Lara Orlandic
Doctoral Student at ESL - Embedded Systems Lab
Lausanne, Vaud, Switzerland
Summary
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Rockstar🎓
Top SchoolLara Orlandic is a doctoral student and embedded systems researcher at EPFL with six years of hands-on experience designing ultra-low-power edge-AI and biomedical signal processing solutions for wearable devices. She combines deep academic rigor—leading the Machine Learning course as Head TA and supervising large student teams—with practical R&D, from semi-custom processor design at imec to sensor-fusion and deep learning for real-time energy-expenditure estimation at Logitech. Her work on crowdsourced datasets (COUGHVID) and contributions to EPFL’s ML course materials demonstrate a commitment to reproducible ML education and applied healthcare analytics. Lara’s background spans biosignal modalities from ECG to seismocardiography and system-level implementation, enabling end-to-end translation of algorithms into constrained hardware. Notably, she bridges interdisciplinary teams across academia and industry, with a track record of shipping prototypes and building datasets that advance respiratory and wearable health monitoring.
6 years of coding experience
8 years of employment as a software developer
Bachelor's degree, Electrical Engineering, 3.96 / 4.0, Bachelor's degree, Electrical Engineering, 3.96 / 4.0 at Georgia Institute of TechnologyMaster's degree, Electrical and Electronics Engineering, 5.66 / 6.0, Master's degree, Electrical and Electronics Engineering, 5.66 / 6.0 at EPFL (École polytechnique fédérale de Lausanne)High School Diploma, 4.0 / 4.0, High School Diploma, 4.0 / 4.0 at University of Illinois Laboratory High School
English, French, Croatian
Github Skills (7)
machine-learning10
logistic-regression10
python10
jupyter-notebook9
image-segmentation8
computer-vision8
segmentation8
Programming languages (3)
Github contributions (5)
epfml/ML_course
Oct 2022 - Dec 2022
EPFL Machine Learning Course, Fall 2024
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 1 PR, 14 pushes in 1 month
Contributions summary:Lara made contributions to the `ex05.ipynb` file, indicating involvement in machine learning coursework. Their commits focused on updating and refining code related to logistic regression and Newton's method, likely to enhance the template or address test case failures. Additionally, the user added helper files and example code for project 2, specifically within the context of aerial image segmentation. This suggests active involvement in developing and preparing learning resources.
falldata-sciencemachine-learning-coursemachine-learningepfl
esl-epfl/edge-ai-cough-count
Jan 2023 - Apr 2024
Contributions:1 PR, 6 pushes, 1 branch in 1 year 3 months
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