Raphaël Attias

Raphaël Attias

Software Engineer @ Databricks || Visiting Postgraduate Researcher @ Harvard || MS in Data Science and BS in Mathematics from EPFL.

Robust Journey Planning for CFF Zurich

less than 1 minute read

Published:

Labs in Data Science, 100/100, Best Class Project, 2021
Developed and deployed a journey planner using Spark to compute and visualize the best transportation and path using Zurich Transportation System. Using CFF data, we built a predictive model that solved efficiently the transportation problem. Given a desired arrival time, our route planner will compute the fastest route between departure and arrival stops within a provided confidence tolerance expressed as interquartiles. For instance, “what route from A to B is the fastest at least Q% of the time if I want to arrive at B before instant T”. We used the Connection Scan Algorithm, handled data with and Hive PySpark and presented resutls with ipywidget.

Github Youtube Presentation

You May Also Enjoy

Vision Transformers for Analyzing High-Resolution Pathology Images

Master Thesis
Transformers are powerful models that can capture long-range dependencies between data using an attention mechanism. However, applying transformers to medical problems poses challenges due to the high-resolution and complexity of pathology images, as well as the scarcity and noise of labels. In this thesis, we have provided a comprehensive overview of the state-of-the- art transformers method for analyzing high-resolution pathology images, using the glioblastoma dataset IvyGAP and the renal cancer dataset as case studies. We have discussed how to pre- train transformers using self-supervised learning methods that can learn useful representations from unlabeled data. We have also explored how to find regions of interest within a whole slide image using different levels of supervision: self-supervised, weakly supervised and strongly supervised. We have demonstrated that transformers can provide a semantic understanding of the data and outperform convolutional-based models on downstream tasks such as classification and segmentation. Finally, we employ posterior networks to estimate the aleatoric and epistemic uncertainty of ViT predictions and evaluate their usefulness for clinical decision making. We propose a novel method to filter potentially mislabeled data in order to make more accurate and confident predictions. Our experiments show that our methods achieve state-of-the-art results on various glioblastoma tasks and provide meaningful insights into the behavior and limitations of ViTs for medical machine learning.

BERT fine-tuning for Sentimental Analysis with deployment using Ray Serve

Personal Project, 2022
This project has for purpose to practice the full pipeline of solving a problem using modern MLOPS tools. The pre-trained BERT weights have been fine-tuned on a sentimental analysis task on the iMDB dataset. Logging and hyper-parameter tunning has been done using W&B. A Docker image is available for production and deployment is done using Ray Serve to obtain a REST API.

Improving Domain Generalization for Deep Learning-Powered Cancer Cell Detectiong

Internship, 2022
In 2022, I have worked as a research & software intern at NEC Laboratories in Princeton, a brilliant research company along with world-renowned scientists. I have implemented and researched the various techniques of data augmentation to improve results on a complex multi-class segmentation problem. In the Medical Machine Learning department, I motivated the use of GANs to produce new samples for cancer detection, explored the use of self-supervised method such as MixMatch, and studied ways to make model more robust to domain generalization.