Welcome To
My Personal Portfolio
Welcome to my portfolio! I'm Sahil Lamba, a Masters of Science in Computer Science student at the University of Illinois at Chicago, showcasing my work in machine learning, AI, and web development.
Projects
Self Driving Car using Behavioral Cloning
An autonomous car model using a regression CNN to complete laps in a virtual simulator. Conducted data collection, augmentation, and pre-processing of frames and steering angle to maintain the car's position on the road, including sharp turns.
- Python
- Keras
- Pandas
- Numpy
Algorithm Visualization
The project is an interactive web tool designed to help users witness and understand the working of various search algorithms. It allows users to set a starting point, a goal, and obstacles along the path. By doing so, they can watch the algorithms navigate their way to the goal and compare their performance firsthand. The tool provides a hands-on learning experience that enables users to gain a deeper understanding of how search algorithms function in real-world scenarios.
- React
- JavaScript
- HTML
- CSS
Anime Face Generator
A GAN model on AWS cloud infrastructure to generate anime faces using a discriminator and generative model with 128-bit latent input. Used a Kaggle anime face dataset for the project.
- PyTorch
- GANs
- AWS
- Torch
Name Generator
The project serves as an example of how Natural Language Processing models can be used for generating new names or words that adhere to a specific naming convention. It involves using a Seq2Seq model architecture for Natural Language Processing (NLP) based on LSTM (RNN). The model is trained on a dataset of 2000 names using Python and Torch. A sampler was designed to generate 20 names, starting with any alphabet, using the trained model.
- PyTorch
- RNN
- Seq2Seq
- NLP
Shape Differentiating Model
This project involved building a multi-class classification model using Convolutional Neural Network (CNN) written in Torch and Python. The model was trained on a dataset of 90,000 images, each with dimensions of 200x200 pixels, and containing 9 classes of different shapes. The model achieved an impressive accuracy rate of over 98%
- PyTorch
- CNN
- Matplotlib
- Numpy
Handwriting Recognition Web - App
This project aimed to build a Machine Learning (ML) model that could recognize handwritten digits from 0-9 using Convolutional Neural Networks (CNN) in Keras and Python. The model achieved an impressive accuracy rate of over 99.9% on the well-known MNIST digit dataset. To make the model more accessible, a user-friendly web application was developed using Flask container.
- TensorFlow
- CNN
- Flask
- JavaScript
Technologies
I've Worked with a range of technologies in ML, AI and web development world.
Machine Learning
Proficient in Regression,
Classification, Ensemble
methods, and Deep Learning
with a focus on Computer
Vision, including CNN,
RNN, and GANs.Artificial Intelligence
Experience with
Markov Decision models
and Reinforcement Learning
models.ML/AI Tools
Experience with
PyTorch, Keras, Numpy,
Scikit-Learn, OpenCV,
Python, MatLab.Data Science and Big Data
Experience with Data
Science tools such
as Beautiful Soup,
Selenium, Spark.Front-End
Experience with
React.jsBack-End
Experience with
SpringBoot, Node
and Databases.
Timeline
2016
Started my Bachelors of Technology in Information Technology at Guru Gobind Singh Indraprastha University.
2019
Published my first Research paper in IEEE Xplore
2020
Graduated my Bachelors.
2020
Started to work at TATA Consultancy Services as a Backend Developer
2021
Began my MS in Computer Science at University of Illinois at Chicago
2023
Will graduate from my Masters in May 2023.
Personal Accompalishments
20+
Open Source Projects
6
Academic Paper Publications
3.85
Current cumulative GPA
Member of UIC Badminton Team