🐧 William Lindskog

Hi, I am Will. PhD in Federated Machine Learning - Welcome to my GitHub!

[]

I am an industrial PhD working with Federated Learning for Connected Vehicles. I am generally interested in anything that has to do with AI but special interests include AI Hardware, AI in Space, and AI in Medicine. I personally enjoy various types of music.

🤝 Connect with me on LinkedIn:

I am active there most days.

🔭 I’m currently working on:

This profile page.
Personalized Federated XGBoost for Tabular Data.

🌱 I’m currently learning more about:

NVIDIA Flare
👯 I’m looking to collaborate on AI for Medicine or Space Exploration.
🤔 I’m looking for help with Federated Learning and Labelling Techniques.
💬 Ask me about how to connect with Top AI Students in Germany.
📫 How to reach me: [email protected]
😄 Pronouns: He / Him
⚡ Fun fact: Apart from programming languages I know Swedish, English, Spanish and German.

📈 GitHub Stats

William Lindskog's Projects

cav-sensor-anomaly-detection-dataset

This is the dataset for the work: https://ieeexplore.ieee.org/abstract/document/8684317

ev_energy_fl

Using federated learning (FL) to predict energy consumption for electric vehicles (EVs).

fedmodal

This is a federated multi-modality repository. Currently under development and a part of TUM AI Munich, Germany.

fman45_ml

Course at Lund University, Faculty of Engineering. This repository includes four assignments that are mandatory for the course. They are completed in MATLAB.

ml_mentor

General Machine Learning repository for mentoring of members in TUM AI.

mlflow-examples

Basic and advanced MLflow examples for many ML flavors

monte_carlo

Labs for course in 'Monte Carlo and Empirical Methods for Stochastic Inference'

nvflare

NVIDIA Federated Learning Application Runtime Environment

standard-traffic-data

The 1st open-source traffic data & knowledge collection

subtab_extended

Unofficial repo for SubTab with additional code and data for Adult Income and BlogFeedback datasets. BlogFeedback data is attached as zip file in the release due to its file size.