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About me


  • 2022 - 2023
    State University of New York at Buffalo,

    Master of Science, Computer Science and Engineering

    Coursework: Algorithm Analysis & Design, Machine Learning, Information Retrieval, Data Model & Query Languages

  • 2015 - 2019
    Thapar Institute of Engineering and Technology (TIET),

    Bachelor of Engineering, Electronics Engineering,

    Cumulative GPA: 8.94/10 Coursework: Artificial Intelligence Techniques, Robotics & Automation, Fundamentals of Microprocessors & Microcontrollers, Optimization Techniques, Numerical Analysis


  • 2019 - 2022
    Wipro Ltd., Hyderabad, TG, India

    Senior Software Engineer, Health IP & Innovations Team (Artificial Intelligence Practice)

    Clinical Trials Feasibility Platform: Tech Stack- Power BI, SQL, Python, RDS, EC2, Data Preprocessing
    ● Developed a cloud enabled SaaS analytics platform on Power BI to assist pharmaceutical company stakeholders make informed trial
    decisions from planning to execution and completion of clinical studies
    ● Implemented data wrangling techniques on data extracted from 4 public databases (Pubmed, clinicaltrials.gov, Open Payments, NPI)
    using web APIs for 5900+ records in lung cancer therapeutic area
    ● Collaborated with domain experts to create a 7 screen BI dashboard each comprising of 15+ visualization charts using 21 data tables
    Pharma Lab Virtual Assistant: Tech Stack- Python, Lambda, DynamoDB, Pandas, NumPy, Alexa Skill Kit, SES
    ● Conceived and built 3 virtual voice assistants for lab usage by scientists with functionalities- inventory management, safety assistant and
    lab experiment knowledge, by applying Amazon Alexa Skill Kit software development framework
    ● Wrote 5 programs in Python, deployed in AWS Lambda and integrated it with 2 DynamoDB tables , Alexa Voice Development and SES
    AI based medical code envisioning tool: Tech Stack- Python, NLTK, Tkinter, Pandas, Gensim, Pillow
    ● Constructed an automated ICD-10AM medical coding tool to suggest code scenarios on 2000+ lines of electronic health records (EHR)
    ● Applied NLP techniques of sentiment analysis, sentence segmentation and fuzzy string matching on EHR and computed medical
    diagnosis with 80% accuracy
    ● Explored Word2vec for word embeddings utilizing shallow neural networks with Python- Gensim library
    AI based indexing tool: Tech Stack- Python, OpenCV, Tesseract OCR, NLTK, Tkinter
    ● Upgraded an automated medical chart indexing tool to provide one-click access and keyword identification in chart sections of EHR
    ● Employed Tesseract OCR engine to read 2000+ pages scanned PDF EHR and Ghostscript to process nearly 1 lakh files
    ● Utilized multiprocessing and threading techniques to minimise processing time by 50% and improved tool efficiency