جست‌وجوی استاد و پژوهشگر

Carmela Troncosoمشاهده پروفایل

دانشیار

• Security and Privacy Engineering
• Machine Learning
• Privacy Enhancing Technologies
• +۵ مورد دیگر

Carmela Troncoso is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), heading the Security and Privacy Engineering Lab (SPRING Lab) in the School of Computer and Communication Sciences. She holds a PhD in Engineering from KU Leuven (2011) and has held academic roles at IMDEA Software Institute and Gradiant, along with postdoctoral research at COSIC Group. Research Focus: Security and privacy engineering, machine learning's societal impact, privacy-enhancing technologies, and decentralized privacy-preserving systems. Publications: Her work spans top venues like IEEE S&P, CCS, NeurIPS, and PETS, with recent emphasis on adversarial robustness, proximity tracing, and privacy metrics. Awards: IEEE S&P Distinguished Paper Award (2022) EPFL Latsis University Prize (2022) Caspar Bowden Award Runner-up (2021) CNIL-INRIA Privacy Protection Award (2017) ERCIM PhD Thesis Award (2011) She supervises active PhD students and collaborates with institutions like ICIJ. Her lab's SPRING Lab website details current projects.

• Applied Machine Learning
• Computer Vision
• Signal Processing
• +۳ مورد دیگر

Atzori Manfredo is a Researcher at the School of Management, HES-SO Valais-Wallis, focusing on machine learning applications in biomedical engineering and digital health. His work bridges computer vision, signal processing, and clinical diagnostics, with emphasis on EMG-based prosthetic control, computational pathology, and neuroimaging reproducibility. Research interests span: Applied ML : Transfer learning, explainable AI, and self-supervised methods for medical data. Biomedical Engineering : EMG signal analysis for hand prosthetics, wearable sensors, and rehabilitation robotics. Digital Pathology : Whole slide image analysis, artifact mitigation, and multimodal data fusion for cancer diagnostics. His publications (2022-2024) reveal strong trends in addressing real-world challenges: improving robustness of EMG classification under dynamic conditions, enhancing reproducibility in neuroimaging studies, and developing open-source tools (e.g., SelfEEG, BIDSAlign) for EEG/EMG data processing. Cross-disciplinary collaboration is evident in veterinary radiology, ophthalmology, and ontology development for pathology data.

Michael C. Knausمشاهده پروفایل

استادیار

• Causal Inference
• Machine Learning
• Econometrics
• +۳ مورد دیگر

Michael C. Knaus serves as Assistant Professor of "Data Science in Economics" at the School of Business and Economics, University of Tübingen. He is actively involved in the "Machine Learning for Science" Cluster of Excellence, which recently secured continued funding from the German Research Foundation for seven additional years. His academic work bridges theoretical econometrics with practical machine learning applications, focusing on empirical labor economics research. Dr. Knaus's research centers on the intersection of causal inference and machine learning, with particular emphasis on identification, interpretation, and estimation of average and heterogeneous treatment effects. His methodological contributions include developing transparent weighting frameworks for causal estimators and improving covariate balancing procedures. He investigates why causal machine learning methods succeed or fail in real-world applications, such as explaining cases where algorithms cannot detect treatment effects in noiseless outcomes. His recent publications reveal a strong trend toward making causal machine learning more accessible and interpretable for economists. Through arXiv working papers, R packages, and teaching materials, he emphasizes practical implementation of methods like Double Machine Learning while maintaining rigorous theoretical foundations. His work provides essential tools for evaluating when causal ML approaches are appropriate for policy-relevant economic questions. Dr. Knaus demonstrates strong commitment to academic community building through his Causal ML Workshop for Ukraine, GitHub-hosted teaching materials, and active scholarly discourse on Bluesky. He regularly shares methodological insights, reproduces seminal papers, and promotes collaborative learning in causal inference techniques across international boundaries.

Federico Wadehnمشاهده پروفایل

مدرس ارشد

• Digital Signal Processing
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Dr. Federico Wadehn is a Senior Lecturer at the Institute of Signal Processing and Wireless Communications within the School of Engineering at Zurich University of Applied Sciences (ZHAW), specializing in digital signal/image processing and machine learning applications for biomedical and engineering systems. His role includes teaching core courses and supervising graduate theses. Education: PhD in Statistical Signal Processing with Probabilistic Graphical Models, ETH Zürich (2014-2018) Teaching Certificate, ETH Zürich and ZHAW (2017-2018) Visiting Researcher in Neuromonitoring, MIT (2016) MSc/BSc in Electrical Engineering and Information Technology, ETH Zürich (2009-2014) Erasmus Exchange in Control Engineering, Imperial College London (2012-2013) Continuing Education: MATLAB C Coder (2023), Embedded C (2021), IBM Rational Rhapsody (2020) Research Interests: His work bridges Digital Signal Processing (classical, ML, and deep learning), Computer Vision , Sensor Fusion/Kalman Filtering for inertial systems, Probabilistic Graphical Models for navigation, and Physiological System Modeling (respiratory/cerebrovascular). He emphasizes model-based approaches for real-world biomedical applications, particularly in neuromonitoring and respiratory care. Publication Trends: His 2015-2020 publications reveal a consistent focus on advanced signal processing for physiological monitoring, featuring model-based separation of biological signals (eye movements, blood flow), sparse state-space modeling, and Kalman smoothing innovations. The work integrates probabilistic graphical models with biomedical constraints, demonstrating strong cross-disciplinary impact in IEEE journals and conferences. Scientific Awards: Study and doctoral scholarship from German Academic Scholarship Foundation (2014) Abiturpreis Physik from Deutsche Physikalische Gesellschaft (2008) Advising and Professional Activities: He supervises bachelor's/master's theses at ZHAW while maintaining industry connections through prior roles as Systems Engineer at Bosch Sensortec (2021-2023), Research Engineer at Hamilton Medical AG (2019-2021), and Computer Vision Freelancer (2021). His dual academic-industry experience informs practical curriculum development. Laboratories and Teams: He contributes to ZHAW's Digital Health Lab and Medical Systems Platform, focusing on translating signal processing innovations into clinical tools for respiratory monitoring and motion analysis, with recent patent work in ventilation technology.

Serina Changمشاهده پروفایل

استادیار

• Artificial Intelligence
• Human Behavior
• Public Health
• +۵ مورد دیگر

Serina Chang is an Assistant Professor at the University of California, Berkeley , jointly appointed in Electrical Engineering and Computer Sciences (EECS) and Computational Precision Health , with affiliations to the Berkeley AI Research (BAIR) Lab . Her research addresses AI for public health, focusing on human behavior modeling, network inference, and policy decision support. PhD in Computer Science from Stanford University (2024) Postdoctoral research in Microsoft Research's Computational Social Science group Her work spans three thematic areas: inferring human behaviors from novel data sources (search logs, mobility data), simulating behaviors with LLMs for public health applications, and AI-driven policy support through models for vaccine distribution, epidemic forecasting, and equity-focused reopening strategies. She has pioneered AI models using anonymized mobility data to analyze socioeconomic disparities in pandemic impacts. Recent publications highlight cross-disciplinary innovations: Nature (2025) perspective on AI for infectious disease modeling ACL 2025 work on human-AI interaction evaluation ICWSM 2025 paper demonstrating LLM capabilities in network generation Scientific recognition includes: Google Research Scholar Award (2025) NSF Graduate Fellowship (2024) KDD Dissertation Award (2025) Meta PhD Fellowship (2023) She advises PhD students in EECS and Computational Precision Health , emphasizing skills in ML research , interdisciplinary collaboration , and real-world impact . Her lab integrates computational methods with public health practice through partnerships with WHO , Chan Zuckerberg Biohub , and United Nations Development Programme . Current teaching includes CS 294-286: Machine Learning & Human Behavior . She explicitly invites students with interests in AI , human behavior , network science , and public health applications to apply for PhD advising.

Zahraa S Abdallahمشاهده پروفایل

مدرس ارشد

• Data Science
• Machine Learning
• Stream Mining
• +۶ مورد دیگر

Dr Zahraa S Abdallah is a Senior Lecturer at the University of Bristol's School of Engineering Mathematics and Technology, specializing in data science and machine learning applications for healthcare, climate change, and smart metering systems. Her research bridges theoretical algorithm development with real-world implementation challenges in evolving data environments. Education: PhD BSc Research Focus: Dr Abdallah's work centers on time series and streaming data analysis using adaptive machine learning techniques. She develops novel frameworks for concept drift handling and real-time pattern recognition, with particular emphasis on healthcare monitoring systems and environmental data processing. Her fingerprint analysis reveals strong activity in Activity Recognition (100%), Data Streams (59%), and Time Series Data (37%) within computer science. Publication Trends: Her recent work demonstrates consistent innovation in streaming data applications, evolving from mobile activity recognition systems to sophisticated time series classifiers. The research trajectory shows increasing focus on healthcare integration, particularly through diabetes management applications using physiological data streams. Scientific Recognition: Australian Postgraduate Award (2010) for doctoral studies Mollie Holman Award (2016) for outstanding thesis contribution Research Leadership: As Co-Investigator on the UK T1D closed-loop systems project (2023-2024), she led AI components for physiological data integration. Her dataset contributions include the BrisT1D-Restricted Dataset (2025) for diabetes research and ML surrogates for genome design (2024). Collaborative Networks: Actively engaged with the Intelligent Systems Laboratory and Infection and Immunity research theme, maintaining international collaborations evidenced by multi-country research networks and 106+ Mendeley readers across publications.

• Machine Learning
• Artificial Intelligence
• Explainable AI (XAI)
• +۱۰ مورد دیگر

Prof. Dr. Wojciech Samek is a leading researcher in machine learning and artificial intelligence, currently serving as a professor in the Department of Electrical Engineering and Computer Science at Technical University of Berlin and Head of the AI Department at Fraunhofer Heinrich Hertz Institute (HHI) in Berlin, Germany. His work focuses on Explainable AI (XAI) , Trustworthy Deep Learning , and Machine Learning for Communications . Samek has made significant contributions to the development of methods for explaining and interpreting deep neural networks, particularly with the Layer-wise Relevance Propagation (LRP) technique. His research spans multiple domains including computer vision, natural language processing, medical applications, and 5G networks. He has developed various software toolboxes such as the Keras Explanation Toolbox, LRP Toolbox, TensorFlow LRP Wrapper, and Quantus Toolbox for explainable AI research. His work in Explainable AI has led to numerous publications on visualizing, explaining and interpreting deep learning models He has pioneered Distributed and Federated Learning techniques that reduce communication costs while maintaining model performance Samek has contributed to Medical AI applications including ECG, EEG, and neuroimaging data analysis He has developed Neural Network Compression methods applicable to mobile and embedded systems Prof. Samek has received multiple best paper awards and has co-authored over 200 peer-reviewed publications. He is actively involved in standardization activities as part of the ISO/IEC MPEG-17 NNC working group and serves as a Fellow at BIFOLD and the ELLIS Unit Berlin. He has organized numerous tutorials and workshops on Explainable AI at major conferences including ICML, CVPR, ECML-PKDD and ICASSP.

Bartolomeo Stellatoمشاهده پروفایل

استادیار

• Mathematical Optimization
• Machine Learning
• Optimal Control
• +۳ مورد دیگر

Bartolomeo Stellato is an Assistant Professor at Princeton University's Department of Operations Research and Financial Engineering. He is associated faculty in Electrical and Computer Engineering and Computer Science, and affiliated with Princeton's AI, Robotics, and Center for Statistics and Machine Learning initiatives. His research bridges mathematical optimization, machine learning, and optimal control for real-time decision-making in uncertain environments. PhD advisors: Georgina Hall, Brandon Amos Key collaborators: Bart Van Parys (MIT), Jaime Fernandez Fisac (UPenn), Adrien Taylor (INRIA) His work on Mean Robust Optimization combines robust and distributionally robust optimization through data clustering, achieving 3+ orders of magnitude speedups in portfolio optimization. He also develops learned optimizers (e.g., L2WS, GeNIOS) and OSQP , which won the Beale–Orchard-Hays Prize. Recent grants include NSF CAREER (2023) and ONR Young Investigator (2025) awards. Article Trends : His research spans optimization verification (3/7 of 2025 articles), data-driven methods (5/7 of 2024-2025 articles), control applications (4/15 articles), and equitable resource allocation (2/15 articles). Key subfields include algorithm verification, stochastic optimization, multi-agent systems, and hardware acceleration. Scientific Awards : ONR Young Investigator (2025), Howard B. Wentz Award (2024), NSF CAREER (2023), ISSNAF Strazzabosco (2022) Students : Mentored 12+ graduate students/postdocs including Irina Wang (Wallace Fellowship), Rajiv Sambharya (Best Paper Award at JMLR), and Vinit Ranjan (co-author Mathematical Programming paper) He leads the Princeton Optimization, Learning, and Control Workshop and collaborates with institutions like MIT, INRIA, and Los Alamos National Laboratory. His group develops open-source tools (OSQP, L2WS) with over 300,000 downloads.

• Artificial Intelligence
• Data Management
• Machine Learning
• +۵ مورد دیگر

Dr. Jonathan Fürst is a Researcher at the ZHAW School of Engineering, part of the Intelligent Information Systems research focus at ZHAW Zurich University of Applied Sciences. His work spans AI-driven data systems, ontology matching, IoT applications, and cross-domain data integration. Key research interests include: AI applications in healthcare and education Machine learning for data integration and systems Multi-modal data exploration and natural language interfaces IoT networks and smart building technologies His projects include: AI-based Quality Management of BIM Models (project leader) DataGEMS : A data discovery platform for generalized exploratory search Multi-lingual data exploration systems Digital Health Zurich: Patient-centered clinical innovation Recent research trends emphasize explainable AI, weakly supervised learning, and real-world system robustness. His work bridges theoretical advancements with practical implementations in domains like healthcare, education, and smart infrastructure. No scientific awards are explicitly listed, though his active role in Horizon Europe projects reflects collaborative excellence. He contributes to interdisciplinary teams across data science, engineering, and healthcare.

Monika Ulrike Reifمشاهده پروفایل

استاد

• Functional Safety
• AI Certification
• Autonomous Systems
• +۳ مورد دیگر

Prof. Dr. Monika Ulrike Reif is Professor of Safety Critical Systems and Head of Research for the Safety Critical Systems focus area at the Zurich University of Applied Sciences (ZHAW) School of Engineering. Her research focuses on safety assurance for autonomous systems and AI-based technologies. Her research program develops certification frameworks and verification methods for AI systems, with applications in railway safety, autonomous vehicles, and medical devices. She leads projects on trustworthy AI assessment, safety-critical software verification, and resilience engineering for complex systems. Current work includes developing MLOps pipelines for safety-critical AI and adversarial training methods for robust autonomous systems. Dr. Reif has received the VDI Ring of Honors (2010) and Artur-Fischer-Preis (2003) for her contributions to engineering. She directs the Safety Critical Systems Research Lab and teaches courses in functional safety, RAMS (Reliability, Availability, Maintainability, Safety), and systems engineering. Her industrial experience includes safety engineering roles at Bombardier Transportation and BMW.

• Algorithmic Fairness
• Ethics of AI
• Data-driven Decision Making
• +۳ مورد دیگر

Christoph Heitz is a Professor at the Zurich University of Applied Sciences (ZHAW), leading the Research Group Smart Services and Maintenance and serving on the Board of the Institute of Data Analysis and Process Design. His roles include core team member of ZHAW Digital for innovation in digitization and Co-Founder/President of the European Association for Algorithmic Fairness. He holds a PhD in Theoretical Physics from the University of Freiburg (1996) and a Diploma in Physics (1992). Research focuses on Algorithmic Fairness, Ethics of AI, Data-driven decision making, and Service Innovation. Key projects include AI-BRIDGE (responsible AI development) and studies on fairness in machine learning systems. He teaches Algorithmic Fairness, Ethics of Algorithms, and Service Operations Management. Publications span peer-reviewed journals (e.g., AI & Society, Journal of Creating Value) and conference proceedings on topics like predictive modeling for bike-sharing systems and fairness metrics in ML. Active in networks like the data innovation alliance and ISSIP. Labs/teams include the Smart Services and Maintenance group and collaborations on ethical AI frameworks. Projects emphasize practical applications of data science in asset management, free-floating mobility systems, and industrial services.

Giovanni Ansaloniمشاهده پروفایل

مدرس

• Embedded Systems
• Reconfigurable Computing
• Low-Power Design
• +۲ مورد دیگر

Dr Giovanni Ansaloni is Senior Scientist and Lecturer at EPFL's Embedded Systems Laboratory. His research focuses on energy-efficient computing architectures including reconfigurable systems, near-memory processing, and hardware accelerators for edge AI and biomedical applications. He develops frameworks for deploying machine learning models on resource-constrained devices, specializing in hardware-software co-design for embedded systems. Recent work explores RISC-V based accelerators, in-memory computing architectures, and optimization techniques for health monitoring applications. His teaching covers embedded systems design and applications, bridging theoretical concepts with practical implementation challenges in low-power computing platforms.

• Control Systems
• Optimization Algorithms
• Machine Learning
• +۳ مورد دیگر

Roland Schwan is a researcher at the Automatic Control Laboratory (LA) within the School of Engineering at the École Polytechnique Fédérale de Lausanne (EPFL) . His work focuses on control systems, optimization algorithms, and the integration of machine learning with physical models. He is actively involved in projects involving model predictive control (MPC), stability verification of neural network controllers, and real-time optimization-based control for robotics applications. His research interests span physics-informed machine learning , quadratic programming solvers , and embedded systems . Key contributions include the development of the PIQP solver for convex quadratic programming and stability analysis frameworks for neural network controllers. He has also explored applications in hovercraft dynamics identification and rocket trajectory control using advanced MPC techniques. Schwan collaborates closely with institutions like the Swiss National Science Foundation and has published extensively in venues such as IEEE Transactions on Automatic Control and IEEE Conference on Decision and Control . His work bridges theoretical control methodologies with practical embedded system implementations.

• Applied Machine Learning
• Large Language Models
• Generative ML
• +۴ مورد دیگر

Dr. Andrei Kucharavy is an Assistant Professor at HES-SO Valais-Wallis in the Faculty of Economics and Services, where he teaches Business Economics courses including Fundamentals of Algorithms and Computational Thinking. He co-founded the GenLearning Center, an applied research entity focused on safety and deployment challenges of Generative AI technologies. His research centers on machine learning safety, privacy, and distribution from cybersecurity and human factors perspectives. PhD from Paris Sorbonne University (research at Johns Hopkins University and Stowers Institute for Medical Research) Engineer degree from Ecole Polytechnique Former postdoctoral researcher at EPFL's Distributed Computing Lab Dr. Kucharavy's research spans Applied Machine Learning, Large Language Models, and Cybersecurity with particular expertise in generative ML since 2018 and its cybersecurity applications since 2020. His work bridges theoretical machine learning with practical security implementations in Swiss defense contexts. His publications reveal strong focus on LLM vulnerabilities, privacy leakage detection, and developing bibliometric methods resilient to AI-generated content. His recent publications demonstrate expertise in LLM security vulnerabilities, code generation risks, and developing frameworks for technology forecasting using iterative self-prompting. The research shows consistent progression from foundational LLM understanding to specialized security applications, particularly in code vulnerability injection through dataset poisoning and malicious fine-tuning. Distinguished Cyber-Defence (CYD) Postdoctoral Fellowship recipient Dr. Kucharavy leads multiple Cyber-Defence Campus-funded projects totaling over CHF 430,000, including work on characterizing LLM attacks in code generation, developing vulnerability characterization frameworks, and secure privacy leakage detection protocols. His research team includes Percia David Dimitri and Vallez Cyril, with collaborations extending to Cyber-Defence Campus experts like Dr. Ljiljana Dolamic. He actively develops applied solutions through the GenLearning Center, focusing on on-premises deployment of generative models for healthcare applications and knowledge transfer tools that maintain data privacy while enhancing educational outcomes. His work directly informs Swiss defense technology standards for LLM-based solutions in cyber-physical systems.

Rishubh Singhمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Neuroscience
• +۴ مورد دیگر

Rishubh Singh is a Doctoral Assistant at the Visual Intelligence and Learning Lab (VILAB) at École Polytechnique Fédérale de Lausanne (EPFL), pursuing his PhD in Computer and Communication Sciences under Prof. Amir Zamir's supervision. His dual status includes academic staff at the School of Computer and Communication Sciences and doctoral student in the EDIC program. His educational background includes: PhD in Computer and Communication Sciences (ongoing) at EPFL Dual Degree (B.Tech & M.Tech) in Computer Science and Engineering from Indian Institute of Technology Delhi (2019) Exchange program at KTH Royal Institute of Technology, Stockholm (2016) Singh's research bridges machine learning with neuroscience and psychology, focusing on bio-inspired deep learning for perception, reasoning, and computational design. He actively explores ML applications for assistive technologies addressing learning and communicative disorders, emphasizing inclusivity in AI systems. His publication record shows expertise in computer vision fundamentals, with recent work spanning single-pixel imaging, robust vision systems through adversarial augmentation, and neural architectures for language processing. These publications in top venues (CVPR, ECCV, ACL) demonstrate methodological innovation across multiple AI subfields. Professional collaborations include work with Dr. Pradeep Shenoy at Google Research India, Prof. Ravi Kiran Sarvadevabhatla (IIIT Hyderabad), Prof. Virginia De Sa (UC San Diego), and Dr. Mike Mozer (Google Research). His career progression shows: Doctoral Assistant at EPFL (2023-Present) Pre-Doctoral Researcher at Google Research India (2020-2023) Position at Graviton Research Capital LLP (2019-2020)