kirscher / segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Computer Vision Research Engineer @SYSNAV with a strong background in Data Science and Statistics.

Some Projects 🛠️

Health Trajectories and Survival Outcomes in HF Patients Repo

• "A Novel Methodological Framework for Analyzing Health Trajectories and Survival Outcomes in Heart Failure Patients".
• Accepted Paper at ICLR 2024 Learning from Time Series for Health Workshop

Topical State Space LSTM for NLP Repo

• Implementation of the Topical State Space LSTM model for text sequence analysis, as described in "State Space LSTM Models with Particle MCMC Inference" by Xun Zheng et al., 2017

Education 📚

• MSc. in Statistics and Economics at ENSAE Paris - IP Paris (FRA)

  • Focus on Statistics, Machine Learning, and Deep Learning.

• MSc. in Engineering at Mines de Saint-Etienne (FRA)

  • Achieved the title of "Ingénieur Civil des Mines" (general engineering degree) with expertise in Data Science, Big Data, and Numerical Analysis.

• Erasmus+ Programme at Karlsruher Institut für Technologie: KIT (DE)

  • MSc. courses in Machine Learning, Knowledge Discovery, Applied Econometrics, Risk Management, and Financial Accounting.