Knightscope Inc.

Jan 2021 - Present
  • 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.


Oct 2020 - Dec 2020
  • 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).

UII America Inc.

Sept 2019 - Jan 2020
  • 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.

Northeastern University

Jan 2019 - Apr 2020


Sept 2017 - May 2018
  • 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.

K. J. Somaiya College of Engineering

May 2017 - Aug 2017
  • 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