Division of Occupational and Environmental Medicine, Department of Medicine, MSU
Duration: 12 October, 2023 - Present
Currently I am working on developing LLM based methods to evaluate SOAP notes, and identifying bias in clinical director comments.
AI Research Team, JP Morgan & Chase
Duration: 04 June 2023 - 26 August 2023
I worked on developing state of the art financial question answering system using Retrieval Augmented Generation paired with advanced prompting techniques.
Real World Data (RWD) Team, ConcertAI (SymphonyAI)
Duration: 06 Jan 2021 - 30 July 2022
I worked on developing NER and Relationship classification models on EHR data to better assess patients fit for clinical trials.
Innovation Team, Innoplexus Consulting Pvt. Ltd.
Duration: 15 August 2017 - 05 January 2021
I worked on building NER, knowledge graph, sentiment analysis and topic extraction models on biomedical datasets.
Innovation Team, Innoplexus Consulting Pvt. Ltd.
Duration: 17 May 2017 - 20 July 2017
I worked on bulding web-scraping pipelines and developing ml methods to improve sharding in MongoDB database.
Short description of portfolio item number 1
Short description of portfolio item number 2
Our project aims to provide a robust framework for forecasting funding flows in public and private markets by harnessing the power of data science, time series analysis, and NLP. (Repo Link)
A browser extension which will take the selected text on a website as input and create a concept map out of it. (Repo Link)
A repo for training bert based models for biomedical named entity recognition models. (Repo Link)
Identifying abbreviations and their corresponding long forms from biomedical abstracts. (Repo Link)
Model Finetuning Service using TorchServe. (Repo Link)
Paper implementation. (Repo Link)
Paper implementation. (Repo Link)
Code pieces. (Repo Link)
Published in Arxiv Preprint, 2020
This paper focuses on extracting COVID-19 entities and relationships from a corpus of articles using a novel model. Through multi-task learning on annotated data, we train entity recognition and relationship discovery models. Employing concept masking, we prevent neural networks from functioning as associative memory, promoting contextual inference. Our findings reveal significant subnetworks, emphasize key terms, and illuminate past treatment modalities for related ailments.
Recommended citation: K Singh, P Singla, K Sarode, A Chandrakar and C Nichkawde (2020). " Uncovering the Corona Virus Map Using Deep Entities and Relationship Models" preprint ArXiv:2009.03068. 1(1). https://arxiv.org/abs/2009.03068
Published in 3rd International Conference on Data Intelligence and Cognitive Informatics, 2022, Tirunelveli, India, 2022
This paper explores diverse applications, delving into model mechanics and decision-making processes. Analyzing extensive text data, we employ interpretable algorithms to extract insights. Specifically, our study focuses on extracting information from political subreddits using sentiment analysis and classification model features.
Recommended citation: Singh, K., Anne, S.V.N.S. (2023). "NLP Driven Political Analysis of Subreddits" Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore.. 1(2). https://link.springer.com/chapter/10.1007/978-981-19-6004-8_54
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Undergraduate course, Michigan State University, Department of Computational Mathematics, 2023
Worked as a Teaching Assistant for CMSE 180, Introduction to Data Science.