جستجوی استاد راهنمای دانشگاهی

Maria Hondeleمشاهده پروفایل

استادیار

• Membraneless Organelles
• Biomolecular Condensates
• Liquid-Liquid Phase Separation
• +۷ مورد دیگر

Maria Hondele is a Tenure-track Assistant Professor at the Biozentrum, University of Basel, Switzerland, where she leads a research group dedicated to understanding the formation, regulation, and function of membraneless organelles, particularly those associated with RNA processing. Her interdisciplinary work bridges biochemistry, biophysics, and cell biology to dissect how RNA-protein condensates influence gene expression. Her research focuses on liquid-liquid phase separation and the role of DEAD-box ATPases as master regulators of biomolecular condensates. She investigates how these dynamic structures control RNA flux, processing, and localization within cells. Her lab employs a multidisciplinary approach including biochemical reconstitution, biophysical measurements, high-throughput screening, and advanced imaging techniques to uncover the molecular mechanisms underlying condensate formation and function. The recent publications of her group reveal a strong thematic focus on RNA-protein interactions, phase separation, stress granules, and the enzymatic regulation of condensates by ATPases. These studies span model systems from synthetic coacervates to human cells, reflecting a comprehensive strategy to understand both fundamental principles and biological implications of membraneless organelles. ERC Starting Grant (2020) SNSF Eccellenza Professorship (2019) HFSP Long-Term Postdoctoral Fellowship (2015–2018) ETH and EMBO Postdoctoral Fellowship (2015) PhD Prize, University of Munich (2014) Boehringer Ingelheim PhD Fellowship (2008–2011) Dr. Hondele advises a vibrant team of postdoctoral fellows, PhD students, and master’s students, indicating an active and expanding research program. She has secured competitive grants and leads a productive research group contributing significantly to the field of RNA biology and cellular organization. She is also an Associate Member of the National Center of Competence in Research (NCCR) RNA & Disease, reflecting her integration into major national research initiatives. Her research group is embedded within the Biozentrum, a leading interdisciplinary research center at the University of Basel, providing access to state-of-the-art facilities and collaborative networks in molecular and cellular biology.

Natalia Díaz Rodríguezمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Reinforcement Learning
• +۶ مورد دیگر

Natalia Díaz Rodríguez is an Assistant Professor of Artificial Intelligence at ENSTA ParisTech, where she works in the Computer Science and Systems Engineering department within the Autonomous Systems and Robotics Lab (U2IS). She is also affiliated with the INRIA Flowers team, focusing on developmental robotics. Her research spans deep learning, reinforcement learning, continual learning, and symbolic AI, with applications in explainable AI, computer vision, and robotics for social good. Her academic background includes a double PhD in Artificial Intelligence from Abo Akademi University and the University of Granada, alongside MSc degrees in Soft Computing and Computer Engineering from the University of Granada. She contributes to interdisciplinary AI, particularly in robotics, ethics, and healthcare applications, and co-organizes workshops on continual learning. Double PhD in Artificial Intelligence (2015), Abo Akademi University and University of Granada Doctoral diploma on Innovation and Entrepreneurship (2017), EIT Digital MSc in Soft Computing and Intelligent Systems (2012), University of Granada MSc in Computer Engineering (2010), University of Granada Her recent publications focus on trustworthy AI, including bias identification, counterfactual explanations, and continual learning strategies, reflecting her commitment to ethical and robust AI systems. She also explores AI applications in structural engineering, climate visualization, and financial risk assessment, emphasizing practical deployment and interpretability.

• Political Communication
• Digital Democracy
• Public Debate
• +۳ مورد دیگر

Regula Hänggli Fricker is a Full Professor at the Department of Communication and Media Sciences, Faculty of Economics and Social Sciences and Management, University of Fribourg, Switzerland. She previously held positions at the Amsterdam School of Communication Research (ASCoR) and has been a visiting researcher at ETH Zurich (TdLab and COSS initiative), Northwestern University, and the University of Bergen. Her research focuses on Political communication Comparative perspectives on institutional reforms Digital democracy Public debate and opinion formation Media dialogue in political contexts Analysis of her 15 most recent publications reveals trends in participatory budgeting, digital governance, media framing of religious/political topics, and computational social science applications. These works span disciplines like political science, computer science, and media studies, with subfields including AI debates, voting system design, religious representation, and polarization dynamics. Her academic credentials include a PhD from the University of Zurich and a Master's in Political Science (major) and Economics (minor) from the University of Bern. Collaborations with Evangelos Pournaras, Dirk Helbing, and others highlight interdisciplinary projects in smart city ethics and democratic innovation.

• Databases
• Transaction Management
• Data Management in the Cloud
• +۱۲ مورد دیگر

Heiko Schuldt is a Full Professor of Computer Science at the University of Basel and leads the Databases and Information Systems (DBIS) group. His research spans databases, transaction management, cloud data systems, digital libraries, and multimedia retrieval, with a focus on distributed systems, data streams, and service-oriented architectures. He studied at the University of Karlsruhe (KIT) and received his PhD from ETH Zurich in 2001. From 2003-2006, he served as an associate professor at UMIT, Austria. Education: University of Karlsruhe (Computer Science), ETH Zurich (PhD, 2001) Research Interests: His work integrates databases, cloud computing, and multimedia retrieval, emphasizing scalable systems for lifelog data, sports analysis, and VR/AR environments. Projects include vitrivr, Polypheny-DB, and StreamTeam. Recent Publications: Trends focus on VR/AR multimedia retrieval, cross-modal analysis, and polystore systems. Key contributions include open-source frameworks for video/image retrieval and immersive analytics. Advising: Supervised over 50 theses in areas like mixed reality, polystore optimization, and gesture-based interfaces.

David Cockمشاهده پروفایل

مدرس ارشد

• Formal Verification
• Operating Systems
• Hardware-Software Co-Design
• +۳ مورد دیگر

Dr. David Cock is a Senior Lecturer and Senior Researcher at ETH Zürich's Department of Computer Science, affiliated with the Systems Group. He holds a PhD from UNSW (2014) and a B.Sc. (hons) from UNSW (2004). His research focuses on formal verification, trustworthy systems, and hardware-software co-design, with notable contributions to projects like Enzian (a CPU/FPGA platform) and seL4 (formally verified kernel). He teaches Advanced Operating Systems and Informal Methods courses. Key achievements include the ACM Software System Award (2022) for seL4 and leadership in projects addressing hardware complexity and security. Research interests include formal methods for hardware modeling (Sockeye project), runtime verification, and mitigating timing channels. His work bridges theoretical foundations with practical systems, emphasizing secure and reliable computing platforms. Projects like Trustworthy BMC aim to enhance baseboard management systems' assurance. Collaborations span academia and industry, with open-source contributions to hardware designs and formal tools. Publications span formal verification, hardware modeling, and secure systems, with recent focus on heterogeneous computing and declarative hardware specifications. Teaching emphasizes practical formal techniques and OS design, leveraging real-world hardware (e.g., Barrelfish). His lab, the Systems Group, explores cutting-edge challenges in systems software and architecture.

Christoph Leitnerمشاهده پروفایل

پژوهشگر ارشد

• Convergent Technologies
• Ultrasonics
• Machine Learning
• +۳ مورد دیگر

Dr. Christoph Leitner is a Research Fellow at ETH Zurich's Integrated Systems Laboratory under Prof. Luca Benini, focusing on biomedical and IoT applications. His work integrates printed piezoelectric transducers and flexible electronics with energy-efficient systems. He holds a PhD in Biomedical Engineering from TU Graz (2022) and has collaborated with institutions like Sant'Anna School of Advanced Studies and KTH Stockholm. Notable achievements include the Josef Krainer Young Researcher Award and Motorik Scholarship. Leitner's research spans ultrasonics, machine learning, and wearable devices, with contributions to muscle-tendon dynamics and real-time biofeedback systems. Education: PhD in Biomedical Engineering, TU Graz (2022), Advisor: Prof. Christian Baumgartner Previous roles: University Assistant (2006–2011) and R&D Engineer at Virtual Vehicle GmbH Research Interests: Convergent technologies merging biomedical engineering and IoT Ultrasound-based monitoring for musculoskeletal systems Energy-efficient embedded systems for wearable applications Collaborations & Awards: Recipient of Josef Krainer Young Researcher Award (2023) and Motorik Scholarship (2018) Active collaborations with University of Zurich, Veterinary University of Vienna, and Queensland University of Technology Labs & Projects: Integrated Systems Laboratory at ETH Zurich Developed a patented ultrasound-transparent tattoo-based SEMG system with Prof. Francesco Greco

Pasquale Davide Schiavoneمشاهده پروفایل

مدرس

• Embedded Systems
• RISC-V Architecture
• Ultra-low-power Computing
• +۵ مورد دیگر

Pasquale Davide Schiavone holds multiple research and teaching positions at École Polytechnique Fédérale de Lausanne (EPFL), serving as a Lecturer at the School of Computer and Communication Sciences (IC) and as a Scientist at both the Embedded Systems Laboratory (ESL) within the School of Engineering (STI) and PAT Administration. His interdisciplinary work bridges computer architecture, embedded systems design, and biomedical applications, with office located at ELG 136 in Lausanne, Switzerland. Dr. Schiavone's research focuses on ultra-low-power computing systems, particularly RISC-V architectures and TinyML applications for edge devices. His work develops open-source hardware platforms like X-HEEP and HEEPOCRATES that enable energy-efficient AI at the edge, with applications spanning biomedical monitoring, neural interfaces, and wearable computing. He explores innovative hardware-software co-design approaches to overcome energy constraints in resource-limited environments. His recent publications reveal a consistent research trajectory centered on open, configurable computing platforms for specialized applications. The work spans from fundamental RISC-V architecture improvements (ARCANE, e-GPU) to application-specific implementations for biomedical contexts (BiomedBench, neural interfaces). A strong emphasis on energy efficiency permeates all his research, whether through novel arithmetic approaches (Posit), system architecture (near-memory computing), or specialized accelerators (Strela, Quadrilatero). Lecturer, School of Computer and Communication Sciences (IC) Scientist, Embedded Systems Laboratory (ESL), School of Engineering (STI) Scientist, PAT Administration, School of Engineering (STI) Dr. Schiavone teaches courses on hardware compilation, presenting algorithms and methods for transforming hardware description languages into optimized circuit implementations. His Embedded Systems Laboratory work places him at the forefront of developing practical, open-source solutions for next-generation computing challenges in energy-constrained environments.

• Natural Language Processing
• Question Answering
• Human-AI Collaboration
• +۷ مورد دیگر

Jordan Boyd-Graber is a Professor in the Department of Computer Science at the University of Maryland's College of Computer, Mathematical, and Natural Sciences. He serves as a leading researcher in Natural Language Processing with significant contributions across multiple NLP subfields. His work bridges theoretical advances with practical applications requiring human-AI collaboration. His research interests span Natural Language Processing , Question Answering systems , Human-AI collaboration , Machine Translation , and Topic Modeling . He focuses on developing systems that work effectively with humans rather than replacing them, emphasizing interpretability and user-centered design. His work often involves creating evaluation frameworks that better capture real-world utility rather than just technical metrics. His publication record shows consistent leadership in the field, with numerous papers at top venues including ACL, EMNLP, and NAACL. Recent work (2023-2024) demonstrates strong engagement with LLMs, human evaluation methodologies, and practical applications in health and translation domains. His research often involves student collaborators, indicating active mentorship. ACL Fellow (2021) Program Chair for ACL 2023 Organizer of prompt hacking competition Leader in human-centered NLP evaluation Boyd-Graber has secured substantial funding for his research, particularly in projects involving human-AI collaboration and question answering systems. His work often involves interdisciplinary teams spanning computer science, linguistics, and domain-specific applications. He has mentored numerous graduate students who have gone on to successful careers in academia and industry. He leads research groups focused on developing interpretable NLP systems that work effectively with humans, particularly in high-stakes domains like healthcare and education. His lab frequently develops novel evaluation methodologies that better capture real-world utility rather than just technical metrics.

Paolo Prandoniمشاهده پروفایل

مدرس

• Signal Processing
• Audio Processing
• Image Processing
• +۵ مورد دیگر

Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.

Xuming Heمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۹ مورد دیگر

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

David Atienzaمشاهده پروفایل

استاد

• Embedded Systems
• Low-Power Computing
• Edge AI
• +۳ مورد دیگر

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.

Robert Souléمشاهده پروفایل

دانشیار

• Distributed Systems
• Networking
• Programming Languages
• +۴ مورد دیگر

Robert Soulé is an Associate Professor in the Departments of Computer Science and Electrical Engineering at Yale University, and holds an Adjunct Professor position at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His research focuses on distributed systems, networking, and applied programming languages, with notable contributions to in-network computing, consensus protocols, and energy-efficient systems. He received his B.A. from Brown University and his Ph.D. from New York University, followed by postdoctoral work at Cornell University. Education: B.A. in Computer Science, Brown University, 1999 Ph.D. in Computer Science, New York University, 2012 Research Interests: Dr. Soulé’s work spans distributed systems, networking, and programming languages, emphasizing practical systems such as in-network computing, consensus algorithms (e.g., NetPaxos), and carbon-aware networking. His research bridges theory and practice, addressing challenges in scalability, performance, and sustainability. Articles Trends: Recent work includes innovations in quantum networks (algebraic specifications), carbon-aware networking (energy efficiency), and system optimization (e.g., P4-based data plane verification). He explores network programmability, microservices acceleration, and zero-copy serialization techniques. Awards: Best Paper Awards at ACM DEBS 2012, NSDI 2018, and CoNEXT 2020 Google Faculty Research Award IBM Invention Plateau Award Advising and Grants: He has advised numerous PhD students and postdocs, including Pietro Bressana (Intel Corporation) and Theo Jepsen (Stanford Postdoc). His grants support projects in networked systems, distributed computing, and sustainable infrastructure. Labs/Teams: Active in Yale’s Systems Research group, collaborating on projects like NetChain (sub-RTT coordination) and P4-based systems (e.g., P4xos for consensus). Engages with industry through partnerships on microservices optimization and energy-efficient networking.

Joris Pascalمشاهده پروفایل

مدرس

• Medical Sensor Systems
• Biomedical Engineering
• Magnetic Field Sensing
• +۳ مورد دیگر

Prof. Joris Pascal is a Lecturer in Life Science Technologies at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW), affiliated with the School of Life Sciences and the Institute for Medical Engineering and Medical Informatics in Muttenz, Switzerland. He holds a Ph.D. in Microelectronics from the University of Strasbourg (2008), an M.S. in Electrical Engineering from Supélec (2005), and a B.S. in Electrical Engineering from the University of Strasbourg (2003). His professional experience includes roles as Senior Scientist and Scientist at ABB Switzerland Ltd. (2009–2015), and Research Assistant at the iCube laboratory (2005–2009). His research focuses on advanced biosignal processing, medical sensor systems, magnetic field sensing, and miniaturized diagnostic/therapeutic tools. Key projects include electromagnetic tracking systems for neurosurgery, magnetic safety assessment for MRI workers, and wearable sensor integration. He has 9 patents (5 granted, 4 pending) and extensive IEEE review involvement. Recent publications emphasize magnetic field cameras, MRI safety protocols, and nanoscale sensor technologies. His work bridges electrical engineering and biomedical applications, with contributions to medical robotics, portable NMR devices, and cardiac implant safety. Active in PhD committee roles and industry-academia collaborations, he leads projects at the intersection of sensor innovation and clinical needs.

Daniele Guido Allegriمشاهده پروفایل

استاد

• Biomedical Engineering
• Microelectronics
• Embedded Systems
• +۵ مورد دیگر

Daniele Guido Allegri is a Professor at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), affiliated with the Department of Innovative Technologies (DTI) and the Institute of Systems and Applied Electronics (ISEA). He became a professor in 2021 and serves as the director of ISEA since August 2023. His roles include leading the "Digital Electronics, Microelectronics, and Bioelectronics" scientific area since 2019. Allegri's research spans biomedical electronics, microelectronics, and embedded systems. Key projects include the development of radiation-hardened PLL ASICs (Cadagno, Quarnei), a Cardio Pulmonary Rescue Support System, and signal processing solutions for myopathy diagnosis and renal dialysis monitoring. He teaches courses on machine learning, analog/digital electronics, and embedded systems design. His publications focus on CMOS multifrequency impedance analyzers for biomedical applications (2018), real-time hydration monitoring via bioimpedance (2016-2017), and the European Solar Telescope (2021), which integrates advanced technologies for solar physics. These works reflect interdisciplinary expertise in circuit design, medical devices, and astrophysical instrumentation. Allegri's current affiliations include the Department of Innovative Technologies and ISEA at SUPSI. He has worked extensively on integrated circuits for biomedical applications, signal processing, and radiation-hardened electronics for space systems.

Krisztian Balogمشاهده پروفایل

استاد

• Information Retrieval
• Natural Language Processing
• Machine Learning
• +۴ مورد دیگر

Krisztian Balog is a Professor of Computer Science at the University of Stavanger and a Staff Research Scientist at Google DeepMind. His work focuses on advancing AI-driven information retrieval, natural language processing, and machine learning for user-centric systems. Key affiliations include co-organizing the Sim4IA 2025 Workshop at SIGIR, leading tutorials on user simulation in generative AI, and directing the NorwAI research center’s PhD project on LLMs for recommendations. Research Interests: User simulation, conversational AI, transparency in recommender systems, and simulation frameworks like SimIIR 3 . Scientific Recognition: Recipient of the Karen Spärck Jones Award (2018) and Best Resource Paper Award at CIKM’23 . Leadership: Serves on program committees for SIGIR, WSDM, WWW, and ECIR. Co-organized workshops and tutorials at SIGIR, AAAI, and WWW. Recent Publications highlight advancements in user simulation methodologies, generative AI applications, and open web infrastructure (e.g., SimIIR 3 , OpenWebSearch.eu ). His work bridges theoretical models (e.g., Markov Decision Processes) with practical toolkits for synthetic data generation and system evaluation.