جستجوی استاد راهنمای دانشگاهی

Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.

• Machine Learning
• Deep Learning
• Computational Imaging
• +۵ مورد دیگر

Ueli Grossniklaus is an Ordinary Professor at the University of Zurich within the Faculty of Mathematical and Natural Sciences , affiliated with the Department of Plant and Microbiology . His work focuses on plant developmental biology, particularly epigenetic and genetic mechanisms governing reproduction and adaptation. Key Courses: Epigenetics, Plant Biology Workshop, Group Seminars on Current Research Laboratory Techniques: Advanced methods in plant cell mechanics, transcriptomics, and genome editing Research Interests span plant epigenetics, reproductive biology, and the interplay between environmental stress and genetic regulation. He investigates: Mechanistic control of gametogenesis and fertilization Epigenetic contributions to plant adaptation Evolutionary implications of asexual reproduction Biophysical forces in plant cell growth Publication Trends (2025–2018) reveal expertise in: Arabidopsis and fern model systems Epigenetic regulation (DNA methylation, histone dynamics) Apomixis and hybrid seed failure mechanisms Biomechanics of pollen tubes and carnivorous plants Genome editing tools (CRISPR) and long-read sequencing Scientific Collaborations include interdisciplinary projects on: Microfluidic devices for plant cell analysis Gene drive ecology and ethics 3D imaging of plant reproductive structures Advising and Grants focus on mentoring through research internships in developmental biology, genetics, and systems biology. His lab engages in: Epigenetic response to environmental stress Cell wall mechanics in reproduction Computational modeling of plant growth Laboratory Teams integrate plant biologists, bioengineers, and computational scientists to study: Mechanistic gene regulation Evolutionary developmental biology Microrobotics for cellular force measurement

• Plant Developmental Biology
• Epigenetics
• Plant Genetics
• +۵ مورد دیگر

Prof. Robert Grass is a Lecturer at the Department of Chemistry and Applied Biosciences at ETH Zurich, affiliated with the Institute for Chemical and Bioengineering Sciences. His research focuses on innovative applications of nanotechnology, DNA-based storage systems, and sustainable catalytic processes for CO2 valorization. Grass has pioneered silica-encapsulated DNA technologies for traceability in healthcare, environmental monitoring, and anti-counterfeiting measures. His work bridges chemical engineering with information technology, addressing challenges in long-term data preservation and molecular-level security. Current projects include developing compostable DNA storage materials and designing catalysts for methanol synthesis from CO2, contributing to both environmental sustainability and energy systems. Grass's interdisciplinary approach integrates nanomaterials design, enzymatic processes, and machine learning to advance next-generation storage and sensing technologies. Research Interests: Development of DNA-based storage systems with error-correction mechanisms Nanoparticle engineering for medical and environmental applications Catalytic materials for CO2 conversion and green chemistry Bio-inspired security systems using molecular randomness Sustainable materials for long-term data preservation His recent work highlights advancements in silica-encapsulated DNA tracers for tracking pathogen transmission dynamics, as well as low-nuclearity catalysts enabling efficient methanol synthesis from CO2. Grass actively explores the intersection of nanotechnology and digital information, including cryptographic applications leveraging DNA's inherent complexity.

• Chemical Engineering
• Materials Science
• Nanotechnology
• +۳ مورد دیگر

Camille Delavaux is a researcher affiliated with ETH Zurich's Department of Global Ecosystem Ecology, part of the Professorship for Global Ecosystem Ecology. Her work focuses on plant-microbe interactions, ecosystem ecology, and biogeographic patterns. Key research interests include island biogeography, microbial diversity, invasion biology, and the application of environmental DNA (eDNA) techniques. She has conducted fieldwork in diverse locations like the Galapagos Islands, Panama, and semi-arid prairie ecosystems. Her research explores how mutualistic relationships (e.g., mycorrhizal fungi) influence plant diversity and community dynamics, with studies addressing topics such as latitudinal diversity gradients, invasive species impacts, and soil microbial feedback mechanisms. She has contributed to developing molecular tools for identifying arbuscular mycorrhizal fungi and advocated for integrative approaches in ecological research, including the use of large language models in science. Delavaux collaborates on projects spanning tropical forests, post-agricultural grasslands, and island ecosystems, emphasizing the role of microbes in planetary health and the Sustainable Development Goals. Her work bridges field ecology, molecular biology, and computational methods to address global ecological challenges.

• Ecosystem Ecology
• Biogeography
• Microbial Diversity
• +۳ مورد دیگر

Touradj Ebrahimi is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he heads the Multimedia Signal Processing Group (MMSPG) within the School of Engineering's Institute of Electrical Engineering. He holds a Ph.D. in Electrical Engineering from EPFL and serves as the Convenor of the JPEG standardization committee. His research spans AI-powered imaging, multimedia signal processing, and media security. Research Interests: Professor Ebrahimi's work focuses on next-generation image/video compression (including JPEG AI and DNA-based storage), quality of experience in immersive media, trustworthy media authentication (JPEG Trust), and explainable AI for biometric systems. His group pioneers methods for point cloud compression, deepfake detection, and privacy-preserving multimedia. Publication Trends: Recent articles emphasize AI-driven compression standards, quality assessment for emerging media (point clouds, HDR), and explainability in computer vision. Key themes include JPEG AI standardization, DNA storage for visual data, and robustness evaluation of deepfake detectors. Awards & Honors: IEEE Star Innovator Award in Multimedia IEEE and Swiss National ASE Awards ISO Certificates for MPEG-4/JPEG 2000 contributions Best Paper Award (IEEE Trans. Consumer Electronics) Fellow of IEEE and SPIE Leadership: He founded RayShaper SA, Genista SA, and Emitall SA. Represents Switzerland in ISO/IEC JTC1/SC29 and ITU, advising European Commission initiatives and venture capital firms on multimedia technologies. Facilities: Leads the MMSPG lab at EPFL, focusing on intelligent multimedia systems. The group collaborates globally on standardization (JPEG, MPEG) and EU projects.

• Image and Video Compression
• Media Security
• Multimedia Signal Processing
• +۵ مورد دیگر

Natalie Ceperley is a researcher at the University of Bern's Institute of Geography, specializing in ecohydrology with a focus on Alpine and dryland systems. She holds a Ph.D. from EPFL and has conducted fieldwork across West Africa and the Swiss Alps. Her research integrates stable isotopes, environmental DNA (eDNA), and participatory methods to study hydrological processes and ecosystem interactions. Current projects include forest hydrology collaborations with the Bern University of Applied Sciences, examining the impacts of forest expansion and snow cover changes on Alpine watersheds. Education: B.A. in Biology (Grinnell College), M.Env.Sc. (Yale), Ph.D. (EPFL) Research Themes: Alpine hydrology, dry-land ecohydrology, water tracers, participatory research Key Projects: Vallon de Nant catchment studies, Volta River Basin climate impact modeling Her interdisciplinary work bridges physical and social sciences, emphasizing field-based innovation and community engagement. Notable contributions include using isotopes and eDNA to trace water pathways and co-creating solutions for water security in transboundary contexts.

• Ecohydrology
• Alpine Hydrology
• Dry-land Ecohydrology
• +۴ مورد دیگر

Jonathan Scarlett is an Associate Professor at the National University of Singapore (NUS), holding joint appointments in the Department of Computer Science, Department of Mathematics, and the Institute of Data Science. His research focuses on information theory, machine learning, and high-dimensional statistics, with contributions to topics such as group testing, Bayesian optimization, and coding theory. He has received notable awards including the NUS Presidential Young Professorship and MIT TR35 Innovators (Asia Pacific) recognition. Scarlett advises numerous PhD students, including Yang Sun, Zihan Li, and Arpan Losalka, whose theses address challenges in inverse problems, kernel-based optimization, and safety in Bayesian optimization. His work spans theoretical foundations and practical applications, with recent publications exploring optimal group testing algorithms, error exponents in DNA storage, and robust bandit optimization. He leads collaborative projects on distributed statistical estimation and adversarial machine learning, leveraging interdisciplinary methods from signal processing and information theory. His academic journey includes a postdoc at the LIONS laboratory (2014–2017) before his promotion to Associate Professor in 2024. Scarlett actively contributes to the academic community through editorial roles, conference organization, and over 100 peer-reviewed publications. His lab develops novel frameworks for handling uncertainty in data-driven systems, with applications to healthcare, communication networks, and autonomous systems.

• Information Theory
• Machine Learning
• High-Dimensional Statistics
• +۳ مورد دیگر

Emily Clare Groves is a Lecturer at École polytechnique fédérale de Lausanne (EPFL) working within the College of Management of Technology, specifically at the EPFL+ECAL Lab. She also serves as an Associate Lecturer for the MA User Experience Design program at University of the Arts London, a position she has held since 2018. Her academic career bridges design, technology, and human experience across multiple domains, with a strong emphasis on practical applications. Dr. Groves' research focuses on the intersection of design and technology with particular emphasis on user experience, digital health applications, and interactive systems. Her work explores how design can enhance wellbeing through innovative technological solutions, particularly in healthcare contexts. She has conducted significant research on self-tracking technologies, augmented reality applications for consumer education, and haptic feedback systems for meditation. Her approach consistently emphasizes qualitative methods to understand user needs and experiences with emerging technologies. Her scientific contributions demonstrate a consistent focus on human-centered design approaches that address real-world challenges. Dr. Groves has published extensively on topics ranging from urine home monitoring systems to cultural archive preservation using synthetic DNA, showing remarkable breadth in her research interests while maintaining a core focus on user experience and interaction design. Her work frequently appears in design and human-computer interaction conferences and journals. Dr. Groves has made notable contributions to the field of Human-Computer Interaction education, particularly through her work on global HCI curricula development that emphasizes creativity in technology education. Her teaching focuses on practical applications of design principles in real-world contexts. She collaborates extensively with researchers across disciplines, as evidenced by her co-authorship on papers involving medical researchers, computer scientists, and design specialists. Her current position at the EPFL+ECAL Lab indicates active engagement in design research and innovation at the intersection of academia and industry.

• User Experience Design
• Digital Health Technology
• Augmented Reality
• +۳ مورد دیگر

Dr. Hella Bolck is a Third Space Scientist and Research-Project Manager at the ZHAW School of Engineering's Machine Perception & Cognition Group. She specializes in applying AI to analyze biological and medical images/data, managing complex interdisciplinary projects, and advancing the 'academic third space' through collaborative research initiatives. Her work bridges academic, industrial, and societal sectors, emphasizing responsible AI development for healthcare applications. Education includes a PhD in Cancer Biology (University of Zurich, 2016), MSc in Biochemistry and Molecular Biology (Friedrich-Schiller University Jena, 2011), and prior studies in biochemistry. She held roles as Postdoctoral Scientist (2018–2024) and Senior Research Scientist (2016–2018) at University Hospital Zurich. Research focuses on AI-driven medical imaging analysis, cancer biology (especially renal and pancreatic cancers), genomics, and liquid biopsy technologies. She leads projects like AI-BRIDGE (responsible AI development) and LINA (autonomous systems testing), and is deputy leader of the REAL-world NETwork operation initiative. Her work integrates AI, bioinformatics, and clinical applications to improve diagnostics and personalized therapies. Key collaborations involve biobanking, drug testing using patient-derived models, and synthetic lethality mechanisms in tumor suppression. She actively contributes to educational programs like the CAS KI (No-Code AI solutions for industry) and WBK Generative AI in education.

• Artificial Intelligence
• Medical Imaging Analysis
• Cancer Biology
• +۵ مورد دیگر

Dr. Xiang Kong is a Researcher affiliated with the Department of Materials at ETH Zurich, specifically within the Professorship for Soft Materials. His work focuses on fluid dynamics in porous media, geothermal energy systems, and carbon capture and storage (CCS). He leads experimental and numerical studies to understand subsurface processes, including fluid-rock interactions, CO2 sequestration, and fracture permeability evolution under thermal and mechanical stresses. His research integrates advanced computational methods like lattice-Boltzmann simulations and machine learning to address challenges in energy and environmental engineering. Key areas of investigation include optimizing CO2 storage in salt caverns, enhancing geothermal energy extraction through phase-transition fracturing, and modeling mineral precipitation dynamics during geothermal reinjection. Dr. Kong collaborates on projects such as ZoDrEx, aiming to improve zonal isolation and drilling techniques in enhanced geothermal systems (EGS). His experimental methods involve hydraulic tomography, laser-induced fluorescence (LIF), and 3D-printed fractured media to study solute transport and flow path evolution. Notable contributions include developing novel tracers (e.g., DNA-labeled nanoparticles) for subsurface monitoring and advancing physics-informed machine learning models for direct inversion of subsurface flow systems. His work bridges fundamental materials science with applied geoscience, addressing critical global challenges in energy transition and climate mitigation.

• Geothermal Energy
• Carbon Capture and Storage
• Materials Science
• +۳ مورد دیگر