جست‌وجوی استاد و پژوهشگر

Ayush Bhartiمشاهده پروفایل

پژوهشگر ارشد

• Simulation-Based Inference
• Approximate Bayesian Computation
• Machine Learning
• +۴ مورد دیگر

Ayush Bharti is an Academy Research Fellow at the Department of Computer Science, School of Science, Aalto University, supported by the Research Council of Finland. He is affiliated with the Probabilistic Machine Learning research group and the Finnish Centre for Artificial Intelligence (FCAI). Previously, he was a postdoctoral researcher working with Prof. Samuel Kaski at Aalto University. Dr. Bharti received his PhD from the Department of Electronic Systems, Aalborg University, Denmark, where he focused on making approximate Bayesian computation methods for estimating parameters of stochastic models in radio propagation. His primary research area is simulation-based inference (or likelihood-free inference), with specific interests in developing approximate inference methods that are (i) robust to model misspecification, and (ii) computationally efficient. His work bridges machine learning, statistics, and wireless communications, with applications in radio channel modeling and parameter estimation for stochastic systems. His recent publications show a strong trend toward robustness in simulation-based inference, with multiple papers on handling model misspecification, missing data, and cost-aware approaches. His research increasingly integrates neural networks and deep learning techniques into traditional statistical inference frameworks. Academy Research Fellowship grant from the Research Council of Finland (June 2024) Dr. Bharti has received significant research funding through his Academy Research Fellowship. He has supervised student projects on radio channel model calibration and recently welcomed Yuga Hikida as a PhD student on his Research Council of Finland project. His collaborative work spans institutions including Aalto University, University College London, and the Alan Turing Institute. He is actively involved in the Probabilistic Machine Learning research group at Aalto University and contributes to the Finnish Centre for Artificial Intelligence, fostering collaborations between Finnish and international researchers in machine learning and artificial intelligence.

• Computational statistics
• Sequential Monte Carlo methods
• Forward-backward filters
• +۵ مورد دیگر

Paul Fearnhead is Professor of Statistics at Lancaster University since 2001, where he started as a lecturer. His academic journey includes a DPhil with Peter Clifford and a post-doctoral position with Peter Donnelly at the University of Oxford. He has been Editor of Biometrika since January 2018. Education : DPhil in Statistics (Oxford), Post-doctoral research (Oxford) Research Interests : Computational statistics with focus on sequential Monte Carlo methods, forward-backward filters, and changepoint detection algorithms; population genetics including bacterial inference for Campylobacter jejuni; Bayesian inference for diffusions and continuous-time stochastic processes. Research Funding : Lead investigator for EPSRC grants on Statistical Scalability (2016-2022), Health Sciences Bayesian Data Science (2018-2023), and CoSInES program (2018-2023) involving multiple UK institutions. Scientific Awards : 2006 Adam's Prize 2007 Royal Statistical Society Guy Medal in Bronze His collaborative work includes teaching the 2012 Modern Computational Statistics course through the Graduate Training Programme and interdisciplinary research on US college basketball ranking systems.

Nuria Oliverمشاهده پروفایل

پژوهشگر

• Computational Models of Human Behavior
• Human-Computer Interaction
• Intelligent User Interfaces
• +۱۲ مورد دیگر

Nuria Oliver, PhD is a pioneering computer scientist and ACM Fellow , recognized as the first female Spanish computer scientist to achieve both ACM Distinguished Scientist and IEEE Fellow status. Current affiliations include Microsoft Research and former roles at MIT Media Lab. First female Spanish ACM Fellow IEEE Fellow Academia Europaea member Her research spans human behavior modeling , intelligent user interfaces , and mobile computing , with notable work in: Wearable context-aware systems (DyPERS, HealthGear) Music-adaptive exercise platforms (MPTrain, TripleBeat) Social network analysis (information propagation, community link modeling) Computer vision interfaces (LAFTER facial expression recognition) Scientific achievements include: 41 patents 15+ years of continuous multimodal systems research Recognized for LAFTER system (2000 paper) She actively promotes technology accessibility through: Media collaborations Technical/non-technical keynotes STEM outreach for girls

Susan Atheyمشاهده پروفایل

استاد

• Economics of Digitization
• Machine Learning and Causal Inference
• Marketplace Design
• +۳ مورد دیگر

Susan Athey is the Economics of Technology Professor at Stanford Graduate School of Business and a Professor of Economics (by courtesy) at Stanford's School of Humanities and Sciences. She is also a Senior Fellow at the Stanford Institute for Economic Policy Research and Stanford Institute for Human-Centered Artificial Intelligence. Her primary affiliation is with the Graduate School of Business, where she leads the Golub Capital Social Impact Lab. She previously served as Chief Economist at the U.S. Department of Justice Antitrust Division (2022–2024) and has held roles at MIT, Harvard, and Microsoft. Her research focuses on the economics of digitization, machine learning applications in causal inference, and marketplace design. Key areas include platform markets, online advertising, vaccine policy, and social impact technology. She earned her PhD from Stanford University and a BA from Duke University. Athey's work bridges economics and technology, with contributions to policy design, antitrust analysis, and digital health interventions. Her recent projects include evaluating the impact of social media campaigns on vaccine adoption and designing auction mechanisms for pandemic vaccine distribution. Education: PhD, Stanford (1995); BA, Duke (1991) Affiliations: Stanford GSB, Stanford Institute for Economic Policy Research, National Academy of Sciences Her research has addressed critical issues such as consumer privacy (The Digital Privacy Paradox), AI governance, and experimental methods for policy evaluation. She is a recipient of the John Bates Clark Medal (2007) and R. K. Cho Award (2024). Current initiatives include the Golub Capital Social Impact Lab, which develops technology-driven solutions for social sector challenges. She also advises governments and tech firms on market design and innovation policy.

Maria Giulia Pretiمشاهده پروفایل

مدرس

• Neuroimaging
• Brain Connectivity
• Alzheimer's Disease
• +۳ مورد دیگر

Maria Giulia Preti is a Senior Scientist at MIPLAB, Research Staff Scientist at the CIBM Center for Biomedical Imaging (Switzerland), and a Lecturer (Maître-Assistante) at the University of Geneva. She holds a PhD in Bioengineering from Politecnico di Milano (2013) and completed postdoctoral research in Dimitri Van De Ville’s group at EPFL. Her work focuses on integrating neuroimaging techniques (fMRI, DTI, EEG, fNIRS) with graph signal processing to study brain function-structure relationships in health and disease. Key clinical applications include Alzheimer’s disease, epilepsy, multiple sclerosis, and stroke. She has pioneered methods like groupwise fMRI-guided tractography to study neurodegenerative pathologies. Education: PhD in Bioengineering (2013), Politecnico di Milano MSc in Biomedical Engineering (2009), Politecnico di Milano BSc in Biomedical Engineering (2007), Politecnico di Milano Research emphasizes multimodal neuroimaging integration and computational neuroscience. Notable achievements include developing connectome embedding techniques and demonstrating brain fingerprinting via spectral signatures. She received the Progetto Rocca fellowship (2011) supporting her work at MIT/Harvard Medical School. Ongoing projects investigate structure-function coupling dynamics, drowsiness markers, and DBS mechanisms in depression. Labs/Teams: MIP:Lab (part of the Van De Ville group at EPFL and University of Geneva collaborations).

Leonardo Santoroمشاهده پروفایل

پژوهشگر

• Statistical Inference
• Optimal Transport
• Functional Data Analysis
• +۴ مورد دیگر

Leonardo Santoro is a Postdoctoral Researcher at EPFL's Chair of Mathematical Statistics within the School of Basic Sciences. His academic appointments include current research roles at EPFL (2024–present) and a research stay at UC Berkeley (2023). His research focuses on theoretical intersections of statistics and machine learning. Key interests include kernel methods for machine learning, nonparametric inference for high-dimensional and functional data, and optimal transport theory. His work addresses inverse problems through mathematical frameworks like covariance operators, transport flows, and diffusions. His publication trends reveal specialization in statistical methodology development, particularly for complex data structures. Research outputs emphasize mathematical rigor in machine learning applications, with emphasis on geometric statistics and probabilistic modeling. Professional trajectory includes doctoral training under Victor Panaretos at EPFL, preceded by masters studies at ETH Zurich and undergraduate education at Università di Trento. His technical profile demonstrates applied experience through an industry internship at Teralytics AG developing mobility models from telco data.

Ya-Ping Hsiehمشاهده پروفایل

مدرس

• Machine Learning
• Optimization
• Stochastic Processes
• +۳ مورد دیگر

Dr. Ya-Ping Hsieh is a Lecturer at the Department of Computer Science at ETH Zürich, affiliated with the Institute for Machine Learning. His research focuses on advanced optimization techniques, stochastic processes, and their applications in machine learning. Key areas include non-convex optimization, diffusion models, and optimal transport theory. His work often addresses convergence properties of algorithms and their theoretical guarantees in complex systems. Dr. Hsieh's recent publications explore topics such as entropy-maximizing exploration via diffusion models, Riemannian stochastic optimization frameworks, and the dynamics of min-max algorithms. His contributions bridge theoretical foundations and practical implementations, emphasizing rigorous mathematical analysis. No scientific awards or grants are explicitly listed in the provided materials. He is part of the Institute for Machine Learning at ETH Zürich, contributing to both academic and applied research in computational methods.

• Aerothermodynamics
• Mechanical Engineering
• Process Engineering
• +۳ مورد دیگر

Dr. Ndaona Chokani is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. Their research focuses on aerothermodynamics, mechanical engineering, and process engineering, with a recent emphasis on interdisciplinary applications in computer vision and image processing. Affiliated with the Professur f. Aerothermodynamik, their work spans theoretical and applied domains. While explicit educational background details are not provided, their academic career includes contributions to fields such as fluid dynamics and process systems engineering. Research interests are inferred from departmental affiliation and recent publications, emphasizing computational methods in mechanical systems and image quality assessment. Recent publications highlight work in perceptual quality metrics for images and videos, immersive systems (VR/360 content), and machine learning applications in 3D pose estimation and generative models. Key areas include diffusion models for low-light imaging, spatial-temporal geometric networks, and benchmarking frameworks for image harmonization. No awards or grants are explicitly listed in the provided text. Their research often involves collaborations on datasets like Salient360! and tools for gaze data analysis in 3D environments. Future work appears to focus on improving perceptual metrics and expanding applications in healthcare and immersive technologies.

Michele Magnoمشاهده پروفایل

مدرس ارشد

• Smart Sensing
• Low-Power Machine Learning
• Wireless Sensor Networks
• +۴ مورد دیگر

Michele Magno is a Senior Lecturer and Privatdozent at ETH Zürich's Department of Information Technology and Electrical Engineering (D-ITET), leading the D-ITET Center for Project-based Learning (pbl.ee.ethz.ch). He holds a PhD in Electronic Engineering from the University of Bologna (2010) and has held visiting roles at institutions like the University of Nice and Mid Sweden University. His research focuses on low-power systems, wearable devices, energy harvesting, and IoT applications. Magno has authored over 350 peer-reviewed papers, with a Google H-index of 49. Notable awards include the 2024 Best Paper Award at ECCV and multiple best poster/demo recognitions at IEEE conferences. His industrial collaborations include projects with STMicroelectronics, Texas Instruments, and Logitech. Teaching contributions include courses on embedded systems, FPGA programming, and machine learning on microcontrollers. Magno's innovations span smart sensors for wind turbines, bio-medical monitoring, and autonomous racing systems, with patents in touch communication and energy-neutral devices. Recent work emphasizes ultra-low-power solutions for AI-integrated wearables, energy-efficient IoT nodes, and real-time embedded vision systems. His labs and teams pioneer technologies like TinyssimoRadar for in-ear gesture recognition and WakeMod for ultra-low-power IoT connectivity.

Nicolò Paganمشاهده پروفایل

پژوهشگر

• AI Ethics
• Social Network Analysis
• Machine Learning
• +۳ مورد دیگر

Dr. Nicolò Pagan is a Postdoctoral Researcher at the Social Computing Group within the Department of Informatics at the University of Zurich. He is also a member of the National Centre of Competence in Research (NCCR) Automation. Pagan holds a Ph.D. from ETH Zurich (2021), an M.Sc. from EPF Lausanne, and a B.Sc. from Politecnico di Torino. His research focuses on AI ethics, fairness in automated decision-making systems, and generative AI applications for health interventions. He has published in top venues like Nature Communications and has supervised multiple student projects on topics like feedback loops in recommendation systems and generative AI modeling. Education: Ph.D. in Automatic Control, ETH Zurich (2021) M.Sc. in Computational Science and Engineering, EPF Lausanne B.Sc. in Applied Mathematics, Politecnico di Torino Research Interests: AI ethics and societal impact Feedback loops in automated systems Generative AI for health interventions Algorithmic fairness in social media Network formation dynamics His recent work explores fairness effects of algorithmic systems and uses LLM-based generative AI to model large-scale human behavior for health campaigns. Over 10 peer-reviewed publications span topics like recommendation systems' societal impact and game-theoretic network analysis. Advising and Grants: Supervised 5+ student projects on topics like opinion dynamics and fairness in ride-hailing systems. Active in NCCR Automation's interdisciplinary research initiatives. Labs/Teams: Member of the Social Computing Group and NCCR Automation, collaborating on projects linking AI ethics to real-world systems.