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David AtienzaView profile

Professor

• Embedded Systems
• Low-Power Computing
• Edge AI
• +3 more

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.

• Hospitality Technology
• Service Robotics
• Customer Experience
• +5 more

Reza Etemad is a Professor at EHL Hospitality Business School (part of HES-SO), specializing in marketing and hospitality technology. His research focuses on human-robot interaction, healthcare technologies in elderly care, and the application of technology in service delivery. He has led multiple projects funded by HES-SO and private partners, including studies on service robots' ethical implications, connected health technologies for seniors, and virtual agents in B2B/B2C contexts. Notable achievements include extending the Technology Acceptance Model (TAM) to healthcare and robotic service contexts, analyzing cultural impacts on online shopping risks, and investigating customer perceptions of revenue management practices in restaurants. He has published widely in journals such as International Journal of Hospitality Management and International Journal of Social Robotics , with a focus on bridging technological advancements and human-centric service solutions. Education: BSc in Hospitality from EHL. Key Projects (2010-2021): Led projects totaling over 298,600 CHF, including studies on robot ethics, smart home healthcare, and virtual agent impacts on B2B platforms. Awards: None explicitly listed, though his impactful research has driven industry-relevant insights. Labs/Teams: Collaborates with researchers like Justine Gentinetta and Valentina Clergue on robotics and healthcare tech. Partnerships include Touchmind for B2B digital solutions. Future Work: Expanding studies on AI ethics in service robots, sustainable healthcare technologies, and post-pandemic digital education strategies. His work emphasizes balancing technological innovation with ethical considerations, particularly in preserving human interaction's role in healthcare and hospitality sectors.

Andreas HeinView profile

Assistant Professor

• Digital Services
• AI Literacy
• Conversational Agents
• +5 more

Andreas Hein is an Assistant Professor of IT Management at the University of St. Gallen's Institute of Information Systems and Digital Business (IWI-HSG). His research focuses on digital services, AI literacy, conversational agents, and ethical design in education and business contexts. He holds a PhD (summa cum laude) from the University of Kassel and has led projects funded by SNSF and Innosuisse. Hein is an AIS Distinguished Member Cum Laude and has received numerous awards for research and academic service, including the AIS Best Conference Paper Award (2024) and Best Paper Awards at DESRIST (2023) and HICSS (2020). His work bridges design science and interdisciplinary collaboration, addressing topics like privacy nudges, gamification in learning, and lawful technology development. Hein actively contributes to academic communities, serving as associate editor for ECIS, ICIS, and AOM divisions, and has organized conferences like the Wirtschaftsinformatik-Nachwuchs-Treffen 2023. His teaching spans undergraduate to graduate levels, emphasizing data-driven service innovation and research practices. Hein's research has been published in top journals (ISR, JAIS, EJIS) and frequently recognized for innovation and impact. Education: PhD in Business Information Systems (Kassel University, 2018), Master of Arts in Communication Management, Diplom in Economic Sciences (Kassel University). Key Achievements: Over €2.2m in third-party funding, 60+ co-authors, and impactful contributions to digital education and AI ethics. His work on privacy nudges and conversational agents has been featured in leading conferences and journals.

Grzegorz ChrupałaView profile

Associate Professor

• Computational Linguistics
• Artificial Intelligence
• Cognitive Science
• +3 more

Grzegorz Chrupała is an Associate Professor at the Department of Cognitive Science and Artificial Intelligence , Tilburg University, where he leads research in computational approaches to multimodal communication. Previously, he was a postdoctoral researcher at Saarland University's Spoken Language Systems group and earned his PhD from Dublin City University's School of Computing. His research bridges biological and artificial computation , focusing on enabling machines to learn language from multimodal data (speech, gestures, visual-auditory stimuli) as children do naturally. This involves developing and interpreting deep learning architectures, analyzing emergent representations, and advancing speech technology for under-resourced languages. Key themes include Visually grounded speech modeling Feature attribution and model interpretability Human-inspired learning paradigms BlackboxNLP workshop leadership His recent publications examine speech model reliability , lexical tone encoding , and contextual dependencies in NLP systems. He mentors a team of PhD candidates and alumni working on topics like user-centric interpretability, bioacoustics, and disentangled speech representations. He also serves on the board of the Dutch Open Speech Technology Foundation, chairs Interspeech 2025 tutorials, and contributes as an Action Editor for TACL.

Adrian PerrigView profile

Professor

• Network Security
• Cybersecurity
• Distributed Systems
• +3 more

Adrian Perrig is a Full Professor at the Department of Computer Science at ETH Zürich. He leads research in network security, distributed systems, and internet architecture, focusing on projects like the SCION secure internet architecture and its commercialization through Anapaya Systems. His work emphasizes secure communication, denial-of-service defense, and public key infrastructure (PKI) innovations. Affiliations: ETH Zürich, Institute for Information Security Key Contributions: SCION, SAGE, RHINE, F-PKI Research interests include path-aware networks, cryptographic protocols, and resilient systems. His publications span over 295 results since 2005, with notable awards including the Best Paper Award (CoNEXT 2021) and ANRP 2023. He has contributed to foundational work in secure routing, DNS security, and GPU attestation. Scientific awards include Best Paper Awards at CoNEXT and ACM SIGCOMM, as well as recognition for applied networking research. His work bridges academia and industry, addressing challenges in global network security and scalability.

• Security of Deep Learning Systems
• Side-Channel Attacks
• Privacy-Preserving Machine Learning
• +3 more

Dr. Yuanyuan Yuan is a Researcher at the Department of Computer Science, ETH Zurich, Switzerland, based at CNB H 104.1, Universitätstrasse 6, 8092 Zurich. Her work bridges computer security and machine learning with a focus on practical vulnerabilities in deployed AI systems. Her research centers on exposing and mitigating security flaws in deep learning deployments, particularly targeting trusted execution environments (TEEs) and on-device inference systems. Key contributions include pioneering side-channel attacks against TEE-shielded neural networks (CipherSteal, HyperTheft), bit-flip attack surfaces in DNN executables (BitShield), and novel testing methodologies for neural network robustness. She investigates cache/timing side channels, ciphertext analysis, privacy leakage in partitioned ML, and concept-based explainability. Analysis of her 2023-2025 publications reveals a cohesive focus on offensive security research for AI infrastructures, with consistent contributions to top venues in security and machine learning. Her work demonstrates expertise in low-level system interactions (memory, cryptography) applied to ML security, spanning attack vectors, defensive mechanisms, and validation frameworks. The research trajectory shows increasing sophistication in exploiting hardware-software interfaces while developing practical hardening techniques for real-world deployments.

• Wearable Computing
• Human-Computer Interaction
• Machine Learning
• +3 more

Silvia Santini is an Associate Professor at the Faculty of Informatics of USI since 2016, leading the People-Centered Computing Lab. Previously, she held roles at TU Dresden (2014–2016) and TU Darmstadt (2011–2014), focusing on embedded systems and wireless sensor networks. She completed her PhD at ETH Zurich in 2009 and earned a Telecommunication Engineering degree from Sapienza University of Rome (2004). Her research centers on wearable computing, human-computer interaction, and sensor networks applied to health monitoring, activity recognition, and physiological signal analysis. Notable projects include BiHeartS (bilateral heart rate monitoring) and studies on sleep quality using wearable devices. Key achievements include being ranked among the World’s top 2% of Scientists (2022) and securing grants for projects like XAI-FinCrime. She advises on PhD and postdoc positions, such as the 2025 Innosuisse-funded project in explainable AI for financial crime detection. Labs/Teams: People-Centered Computing Group (USI), collaborating on cross-disciplinary projects in pervasive computing and healthcare technology.

Nirupam GuptaView profile

Assistant Professor

• Distributed Machine Learning
• Federated Learning
• Privacy-Preserving Algorithms
• +4 more

Nirupam Gupta is an Assistant Professor at the Department of Computer Science (DIKU), University of Copenhagen. His research focuses on robustness, privacy, and fairness in distributed machine learning systems, with particular emphasis on federated learning and Byzantine fault tolerance. He actively contributes to the design of secure, resilient, and privacy-preserving algorithms for collaborative AI systems. He teaches courses in Privacy in Machine Learning (PriMaL) Machine Learning B (MLB) both offered in hybrid formats with full remote participation support. His latest book, Robust Machine Learning: Distributed Methods for Safe AI , provides foundational insights into the field. Key research themes in his work include: Byzantine-Robust Optimization Differential Privacy in Federated Systems Personalization under Adversarial Conditions Secure Aggregation Techniques Contact: nigu@di.ku.dk

Debabrota BasuView profile

Assistant Professor

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +6 more

Debabrota Basu is a tenured faculty member (Inria Starting Faculty Position - ISFP) at the Scool team (previously called SequeL) of Inria Centre at University of Lille in France. He teaches postgraduate-level courses on privacy, responsible machine learning, and research methods in AI at École normale supérieure-PSL University, Université de Lille, and Centrale Lille. He is also a member of the ELLIS Society (European Laboratory for Learning and Intelligent Systems) and the Paris unit of ELLIS. Dr. Basu earned his PhD in Computer Science from the Department of Computer Science, School of Computing, National University of Singapore, advised by Stéphane Bressan and Pierre Senellart. Prior to that, he obtained a B.E. degree with Honours in Electronics and Telecommunication Engineering from Jadavpur University. Before joining Inria, he was a postdoctoral researcher at Chalmers University of Technology's Data Science and AI Division. Dr. Basu's research focuses on constructing algorithms for developing efficient, robust, private, and ethical learning machines that solve real-world problems. His methodological approach blends statistics, machine learning, and optimization. His application interests span sustainable agro-ecology, medical and pharmaceutical applications, energy-efficient autonomous systems, and algorithmic audits. Recent collaborations include the Inria-Indian Statistical Institute associate team SeRAI for developing Sequential Testing and Learning Algorithms for Verifiably Robust and Responsible AI, and the Inria-INRAE collaboration on Resilient Agricultural Decision Making under Environmental Risks. His publication record demonstrates expertise in bandit algorithms, reinforcement learning, and privacy-preserving machine learning. Recent work shows a strong theoretical foundation with practical applications, particularly in pure exploration bandits, differential privacy mechanisms, constrained optimization, and fairness verification. His research bridges the gap between theoretical guarantees and real-world deployment challenges across multiple domains. Dr. Basu has received notable recognition for his contributions: Best Student Paper Award at ACM EAAMO 2022 for 'On Meritocracy in Optimal Set Selection' Young researcher (JCJC) grant from the French National Research Agency (ANR) in 2022 in 'Artificial Intelligence and Data Science' He leads the project 'RL under Real-life Constraints: Regrets and Algorithms' and supervises PhD students and postdoctoral researchers. His research is supported by multiple projects including REPUBLIC ('Vers l'IA responsable avec l'apprentissage par renforcement sous contraintes') and 'Foundations of robustness and reliability in artificial intelligence.' Dr. Basu actively collaborates with institutions worldwide, including establishing the RELIANT associate team with Kyoto University for investigating structured multi-armed bandit problems.

• Natural Language Processing
• Machine Learning
• Medical Language Models
• +4 more

Syrielle Montariol is a Researcher and Course Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Natural Language Processing Lab (NLP) under the School of Computer and Communication Sciences (IC). She holds a postdoctoral position and teaches courses related to computational linguistics and AI applications. Her research focuses on advancing NLP, medical language models, multimodal learning, and AI ethics. She works in the INR 240 office and maintains collaborations across EPFL's academic divisions. Research Interests: Her work spans interpretability of AI systems, cross-modal reasoning, medical domain adaptation, sustainability text analysis, and the societal impact of AI. Recent projects include developing explainable models (e.g., global mixture-of-experts frameworks) and benchmarking tools like Vinabench for visual narratives. Publications: Her recent work addresses critical challenges in AI, including vulnerability of higher education to LLMs, medical language model adaptation (Meditron), and robust geo-localization systems. Key themes include ethical AI, multimodal learning, and domain-specific NLP applications. Labs & Teams: She contributes to the NLP lab's initiatives on visual-language models and collaborates with interdisciplinary teams on projects like PAN-RSVQA for remote sensing and PICLe for low-resource NER systems.

Carlos Cotrini JimenezView profile

Lecturer

• Machine Learning
• Information Security
• Privacy-Preserving Technologies
• +3 more

Dr. Carlos Cotrini Jimenez is a Lecturer at the Department of Computer Science at ETH Zurich, affiliated with the Institute of Machine Learning under Prof. Joachim Buhmann. He holds a PhD in Information Security from ETH Zurich (supervised by Prof. David Basin) and previously worked on infon logic under Prof. Yuri Gurevich. His research focuses on privacy-preserving machine learning, security analysis, and educational methodologies. Education: PhD in Information Security (ETH Zurich), prior work on infon logic. Research Interests: Privacy-preserving technologies, robust machine learning, security compliance analysis, and educational frameworks for complex concepts. His recent work includes developing distributed differentially private algorithms and analyzing cookie notice compliance at scale. He teaches courses on machine learning, software engineering, and AI applications, emphasizing theoretical foundations and practical implementations. Current opportunities include graduate research projects in privacy-preserving ML, security compliance, and robust algorithm development. He leads the Institute of Machine Learning’s educational initiatives and contributes to open-access materials for machine learning education.

Ivan De Oliveira NunesView profile

Assistant Professor

• Security & Privacy
• Computer Networking
• Embedded Systems
• +6 more

Prof. Ivan De Oliveira Nunes is an Assistant Professor of Cybersecurity at the University of Zurich (UZH), affiliated with the Department of Informatics (IfI) and the Digital Society Initiative (DSI). Previously, he held a similar position at the Rochester Institute of Technology (2021–2025). He earned his Ph.D. from the University of California, Irvine (UCI) in 2021, advised by Prof. Gene Tsudik, and was part of the Sprout Lab. His academic journey includes a B.S. in Computer Engineering from the Federal University of Espirito Santo (Brazil) and an M.Sc. in Computer Science from the Federal University of Minas Gerais (Brazil). His research focuses on Security & Privacy, Computer Networking, Embedded Systems, and Applied Cryptography. Key areas include IoT/CPS Security, System Security, and Provable Execution in resource-constrained systems. Notable contributions include work on remote attestation, secure software updates, and formally verified security protocols. Prof. Nunes advises multiple Ph.D., M.Sc., and undergraduate students, including current Ph.D. candidates Antonio Joia Neto, Liam Tyler, and Alexandra Lengert. Former advisees include Adam Caulfield (now at U. of Waterloo) and Spencer Roth (M.Sc. 2023). He leads the SPINS Research Group, exploring secure systems design, and has secured grants such as the CRII: SaTC grant (2023). His work bridges theoretical foundations and practical implementations, emphasizing real-world applicability in embedded and IoT systems.

Michael BaudisView profile

Professor

• Bioinformatics
• Tumorgenomics
• Genomics
• +4 more

Michael Baudis is a Professor of Bioinformatics and Tumorgenomics at the Institute of Molecular Biology , Faculty of Science, University of Zurich. He leads the development of the Progenetix database, a global reference for cancer genomic copy number alterations, and contributes to international standards through his membership in the Global Alliance for Genomics and Health (GA4GH) . Research focuses on genomic data representation , cancer subtype classification , and data sharing protocols Key projects include Beacon networks , Phenopackets , and GA4GH standards Email: michael.baudis@uzh.ch His recent work explores short tandem repeat variations , attention-based deep learning for CNAs , and heterogeneity in cancer classifications . Methodological contributions include segment_liftover , CNARA , and pgxRpi for genomic data calibration and analysis.

Alexandre de SpindlerView profile

Professor

• Conversational Agents
• Generative AI
• Information Systems
• +8 more

Prof. Dr. Alexandre de Spindler is a Professor of Information Systems at the Zurich University of Applied Sciences (ZHAW) within the School of Management and Law . He co-leads the Center for Information Systems and Technologies and focuses on application development frameworks for conversational interactions with information systems, leveraging multimodal foundation models while enhancing their controllability and reliability. His work extends to improving social skills in digital interactions and teaching agile requirements engineering. University: Zurich University of Applied Sciences School: School of Management and Law Department: Institute of Business Information Technology Academic Rank: Professor His research explores conversational systems , generative AI , and stateful prompt orchestration for complex interactions, particularly in healthcare and financial sectors. Recent work includes frameworks like PROMISE for model-driven prompt management and Science Fiction Prototyping for responsible AI innovation. Teaching contributions span data science programs (BSc, MSc, CAS) covering machine learning, generative AI, and full-stack application validation. He collaborates across disciplines including gerontology and forensic phonetics, with a focus on applying AI to societal challenges.

Rafael Pereira PiresView profile

Lecturer

• Distributed Systems
• Machine Learning
• Privacy-Preserving Technologies
• +3 more

Rafael Pereira Pires is a Lecturer and researcher at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Scalable Computing Systems Laboratory (SACS) and IC-SIN units. His research focuses on systems solutions at the intersection of privacy, efficiency, and machine learning in distributed environments. Education PhD in Computer Science (2019, University of Neuchâtel, Switzerland) Professional Master in Mechatronics (2014, IFSC, Brazil) Master in Computer Science (2009, UFSC, Brazil) His work explores privacy-preserving decentralized learning , trusted execution environments , and resource-efficient distributed systems . Recent publications address techniques like model fragmentation, approximate caching, and secure aggregation in decentralized learning contexts. Key trends in his 2023-2025 publications include: Advancements in federated learning and Mixture-of-Experts (MoE) models Applications of Trusted Execution Environments (SGX) to decentralized systems Optimization techniques for energy-aware and low-cost learning Scientific recognition includes the 2019 Léon Du Pasquier et Louis Perrier award for his PhD thesis. He has contributed to open-source tools like DecentralizePy and served as reviewer/PC member for top conferences including NeurIPS , Middleware , and ICDCS .