Find Academic Supervisors

Aldo BrandiView profile

Lecturer

• Urban Systems
• Environmental Systems
• Sustainable Development
• +2 more

Aldo Brandi serves as a Lecturer and Scientist at the Swiss Federal Institute of Technology in Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC). Within ENAC, he operates through the Institute of Architecture (IA) and the Laboratory of Urban and Environmental Systems (URBES), while concurrently contributing to the Teaching Support Service (SSIE) for school-wide instruction. His research centers on Urban Systems and Environmental Systems , exploring sustainable development frameworks and environmental engineering solutions for urban contexts. Work at URBES emphasizes interdisciplinary integration of architectural design, civil infrastructure, and ecological principles to address contemporary urbanization challenges, with particular focus on system resilience and resource optimization in built environments. Dr. Brandi's teaching portfolio includes Environmental Sciences and Engineering coursework and the "Projeter ensemble ENAC" collaborative program, fostering practical application of theoretical concepts through project-based learning. His laboratory engagement drives applied research connecting architectural innovation with environmental sustainability metrics, utilizing systems analysis methodologies to evaluate urban ecological footprints.

Hendrik HuwaldView profile

Academic

• Cryospheric Sciences
• Climatology
• Hydrology
• +10 more

Hendrik Huwald is a Scientist at the Laboratory of Cryospheric Sciences (CRYOS) and a Lecturer at the Environmental Sciences and Engineering Section (SSIE) at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He is affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC), and maintains offices in both Sion and Lausanne. His work bridges research and teaching in environmental and atmospheric sciences. His research focuses on surface mass and energy balance in alpine and polar environments , stream temperature dynamics under climate change , and development of environmental measurement techniques and sensors . His expertise lies in field experimentation, environmental monitoring, and modeling of snow-atmosphere interactions, boundary layer meteorology, and hydrological systems. He has led and participated in numerous large-scale field campaigns across diverse environments. The recent publication trends indicate a strong focus on complex terrain meteorology (e.g., banner clouds at the Matterhorn), blowing snow dynamics , urban and alpine climate , and river temperature modeling under climate change . His work combines observational data with advanced modeling (e.g., large-eddy simulations, Alpine3D) and machine learning, particularly in energy and hydrological applications. He has supervised several PhD students including Elizaveta Sharaborova, Brandon Van Schaik, Tristan Brauchli, Aurélien Gallice, and Adrien Michel. His work is supported by extensive project management experience at national and international levels. He is actively involved in teaching courses such as Physics and Chemistry of the Atmosphere and Physics and Hydrology of Snow . While no formal awards are listed in the provided text, his extensive publication record and leadership in major environmental research initiatives reflect significant scholarly impact. Huwald's work is central to understanding cryospheric processes under climate change, with applications in hydrology, renewable energy, and environmental policy. His integration of field measurements, sensor development, and modeling places him at the forefront of experimental environmental science.

• Environmental Engineering
• Environmental Sciences
• Sustainable Development
• +1 more

Lucas Uhlmann serves as a Lecturer at the Swiss Federal Institute of Technology in Lausanne (EPFL) within the School of Architecture, Civil and Environmental Engineering (ENAC), specifically affiliated with the Section of Environmental Sciences and Engineering (SSIE-ENS). Holding the status of Invited guest, his primary responsibilities center on teaching within the Environmental Sciences and Engineering program. His academic focus spans Environmental Engineering, Environmental Sciences, and Sustainable Development, with emphasis on practical application through project-based learning. He actively contributes to courses such as 'Environmental Sciences and Engineering' and 'Projeter ensemble ENAC', which integrate collaborative problem-solving approaches for complex environmental challenges in civil infrastructure contexts.

• Artificial Intelligence
• Signal Processing
• Machine Learning
• +4 more

Zohreh Mostaani is a Doctoral Assistant at the IDIAP Research Institute, affiliated with the École Polytechnique Fédérale de Lausanne (EPFL), within the School of Engineering, Institute of Electrical Engineering. Her work is embedded in a leading research environment focused on intelligent systems and signal processing. Her research interests are centered around artificial intelligence and its applications in human-centered technologies. Given her affiliation with IDIAP, her work likely spans machine learning, speech recognition, computer vision, biometrics, and human-computer interaction . These domains reflect the core strengths of the laboratory and its mission to advance intelligent systems that interact naturally with humans. The trends in her research domain emphasize robust AI models for real-world multimodal data analysis, particularly in security, healthcare, and assistive technologies. While specific publications are not listed, the thematic focus of IDIAP suggests a strong orientation toward applied AI with societal impact. No scientific awards were mentioned in the provided text. As a Doctoral Assistant, she is actively conducting research toward a PhD degree, contributing to ongoing projects at LIDIAP. While no formal advisees are listed, she likely collaborates within a structured supervision framework typical of doctoral programs at EPFL. There is no mention of independent grants, but her position suggests funding through institutional or project-based support. She is part of the LIDIAP (IDIAP Research Institute) research team, a prominent Swiss research institute specializing in signal processing and artificial intelligence, closely integrated with EPFL. The lab fosters interdisciplinary collaboration in AI-driven technologies for real-world applications.

• Computer Science
• Information Theory
• Machine Learning
• +3 more

Cemre Çadir is a Doctoral Assistant at the Mathematics of Information Laboratory (MIL), within the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). She is concurrently pursuing her doctoral studies in the Doctoral Program in Computer and Communication Sciences (EDIC). Her academic work is centered on the mathematical foundations of information processing, with research interests spanning computer science, information theory, machine learning, and data science. As a Doctoral Assistant, she contributes to research and academic activities at MIL, a leading laboratory in theoretical and applied aspects of information sciences. The available publications and research outputs could not be determined from the provided text. However, her affiliation with MIL suggests strong engagement in cutting-edge research in intelligent systems and information theory. Scientific Awards: No awards listed in the provided text. Advising and Grants: There is no public information available about advisees or funded research grants associated with Cemre Çadir in the current data. As a doctoral researcher, she may be supported through institutional or project-based funding at EPFL. Labs and Research Teams: She is an active member of the Mathematics of Information Laboratory (MIL) at EPFL, which focuses on the theoretical underpinnings of machine learning, signal processing, optimization, and statistical inference.

Zhen WeiView profile

Researcher

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +4 more

Zhen Wei is a Doctoral Assistant and PhD student at École polytechnique fédérale de Lausanne (EPFL), affiliated with the Computer Vision Laboratory (CVLAB) within the School of Computer and Communication Sciences (IC) and the Institute of Computer Science (IINFCOM). He is actively engaged in research and holds a formal staff position at the university. His research interests span Computer Vision , Machine Learning , Deep Learning , and Artificial Intelligence , with additional expertise in Multidisciplinary Design Optimization and Aerospace Engineering , as evidenced by his awards. His work is conducted primarily within the CVLAB, a leading research group in vision and machine learning. Zhen Wei has been recognized with prestigious awards including the AIAA Multidisciplinary Design Optimization Best Paper Award and the AIAA Aviation Forum Best Student Paper Award in Multidisciplinary Design Optimization. He is also a recipient of the Toulouse Graduate School of Aerospace Engineering (TSAE) Scholarship. As a doctoral researcher, he contributes to academic advising and research projects within CVLAB. Though no formal advisees are listed, his role involves collaborative research and technical contributions to the lab’s scientific output. He is also Treasurer of the Chinese Students & Scholars Association Lausanne (CSSA), demonstrating leadership in student community engagement. His research is supported through institutional affiliation with EPFL and scholarship funding. He is based at BC 303, Building BC, Station 14, 1015 Lausanne, Switzerland, and is actively involved in both academic and student life at EPFL.

Sibo WangView profile

Academic

• Computational Neuroscience
• Neuroengineering
• Bio-inspired Robotics
• +7 more

Sibo Wang is a Doctoral Assistant in the Neuroengineering Laboratory at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Life Sciences and the Brain Mind Institute . He is currently pursuing his PhD in the Doctoral Program in Computational and Quantitative Biology . His research integrates neuroscience and artificial intelligence to develop robotic controllers inspired by animal movement. Institution: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Life Sciences Department: Brain Mind Institute Research Group: Prof. Ramdya Group Office: AI 1125, Station 19, 1015 Lausanne Email: sibo.wang@epfl.ch Phone: +41 21 693 81 89 Website: https://ramdya-lab.epfl.ch Sibo Wang's research is centered on computational neuroscience and bio-inspired robotics . He constructs artificial neural networks based on the fully mapped connectome of the fruit fly's brain and nerve cord to model and understand movement control. His work aims to bridge neurobiology with AI, offering insights into natural systems while inspiring more efficient and adaptive neural architectures for robotics. His research interests include artificial neural networks, machine learning, systems neuroscience, and neuromorphic engineering. Prior to his doctoral studies, Sibo Wang served as Chief Research Officer at HabiTerre , a startup focused on ecosystem modeling and sustainability. In this role, he led an NSF-funded project as Principal Investigator, developing generative machine learning models to assess farmland resilience using satellite imagery. He also secured a USDA grant for research on smart irrigation systems. Earlier in his academic journey, he contributed to astrophysics research, building machine learning models for the Laser Interferometer Gravitational-Wave Observatory (LIGO) to detect and analyze gravitational waves, and for the Dark Energy Survey (DES) to curate observational data. Scientific Awards and Funding: Principal Investigator, NSF-funded project on farmland resilience Recipient of USDA grant for smart irrigation technology Sibo Wang is also involved in academic community service, serving as Logistics Manager for the Association des doctorants en sciences de la vie (ADSV), a student association at EPFL. This role, while outside his core research, demonstrates his engagement with the academic community. He has not been mentioned as retired or former staff, and there is no indication of part-time status; he is an active doctoral researcher and staff member at EPFL. No information is available on his advisees or recent publications within the provided text.

• Signal Processing
• Scientific Machine Learning
• Inverse Problems
• +7 more

Prof. Dr. Ivan Dokmanić is an Associate Professor for Data Analytics at the Department of Mathematics and Computer Science, University of Basel, where he leads the Research Group Signal and Data Analytics. His work bridges data science, signal processing, and physics to address challenges in computational imaging, acoustics, and audiovisual reasoning. His research focuses on developing principled machine learning and signal processing techniques grounded in physical models. Key interests include: Signal Processing (especially scientific and wave-based) Scientific Machine Learning (SciML) Inverse Problems and Imaging Computational Acoustics and Audio Seismic Signal Processing Dokmanić is the principal investigator of the ERC Starting Grant project SWING (Signals, Waves, and Learning), which aims to create a data-driven framework for wave-based inverse problems. This project underscores his leadership in integrating physical models with modern learning techniques. He has received significant recognition through competitive funding, most notably the ERC Starting Grant, highlighting the innovative and high-impact nature of his research. While no detailed list of publications is provided in the text, his work appears to trend toward interdisciplinary methodologies combining physics, signal processing, and machine learning for scientific discovery. Dokmanić is actively involved in research and academic leadership. He supervises a research group and contributes to the academic mission of the University of Basel. Although specific advisees are not listed, his group likely includes PhD and master’s students working on advanced topics in data analytics and signal processing. He has secured substantial grant funding, indicating active research projects and collaboration opportunities. He is affiliated with the Research Group Signal and Data Analytics, which serves as the hub for his interdisciplinary investigations into wave phenomena, inverse modeling, and data-driven scientific computing.

Pier Luigi DragottiView profile

Professor

• Signal Processing
• Wavelet Theory
• Sampling Theory
• +7 more

Prof. Pier Luigi Dragotti is a Professor of Signal Processing in the Department of Electrical and Electronic Engineering at Imperial College London, Faculty of Engineering. He leads the Communications, Signal Processing, and Control (CSP) research group and is actively engaged in both theoretical and applied research in signal processing and computational imaging. His research focuses on wavelet theory, sampling theory, sparse signal processing, computational imaging, and data-driven image processing . He integrates classical signal processing frameworks with modern deep learning techniques, particularly in solving inverse problems and image restoration. His work bridges model-based algorithms with data-driven approaches, enabling breakthroughs in fields such as art conservation and neuroscience imaging. The recent publications highlight a strong trend towards invertible neural networks, physics-informed deep learning, and hybrid model-data methods for image restoration, microscopy, and cultural heritage analysis. His team develops algorithms that are not only accurate but also interpretable and grounded in physical models. Scientific Awards and Recognitions: Editor-in-Chief of IEEE Transactions on Signal Processing (2018–2020), Certificate of Merit Supervised students winning: Eurasip 3M Thesis Competition, IEEE MMSP Best Paper Award, Outstanding PhD Thesis Awards (2020–2022), Ivor Tupper Prize Student Adam won Gold at the Commonwealth Games Advising and Grants: Prof. Dragotti has successfully supervised numerous PhD and MSc students, many of whom have gone on to achieve significant recognition. His group has secured funding that supports interdisciplinary research in imaging science, and several of his former students have launched innovative projects, including a start-up company. He fosters a dynamic research environment with strong industry and cross-institutional collaborations. Labs and Research Groups: He is a key member of the CSP (Communications, Signal Processing, and Control) group at Imperial College London, where he leads a vibrant team working on cutting-edge signal processing challenges. The group is known for its contributions to sampling theory, wavelets, and the integration of deep learning in imaging systems.

Ekansh SareenView profile

Research Fellow

• Medical Image Processing
• Biomedical Engineering
• Signal Processing
• +7 more

Ekansh Sareen is a Doctoral Assistant at the Medical Image Processing Laboratory (MIPLAB) within the School of Engineering at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He is concurrently enrolled in the Doctoral Program in Electrical Engineering (EDEE) under the Doctoral School (EDOC), reflecting his dual role as a researcher and PhD candidate. His work is based at Campus Biotech in Geneva, where MIPLAB conducts cutting-edge research in biomedical imaging and computational methods for healthcare applications. His research interests lie at the intersection of engineering and medicine, with a focus on medical image processing , signal processing , and machine learning . These areas are central to developing automated tools for disease detection, image segmentation, and diagnostic support systems. Given his affiliation with MIPLAB, his work likely involves deep learning models applied to MRI, CT, or ultrasound data, contributing to advancements in precision medicine and digital health. While no publications are listed in the provided text, the research output from MIPLAB typically spans computer vision, artificial intelligence in healthcare, and biomedical signal analysis. The lab’s work often emphasizes translational research, aiming to bridge the gap between algorithmic innovation and clinical utility. Future trends in his research may include explainable AI, federated learning for medical data, and integration with electronic health records. Scientific Awards: No awards mentioned. As a Doctoral Assistant, Ekansh is involved in research and potentially teaching or mentoring, though no advisees are listed. He does not appear to lead independent grants, but likely contributes to larger collaborative projects within EPFL or with clinical partners. MIPLAB is part of a broader ecosystem of health technology innovation at EPFL, collaborating with hospitals and industry partners to develop real-world medical solutions. The laboratory environment fosters interdisciplinary collaboration, combining expertise from electrical engineering, computer science, and clinical medicine. Ekansh’s position places him at the forefront of academic research in medical AI, with opportunities to publish in top-tier journals and present at international conferences. His career trajectory is aligned with academic or industrial research in biomedical engineering and AI-driven healthcare technologies.

• Computational Systems Biotechnology
• Systems Biology
• Bioinformatics
• +3 more

Konrad Lagoda is a Doctoral Assistant at the Laboratory of Computational Systems Biotechnology (LCSB) within the Institute of Chemical Sciences and Engineering (ISIC), School of Basic Sciences (SB) at École polytechnique fédérale de Lausanne (EPFL). He is also a doctoral student in the Chemistry and Chemical Engineering program under the EDOC doctoral school. His research is situated in the domain of computational systems biotechnology, leveraging advanced computational methods to model and analyze complex biological systems. The LCSB focuses on integrating high-throughput biological data with genome-scale metabolic models, network analysis, and machine learning techniques to understand disease mechanisms and microbial metabolism. Konrad Lagoda is part of a multidisciplinary research environment that emphasizes data-driven approaches in systems biology. The laboratory fosters collaboration across bioinformatics, biotechnology, and biomedical research, contributing to innovations in personalized medicine and synthetic biology. No scientific awards or publications were listed in the provided information. As a Doctoral Assistant, Konrad contributes to research projects while pursuing his PhD. There is no indication of formal advising roles or grant leadership at this stage. He is based at the EPFL campus in Lausanne, Switzerland, with office location CH H4 605 and laboratory space in CH H4 615. The Laboratory of Computational Systems Biotechnology (LCSB) is a leading research group at EPFL dedicated to advancing systems-level understanding of biological processes through computational modeling and big data analytics. The lab hosts researchers from diverse backgrounds including computer science, biology, chemistry, and engineering, promoting an interdisciplinary approach to solving complex biomedical challenges.

Christoph BaderView profile

Researcher

• Sustainable Economies
• Poverty and Inequality
• Sustainable Consumption Patterns
• +3 more

Christoph Bader is a researcher at the University of Bern's Centre for Development and Environment (CDE) , where he leads the Transformative Education and Science Impact Area and serves as Director of Studies. His work focuses on sustainable economies, poverty/inequality analysis, and fostering sustainable consumption patterns. He is also involved in teaching within CDE's academic programs. Key Research Areas: Sustainable consumption, multidimensional poverty, post-growth economics, part-time work impacts, social innovation Projects: 'Time is wealth' examining part-time work and sustainability, 'From niche to society' analyzing social innovation diffusion, and multidimensional poverty studies in Laos Publications: Recent works explore sustainability skills in higher education, civil society initiatives for ecological transition, and worktime reduction policies in the Global North Networks: Affiliated with Rethinking Economics, WWF Netzwerk Nachhaltige Wirtschaftswissenschaften, World Economics Association, and Netzwerk Nachhaltige Ökonomie Contact: christoph.bader@unibe.ch | Office 262, Mittelstrasse 43, Bern

Albrecht EhrenspergerView profile

Researcher

• Agroecology
• Agrobiodiversity
• Invasive Species Management
• +5 more

Dr. Albrecht Ehrensperger is a Senior Research Scientist at the Centre for Development and Environment (CDE) of the University of Bern. His work focuses on agroecology, invasive species management, and spatial analysis in global regions including Southeast Asia, Central Asia, Eastern Africa, Horn of Africa, and Western Africa. He specializes in stakeholder engagement and sustainable land use systems. Research Areas: Agroecology, Agrobiodiversity, Invasive Species Management, GIS, Participatory Mapping Projects: Woody Weeds, ASSET, Pha Khao Lao, Agriculture for Nutrition (AFN II) Languages: French, German, English, Kiswahili, Lao Recent Publications highlight invasive species dynamics in Eastern Africa, agroecological transitions in the Mekong region, and stakeholder-driven land use governance. His work often integrates GIS tools, climate resilience frameworks, and participatory planning methodologies. Network Affiliations include Agroecology Works! and the Agroecology Learning Alliance in Southeast Asia (ALiSEA) .

Tobias HalbherrView profile

Researcher

• Educational Technology
• Cognitive Sciences
• Higher Education
• +6 more

Tobias Halbherr is a Researcher at ETH Zurich, working in the Digital Education Services group within the university's IT Services. He has been active in the field of learning technologies and teaching innovation since 2012. Doctoral degree (2020) from the Chair of Learning Sciences and Higher Education at ETH Zurich (completed part-time) Undergraduate: Computer Science at ETH Zurich Postgraduate: Psychology (with a minor in Computer Science) at the University of Zurich 2024: Advanced training in General Management (CAS) from Rochester-Bern Executive Programs Halbherr's research focuses on educational technology, particularly adaptive computer-based learning and assessment systems. His work bridges cognitive sciences and computer science, with applications in aviation security and higher education innovation. His prior activities included research in adaptive learning environments, software development for the Engineering Treasury of Zürcher Kantonalbank, and freelance event management and live video art projects. Halbherr also leads the SWITCH eduhub community's Special Interest Group (SIG) E-Assessment.

• Chemical and Bioprocess Engineering
• Process Monitoring and Control
• Process Analytical Technology (PAT)
• +5 more

Michal Dabros is an Associate Professor at the ChemTech - Institute of Chemical Technologies within HES-SO (University of Applied Sciences and Arts Western Switzerland) . His work spans chemical and bioprocess engineering, focusing on thermal safety, process analytical technology (PAT), and sustainable chemistry solutions. Education : MSc HES-SO in Life Sciences, BA HES-SO in Architecture Research Interests : Process monitoring, bioplastic development, nanoparticle synthesis, and reactor design for industrial applications. Recent Research Trends include continuous flow reactors for heterogeneous reactions, waste valorization via bio-cementation, and automation in bioprocesses. His publications emphasize sustainability, safety, and digital advancements in chemical engineering. Project Leadership involves international collaborations such as bio-cementation for construction materials and automated hydroponic systems in Haiti. He also contributed to remote laboratory platforms and thermal safety modeling.