Some things I know in life! 😎

Hi Guys. I am a passionate engineer interested in Deep Learning, Computer Vision, and Generative AI. I love to develop deep learning based computer vision algorithms that can have a real imapct in terms of performance, generalizability, and outreach of its solution. Developing the algorithms for surviellance industry for more than five years have given me the “deep” understanding behind DNNs(Pun Intended !) and I will cherish and contine my chase for Applied AI in solving real world problems. My Masters thesis is also based on the Highway Traffic Analytics where I need to develop real-time high performance analytics pipeline mainly optimized for the highway networks.

I also like to write the technnical explainations of the Deep Learning Research Papers. Dont forget to visit on my Medium Page.

Apart from the academica, I like to play cricket 🏏, football ⚽, and badmintion 🏸. I also like to read books related to sci-fi, thriller, cosmology, and astrophysics. Not to forget, I am a huge Real Madrid Fan. HALA MADRID !.

Last but not least, I am a Pokémon freak. I judge a person based on which starter pokemon they choose. I choose Charmender!

Education

College/School	Degree	GPA/Percentage
Lassonde School of Engineering, York University	Masters in Computer Science - Thesis	3.9 (Ongoing)
Ahmedabad University	BTech. Information and Communication Technology	3.61
Swastik Sindoor	Higher Secondary Board	92%
Swastik Sindoor	Secondary Board	85%

Courses and Electives

Introduction to Blockchain
High Performance Computing
System Design for Societal Problems
Computer Vision
Advanced Data Structures and Algorithms
Machine Learning
Cloud Computing
Data Analytics and Visualization
Core Courses
- Basic Electric Circuits
- Calculus and Differential Equations
- Communication Skills
- Introduction to Computer Programming
- User Centered Design
- Digital Design
- Discrete Mathematics
- Electromagnetic Theory
- Enviornmental Studies
- Object Oriented Programming
- Computer Organization
- Data Structures and Algorithms
- Ethics
- Linear Algebra
- Signals and Systems
- Analog and Digital Communication
- Database Management Systems
- Embedded System Design
- Probability and Random Processes
- Computer Networks
- Operating Systems
- Wireless Communication
- Digital Signal Processing
- Integrated Circuit Device and Technology
- Software Engineering

Languages, Frameworks, and Tools

Languages I am most confident in

C
C++
Python
Other Languages
- Java
- SQL
- HTML
- CSS
- Ruby
- LateX
- Markdown
- JavaScript

Frameworks and Tools I can definitely work on

PyTorch
TensorFlow-2.0
TensorRT
OpenVino
MLFlow
Docker
Other Tools/Frameworks
- MatLab
- GCP
- MySQL
- Darknet
- OpenCL
- GIT
- Docker

TODO List

Experience

Deep Learning Enginner at Eagle Eye Networks (April 2024 - Present) - Part time
- Working on the Gun Detection Algorithm from an action localization perspective. Main goal is to optimally localize the gun using the human pose, person detection, action localization.
Machine Learning Researcher at Lassonde School of Engineering (Sept 2022 - Current) - Full time
- Working on my thesis titled Design and Evaluation of Real-time Daytime/Nighttime Highway Traffic Analytics System which focuses on optmizing computer vision based deep learning pipeline using traditional single-view geometry and image processing in collaboration with Ministry of Transport Ontario (MTO)
- End goal is to deploy the real-time pipeline developed using Nvidia’s Deepstream pipeline on the highway traffic site. This will in real-time 24x7 giving the analytics of the traffic include vehicle counting/lane-by-lane counting, anomaly detection, speed estimation, etc.
🏛️ TA Duties at Lassonde School of Engineering, York University (Sept 2022 - Current) - Part time
│
└── ⬤ Graduate Teaching Assistant 📅 Jan 2025 – April 2025.
📖 Teaching assistant for the course EECS1730 - Building
Interactive Systems
│
└── ⬤ Graduate Teaching Assistant 📅 Jan 2024 – May 2024.
📖 Teaching assistant for the course EECS2101 - Fundamentals of Data Structures
│
└── ⬤ Graduate Teaching Assistant 📅 Sep 2023 – Dec 2023.
📖 Teaching Assistant for the course EECS1015: Introduction to
Computer Programming
│
└── ⬤ Graduate Teaching Assistant 📅 Jan 2023 – April 2023.
📖 Teaching assistant for the course EECS1720 - Building
Interactive Systems
│
└── ⬤ Graduate Teaching Assistant 📅 Sep 2022 – Dec 2022.
📖 Teaching Assistant for the course EECS1015: Introduction to Computer Programming
Deep Learning Enginner at Eagle Eye Networks (Aug 2019 - July 2022) - Full time
Founder and Editor at VisionWizard Inc.(Mar 2020 - Aug 2022) - Part time
- Leveraged a Medium platform so that researchers or newly coming beginners can share their views on the upcoming work/novelty in Deep Learning and AI mainly focused on thorough explanations and intact format for proper grasping
- Different writers shared their implications on state-of-the-art research using the platform getting standout traction on Medium.
- Publication Link
Machine Learning Intern (Jan 2019 - April 2019 ) - Internship
- Network Pruning of Deep CNNs
- INT8 Quantization of Object Detectors
  
  Ablation Studies and Results can be obtained from my BTech Report: Link

Projects

Intelligent Traffic Light Control System

Created a prototype for automating the traffic light timers by doing dynamic timer set-stop transitions at the crossroads for Ahmedabad. Density calculation was done through two different approaches viz Deep Learning: TFNet YOLO Detection and Computer Vision/ Image Processing: Foreground extraction.
Classification of Gender from Tweets and their Profile Description

Gender classification from Tweets and their profile description through Natural Language Processing for formatting the tweets and C 4.5 Decision for featurization and Shallow Neural Networks for classification of the gender from the formatted tweets. We reached 84% accuracy on the test set
Thread Management System

We developed a thread management library to create, schedule, and kill threads(Operating System). All these operations were done on the kernel level with the use of Linux system calls. The library had similar functionalities as pThread in Linux.
Saliency Map Generation from Images

Extraction of saliency maps from two different approaches i.e., Supervised and Semi-Supervised. The supervised approach follows discriminative features integration with superpixel image segmentation followed by finding saliency scores with Random Forest Regression. The semi-Supervised approach follows Autoencoder based approach with an attention layer to find the saliency maps from images.