Hi, I'm Sushant Gautam.
As a dedicated Computer Science graduate student with a focus on Machine Learning and Data Science, I am seeking an internship/full-time machine learning engineer position where I can leverage my expertise in Machine Learning, Deep Learning, Computer Vision, and AWS Cloud technologies. My goal is to apply my theoretical knowledge and innovative ideas to challenging projects, thereby gaining invaluable hands-on experience.
The only way to do great work is to love what you do.
– Steve Jobs
About
Machine Learning Engineer with Expertise in AI Development and MLOps
As a Computer Science graduate student, I specialize in Machine Learning, Data Science, and AI development. Since 2017, I have been driven by a passion for leveraging AI to transform complex data into actionable insights.
My expertise lies in building machine learning models, deploying robust MLOps pipelines, and optimizing workflows for secure and efficient AI applications. I am also actively expanding my knowledge in scalable cloud-based deployment architectures to further enhance my skill set.
Inspired by the transformative potential of AI, I am dedicated to solving real-world challenges and collaborating with innovative teams to deliver impactful, data-driven solutions.
Experience
- Improved image restoration accuracy by 20% through the implementation of novel generative models, enhancing the overall quality of visual data for the WRIVA project in collaboration with Stanford, Princeton University and BlueHalo. Used different GAN and diffusion models to restore global artifacts like blurring, overexposure, lens flare, and JPEG compression, resulting in a 15% increase in image quality. .
- Created CI/CD process that lints builds into the container, tests, vulnerability scans, and pushes machine learning software to the registry for use in orchestration software airflow.
- Pytorch-Lightning, Optuna, PyTorch, Scikit-learn, and Docker are some of the tools used during my research.
- Spearheaded the research and development of advanced AI models for detecting and classifying brain-related diseases, including Large Vessel Occlusion (LVO), and hypodensity, with deep learning models like YOLOv7, detectron2 enhancing diagnostic accuracy and efficiency.
- Engineered and deployed robust APIs for the AI models, facilitating seamless integration into production environments. This deployment significantly improved the operational workflow and led to more reliable and rapid diagnosis of disease
- Pioneering Research in Synthetic Datasets: Spearheaded research initiatives on synthetic datasets derived from a limited number of authentic disease images. This innovative approach focused on enhancing data availability for robust model training, particularly in scenarios with scarce real-world data.
- Development of GANs for Disease Image Generation: Played a crucial role in designing and developing Generative Adversarial Networks (GANs). These models were adept at producing high-quality synthetic disease images. The use of these GAN-generated images was instrumental in significantly elevating the accuracy of disease identification models. This breakthrough demonstrated the potential of GANs in overcoming data limitations in medical imaging.
- Leadership and Training: As the team leader, led a group of researchers and developers in applying this cutting-edge research to product development. Additionally, undertook the responsibility of trainer, imparting essential skills and knowledge related to GANs and synthetic dataset utilization. This role encompassed mentoring the team to efficiently translate research findings into practical, market-ready healthcare solutions.
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Spring 2022
- CACS354 - Advance Java Programming (3 cr)
- CACS355 - Network Programming with Java (3 cr)
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Summer 2022
- CACS204 - Object Oriented Java Programming (3 cr)
- CACS402 - Cloud Computing (3 cr)
- CACS456 - Machine Learning (3 cr)
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Spring 2023
- CACS354 - Advance Java Programming (3 cr)
- CACS355 - Network Programming with Java (3 cr)
- Worked under the Prof. Dr. Suresh Manandhar
- Collaborated as a key member of an international team specializing in the research and design of machine learning and computer vision solutions for healthcare applications.
- Led the development of APIs and user interfaces for disease recognition models, enhancing the usability and accessibility of machine learning tools in medical diagnostics.
- Engineered a custom Machine Learning Pipeline tailored for medical image diagnosis, enabling dynamic, user-driven model training online for various diseases.
- Innovatively researched and developed a Deep Learning Medical Image Diagnosis Model, achieving significant performance improvements in detecting diabetic retinopathy, skin lesions, and lung diseases.
- Made video summarization and scene finding project using state-of-art Deep Learning model.
- Work under the Prof. Dr. Suresh Manandhar
- Developing language models to improve offline handwritten recognition tasks
- Synthesize handwritten images with GAN.
- Used of different types of GAN like Cycle GAN, Text2Image GAN, and Variational Encoder
PROJECTS
Deep Learning Projects
Handwritten Line Text Recognition using Deep Learning with Tensorflow
- Tools: Flask, HTML, CSS, Bootstrap, Tensorflow, OpenCV
- This is my major project which I do with quiet research and found the state-of-the-art architecture for real-time handwritten recognition with CER 4.32%.
- Uses CNN, LSTM, CTC loss function and trained on an IAM dataset with a self-created dataset that can detect handwritten text in real-time.
- Perform data augmentation to make a robust model and improve accuracy.
- Also make some changes on the current architecture which is able to detect handwritten images in Devnagari Script with CER 8.32%.
Nepali Handwritten Line Text Recognition using Deep Learning with Tensorflow
- Tools: Flask, HTML, CSS, Bootstrap, Tensorflow, OpenCV
- This is my major project which I do with quiet research and found the state-of-the-art architecture for real-time handwritten recognition with CER 4.32%.
- Uses CNN, LSTM, CTC loss function and trained on an IAM dataset with a self-created dataset that can detect handwritten text in real-time.
- Perform data augmentation to make a robust model and improve accuracy.
- Also make some changes on the current architecture which is able to detect Nepali handwritten images in Devnagari Script with CER 10.32%.
A Nepali POEM Generation using AI
- Tools: Numpy, Tensorflow, keras, NLP
- This project use the char-rnn model.
- Train on Nepali POEM by Adikabi "Laxmi Prasad Devkota" who is pioneer in Nepali Literature with more than 200k characters.
- The char-RNN model is here used to generate the Nepali Poem
- Accuracy will be increase if more amount of data is available.
Reverse Search Engine with Transfer Learning and K nearest-neighbors.
- Tools: Numpy, Scikit-learn,Tensorflow v2, Flask
- Image Search Engine using Deep Learning Model (ResNet50)
- Use ResNet=50 model to extract features from Caltech101 datasets, train a K nearest-neighbors model using the brute-force algorithm to find the nearest n neighbors based on Euclidean distance.
- Based on less distance, top n images are returned.
- Also some analysis on how to increase speed and accuracy is given.
This project presents an innovative simple approach to enhancing the interaction with scientific papers through a blend of summarization and question-answering capabilities, leveraging the strengths of large language models (LLMs) and retrieval-augmented generation (RAG) methodologies.
Other Projects
A simple and powerful Encryption and Decryption Application with own ASCII shifting algorithm.
Blog Posts & Paper Reading
In my free time, I usually create blog posts on fascinating deep learning and computer vision studies that go into extensive detail about ideas, architecture, and mathematical equations.
View all postsI annotated numerous interesting computer vision papers, particularly GANs and Vision Transformers, by providing an overview of each with details in mathematical calculations.
Read moreDeep Learning Paper Scratch Implementation with PyTorch & Tensorflow.
Read moreSkills
Skills Word Cloud
Languages and Databases
Python
HTML5
CSS3
MySQL
PostgreSQL
Shell Scripting
Libraries
NumPy
Pandas
OpenCV
scikit-learn
matplotlib
Frameworks
Django
Flask
Bootstrap
Keras
TensorFlow
PyTorch
Other
Git
AWS ML
CI/CD
Certificates
Course Project Repository
Education
2023-2025| Starkville, USA
- Degree:Masters In Computer Science(Thesis Track)
- Concentration: Artificial Intelligence
- Relevant Coursework: Algorithms, Machine Learning, AI Robotics
- GPA: 4.0
2015-2019 | Kathmandu, Nepal
Degree: Bachelor In Computer Engineering
Grade: A
- For four years during my undergraduate studies, I received a merit-based scholarship for outstanding academic excellence.
- Completed Undergraduate In Computer Engineering with Distinction.
- Topper of my Batch in my University
- First position in instant software competition in my university.
- Data Structures and Algorithms
- Database Management Systems
- Operating Systems
- Artificial Intelligence
- Digital Image Processing And Pattern Recogntion
- Big Data Technologies
- Advance Mathematics
- Probability And Statistics
Accomplishments
Relevant Courseworks: