Arthi Rajendran

Arthi Rajendran

About

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AI Entrepreneur, The Arthi AI Collective | AI Education for Real-World Professionals | Host, The AI Sisterhood Podcast | Speaker
Greater Chennai, Tamil Nadu, India

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Résumé


Jobs verified_user 0% verified
  • The Arthi AI Collective
    Solopreneur
    The Arthi AI Collective
    Jul 2020 - Current (6 years 2 months)
    The Arthi AI Collective is not an agency or a course platform. It's a place where AI is translated, tested, and applied — so people don't just learn AI, they start using it.
  • Zoho
    MarTech AI Specialist
    Zoho
    Jan 2024 - Dec 2025 (2 years)
  • Pfizer
    Data Scientist
    Pfizer
    Dec 2022 - Jan 2024 (1 year 2 months)
  • Pfizer
    Statistical Programmer
    Pfizer
    Jul 2021 - Nov 2022 (1 year 5 months)
  • Whirldata Inc
    Data Analyst
    Whirldata Inc
    Jan 2021 - Jun 2021 (6 months)
    As a Biostatistician at the new life science wing of Whirldata Inc. since January 2021. I am responsible for Market analysis, training new resources in Clinical SAS Programming, and providing Biostatistics consultation to clients. Basically building a whole domain from scratch.
  • M
    Visiting Faculty Member
    MMM COLLEGE OF HEALTH SCIENCES Inst Code CHENNAI
    Aug 2020 - Jun 2021 (11 months)
    I currently teach Biostatistics paper to the enthusiastic Undergraduate and postgraduate students in the field of MLT, Clinical Nutrition, and Medical sociology.
  • P
    Data Analyst Intern
    Palms Connect LLC
    Jun 2020 - Jan 2021 (8 months)
    Key roles during this internship include: Analyzing health science datasets and arriving at significant findings. Authoring a manuscript presenting the analyzed data. Working hand in hand in with the company's director, Dr. Balu Ranganathan to perform several statistical analyses, using R programming.
  • AstraZeneca
    Associate Engineer
    AstraZeneca
    Mar 2019 - Jul 2019 (5 months)
  • AstraZeneca
    Junior Engineer
    AstraZeneca
    May 2018 - Mar 2019 (11 months)
  • AstraZeneca
    Graduate Trainee
    AstraZeneca
    Jul 2017 - May 2018 (11 months)
Education verified_user 0% verified
  • SRM Institute of Science and Technology SRMIST
    Master's degree, Biostatistics and Epidemiology
    SRM Institute of Science and Technology SRMIST
    Jan 2019 - Jan 2021 (2 years 1 month)
  • S
    Bachelor of Technology - BTech, Biotechnology
    Sri Venkateswara College Of engineering
    Jan 2013 - Jan 2017 (4 years 1 month)
  • Shrishti Schools
    High School
    Shrishti Schools
    Jan 2005 - Jan 2013 (8 years 1 month)
Projects (professional or personal) verified_user 0% verified
  • D
    Detecting Pneumonia and Breast Cancer using Convolutional Neural networks
    This code in Python uses Tensorflow and Keras. The chest X-ray images of pneumonia obtained from Kaggle were used to train the model. The model produced an accuracy of 95.11%. The same model was trained by means of transfer learning to detect Breast cancer in Chest ultrasound images. This produced an accuracy of 80%.
Publications verified_user 0% verified
  • I
    Enhancing Medical Images using Non-Local MEANS Filter to Detect Abnormalities in the Chest Region
    International Journal of Scientific Research amp Engineering Trends May
    This project in Python uses Tensorflow and Keras. The chest X-ray images of pneumonia obtained from Kaggle were used to train the model. The model produced an accuracy of 95.11%. The same model was trained by means of transfer learning to detect Breast cancer in Chest ultrasound images. This produced an accuracy of 80%.
  • J
    Using large language models for safety-related table summarization in clinical study reports
    Jamia Open May
    Key Highlights: - Application of LLMs: The generation of highly structured regulatory documents for clinical trials is a promising application of large language models (hashtag#LLMs). - Case Report: We share opportunities, learnings, and impediments from a competitive challenge organized by hashtag#Pfizer to use LLMs for automating clinical trial documentation from a drug development perspective. - Evaluation Framework: A combination of automated metrics and expert review was critical to evaluate the output from hashtag#GenerativeAI models. - Prompt Engineering: All participants in the challenge used prompt engineering combined with pre-trained LLM models to address the challenge statement. Significance: - This work represents a sign
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