Rohith Teja M
Angestellt, Data Scientist, CEA
Paris, Frankreich
Über mich
Expert in Machine Learning and Data Mining with a demonstrated history of working in Bioinformatics and IoT domains where I dealt with data structured in form of a graph and highly interested to work in interdisciplinary fields using machine learning. • Experienced in building end-to-end machine learning pipelines using python programming language to tackle tasks like spam detection, text classification, protein interaction prediction, protein clustering, and also knowledge graph related tasks like node classification, link prediction and triple classification. • Worked with exploratory data analysis using data visualization using python libraries like MatplotLib, Seaborn and Plotly and several predictive modeling techniques. Linux based environment and GIT project management is what I often use to build machine learning projects. • Love travelling, discovering new places and things and also recently I started playing tennis which I enjoy a lot.
Werdegang
Berufserfahrung von Rohith Teja M
Bis heute 2 Jahre und 10 Monate, seit Sep. 2021
Data Scientist
CEA
- Focus on methods to quantify regional budgets of anthropogenic CO2 emissions using ground-based near real-time activity data on energy and mobility. - Carbon Monitor: https://carbonmonitor.org
Bis heute 2 Jahre und 11 Monate, seit Aug. 2021
Technical Writer
Medium
7 Monate, Feb. 2021 - Aug. 2021
Machine Learning Intern (IoT Knowledge Graph)
Orange Labs, Cesson-Sévigné, France
- Validated and enhanced data within a knowledge graph in the Internet of Things domain using state of the art algorithms for deep graph learning and graph embeddings for knowledge graphs - Achieved excellent performance for embeddings with 98% ROC-AUC and > 80% average precision scores in prediction of missing links in the graph by using an end-to-end machine learning pipeline
4 Monate, Apr. 2020 - Juli 2020
Machine Learning Intern (Bioinformatics)
Laboratoire Hubert Curien, Saint-Étienne, France
- Generated dynamic graph embeddings (numerical representation of proteins) using deep Autoencoders and predicted missing protein interactions in the network with an accuracy score of 80% using SVM - Identified important protein clusters (ranging from 1 to 24 clusters depending on the cell type) from dynamic graph embeddings using K-NN and clustering algorithms
Ausbildung von Rohith Teja M
1 Jahr und 11 Monate, Sep. 2019 - Juli 2021
Master Machine Learning Data Mining
Université Jean Monnet, Saint-Étienne, France
- Machine learning, Deep Learning, Data Analysis, Computer Vision, Big Data & Project Management - Course website: https://mldm.univ-st-etienne.fr/course_organisation.php
Sprachen
Englisch
Fließend
Französisch
Gut
Deutsch
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