Jayant Sharma

- Implement metrics to quantify the reliability of machine learning models - Disentanglement of model uncertainty into aleatoric and epistemic uncertainties - Analyze robustness of probabilistic models, especially timeseries models - Models trained using Tensorflow, Pytorch lightning and GluonTS - Implementation of model monitoring and artifacts logging using MlFlow

- Designing, implementing and deploying deep neural networks using TensorFlow and Keras for image classification to detect fabric defects - Leading the development of a solution for detecting faulty image captures from cameras thereby causing a 14% reduction in false positives leading to better scalability of our operations - Speeding up model training and inference using the XLA compiler

- Knowledge graph construction on historical literature database - Driving research with NLP models like transformers on historical objects - Carried research activities in Database & Information Systems department on database query optimization using RDF triples. - Led experiments with different implementations of a new relational schema called "subject-object bits" in C++.

- Authored article (under review) for IEEE and my Master thesis on drug repurposing and drug adverse event prediction using large-scale electronic health records of more than 4 million patients using SQL and Python - Created an ETL pipeline including data sources from SNOMED, MedDRA, Pubmed, PheWas and other medicine databases on Slurm-managed GPU clusters - Implemented a relational Graph Attention Network (RAGAT) using PyTorch and Tensorflow to perform knowledge graph completion

- Developed graphic models for the Thermal Systems and Buildings division of the Fraunhofer ISE using react.js - Created interactive visualizations of thermal systems using Python’s networkx library, dash framework, and GoJS

- Responsible for credit risk modelling - Implemented classification of loan applications using Logistic Regression and Decision Trees for default risk prediction - Responsible for analysis of loan applicants' profiles and rate of interest • Developed REST-based micro services for equipment insurance and customer history purposes. • Created a feature to enable Business Rules Management in collaboration with Credit Bureaus and Risk Analytics team reducing the mean credit decision time by 35%.

• Wrote new algorithms that increased Auto Decisioning on credit requests by 20%. Won the President's award for implementing this solution. • Collaborated with the Bots team on RPA solutions using the Workfusion tool • Worked to develop new GUIs to better assist Credit Analysts and dealers using our web app • Supported and developed features for John Deere Finance’s critical web application • Served as an SME for a critically essential software component and won the MVP award for the same

- Specialisation in Artificial Intelligence with a final GPA of 1.7 and a potential publication in graph ML. - Master Thesis on large scale Electronic Health Records using Graph Neural Networks. Achieved a grade of 1.1. - Courses on Foundations of Artificial Intelligence, Artificial Intelligence Planning, Machine Learning, Image Processing, Information Retrieval, Bioinformatics, Computational Economics, Mobile Robotics, Deep Learning, Freicar project.

Computer Science(Focused on programming languages and software development)  CGPA - 8.33/10.00 (1.84 in the German grading system)

Non - Medical(Physics, Chemistry, Maths)Final achieved % - 92.00

Physics, Chemistry, Maths, Geography, History and Civics, Environmental EducationFinal achieved % - 91.00 with a scholarship