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Sarah Ebling is a Full Professor of Language, Technology and Accessibility at the University of Zurich's Faculty of Arts and Social Sciences. She leads the Language, Technology and Accessibility research group within the Institute for Computational Linguistics. Her work focuses on computational linguistics applications for assistive technologies targeting disabilities such as hearing impairments, visual impairments, and cognitive disorders. Key areas include sign language technologies, automatic text simplification, and audio description systems. She directs the large-scale Swiss innovation project 'Inclusive Information and Communication Technologies' (2022-2026, CHF12 million budget) and collaborates on EU H2020 and SNSF Sinergia projects. Education: Holds a doctoral degree (summa cum laude, 2016) from the University of Zurich with research on automatic translation to Swiss German Sign Language. Completed studies in German Linguistics, Computational Linguistics, and English Linguistics at Universities of Zurich and Heidelberg, with research stays in Dublin, Chicago, and Rochester. Research emphasizes multimodal accessibility solutions, including sign language fluency assessment, gesture-based interaction, and AI-driven text adaptation. Current projects explore audio description translation systems (SwissADT), sign language corpus development (SwissSLi), and digital tools for comprehensibility assessment in simplified texts. Her work bridges computational linguistics with ethical considerations in assistive technology deployment. Grants and Leadership: Principal Investigator on major accessibility-focused grants, including the CHF12M Swiss innovation project. Supervises PhD candidates in areas like sign language assessment tools and text simplification algorithms. Active in international collaborations, publishing extensively in computational linguistics and accessibility journals/conferences. Technology Development: Created the 'DigiSpon' benchmark for language sample analysis and developed open-source tools for sign language translation baselines. Her team's innovations include the SignCLIP model connecting text and sign language via contrastive learning, and pose estimation frameworks for sign language recognition.

• Computational Linguistics
• Assistive Technologies
• Sign Language Technologies
• +7 more

Philippe Schwaller is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), School of Basic Sciences, within the Institute of Chemical Sciences and Engineering. He leads the Laboratory of Artificial Chemical Intelligence (LIAC), a research group focused on leveraging artificial intelligence to accelerate molecular discovery and sustainable chemistry. He is also a core Principal Investigator of the NCCR Catalysis, a national Swiss research center. His research lies at the intersection of chemistry, materials science, and computer science, with a strong emphasis on developing machine learning models for molecular design and synthesis. LIAC's work is driven by real-world sustainability challenges, aiming to reduce the time and cost of discovering new functional molecules and materials. The recent publications and projects from his lab highlight a strong trend in generative AI for chemistry, including memory-augmented models, hypergraph neural networks, and large language models tailored for scientific discovery. These efforts are complemented by educational initiatives such as the 'AI for Chemistry' course and practical programming resources for chemists. He actively supervises a diverse group of PhD students and contributes to multiple doctoral programs at EPFL, including EDCH and EDPY. His teaching portfolio includes courses on computational chemistry, AI applications in chemistry, and scientific machine learning. Philippe Schwaller is deeply involved in advancing AI-driven scientific discovery through both research and education, positioning his lab at the forefront of artificial chemical intelligence. The lab maintains active open-source contributions on GitHub, fostering collaboration and transparency in scientific AI development.

• Artificial Intelligence in Chemistry
• Machine Learning for Molecular Discovery
• Sustainable Chemistry
• +6 more

David Atienza is a Professor in the Department of Electrical Engineering at the School of Engineering, Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for pioneering embedded systems education and research in ultra-low power computing. His innovative teaching methods, including using Nintendo DS consoles and smartphones to teach embedded systems, earned him the 2015 EPFL Teaching Award in Electrical Engineering. His research focuses on Embedded Systems , Edge AI , and Wearable Healthcare , with breakthroughs in energy-efficient hardware-software co-design for biomedical applications. Key contributions include open-source platforms like X-HEEP and HEEPocrates for ultra-low power edge computing, and frameworks like SzCORE for seizure detection benchmarking. His work bridges computer architecture with real-world healthcare challenges, emphasizing privacy-preserving algorithms and sustainable computing. Recent publications (2023-2025) reveal a dominant trend toward biomedical edge AI and sustainable computing , with 70% of articles targeting healthcare wearables (seizure detection, cough monitoring) and 30% addressing energy efficiency in data centers and edge devices. His research consistently integrates open-hardware principles (RISC-V) with novel algorithm-hardware co-design. Awards include: 2015 EPFL Teaching Award in Electrical Engineering section While specific advising details are unreported, his extensive publication record and leadership in multi-partner projects like Sustainable Textile Electronics (STELEC) indicate active graduate supervision and significant research funding. His group develops open-source hardware frameworks used globally in academia and industry. He leads the Embedded Systems Laboratory at EPFL, driving projects in ultra-low power RISC-V architectures, biomedical wearables, and sustainable computing. Current initiatives include carbon-aware data center frameworks and multi-modal health monitoring systems deployable on commercial wearables.

• Embedded Systems
• Low-Power Computing
• Edge AI
• +3 more

Matthias Bannert is a Lecturer at the Department of Management, Technology, and Economics at ETH Zürich, where he works at the KOF Swiss Economic Institute (Konjunkturforschungsstelle). His work focuses on the intersection of economics, software development, and data management, with particular expertise in time series analysis and official statistics. Bannert designs solutions for state-of-the-art data processing, management, and publishing of economic data and research. Bannert completed his doctoral thesis titled "Survey Based Research in Economics - Essays on Methodology, Economic Applications and Long Term Processing of Economic Survey Data" at ETH Zürich in 2016. His academic journey began when he joined KOF in late 2008, initially working as a researcher for the Business Tendency Survey group before transitioning to the institute's IT department. Dr. Bannert's research interests span several interconnected domains at the nexus of economics and data science. He specializes in developing software environments for official statistics, with particular focus on processing and managing economic time series data through open-source driven data pipelines. His technical expertise includes R programming and PostgreSQL database systems, which he applies to create robust solutions for economic data analysis. Bannert is particularly interested in survey methodology, nowcasting techniques, and the development of reproducible research workflows. His work bridges the gap between theoretical economics and practical software implementation, ensuring that economic research can leverage state-of-the-art data processing techniques. Analysis of Bannert's publication record reveals a consistent focus on the application of data science techniques to economic research problems, particularly in the domain of official statistics and survey-based economics. His work demonstrates a progression from theoretical survey methodology to practical software implementation, with increasing emphasis on real-time economic forecasting and data management systems. A distinctive feature of his research is the development of open-source R packages that make advanced economic data analysis more accessible to researchers and practitioners. As an active contributor to the R language for Statistical computing and the open source community, Bannert has developed several notable software packages including timeseriesdb, tstools, and kofdata, which are available on CRAN. These tools reflect his commitment to creating reproducible, transparent, and efficient workflows for economic data analysis. Bannert serves as a data science supervisor for multiple KOF research projects and is a co-Principal Investigator in an SNF-funded Digital Lives project in collaboration with KOF's labor market expert group. His teaching activities include "Hacking for Sciences - An Applied Guide to Programming with Data" and involvement in the Nowcasting Lab, which provides live out-of-sample forecasting and model testing capabilities for economic researchers. Dr. Bannert is affiliated with the KOF Swiss Economic Institute, where he contributes to several research groups including the KOF Macroeconomic Forecasting group and the KOF Data Science and Macroeconomic Methods group. His work at KOF bridges the institute's traditional economic research with modern data science approaches, helping to position the institute at the forefront of data-driven economic analysis.

• Software Environments for Official Statistics
• Processing and Management of Economic Time Series Data
• Open Source Driven Data Pipelines
• +7 more

Oliver Gassmann is a Full Professor of Technology Management with a focus on Innovation Management at the University of St. Gallen (HSG). He leads the Institute for Technology and Innovation Management (ITEM-HSG), specializing in research areas such as business model innovation, open innovation, pharmaceutical R&D efficiency, and digital sustainability. His work bridges strategic management and innovation ecosystems, emphasizing collaborative advantage in volatile markets. **Education & Affiliations**: While formal educational details are not explicitly stated, his position as a Full Professor implies advanced academic credentials in technology or business management. His primary affiliation is with ITEM-HSG, where he contributes to executive education programs and industry partnerships. **Research Interests**: Gassmann’s research explores how organizations innovate in dynamic environments, particularly in pharmaceuticals, digital platforms, and sustainability. Key themes include R&D productivity, AI-driven innovation, open innovation frameworks, and the governance of innovation ecosystems. He advocates for collaborative strategies to address challenges like VUCA (Volatility, Uncertainty, Complexity, Ambiguity) in business and science. **Publications Trends**: Recent work focuses on pharmaceutical R&D benchmarks, AI’s role in business models, and platform ecosystems. His 2025 studies analyze FDA approval trends and Web3 implications for trust in digital platforms. Earlier publications (2020–2024) address smart cities, patent management in VUCA contexts, and frugal innovation strategies. **Advising & Grants**: Supervised notable students like Charlotte-Katharina Lekkas and Maximilian Böger. His grants and projects likely involve industry collaborations, though specific funding details are not provided here. He actively contributes to policy discussions on intellectual property and innovation policy. **Labs & Teams**: Oversees ITEM-HSG’s research initiatives, including studies on innovation ecosystems and digital transformation. His team collaborates with pharmaceutical companies, tech firms, and public institutions to develop actionable frameworks for innovation management.

• Business Model Innovation
• Open Innovation
• Pharmaceutical Innovation
• +4 more

Diego Ulisse Pizzagalli is an independent researcher in Computational Medicine affiliated with the Faculty of Biomedical Sciences at the Università della Svizzera italiana (USI), the Euler Institute, and the Institute for Research in Biomedicine (IRB). He holds roles as a lecturer at USI and teaching assistant, focusing on machine learning in medicine and signal processing. His work bridges immunology and AI, with projects involving wearable device monitoring in chronic diseases and the development of digital biomarkers. His research focuses on applying artificial intelligence to understand immune responses, tissue remodelling, and chronic diseases. Key areas include trajectory analysis of immune cells using intravital microscopy, predictive models for disease complications, and bioimage analysis. He leads the IMMUNEMAP initiative, an open immunology data platform, and collaborates on hardware prototyping for medical applications. Pizzagalli’s recent work emphasizes AI-driven medical imaging tools (e.g., CompositIA for CT scans, deep learning in forefoot morphology analysis) and trajectory-based studies of immune cell behavior. His projects integrate clinical data with computational models, such as predicting hospital admissions in chronic liver disease patients using wearable sensors. He co-leads projects funded by systemsx.ch, Apple Inc., and others. Supervises BSc/MSc students. The Digital Pathophysiology Lab at Euler Institute serves as his primary research base, promoting interdisciplinary collaboration in biomedical imaging, trajectory mining, and wearable sensor development.

• Computational Medicine
• Immunology
• Machine Learning
• +5 more

Professor Elzbieta Pustulka is a faculty member at FHNW University's School of Business, affiliated with the Institute for Business Informatics. Her research centers on databases, NLP, and AI-driven business process optimization. Research explores database pedagogy through gamification (SQL Scrolls), NoSQL implementations for ERPs, and automated information extraction techniques. Recent work emphasizes CI/CD practices in database development and educational game design for programming concepts. She supervises graduate students in projects spanning database indexing, logistics simulations, and AI-driven reporting systems. No scientific awards are noted.

• Databases and Algorithms
• Data Integration
• Natural Language Processing
• +3 more

Dr. Philipp Ackermann is a researcher and academic leader at ZHAW Zurich University of Applied Sciences, School of Engineering. His roles include Deputy Head of the Human-Centered Computing Research Group, Head of the Visual Computing Lab, Deputy Programme Director for Medical Informatics, and Delegate for Entrepreneurship in the School of Engineering. His research focuses on Visual Computing (especially Augmented Reality), Medical Informatics , and Digital Health . He leads projects like AR Patterns (design patterns for AR experiences) and Automated Recognition of Modular Product Structures in AR , which explore circular economy applications. His work bridges technical innovation with practical implementation, emphasizing collaboration in immersive technologies and data-driven solutions. Key research trends in his articles include: Advancements in XR technologies (e.g., 3D inference pipelines, physically-based rendering) Collaborative systems in AR/VR environments Applications of AR in healthcare and industrial settings Systematic reviews of collaboration in virtual spaces He has contributed to projects as both leader and team member, addressing topics from surgical proficiency training to intelligent viticulture. His work often emphasizes real-world impact through partnerships between academia and industry. Philipp leads the Visual Computing Lab , fostering interdisciplinary innovation in computer graphics and immersive technologies. His educational contributions include teaching the CAS Advanced Statistical Data Analysis course.

• Visual Computing
• Augmented Reality (AR)
• Medical Informatics
• +4 more

Xiaosong Wang is a Professor at the University of Pittsburgh School of Medicine where he directs the Wang Laboratory at UPMC Hillman Cancer Center. His research bridges computational genomics and clinical oncology with a focus on developing precision biomarkers for cancer treatment. His laboratory develops innovative computational approaches to analyze multi-omics data for advancing precision oncology. Dr. Wang's research interests center on computational genomics, precision oncology, and translational cancer biology. His laboratory applies interdisciplinary approaches combining computational genomics, artificial intelligence, cancer genetics, and translational biology to explore uncharted areas of cancer genetics. Key research areas include identifying actionable cancer targets, developing AI-backed genomics tools, and advancing precision oncology based on multi-omics data. His work particularly focuses on breast cancer biomarkers, intragenic rearrangements as 'dark matter' of cancer genetics, and genomic markers for immunotherapy response prediction. Analysis of Dr. Wang's publications reveals a consistent trajectory from foundational computational methods for gene fusion detection (2009-2014) toward increasingly sophisticated AI-driven approaches for precision oncology (2020-2024). His recent work emphasizes the development of the iGenSig-AI and G2K frameworks that integrate mechanism-driven AI with agentic models for autonomous hypothesis generation. A significant portion of his research focuses on translating computational findings into clinically actionable biomarkers, particularly for breast cancer and immunotherapy response prediction. DOD Breakthrough Award for studying predictive genomic biomarkers for precision immuno-oncology Level 2 Breakthrough Award from Department of Defense for TNBC-specific BCL2L14-ETV6 fusions research Women's Cancer Research and Education Pilot Award Dr. Wang leads an active research group with multiple postdoctoral researchers and computational biologists including Dr. Renu Sharma and Dr. Chuang Yang. His laboratory has received substantial funding from the Department of Defense for breakthrough cancer research. Current projects focus on translating computational biomarkers into genotype-directed targeted therapies for therapy-resistant breast cancers and developing the G2K AI framework for autonomous cancer biology discovery. The lab maintains several open-source computational tools including iGenSig, IndepthPathway, and intragenic-rearrangement-burden available on GitHub. The Wang Laboratory operates at the intersection of computational biology and clinical oncology, with research teams dedicated to algorithm development, cancer genomics analysis, and translational validation. The lab has developed multiple computational frameworks including integral genomic signature analysis and the newer iGenSig-AI platform. Current efforts focus on creating a dual-AI strategy that combines mechanism-driven models with autonomous hypothesis-generating systems to accelerate cancer breakthroughs.

• Computational Genomics
• Precision Oncology
• Cancer Genetics
• +5 more

Prof. Timo Kehrer is a Professor and Head of the Software Engineering Group (SEG) at the Institute of Computer Science, University of Bern. He also serves as Deputy Director of Studies, overseeing academic programs in software engineering. His research focuses on variability modeling, model-based systems engineering, simulation-based testing of autonomous systems, and empirical studies in software development practices. Key areas include software product lines, cyber-physical systems, and the application of formal methods in industrial contexts. Notable contributions include work on Community-driven variability management in open-source software, Automated testing tools like ScoutSL and TEASER , Simulation-based frameworks for self-driving cars (e.g., Sensodat datasets), and Empirical analyses of GitHub Actions workflows and merge conflict resolution. His research bridges theory and practice, addressing challenges in software sustainability, AI-driven development, and the integration of formal verification tools like TLA+. He actively participates in conferences such as VaMoS (Variability Modelling) and contributes to open-source projects in model-driven engineering. Prof. Kehrer collaborates with industry partners to advance tools for model repositories, vulnerability detection (e.g., VUDENC ), and semantic analysis of software changes. His work emphasizes reproducibility, tool support for developers, and educational initiatives to modernize software engineering curricula.

• Software Engineering
• Model-Based Systems
• Cyber-Physical Systems
• +7 more

Gabriele Bavota is a Full Professor in the Faculty of Informatics at the Università della Svizzera italiana (USI). He holds a Laurea (cum laude) and PhD in Computer Science from the University of Salerno (2013). Before joining USI in 2016, he was an Assistant Professor at the Free University of Bolzano-Bozen (2014–2016) and a research fellow at the University of Sannio (2013–2014). Research Focus: Software maintenance, mining software repositories, empirical software engineering, and AI-driven code analysis. Awarded the 2018 ACM Sigsoft Early Career Researcher Award and leads the ERC Starting Grant project DEVINTA, exploring deep learning applications in software engineering. Authored over 180 publications in top venues like ICSE, FSE, and TOSEM, with multiple best/distinguished paper awards. His work bridges empirical studies, AI tools, and developer-centric solutions, addressing challenges in code quality, refactoring, and automated testing. He has served as program chair for ICPC 2016, SANER 2017, and ICSME 2023, and holds editorial roles in TSE, EMSE, and JSS. Labs/Teams: Principal Investigator of the DEVINTA project and part of the Software Institute at USI, fostering collaborations in AI, software engineering, and empirical research.

• Software Engineering
• Artificial Intelligence
• Data Mining
• +7 more