جست‌وجوی استاد و پژوهشگر

• 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.

Saqib Javedمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Efficient Neural Networks
• +۴ مورد دیگر

Saqib Javed is a doctoral researcher and Researcher at the Computer Vision Laboratory (CVLab) at EPFL, supervised by Prof. Pascal Fua and Dr. Mathieu Salzmann. His research focuses on energy-efficient deep networks, 3D reconstruction, quantization-aware training, and domain generalization. He holds a master’s degree from TU Munich and ETH Zurich. He is affiliated with the School of Computer and Communication Sciences (IC) and the Department of Computer Science at EPFL. His current projects include compressed Gaussian splatting for dynamic scenes, quantized diffusion models, and reducing inference time for vision-language models. He has been awarded the EPFL IC Distinguished Service Award and is a Global Leaders PhD Fellow. His work spans theoretical research and practical applications in low-power device optimization. Teaching roles include serving as a Teaching Assistant for courses like Introduction to Machine Learning (CS-233) and Probability and Statistics (MATH-232). He has supervised multiple students, including Chengkun Li and Ahmad Jarrar Khan, on projects related to quantization and 3D pose estimation. His research also involves collaborations with industry partners like BMW, Siemens, and Intel, focusing on hardware-friendly neural networks. Awards include the EPFL IC Distinguished Service Award (2024) and recognition for his contributions to efficient deep learning. His recent publications address domain generalization, Gaussian splatting, and modular quantization techniques, reflecting his interdisciplinary approach to advancing machine learning efficiency and applicability.

• 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.

Alexander Artikisمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Distributed Systems
• Complex Event Recognition
• +۴ مورد دیگر

Dr. Alexander Artikis is an Associate Professor of Artificial Intelligence at the University of Piraeus and a Research Associate at the National Centre for Scientific Research (NCSR) "Demokritos". He leads the Complex Event Recognition (CER) group , focusing on symbolic and probabilistic approaches to event recognition and forecasting. University of Piraeus (2025–present) NCSR Demokritos (2017–present) Complex Event Recognition Group (2017–present) His research spans Artificial Intelligence and Distributed Systems , with a focus on: Complex Event Recognition (CER) : Developing logic-based systems for detecting events in real-time data streams Event Calculus : Creating probabilistic and incremental versions for runtime reasoning Multi-Agent Systems : Modeling norm-governed interactions Maritime Informatics : Applying CER to vessel trajectory analysis and fleet management Key publications reveal trends in: Neuro-symbolic forecasting models combining deep learning and logic-based reasoning Symbolic automata with memory for pattern detection Online learning techniques for dynamic event rule generation Tensor-based formalizations for efficient temporal reasoning Handling uncertainty in real-time maritime data streams Optimizing memory usage for scalable stream processing He contributes to open-source tools like RTEC (Run-Time Event Calculus) and holds a European patent on complex event forecasting. His work addresses challenges in: Proactive decision-making systems Knowledge Graph consistency Hybrid human-machine discovery of movement patterns Big Data analytics for time-critical applications

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 .

Giorgia Ramponiمشاهده پروفایل

استادیار

• Machine Learning
• Mathematical Modelling
• Reinforcement Learning
• +۳ مورد دیگر

Giorgia Ramponi is an Assistant Professor with Tenure Track at the Faculty of Business, Economics and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and the Data Science and AI, Computer Science and Engineering department at Chalmers University of Technology. Her educational background includes a Ph.D. in Information Technology from Politecnico di Milano (completed June 2021 with honors), advised by Marcello Restelli, and a Master of Science in Computer Science with Honours Programme (110/110 cum laude) from la Sapienza (July 2017), advised by Flavio Chierichetti and Alessandro Panconesi. Dr. Ramponi's research focuses on machine learning and mathematical modeling, with particular emphasis on reinforcement learning and multiagent learning. Her work bridges theoretical foundations with practical applications, exploring how learning algorithms can make optimal decisions in complex environments. She has made significant contributions to areas including inverse reinforcement learning, multi-agent systems, constrained Markov decision processes, and human-AI interaction through preference learning. Her recent publications demonstrate a strong trend toward addressing fundamental challenges in reinforcement learning, particularly in multi-agent settings, constrained optimization, and learning from human feedback. Her work combines theoretical rigor with practical applications across robotics, economics, and decision-making systems. Hassler Research Grant for "Unified Feedback Integration Framework for Reinforcement Learning" Dr. Ramponi actively contributes to the academic community through conference participation, invited lectures (including at the Mediterranean Machine Learning Summer School), and teaching. She designed and taught the "Data Science and Machine Learning" course for the ETH-Ashesi Master program. She is also a member of the ELLIS community, which connects excellence in AI research across Europe. Her research group focuses on developing frameworks for reinforcement learning with various feedback types, including preferences, rewards, and demonstrations. The group aims to advance the theoretical understanding of learning algorithms while addressing practical challenges in real-world applications.

• Genomics
• Computational Biology
• Cell Biology
• +۵ مورد دیگر

Gioele La Manno is an Assistant Professor (tenure track) at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences (SV) and the Department of Life Sciences Engineering. He leads the Unité du Prof. La Manno (UPLAMANNO) and holds roles in teaching and doctoral education within the SSV-ENS and Programme doctoral Biologie computationnelle et quantitative . His research focuses on single-cell genomics, cellular heterogeneity, and systems biology approaches to study developmental and neural systems. La Manno’s educational background includes a strong foundation in computational and quantitative biology, reflected in his teaching of courses like Biological data science I: statistical learning and Scientific literature analysis in neuroscience . His lab explores interdisciplinary methods combining genomics, epigenomics, and machine learning to dissect cell fate decisions and tissue organization. He actively contributes to doctoral programs, supervising over a dozen PhD students and advising theses on topics ranging from nervous system development to drug sensitivity profiling. His work bridges basic research with translational applications, such as clinically compliant cell cryopreservation for regenerative medicine. La Manno collaborates across EPFL’s Life Sciences Engineering ecosystem, leveraging state-of-the-art technologies like mass spectrometry imaging and single-cell RNA velocity modeling. His lab’s recent studies address lipidome dynamics, glial diversity in the CNS, and the molecular regulation of midbrain dopaminergic neurons.

Lenka Zdeborováمشاهده پروفایل

دانشیار

• Statistical Physics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Lenka Zdeborová is an Associate Professor at EPFL, jointly affiliated with the School of Basic Sciences and School of Computer and Communication Sciences. She leads the Laboratory of Statistical Physics of Computational Systems, where her research bridges statistical physics, machine learning, and computational biology. Education: PhD in Physics, Université Paris-Cité (2012) MSc in Fundamental Physics, École Normale Supérieure (2009) BSc in Physics, École Normale Supérieure de Lyon (2007) Her work focuses on phase transitions in learning algorithms, high-dimensional statistics, and neural network theory. Current projects investigate fundamental limits of machine learning, dynamics of graph neural networks, and applications to biological systems. Recent publications explore attention mechanisms in transformers, neural network depth advantages, and Bayes-optimal learning. Methodological innovations include cavity methods for hypergraphs and analysis of high-dimensional inference problems. Supervises doctoral students researching statistical physics approaches to machine learning and optimization. Teaches graduate courses in data science and machine learning for physicists.

• Finance
• Economics
• Econometrics
• +۳ مورد دیگر

Anouk Bergner is an Assistant Professor at the Institute of Management within the University of Geneva. She holds a Ph.D. from the University of St. Gallen. Her affiliations include the Geneva Finance Research Institute, Research Institute of Economics and Econometrics, and Research Institute for Statistics and Information Science. Her research interests span finance, economics, econometrics, management, statistics, and information science. These areas are inferred from her institutional affiliations and academic position. No specific articles, awards, or grants are listed in the provided texts. Contact details include a phone number (+41 22 379 85 86) and an office location (Uni Mail - 3225).

• Statistics
• Machine Learning
• Statistical Inference
• +۳ مورد دیگر

Peter Grünwald is full professor of Statistical Learning at Leiden University's Mathematical Institute and senior researcher in the Machine Learning group at CWI (Centrum Wiskunde & Informatica) in Amsterdam. His pioneering work on e-values establishes a transformative framework for statistical inference that overcomes critical limitations of classical p-values, enabling flexible experimental designs while maintaining rigorous error control. His research centers on e-values and e-processes as a unifying paradigm between Bayesian and frequentist statistics, with core innovations in safe testing, anytime-valid inference, and optional continuation. These methods allow researchers to gather additional data after initial analysis without inflating Type I errors and to determine significance levels post-hoc—addressing longstanding rigidity in Neyman-Pearson hypothesis testing. His publication trajectory reveals rapid adoption of e-values across disciplines: from foundational theory in PNAS and JRSSB to clinical applications in survival analysis (NEJSDS) and epidemiology (medrxiv meta-analysis). The 2022–2024 publications demonstrate methodological maturation, with implementations in R (safestats package) and growing use in social sciences (PsyArXiv) and causal inference (JASA). Scientific recognition includes: ERC Advanced Grant (2024) for developing flexible statistical inference theory via e-values His ERC-funded project drives current research, while his internship policy restricts non-Dutch master’s/bachelor’s students but welcomes advanced international PhD candidates. Collaborative work spans statisticians (Ly, de Heide, Koolen), machine learning researchers (Ramdas, Shafer), and medical scientists (van Werkhoven). As core member of CWI's Machine Learning group, he advances theoretical foundations with practical impact—evidenced by the first live deployment of e-values in a BCG vaccine meta-analysis. His work redefines statistical practice for adaptive data collection in clinical trials, AI, and social science research.

Alban Moorمشاهده پروفایل

پژوهشگر

• Statistics
• Econometrics
• Time Series Analysis
• +۳ مورد دیگر

Alban Moor is a Researcher at the Research Institute for Statistics and Information Science at the University of Geneva. He holds a Ph.D. from the University of Geneva and specializes in statistical methodology with a focus on time series analysis and econometrics. Education: Ph.D., University of Geneva Research Interests: Moor’s work centers on advancing statistical inference techniques, particularly through the application of cumulant generating functions and bootstrap methods. His research addresses challenges in time series analysis and dependent data, with applications in computational statistics and econometric modeling. He emphasizes methodological innovation to improve accuracy and efficiency in data-driven analyses. Teaching: Moor contributes to executive education through courses on Mixed Linear Models, bridging theoretical statistical foundations with practical applications. Labs/Teams: Affiliated with the Research Institute for Statistics and Information Science, where he collaborates on interdisciplinary projects involving statistical computation and econometric methodologies.

Fabio Nobileمشاهده پروفایل

استاد

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +۵ مورد دیگر

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

Peter Eggerمشاهده پروفایل

استاد

• Applied Econometrics
• Theoretical Panel Econometrics
• International Economics
• +۷ مورد دیگر

Peter Egger is a Full Professor at the Department of Management, Technology, and Economics at ETH Zürich . His research spans applied and theoretical econometrics, international economics, and industrial organization.

• Econometrics
• Financial Econometrics
• Risk Management
• +۶ مورد دیگر

Patrick Gagliardini is a Full Professor of Econometrics at the University of Lugano (USI) within the Faculty of Economics and the Institute of Finance. He also serves as Pro-Rector at USI. His academic journey includes a PhD in Econometrics from USI (2003) and studies in Physics at ETH Zurich (1998). He has held roles such as Visiting Fellow at CREST Paris (2003) and Assistant Professor at the University of St. Gallen (2004–2006). His research focuses on econometric methods (nonparametric techniques, GMM, latent factor models) and financial applications such as credit risk, asset pricing, and risk management. Competence areas include Big Data, investment decisions, and systematic risk analysis. He teaches courses in econometrics, financial econometrics, and time series at the undergraduate, graduate, and PhD levels. Recent publications explore latent factor models, econometric testing (e.g., eigenvalue tests for factor detection), and financial decision-making in small data regimes. His work bridges theoretical econometrics with practical applications in finance and risk modeling. Notably, his research addresses challenges in dynamic latent factor models, hedge fund performance evaluation, and granularity theory in financial systems. He maintains an active academic profile with contributions to both theoretical and applied econometrics.

Marc Paolellaمشاهده پروفایل

استاد

• Financial Econometrics
• Time Series Analysis
• Portfolio Optimization
• +۴ مورد دیگر

Marc Paolella is a Full Professor of Empirical Finance at the University of Zurich's Department of Finance. His academic journey includes roles as Associate Professor (2003–2006) and Assistant Professor (1999–2003) at Kiel University and visiting positions at the University of Geneva. He holds a Dr. sc. pol. (1998) and Habilitation (2002) from Kiel University, alongside degrees from SUNY Stony Brook and Colorado State University. His research focuses on financial econometrics, emphasizing portfolio optimization, risk prediction, and saddlepoint approximation theory. He has authored four textbooks, including works on probability theory, statistical inference, and GARCH models. Over 50 journal articles span leading venues like the Journal of Econometrics and Journal of Financial Econometrics. Paolella's contributions include developing methods like CHICAGO for portfolio risk calculation and the COMFORT model for non-Gaussian returns. He serves as Editor-in-Chief of the journal Econometrics and holds editorial roles at multiple journals. Awards include the Elected Fellowship at the IETI and recognition for impactful research. His teaching spans advanced courses in financial econometrics, probability theory, and mathematical finance. He has supervised numerous theses and leads the Quantitative Finance program at the Science ETH UZH. Research grants include Swiss National Science Foundation funding for portfolio optimization under non-elliptical distributions.