Prof. Dr. Rebecca Buller is a leading researcher at the ZHAW School of Life Sciences and Facility Management , specializing in biocatalysis and enzyme engineering. Her work bridges computational methods with industrial applications, focusing on sustainable chemical synthesis and food safety. Research Interests : Biocatalysis, enzyme design, halogenation reactions, mycotoxin reduction, 3D bioprinting inks, machine learning-driven enzyme optimization. Key Projects : National Competence Center of Research Catalysis , Microbial Epimerases for Drug Synthesis , Reduction of Mycotoxins in Food Streams , and Excelzyme (university-industry collaboration). Publications highlight advancements in: Temperature-induced enzyme stabilization (2025) Computational protein design (2024) Mutational path enrichment (2024) Mycotoxin detoxification (2024) Industrial biocatalysis (2023-2024) Collaborations span institutions like the University of Basel, ETH Zurich, and industry partners in Switzerland. Her work integrates computational analysis, directed evolution, and real-world applications in pharmaceuticals and food safety.
Dr. Carsten Magnus is a Lecturer at the Department of Biosystems Science and Engineering (D-BSSE) at ETH Zurich , Switzerland, with affiliations to the Computational Evolution group in Basel since 2015. His work bridges mathematics and theoretical biology to explore HIV dynamics, antibody neutralization, and viral evolution. Education includes a Mathematics degree from Münster, Germany, followed by a PhD in Theoretical Biology at ETH Zurich (2007–2010) under Roland Regoes. He conducted postdoctoral research at ETH Zurich, Oxford (with Angela McLean), and Zurich (with Alexandra Trkola), supported by Swiss National Science Foundation (SNF) and Deutsche Forschungsgemeinschaft (DFG) grants. His research interests focus on HIV-1 antibody interactions, phylodynamic modeling, and mathematical frameworks for viral entry and immune escape. He extends phylodynamic tools to study within-host HIV diversification and applies stoichiometric analysis to quantify neutralization thresholds. Article trends highlight computational approaches to viral evolution (HIV, Zika, influenza), antibody efficacy, and host adaptation. Key themes include phylodynamics , immunology , mathematical biology , and bioinformatics with subfields like HIV transmission , BEAST software , and antiviral kinetics . Scientific awards include the Flash poster award for innovative science communication using pop culture references (e.g., Minions characters) in virology education. He actively mentors and collaborates on master’s/PhD projects, emphasizing interdisciplinary research. Laboratory : Computational Evolution group at D-BSSE, Basel, Switzerland, where he develops models for HIV dynamics and phylodynamic inference tools.
Professor Gavin Brown is a faculty member in the Department of Computer Science, focusing on theoretical and methodological foundations of Machine Learning. His work bridges statistics, information theory, and information geometry to develop robust ML methods for reproducibility and hypothesis testing in non-standard scenarios. His research has been applied to predictive policing, clinical trials, and energy-efficient ML algorithms for plastic electronics. Key publications include A Unified Theory of Diversity in Ensemble Learning (JMLR, 2023) and On the Stability of Feature Selection in the Presence of Feature Correlations (ECML, 2019), supported by EPSRC funding. Recent work (2024) investigates the distinction between bias-variance and approximation-estimation error, alongside hardware-efficient ML solutions for FPGAs and flexible electronics. His book How to Get Your PhD (OUP, 2021) offers practical and emotional guidance for doctoral students, co-authored with 12 experts on career planning, diversity in science, and risk management in research. Developed theories for ensemble diversity (2020-2023) Created stability metrics for feature selection (2016-2022) Explored ML applications in healthcare and public safety Current activities include a research sabbatical (2021-2022) working on novel ML frameworks. He leads a research group at the Kilburn Building (Office G11) and contributes to pedagogy discussions, particularly around PhD training and conceptual understanding in deep learning.
Manuel Gil is a Researcher at the Zurich University of Applied Sciences (ZHAW), affiliated with the School of Life Sciences and Facility Management and the Institute of Computational Life Sciences. His work focuses on computational methods in bioinformatics, including ancestral sequence reconstruction, multiple sequence alignment, and semantic integration of biological databases. He leads projects such as computational literature-based drug discovery for natural products and the development of tools like ProPIP and ARPIP. His research interests span evolutionary genomics, bioinformatics software development, and semantic querying of biomedical data. Notable contributions include advancing indel-aware phylogenetic analysis and enabling semantic access to federated bioinformatics databases through projects like Bio-SODA. Publications emphasize methodological advancements in sequence alignment, ancestral reconstruction, and knowledge graph applications in biomedicine. Collaborations include work with institutions like the SIB Swiss Institute of Bioinformatics and involvement in international conferences on text and data mining.
Stefan Schneider is a researcher at the University of Geneva, affiliated with the Groupe Systèmes Energétiques and Institut Forel since 2014. He holds a PhD in Applied Mathematics (1994) focused on numerical methods for differential equations and previously worked in R&D at Deram SA (banking software) from 2000-2013. His current research spans energy systems modeling, including territorial heat demand analysis, electricity consumption decomposition, and evaluation of innovative energy solutions for urban environments. Academic Affiliation: University of Geneva (Institut Forel, Groupe Systèmes Energétiques) Industry Experience: R&D at Deram SA (banking software, 2000-2013) Education: PhD in Applied Mathematics (1994) Research interests focus on energy demand modeling, district heating systems, and techno-economic analysis of urban energy solutions. He contributes to projects with industrial partners like Service Industriels de Genève (SIG), including geothermal heat pumps, energy efficiency atlases, and temperature reduction strategies for district heating networks. His earlier work in population genetics (1995-2002) led to the Arlequin software package, widely adopted in genetic data analysis. Recent publications (2017-2025) emphasize building energy modeling, heat demand forecasting, and sustainable urban infrastructure. While no explicit awards or advisees are mentioned, his interdisciplinary career bridges mathematics, computational methods, and applied energy systems research.
Berno Büchel is a Professor in the Department of Economics at the University of Fribourg, Switzerland. He holds a Chair in Microeconomics and is affiliated with the Faculty of Management, Economics and Social Sciences. His research focuses on social networks, game theory, and microeconomic systems, with particular emphasis on strategic behavior, market dynamics, and information sharing. Education and Career: PhD (Dr. rer. pol.) from Bielefeld University (2009) Habilitation in Economics from Hamburg University (2011–2015) Post-doctoral positions at Saarland University (2009–2011) and the University of St. Gallen (2015–2017) Visiting scholarships at Stanford University (2015), Paris 1 Panthéon-Sorbonne (2006–2007), and other institutions Research Interests: Analysis of strategic interactions in markets and networks Behavioral economics and information avoidance Labor market policies and migration impacts Trust dynamics and network structures Key Contributions: His work spans theoretical and empirical studies, including analysis of peer-to-peer platforms, law enforcement mechanisms, and social learning in teams. Recent publications address misinformation dynamics and the economic implications of migration policies. Teaching and Engagement: Leads the Chair of Microeconomics at Fribourg, offering courses on advanced economic theory. Maintains active collaborations with institutions globally, including the Liechtenstein-Institute.
Beat Wellig is a Professor of Thermodynamics, Process Engineering, and Environmental Technology at the Lucerne School of Engineering and Architecture (HSLU T&A). He holds a PhD from ETH Zurich in High-Pressure Process Engineering and has extensive academic and industry experience. His research focuses on thermal separation processes, environmental technology, process integration (including Pinch Analysis), heat pumps, and cooling systems. Wellig has led over 30 projects, including work on carbon capture, solar integration, and energy-efficient industrial processes. He has authored approximately 70 scientific publications and holds a patent for thermal networking of household appliances. His contributions span academia, industry collaboration, and applied research in energy systems. Key projects include the SWEET DeCarbCH initiative for industrial decarbonization and the PinCH software development for process analysis. His work emphasizes energy efficiency, sustainable industrial processes, and innovative thermal technologies.
Prof. Johannes Bohacek is an Associate Professor at ETH Zürich's Department of Health Sciences and Technology, within the Institut für Neurowissenschaften. His research focuses on understanding how acute stress events lead to anxiety disorders, particularly through noradrenaline's role in molecular and circuit-level brain changes. He leads the Molecular and Behavioral Neuroscience (MBN) lab, emphasizing transgenerational epigenetic effects and neuromodulation. Notable contributions include the BehaviorFlow pipeline for rodent behavior analysis and studies on locus coeruleus-norepinephrine signaling. He was a finalist for the 2024 Art of Leadership Award (ALEA). His work is published in top journals like Nature Methods and Nature Neuroscience . Contact: Email , Lab Website .
Carlos Andrés Pena is a Professor and Director of the Institute of Information and Communication Technologies (IICT) at HEIG-VD. His research focuses on Machine Learning, Artificial Intelligence, and Bioinformatics, with applications in healthcare, environmental science, and computational biology. He leads projects like ImpTox and EXPLaiN, addressing challenges in nanotoxicology, phage therapy, and ethical AI. His work bridges AI with real-world problems, including antimicrobial resistance and soil health monitoring. Education: Not explicitly listed in the provided text. Research Interests: Pena’s research integrates AI with interdisciplinary domains. Key areas include: Machine learning for healthcare diagnostics (e.g., diabetic retinopathy, perinatal health) Phage genome engineering using deep learning to combat antibiotic resistance Protist bioindicators for soil quality assessment using metabarcoding Fuzzy logic systems for biomarker discovery and disease classification Grants & Projects: He has secured over CHF 1M in funding, including EU Horizon 2020 projects and Swiss National Science Foundation grants. Notable projects include: ImpTox (2021–2025): Investigating nanoplastics’ toxicity on allergies PERPHECT (2019–2021): Engineering phages via LSTM networks HES-XPLAIN (2023–2024): Open platform for explainable AI Labs/Teams: Directs the IICT lab, collaborating with institutions like CNRS and University of Fribourg on bioinformatics and environmental studies.
Barbora Hudcová is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences and the Department of Mathematics . She works under the Chair of Statistical Field Theory and is involved in teaching courses like "Cellular Automata and Models of Artificial Life" through the SMA-ENS program. Her research focuses on computational and dynamical aspects of discrete systems. Research Interests Cellular Automata Artificial Life Discrete Dynamical Systems Computational Complexity Reservoir Computing Statistical Field Theory Publications Her work explores computational hierarchies, complexity transitions, and mathematical structures in cellular automata, with recent contributions to encoding-decoding relations and dynamical cavity methods. Key topics include artificial life modeling, computational universality, and transient behavior analysis in discrete systems.
Richard Neher is a research group leader at Biozentrum, University of Basel , focusing on the molecular evolution of rapidly adapting pathogens like influenza viruses, HIV, and bacteria. His work combines bioinformatics , population genetics , and statistical physics to develop algorithms for analyzing genomic data and predicting evolutionary trajectories. Key Research Areas: Virus evolution, phylogenetics, computational modeling of pathogen adaptation Projects: Predicting influenza evolution via phylogenetic trees, real-time viral genome analysis, HIV mutation dynamics Tools: Nextclade, TreeTime, PanGraph for genomic and epidemiological data analysis His research has significant implications for vaccine design, public health strategies, and understanding the interplay between pathogens and host immunity. He actively contributes to open-data initiatives and surveillance platforms, emphasizing the importance of real-time pathogen tracking .
Christoph Schindler is a Swiss Professor at the Lucerne School of Design, Film and Art (HSLU), where he heads the Bachelor's Programme in Object Design since 2014. He holds a doctorate in architecture from ETH Zurich and combines academic work with his role as partner in the Zurich-based furniture design firm schindlersalmerón GmbH , which he co-founded in 2005. Research Interests: His work spans digital fabrication , sustainable product design , and woodworking innovation , focusing on merging computational technologies with natural material properties. Key areas include parametric modeling , moldless manufacturing , and material lifecycle systems . He explores the intersection of Swiss craft traditions with algorithmic design through projects like the ZipShape method and Serial Branches series. Scientific Contributions: He has published over 40 works, with recent 2025-2023 articles examining social object design , climate-responsive furniture , and sustainable Swiss wood systems . His research appears in outlets like International Journal of Architectural Computing and Werkspuren , while his ETH dissertation on fabrication-based architectural periodization remains influential. Awards: iF Design Award (2019), Lista Office Design Award GOLD (2012), iF Material Design Award (2008) Teaching: Lecturer at ZHAW (2010-2014), Royal Danish Academy external examiner (2014-2026), and workshop leader at institutions including ETH Zurich, NTNU Trondheim, and Virginia Tech Industry Roles: Founding member of schindlersalmerón (2005-present) and Swiss Design Board representative (2022)
Giovanni Iacca is an Associate Professor at the Department of Information Engineering and Computer Science (DISI) of the University of Trento, Italy, where he leads the Distributed Intelligence and Optimization Lab (DIOL). He serves as Coordinator of the Master's Degree in Computer Science and Deputy Director of the Information Engineering and Computer Science Doctoral School. Dr. Iacca has over 15 years of industrial experience in mechatronics and optimization applied to engineering, logistics, and scheduling. Dr. Iacca received his PhD in 2011 from the University of Jyväskylä, Finland, and his MSc in 2006 from the Technical University of Bari, Italy. His academic career includes: 2021-present: Associate Professor, University of Trento 2018-2021: Tenure-track Assistant Professor, University of Trento 2017-2018: Postdoc, RWTH Aachen University, Germany 2013-2016: Postdoc, EPFL and University of Lausanne, Switzerland 2012-2016: Postdoc, INCAS³, The Netherlands Dr. Iacca's research bridges fundamental and applied aspects of artificial intelligence with particular emphasis on evolutionary computation and explainable AI. His work spans machine learning, optimization techniques, distributed systems, and their practical implementations. Recent research directions include federated learning, interpretable reinforcement learning, neural architecture search, and optimization for resource-constrained environments. He teaches courses on Computer Architectures, Introduction to Machine Learning, Bio-Inspired Artificial Intelligence, Optimization Techniques, and AI in Medicine. His publication record demonstrates a strong trend toward developing transparent and efficient AI systems. Recent papers focus on making complex AI models more interpretable while maintaining performance across diverse domains from healthcare to supply chain management. His work on evolutionary approaches to explainable AI has gained significant recognition in the computational intelligence community. Scientific Awards and Editorial Roles EvoApplications Best Paper Award (2017) UKCI AWARENESS Best Paper Award (2012) IEEE CIS Outstanding Student-Paper Award (2011) IEEE Senior Member (2023) Associate Editor, Evolutionary Intelligence (2024) Editorial Board Member, Memetic Computing (2024) Associate Editor, IEEE Transactions on Evolutionary Computation (2023) Dr. Iacca has successfully supervised multiple PhD students including Andrea Ferigo, Hyunho Mo, and Leonardo Lucio Custode. His research is supported by various grants and collaborations with industry partners like MyAv. He serves as chair for PPSN 2026 and has organized workshops including the Workshop on Awareness and Consciousness in Artificial Intelligence (ACAI). As leader of the Distributed Intelligence and Optimization Lab (DIOL), Dr. Iacca oversees a research team working at the intersection of evolutionary computation, machine learning, and distributed systems. The lab focuses on developing novel algorithms that balance computational efficiency with interpretability, with applications spanning from embedded systems to large-scale distributed computing environments. Current projects include interpretable reinforcement learning, federated neuroevolution, and optimization for edge computing.
Federico Germani, a postdoctoral researcher at the Institute of Biomedical Ethics and History of Medicine (IBME) within the Faculty of Medicine at the University of Zurich, holds a PhD in Molecular Life Sciences and additional qualifications in International Relations. He founded Culturico, a non-profit cultural platform combating misinformation, and serves as a rapporteur for the World Health Organization (WHO) on infodemic ethics. His work bridges biomedical research, digital society, and public health ethics. Bachelor's in Biology, University of Milan (2013) Master's in Cellular and Molecular Biology, University of Milan (2015) PhD in Molecular Life Sciences, University of Zurich (2019) Graduate Diploma in International Relations, University of London (2018) Federico's research focuses on digital misinformation, pandemic preparedness, and ethical frameworks for AI in health communication. His expertise spans infodemic management, information literacy, and the intersection of technology and ethics. His recent work explores AI-driven disinformation mechanisms, systematic evaluation biases in language models, and critical thinking interventions. His 15 most recent publications (2025-2024) address AI ethics, pandemic communication, and misinformation analysis. Articles include topics such as algorithmic accountability, emotional bias in AI, and preference epidemiology for patient-centered healthcare.
Dr. Oliver Strub is a Lecturer at the University of Bern, affiliated with the Group for Business Analytics, Operations Research and Quantitative Methods. His research focuses on quantitative finance, optimization algorithms, and data-driven decision making. He holds a PhD and has expertise in applying mathematical and computational techniques to financial and operational problems. His research interests include index-tracking portfolio optimization, feature selection in machine learning, and the development of heuristic and mathematical programming methods. He has explored hybrid approaches combining genetic algorithms, MILP models, and data-mining techniques to enhance portfolio performance while adhering to regulatory constraints like UCITS. Recent work emphasizes optimization methods for portfolio management, particularly under constraints such as UCITS regulations. He has developed solutions that blend heuristic algorithms with mathematical programming to achieve efficient financial and operational outcomes. His contributions span algorithmic trading, risk management, and compliance-driven portfolio construction. Dr. Strub's affiliations include the Group for Business Analytics, where he collaborates on projects involving business analytics, operations research, and quantitative methodologies. While no awards or grants are explicitly noted, his extensive publication record reflects a strong focus on practical and theoretical advancements in quantitative fields.
