جست‌وجوی استاد و پژوهشگر

Sebastijan Dumancicمشاهده پروفایل

استادیار

• Program Synthesis
• Probabilistic Programming
• Neuro-Symbolic AI
• +۲ مورد دیگر

Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.

Julian Adamekمشاهده پروفایل

پژوهشگر

• Computational Cosmology
• Theoretical Cosmology
• Large-scale structure of the Universe
• +۳ مورد دیگر

Julian Adamek is a computational cosmologist and lead developer of gevolution , a general-relativistic N-body code for cosmological simulations. His work focuses on modeling relativistic effects in cosmic structure formation to better understand gravity’s role on large scales and dark energy. Research Interests: Computational Cosmology, Theoretical Cosmology, Large-scale structure of the Universe, Relativistic N-body simulations. Technical Leadership: Lead developer of gevolution , a public cosmological simulation code available via GitHub. Recent publications span diverse applications of deep learning in geospatial analytics, environmental monitoring, and computer vision, including phenology modeling, biomass mapping, conflict assessment, and 3D reconstruction from point clouds. Key Trends: Integration of AI/ML for environmental tasks, cross-domain applications (cosmology, ecology, forestry), and satellite data processing. Technical Focus: Transformer networks, diffusion models, super-resolution imaging, and ensemble learning for uncertainty quantification. Julian collaborates with researchers in cosmology and geospatial science, though specific students or awards are not mentioned in the provided texts.

Chen Liuمشاهده پروفایل

استادیار

• Machine Learning
• Adversarial Robustness
• Privacy-Preserving Machine Learning
• +۹ مورد دیگر

Chen Liu is an Assistant Professor in the Department of Computer Science at City University of Hong Kong and the Principal Investigator (PI) of the Machine Learning and Optimization (MLO) group. His research focuses on building reliable machine learning models, particularly studying robustness and privacy properties of deep neural networks from an optimization perspective. University: City University of Hong Kong Academic Rank: Assistant Professor Students: Supervises multiple PhD, MPhil, and postdoctoral researchers. Education: Holds a Ph.D. (2022) and MSc (2017) in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), and a BSc (2015) in Computer Science from Tsinghua University. Research Interests: Adversarial robustness, privacy-preserving machine learning, optimization algorithms, dataset distillation, generative models, and theoretical analysis of loss landscapes. His work addresses challenges like catastrophic overfitting, architecture overfitting in distilled data, and stable adversarial training methods. Article Trends: Recent publications explore adversarial robustness under l0/l1 norms, gradient inversion for data reconstruction, evolutionary factor searching in finance, and meta-tuning for out-of-domain few-shot learning. These works emphasize optimization techniques to enhance model reliability and generalization. Scientific Awards: Microsoft Research Ph.D. Scholarship Programme (2017–2019) Advising and Grants: Supervises a diverse team of current and former students, with collaborations across institutions like George Mason University and Zhejiang University. Research supported by academic and industry grants. Labs and Teams: Leads the MLO group, which investigates fundamental ML theory and algorithms to improve system reliability. The group's work spans adversarial training, dataset distillation, and generative model optimization.

Enkelejda Mihoمشاهده پروفایل

استاد

• Digital Life Sciences
• Medical Informatics
• Artificial Intelligence in Healthcare
• +۴ مورد دیگر

Prof. Enkelejda Miho is a Professor of Digital Life Sciences at the School of Life Sciences, FHNW, leading the aiHealthLab. Her work bridges computer science/AI with life sciences, focusing on drug discovery, personalized medicine, and immunology. She holds roles as Team Leader at aiHealthLab and Group Leader at the Swiss Bioinformatics Institute. Research Interests : She applies machine learning to analyze immune repertoires, antibody engineering, and autoimmunity diagnostics. Her lab develops computational tools like the RWD-Cockpit for real-world data analysis and synthetic antibody-antigen models (Absolut!) to advance biotherapeutics. Her work on dengue immunity and monoclonal gammopathies highlights translational applications. Key Projects : The aiHealthLab focuses on AI-driven diagnostics and therapeutics. Her contributions include AI frameworks for antibody specificity prediction, age-related immune repertoire changes, and large-scale network analysis of antibody repertoires. Labs/Teams : Leads aiHealthLab and collaborates with the Swiss Bioinformatics Institute, integrating computational and experimental immunology.

• Computational Materials Science
• Electronic Structure Theory
• Solid-State Physics
• +۵ مورد دیگر

Nicola Marzari is a Professor of Theory and Simulation of Materials at EPFL, where he also serves as Director of the National Centre for Computational Design and Discovery of Novel Materials (NCCD). He is Chairman of Psi-k, an international network for advanced materials' computational design. Previously, he held the Toyota Chair of Materials Engineering at MIT and leadership roles at the University of Oxford, including Director of the Materials Modeling Laboratory and a Statutory Chair in Materials Modeling. His education includes a Laurea in Physics (summa cum laude) from the University of Trieste, a PhD in Physics from the University of Cambridge under Prof. Michael C. Payne, and postdoctoral work at Rutgers University with Prof. David Vanderbilt. Marzari's research focuses on computational materials science, electronic structure theory, and high-throughput simulations. He develops methods for predicting material properties using first-principles approaches, machine learning, and quantum espresso software. Key areas include energy materials (batteries, thermoelectrics), magnetic materials, and optoelectronic systems. His work bridges fundamental physics and practical material design, emphasizing reproducible workflows and open-source tools like koopmans and AiiDA . His recent articles highlight advancements in machine learning for materials interfaces, dynamical Hubbard functionals, and thermal conductivity modeling. He actively contributes to EuroHPC initiatives for exascale materials design and OPTIMADE standards for materials data exchange. Marzari leads interdisciplinary teams at EPFL and collaborates globally on projects ranging from defect engineering in semiconductors to AI-driven materials discovery. His research aims to accelerate the development of sustainable energy and electronic technologies through computational innovation.

Tom Beuclerمشاهده پروفایل

استادیار

• Geo-Environmental Data Science
• Climate Informatics
• Atmospheric Physics
• +۵ مورد دیگر

Tom Beucler is a Conditional Pre-Tenure Assistant Professor in Geo-Environmental Data Science at the University of Lausanne’s Institute for Earth Surface Dynamics (IDYST). He holds a Master’s degree in Science and Mechanics from École Polytechnique (2014) and a PhD in Atmospheric Science from MIT (2019). Postdoctoral research at Columbia University and UC Irvine focused on machine learning applications in climate science under Professors Pierre Gentine and Michael Pritchard. Research Interests: Climate informatics, atmospheric physics, fluid dynamics, tropical meteorology, and integrating machine learning into climate models for extreme weather prediction and hydrological cycle modeling. Collaborations: Works with environmental scientists and computer engineers to improve climate models using neural networks and causal discovery methods. Initiatives: Organizes weekly brainstorming sessions to promote machine learning adoption in environmental sciences. Publications span climate-invariant machine learning, data-driven parameterizations, and hybrid AI-climate modeling frameworks like ClimSim. His work emphasizes causal consistency and generalizability across climate conditions.

• Federated Learning
• Optimization for Machine Learning
• Privacy-Preserving Machine Learning
• +۱۲ مورد دیگر

Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering. He previously held an SNSF postdoctoral fellowship at UC Berkeley under Michael I. Jordan and earned his PhD at EPFL advised by Martin Jaggi. He co-leads the Federated Learning and Data Quality working group at MONAI (NVIDIA) and collaborates with researchers at Apple Research. His research lies at the intersection of optimization, machine learning, statistics, and economics, with a strong focus on federated learning, privacy-preserving machine learning, data valuation, and AI for healthcare. He investigates how data quality, privacy, and incentives shape collaborative ML systems, especially in high-stakes domains like medicine. His work has been deployed at companies such as Meta, Google, OpenAI, and Owkin. His recent publications span top-tier venues including NeurIPS, ICML, ICLR, and JMLR, with influential contributions such as the SCAFFOLD algorithm for federated learning. His research shows a consistent trend toward building robust, private, and incentive-compatible collaborative learning systems, with increasing emphasis on real-world deployment in healthcare and decentralized data markets. 2023 SNSF Mobility Fellowship 2022 Patrick Denantes Memorial Prize for best thesis in computer science 2022 EPFL thesis distinction (top 8%) 2021 Chorafas Foundation Prize for exceptional applied research Capitol One Fellow (2025) He is actively mentoring PhD students and leads a research group focused on foundational and applied challenges in federated and privacy-preserving ML. He teaches graduate courses at USC, including CSCI 599 on Optimization for Machine Learning and CSCI 699 on Privacy-Preserving Machine Learning. He serves as an area chair for ICLR 2025 and co-organizes major workshops on incentives in data sharing and federated learning. His lab collaborates with institutions like NVIDIA, Apple, and Argonne National Laboratory, and he is building a research program centered on sustainable, equitable, and trustworthy AI ecosystems.

• Causal Machine Learning
• Shrinkage Methods
• Regularization Methods
• +۳ مورد دیگر

Jana Mareckova is an Assistant Professor of Econometrics at the Swiss Institute for Empirical Economic Research (SIEW), part of the School of Economics and Political Science (SEPS) at the University of St. Gallen. She joined the university in 2020 after completing a postdoc at SEW-HSG following her PhD from the University of Konstanz (2019). Her research focuses on causal machine learning, shrinkage methods, regularization techniques, and labor economics. She explores applications in labor market outcomes and fairness, leveraging econometric tools to address real-world economic questions. Education: PhD in Econometrics, University of Konstanz (2019); Postdoc at SEW-HSG (pre-2020). Research interests include shrinkage estimation for categorical regressors, causal inference via machine learning, and predicting economic outcomes using noncognitive skills. Her work bridges statistical theory with practical policy analysis, as seen in her 2021 Journal of Econometrics publication on shrinkage methods. Recent projects emphasize causal forests and comprehensive frameworks for policy evaluation. No scientific awards are listed, though her contributions to causal ML and econometric methods are notable. She has no documented advising or grant information. Her research is affiliated with SIEW, focusing on empirical economic research.

Christian Russمشاهده پروفایل

مدرس ارشد

• Digital Leadership
• IT Strategy
• Business-IT Alignment
• +۴ مورد دیگر

Dr. Christian Russ is a Senior Lecturer at the Institute of Business Information Technology , Zurich University of Applied Sciences (ZHAW), with expertise in digital transformation, IT leadership, and business-IT alignment. His work spans healthcare, education, and automotive sectors, focusing on agile IT governance and adaptive business models. Current roles: Program Director MAS IT-Leadership and TechManagement Key projects: ZHAW Digital Culture Assessment, Monitoring eCH Standards Research interests include digital transformation , agile IT , health informatics , and emerging technologies . Recent publications analyze post-pandemic organizational culture, medical AI regulation, and cloud infrastructure in healthcare. 2025: Organizational culture shifts during/after pandemic 2024: ML solutions in Swiss hospitals under MDR 2023: iPaaS cloud security in healthcare 2023: Agile transformation in non-profits Scientific recognitions: Best Paper Award at SMART 2018 "Educate to lead" award (Soroptimist International Europa, 2016) Active in the ZHAW Digital Health Lab, he leads projects on tech startup coaching, digital ecosystems, and IT governance. His teaching covers IT strategy, digital transformation, and enterprise service management at ZHAW.

Shayan Aziznejadمشاهده پروفایل

پژوهشگر

• Machine Learning
• Acoustic Imaging
• Neural Networks
• +۳ مورد دیگر

Shayan Aziznejad is a Senior ML Scientist at Distran, working on the intersection of machine learning and acoustic imaging. He was previously an ML researcher at Daedalean AI (October 2022–December 2024) and a Ph.D. candidate at Ecole Polytechnique Fédérale de Lausanne (EPFL) , where he focused on mathematical optimization and signal processing under Prof. Michael Unser. His academic background includes dual B.Sc. degrees in Electrical Engineering and Pure Mathematics from Sharif University of Technology . Research Focus: Machine learning, neural network certification, wavelet analysis, Hessian-Schatten regularization, and sparse modeling. Scientific Recognition: Swiss National Science Foundation Postdoc Fellowship (2021) Best Student Paper Award at ICASSP (2019) Gold Medalist at Iranian National Mathematics Olympiad (2011) Academic Contributions: Authored 15+ publications in top-tier journals (SIAM, IEEE, etc.) and conferences (ICASSP, EUSIPCO), with a focus on Lipschitz-regularized models, spline-based optimization, and inverse problems. Advising Experience: Supervised 11+ students across master's theses, summer internships, and semester projects, including Eliana Renzo, Joaquim Campos, and Haojun Zhu. Email: shayan.aziznejad@gmail.com

Sara Magliacaneمشاهده پروفایل

استادیار

• Causality
• Causal Representation Learning
• Causal Discovery
• +۴ مورد دیگر

Sara Magliacane is an Assistant Professor at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab . She leads research at the intersection of causality and machine learning , focusing on improving AI robustness, generalization, and safety through causal reasoning. Her work spans causal representation learning , causal discovery , and causality-inspired ML in domains like reinforcement learning and dynamical systems. PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano/Torino BSc in Computer Engineering (2008), Università degli Studi di Trieste Her research explores causal variable identification from high-dimensional data (e.g., images, sequences) and causal graph discovery for domain adaptation. Methods include CITRIS , FANS-RL , and SNAP , with applications in embodied AI and biomedical data. The group emphasizes theoretical guarantees and scalable algorithms for real-world systems. Recent work trends include temporal causal modeling , intervention-efficient learning , and nonstationary reinforcement learning . Publications cover topics like causal discovery in partially observed settings , causal graph pruning , and sample-efficient concept learning , often combining neurosymbolic approaches with deep learning. Scientific Awards : ELLIS Scholar Sara supervises PhD students across universities (UvA, University of Pisa) and collaborates with institutions like TU Delft , Harvard , and IBM Research . She co-organizes workshops at premier conferences (NeurIPS, ICML, AISTATS) and teaches causality courses at the University of Amsterdam and Harvard Data Science Initiative. Her lab, Amsterdam Machine Learning Lab (AMLab) , investigates causal structure in embodied agents , safe reinforcement learning , and hybrid dynamical system modeling . The group maintains active partnerships with institutions such as MIT-IBM Watson AI Lab , Qualcomm , and Adyen .

Jakub Macinaمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Learning Sciences
• Dialogue Systems
• +۶ مورد دیگر

Jakub Macina is a Doctoral Fellow at the ETH AI Center and a PhD Candidate at ETH Zürich . He works in the intersection of Natural Language Processing and Learning Sciences as part of the Language, Reasoning and Education Lab (led by Prof. Mrinmaya Sachan) and the Professorship for Learning Sciences and Higher Education (led by Prof. Manu Kapur). Forbes 30 Under 30 in Science & Education 2023 Recipient of ETH AI Center Fellowship ( Co-founder of a health-tech startup with seed investment Research Interests : Focus on generative large language models (LLMs), dialogue tutoring systems , pedagogical alignment of AI models, and mathematical reasoning . His work explores: Reinforcement learning for pedagogical steering LLM evaluation frameworks Socratic question generation Stepwise error detection and remediation Student-teacher interaction modeling Publications span top conferences like EMNLP , ACL , NeurIPS , and RecSys , with particular emphasis on educational applications of LLMs and dialogue-based learning systems . Scientific Awards : Forbes 30 Under 30 in Science and Education (2023) 2nd Place in ACM IT SPY Computer Science Master's Thesis Competition (2017) ETH AI Center Fellowship (2021) Leadership & Teaching includes: Managing team of 6 data scientists Teaching Assistant for Machine Learning and NLP courses at ETH Zurich Developing large-scale ML pipelines for recommender systems Open-source contributions to Discourse and Google Summer of Code projects

Mauro Pezzèمشاهده پروفایل

استاد

• Software Engineering
• Software Testing and Analysis
• Self-Adaptive Systems
• +۵ مورد دیگر

Mauro Pezzè is a Full Professor of Software Engineering at the Università della Svizzera italiana (USI) and Università di Milano Bicocca, leading the STAR research group since 2006. He holds a laurea from the University of Pisa and a PhD from Politecnico di Milano. His research focuses on software testing, analysis, self-adaptive systems, and cloud systems. He has held editorial roles, including Editor-in-Chief of ACM Transactions on Software Engineering and Methodologies (TOSEM), and served on numerous program committees. Education: Laurea (Pisa), PhD (Politecnico di Milano). Professional roles include Dean of the Faculty of Informatics at USI (2009-2013), visiting scientist at UC Irvine and Edinburgh, and technical lead for international projects. He co-authored a seminal book on software testing (Wiley, 2007), with over 670 citations. Research Interests: Software Testing, Self-Adaptive Systems, Cloud Computing, AI in SE, Sustainable Software. Projects include work on field-based testing, failure prediction in distributed systems, and neuro-symbolic approaches for test oracles. Grants and Advising: Led STAR Lab projects in self-healing systems, GUI testing, and semantic matching. Advised numerous PhD/postdoc students (e.g., Ciniselli, Di Grazia, Qiu). Collaborations with European tech firms on R&D initiatives. Labs/Teams: STAR Group at USI/Constructor Institute, Bicocca, and Politecnico di Milano. Current members include postdocs and PhD students working on AI-driven testing and cloud reliability.

Burcu Demirayمشاهده پروفایل

پژوهشگر

• Cognitive Psychology
• Gerontology
• Machine Learning
• +۳ مورد دیگر

Dr. Burcu Demiray is a researcher at the Department of Psychology, University of Zurich, leading a team focused on real-life cognitive activities in the context of healthy longevity. Her interdisciplinary work integrates smartphone sensing, experience sampling, and machine learning to analyze real-life audio, speech, and text data. She is also developing e-learning concepts for Generation 65+ to combat ageism and enhance digital literacy. Education: Not explicitly detailed in the provided text. Research Interests: Healthy cognitive aging and psychological well-being. Automated semantic analysis of real-life conversations. Machine learning applications in gerontology and memory studies. Digital health interventions and ambient audio monitoring. Publications Trends: Focus on naturalistic observation studies using smartphone sensing and wearable devices. Development of machine learning models for reminiscence and memory function detection. Analysis of gender differences in daily communication and cognitive aging. Scientific Awards: No specific awards mentioned. Labs/Teams: Affiliated with the Healthy Longevity Center at UZH and the Digital Society Initiative (DSI) Community Health.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Prof. Frank-Peter Schilling is a Senior Lecturer at Zurich University of Applied Sciences (ZHAW) School of Engineering and Deputy Director of the Centre for Artificial Intelligence (CAI). He leads the Intelligent Vision Systems group and coordinates the PhD Programme in Data Science with the University of Zurich. As an Adjunct Professor at Victoria University of Wellington, he specializes in AI, Machine Learning, and applications in healthcare and physical sciences. His research focuses on deep learning-based computer vision, MLOps, and trustworthy AI certification frameworks. Education: PhD in Physics (University of Heidelberg, 2001) Dipl.-Phys. (MSc equivalent in Physics, University of Heidelberg, 1998) CAS University Didactics (PH Zurich, 2024) Research Interests: Developing AI systems for medical imaging (e.g., CBCT artifact reduction) Certification schemes for AI trustworthiness (e.g., certAInty project) Applications of deep learning in particle physics and industrial vision Achievements: Recipient of the EPS HEP Prize (2013) for contributions to the Higgs boson discovery at CERN Lead author of over 20 peer-reviewed articles on AI, MLOps, and medical imaging Principal investigator for projects like AI-BRIDGE (responsible AI development) and GenAI4SKA (Square Kilometre Array simulations) Teaching: Courses in MLOps, Machine Learning Operations, and Computer Vision at BSc and MSc levels. Developed the CAS Advanced Machine Learning program. Labs & Networks: Active in ELLIS (European Lab for Learning and Intelligent Systems), CLAIRE (AI research), and ZHAW’s Digital Health/Datalab initiatives.