investigator, EU H2020 INVITE project Organizer, International Quinoa Symposium 2024
Alfonso Emilio Gerevini is a prominent researcher in artificial intelligence with over 30 years of continuous academic contributions. His work spans theoretical foundations of automated planning to practical healthcare applications, with recent publications demonstrating significant impact in both traditional AI domains and emerging interdisciplinary areas. His research interests focus on automated planning systems , temporal reasoning , and multi-agent coordination , with recent expansion into healthcare applications using machine learning techniques. Gerevini has made fundamental contributions to planning algorithms, particularly in width-based search, case-based planning, and privacy-preserving multi-agent planning. His work on PDDL (Planning Domain Definition Language) has been influential in standardizing planning representations. Analysis of his 15 most recent publications reveals a strategic evolution from core planning research toward impactful healthcare applications, particularly during the COVID-19 pandemic. While maintaining his expertise in planning algorithms, he has successfully integrated machine learning techniques to address real-world medical challenges including radiology report analysis, prognosis prediction, and lab test interpretation. His work demonstrates exceptional versatility across both theoretical and applied domains of artificial intelligence. Gerevini maintains a robust collaborative network, primarily with Italian researchers including Ivan Serina, Alessandro Saetti, and Luca Putelli. His publications appear consistently in top-tier AI venues including Artificial Intelligence journal, Journal of Artificial Intelligence Research, and AAAI/ICAPS conferences. The collaborative patterns suggest he leads a significant research group focused on advancing planning systems while applying them to critical real-world problems.
Andrew T. Duchowski is a Professor at Clemson University, specializing in Eye Tracking Methodology, Human-Computer Interaction, and Computer Graphics. His work spans over two decades with significant contributions to gaze-based interaction systems, foveated rendering, and cognitive load measurement. He authored three editions of the influential textbook Eye Tracking Methodology (Springer, 2003/2007/2017). Duchowski's research integrates eye movement analysis with applications in virtual reality, medical imaging (e.g., colonography viewers), and aviation safety. He actively collaborates with institutions globally and serves on editorial boards for journals like Proceedings of the ACM on Human-Computer Interaction . His recent projects include developing real-time gaze analytics pipelines and exploring entropy-based metrics for visual attention analysis. Publications (selected 15 recent): Focus on advancing gaze interaction in immersive environments, optimizing 3D visualization, and measuring cognitive load through pupillary activity and microsaccades. Key co-authors include Krzysztof Krejtz, Matias Volonte, and Donald House.
