Shreejal Trivedi's Portfolio 😎

Hi Guys. I am a passionate engineer interested in Deep Learning, Computer Vision, and General 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 three 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. 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 !.

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Table of Content

• Education 📚
• Courses and Electives ✍
• Languages, Frameworks, and Tools 🔏
• Experience 🧠
• Projects 📝

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Education

College/School	Degree	GPA/Percentage
Lassonde School of Engineering, York University	Masters in Computer Science	3.8 (Ongoing)
Ahmedabad University	BTech. Information and Communication Technology	3.45
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
    

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Languages, Frameworks, and Tools

Languages I am most confident in

• C
• C++
• Python
  Other Languages

  • Java
    
  • SQL
    
  • HTML
    
  • CSS
    
  • Ruby
    
  • LateX
    
  • Markdown
    
  • JavaScript
    </ul> </details>

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
    </ul> Docker
    </details>

TODO List

• Grafana
• Prometheus
• Elastic Search
• Kubernetes
• GoLang
• KubeFlow

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Experience

• Deep Learning Enginner at Eagle Eye Networks
  

  • Redifining One-Shot Object Detector for Two-Class Problem
  • Low-Compute - High FPS BGS Algorithm for Low End Devices
  • Site Specific Training Tool
  • Vehicle Trajectory Anomaly Detection
  • Person Retrieval and Re-identification
  • Unsupervised Domain Adaptation and Semi Supervised Learning
• Founder and Editor at VisionWizard Inc.
  • 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
  • Network Pruning of Deep CNNs
  • INT8 Quantization of Object Detectors

    Ablation Studies and Results can be obtained from my BTech Report: Link

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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.