Experience
Machine Learning Engineer
- Developed a new People Detection model for detecting people at 10-15ft via 4 IP cameras moving at . The
model is robust to capture people in low-light evening conditions with no false-positive detections (avoiding people in
hoardings, billboards, TV screens or newspapers). Only detects real people.
Machine Learning Engineer
- Worked on developing a system to observe and visualize Bias and ensure Fairness in machine learning systems
using Statistical Parity Difference, Equal Opportunity Difference, Average Odds Difference, Disparate Impact and Theil Index Metrics.
- Mitigating Bias and ensuring fairness using optimized pre-processing & adversarial debiasing algorithms.
- Also working on reducing data dimension using t-Stochastic Neighbor Embedding (t-SNE) and developing interpretable
models using Local Interpretable Model Agnostic Explanations (LIME).
Computer Vision Research Intern
- Implemented Dense Correspondence Estimation algorithm for real-time patient inference and increased inference speed
by 50% to ~20-21 FPS using multi-threaded Dockerized architecture on Nvidia P5000 GPU
- Developed a technique to acquire skeletal positions of human body by reading depth image frame
from RGBD images captured using Astra Orbbec Depth Camera.
- Annotated image data using Densepose for keypoint estimation and ran the system on NVidia P5000 GPUs.
Graduate Teaching and Research Assistant
Software Engineer
- Scaled up video production rate for clients from 30 videos/day to 300 videos/day by implementing Geometric
Perspective Transformation on image masks
- Improved software efficiency by 60% by saving 10 mins of rendering time per video by incorporating Face Detection
using Convolutional Neural Networks for automated text positioning
- Parsed news articles for understanding language semantics using Spacy and NLTK for text-to-video translation
- Integrated additional features for our product like providing users the ability to add GIFs, social media widgets, image icons,
etc for contextual representation of information in videos.
Research Assistant
- Worked Under Dr. Jagannath Nirmal, HOD, Electronics Department, KJSCE on industrial Machine Learning and
Computer Vision applications.
- Worked on a project demanding the need to develop a system to classify Dysphonia patients from normal patients using
Machine Learning. It uses an algorithm which uses a robust feature extraction technique named I-Vectors for classification.
Entire implementation done in MATLAB and can be migrated into Python packages. Successfully modelled a system which gives 98%
accuracy using the proposed technique as compared to the 92% before using the technique. Used Support Vector Machines (SVM)
to perform Binary classification.1
- Simultaneously worked on another project requiring the need to classify patients with Dysphonia, Paralysis, Laryngitis and Normal.
Used the above proposed technique of I-Vectors to develop 3 different models to classify these 4 classes. Support Vector Machines (SVM),
Naive Bayes and K-Nearest Neighbours (KNN) were used with multi-class functionality. The entire system was developed in MATLAB and can be
migrated into Python packages.2