Michail VlachosTeaching & PhD Courses Data science and machine learning MGT-502 Hands-on introduction to data science and machine learning. We explore recommender systems, generative AI, chatbots, graphs, as well as regression, classification, clustering, dimensionality reduction, text analytics, neural networks. The course consists of lectures and coding sessions using Python.
Sayan BiswasExpertise
Decentralized Machine Learning and Federated Learning focusing on Privacy, Fairness, and Personalization; Secure and Trustworthy Distributed Systems; Differential Privacy; Location Privacy.
Sayan Biswas (CV) is a Postdoctoral Researcher in the Scalable Computing Systems (SaCS) lab at EPFL, Switzerland, led by Prof. Anne-Marie Kermarrec. He completed his PhD in Computer Science at INRIA and école Polytechnique in France, under the supervision of Prof. Catuscia Palamidessi. Prior to that, Sayan stud
Geovani RizkExpertise
Distributed Machine Learning, Robustness, Safety, Optimization, Multi-Agent, Bandits in Graphs
I am a postdoctoral researcher at école Polytechnique Fédérale de Lausanne since 2022, where I work with Rachid Guerraoui on robustness of distributed machine learning algorithms. From 2018 to 2022, I completed my Ph.D. in computer science at Université Paris Dauphine-PSL on multi-agent bandits in graphs under the supervision of Yann Chevaleyre, Rida Laraki, Igor Colin, and Albert Thomas.Pre-prints Over
Stefana ParaschoTeaching & PhD PhD Students Jingwen Wang, Gabriel Vallat, Maxence Grangeot, Eleni Maria Skevaki, Wenqian Yang, Megi Sinani Courses Capitalizing on Uncertainty AR-809 SUMMER SCHOOL - Capitalizing on Uncertainty is a 6-day Summer school exploring uncertainty as a productive resource in design and fabrication through lectures, hands-on experiments, and the collaborative design and construction of a full-scale structure in a human-robot fabrication setting. Digital design and making: A criti
Vittorio ErbaTeaching & PhD Courses Statistical physics of computation PHYS-512 The students understand tools from the statistical physics of disordered systems, and apply them to study computational and statistical problems in graph theory, discrete optimisation, inference and machine learning.
Lazar MilikicExpertise
LLMs (Large Language Models), NLP (Natural Language Processing), Automated Reasoning
Mission
My work focuses on developing robust methods for autoformalization and automated theorem proving using large language models. I aim to enhance the reasoning reliability of modern AI systems by reducing hallucinations, improving predictability, and integrating formal verification techniques and tools into LLM-based pipelines.
Before joining EDIC, I completed a Bachelor’s in Mathematics and Computer Science
Mohammad Ali ShaeriExpertise
Neural Engineering, Neural Signal Processing, Neural Decoding, Neural Prosthesis, Brain Machine Interface
Jiancheng YangI am a 4th-year PhD student in the Department of Electronic Engineering, Shanghai Jiao Tong University, advised by Prof. Bingbing Ni. I received BEng and MEng degree in the Department of Automation from the same university. Enrolled in a double master degree program, I spent 2 years in France with an Engineer degree. I was a visiting research fellow in Visual Computing Group at Harvard University. I am now visiting EPFL CVLAB, working with Prof. Pascal Fua.I focus on medical image analysis, 3D computer vision and trustworthy machine learning, with a clinical emphasis on lung cancer and thoracic diseases. I have (co-)first-authored more than 10 papers on top-tier journals / conferences, e.g., Cancer Research, CVPR, MICCAI and NeurIPS. I was a reviewer for more than 10 prestigious venues, a top-ranking participant for several AI competitions, and the lead organizer for MICCAI 2020 RibFrac Challenge.Updated on April, 2021.
