Gopal Narmada NaiduExpertise
Researcher with 8+ years of experience in computational physics and multiphysics modeling, focused on numerical simulation of light–matter interactions and photon propagation. Proficient in FEM, FDTD, and Monte Carlo methods to model energy conversion and optical behavior, with additional expertise in AI/ML techniques for model optimization and design exploration. Strong background in photonic devices, photo-thermal, thermo-optical effects, and nanostructure engineering. Adept at translating compl
Olga FinkTeaching & PhD PhD Students Han Sun, Leandro Von Krannichfeldt, Keivan Faghih Niresi, Zepeng Zhang, Vinay Sharma, Chenghao Xu, Raffael Pascal Theiler, Kevin Antonio Steiner, Sergei Garmaev, Amaury Wei Past EPFL PhD Students Ismail Nejjar, Mengjie Zhao Courses Data Science for infrastructure condition monitoring CIVIL-332 The course will cover the relevant steps of data-driven infrastructure condition monitoring, starting from data acquisition, going through the steps pre-processing of real
Andres Osorio SalazarExpertise
Robotics, advanced actuator design, soft actuators, sensor design, embedded systems, human-machine interface architecture, electronic-less control, mechanism design, additive and traditional manufacturing, industrial automation, process control, and fluid robotics.
Current Work
Smart Footwear for Diabetic Foot Care and Ulcer PreventionEvery 20 seconds, a person with diabetes has to face an amputation somewhere in the world. The main risk factor is high plantar pressure combined with foot nephropat
Myrto LimniosTeaching & PhD Courses Empirical processes MATH-522 We learn how to control the nonasymptotic and random behavior of collections of estimators, when indexed by classes of functions/sets. Examples range from prototypical estimators used by statisticians, to nonparametric models used in machine learning. Regression methods MATH-408 General graduate course on regression methods
Théophile BoinnardEducation M2 ICFP - Physique Théorique | 2022 – 2023 École Normale Supérieure Paris, Paris, France M1 Physique Fondamentale | 2020 – 2021 École Normale Supérieure Paris-Saclay, Gif-Sur-Yvette, France L3 Physique Fondamentale | 2019 – 2020 École Normale Supérieure Paris-Saclay, Gif-Sur-Yvette, France Classe Préparatoire PCSI-PSI* | 2017 – 2019 Lycée Fabert, Metz, France Professionals experiences M2 internship "Turbulent
Francesca RodinoExpertise
Electrochemical BiosensorsSensors and Biosensors interfaces | front-end electronicsBiomedical circuits and systems
Francesca Rodino received the B.Sc. and M.Sc. degrees in Biomedical Engineering from the Politecnico di Torino, Turin, Italy, in 2019 and 2022, respectively. She developed her Master's project in the field of cortical neuroprosthesis devices for artificial vision in collaboration with the Integrated Circuits Laboratory (ICLAB) at école Polytechnique Fédérale de Lausanne (EPFL) in Swit
Lenka ZdeborováLenka Zdeborová is a Professor of Physics and of Computer Science in École Polytechnique Fédérale de Lausanne where she leads the Statistical Physics of Computation Laboratory. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director's Postdoctoral Fellow. Between 2010 and 2020 she was a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay, France. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize, in 2021 the Gibbs lectureship of AMS. She is an editorial board member for Journal of Physics A, Physical Review E, Physical Review X, SIMODS, Machine Learning: Science and Technology, and Information and Inference. Lenka's expertise is in applications of concepts from statistical physics, such as advanced mean field methods, replica method and related message-passing algorithms, to problems in machine learning, signal processing, inference and optimization. She enjoys erasing the boundaries between theoretical physics, mathematics and computer science.
Tanja Christina Käser JacoberTanja Käser is an assistant professor at the EPFL School of Computer and Communication Sciences (IC) and head of the D-VET laboratory. Her research lies at the intersection of machine learning, data mining, and education. She is particularly interested in creating accurate models of human behavior and learning.Prior to joining EPFL, Tanja Käser was a senior data scientist with the Swiss Data Science Center at ETH Zurich. Before that, she was a postdoctoral researcher with the AAALab at the Graduate School of Education of Stanford University.
Tanja Käser received her PhD degree from the Computer Science Department of ETH Zurich. In her dissertation, completed at the Computer Graphics Laboratory, she focused on user modeling and data mining in education, which was honored with the Fritz Kutter Award 2015.
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