WORK EXPERINCE
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Machine Learning Intern
LeddarTech (05/2022 – present)
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Implementing a real-time Vehicle Taillights Detection algorithm using machine learning for autonomous vehicle applications.
Developing a single frame annotated dataset for vehicle taillight status recognition
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Leading an Autonomous Driving based Data Acquisition Campaign and automating the data export pipeline to achieve a 99.4% reduction in the time consumed for data preprocessing.
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Verifying perception results leveraging different AI algorithms, performing KPI analysis and gaining strategic insights by analyzing false positive data frames.
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Developing an algorithm for distance estimation of the farthest visible obstacle based on radar/lidar point cloud clustering.
Application Engineering Intern
Data Science Intern
Astrazeneca (Akaike Technologies PVT. LTD.) (05/2021 -11/2021)
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Managed the Pharmaceutical Market Configuration data for Fasenra, an AstraZeneca drug for Eosinophilic Asthma.
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Created different patient cohorts and extracted essential features for training a machine learning model for Fasenra Initiation.
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Built a Streamlit Application for visualizing drug usage patterns for Fasenra patients grouped at multiple levels facilitating the sales team in targeting relevant Healthcare providers and increasing sales by 72%.
Streamlit Application Recorded Demo
Internship Completion Certificate
Machine Learning and Computer Vision Intern
Melzo, ShilpMIS Technologies PVT. LTD. (01/2021 - 05/2021)
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Devised a Scalable Computer Vision and Augmented Reality based Jewelry Try-On Application in Python and Node.js.
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Deployed a Deep Learning based Jewelry Background Removal algorithm as a Flask API using U-Net, HTML & JQuery.
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Presented the product at Sparkle’21, an International Gems and Jewelry Exhibition organized by Indian State Government followed by its commercial launch as ‘Noor’ fetching 5 major Jewelry brand clients.
Research Based Summer Internship
ROBOKITS INDIA, DAZZLE ROBOTICS (06/2020 - 07/2020)
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Used Python libraries to develop an algorithm for IoMT based product WellNest 12L, a Bluetooth 5.0 based portable Healthcare device for extracting ECG signals
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Developed the code base on Ubuntu 20.04 LTS server via SSH/SCP using WinSCP and PuTTY
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Applied signal processing concepts on the ECG signal detection of P, QRS and T waves, along with essential segments and intervals
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Analyzed the data of the 12 LED ECG signals using Matplotlib library
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Based on the analysis, calculated heart rate, cardiac axis and possible cardiac risks
Research paper published:
Internship Completion Certificate