Hi, I'm Abhinav Agarwal, a passionate student recently graduated from Bachelor of Technology in Information Technology program from Manipal Institute of Technology. I am currently pursuing the Master's of Science in Computer Science & Software Engineering degree at the University of Washington Bothell. I will be graduating in May 2023. Through a combination of course work, projects and certifications, I have a wide range of skills across the domains of full stack development, systems engineering and machine learning.
I am currently looking entry level full time software developer roles. I am based in Seattle, WA (PDT timezone).
Hello, I’m
Abhinav Agarwal
Computer Science graduate student skilled in Full Stack developement and Machine Learning
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5 this.age = new Date().getFullYear() - 1999;
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About Me
My Alma Matter
University of Washington Bothell
September 2021 - May 2023
Master's of Science in Computer Science & Software Engineering
Completed Coursework: High Performance Computing, Evidence Based Design, Evaluating Software Design
Expected Coursework: Algorithms Design & Analysis, Software Management, Information Security, Capstone Project
Manipal Institute of Technology
July 2017 - July 2021
Bachelor of Technology in Information Technology
Relevant Coursework: Data Structures, Database Systems, Distributed Systems, Operating Systems, Pattern Recognition, Big Data Analytics, Neural Networks, Data Warehousing, Data Mining, Advanced Calculus and Combinatorics
Kendriya Vidyalaya Sangathan
I did my schooling through various schools operated by the KV Sangathan.
Work Experience
Nvidia Graphics
Systems Software Engineer (Internship)
June 2022 - September 2022
- Worked as a Systems software Engineer (Intern) with the virtual GPU (vGPU) team
- Studied the resource manager architecture in detail, which forms the base for system APIs for GPUs
- Solved bugs and worked on feature requests related to the vGPU stack, using systems level C programming.
ICICI Lombard
Software Developer Intern
February 2021 - July 2021
- Built new APIs for 5 different customer-facing ASP.NET applications using SQL database.
- Migrated a project with multiple tables and procedures from MS SQL to Oracle SQL Database.
Xu Labs, Careneige Mellon University
Summer Intern
July 2020 - November 2020
- Used Parameter Pruning & Quantization to reduce the size of a large and complex Tensorflow convolution neural network by 75% while preserving up to 70% of original accuracy.
- Built a GUI web application for exploration of MRC models using Django framework and Metro UI
My Skillsets
Full Stack Development Projects
(Research) Firefox Browser Extension for MOOC Ontology Based Recommendations
- Developed a Firefox browser extension that scrapes usage data from MOOC platforms.
- Built Using NodeJS running on free GCP AppEngine. Converts JSON data into SQL for storage
- Designed Python scripts to generate recommendations from the collected data by predicting FSLSM styles.
Evaluation System Project
- Full featured production ready electronic testing system which provides a platform for teachers to conduct online tests efficiently. Developed as per the custom requirements of my instructors at my undergrad college.
- Contains 20 AJAX APIs built using NodeJS Express and Nginx. Uses MySQL as the database layer.
- The frontend is built using HTML5/CSS3 based Metro UI. Uses Memcached for session encryption.
AITOM GUI MRC Loader
- Developed during my internship at Xu Labs, CMU to aid zooming into and slicing a tomograph model stored as a MRC file.
- The tool has Django backend and makes use of libraries such as VTK, mayavi and django2_resumable. For frontend CSS3 is used.
- The model can be either uploaded for exploration, or an existing model can be selected from the library.
Machine Learning Projects
(Research) A study on parameter pruning and quantization for faster alignment and averaging of subtomograms captured by cryo-electron tomography
- We study how parameter pruning and quantization, can be applied to the alignment of subtomograms captured by cryo-electron tomography.
- We try to explain the physical significance of the model compression obtained through this method. Later, we compare the compression achieved and the error incurred for the compressed models through each technique. We present the results for five datasets, all simulated at different SNR levels.
- Model and compression methods implemented in Tensorflow
(Research) Natural Language Query Formalization to SPARQL For Querying Knowledge Bases Using Rasa
- Designed an NLP-based conversational AI agent (chatbot) using RASA NLU and Python to query OWL ontologies.
- Converts plain english language queries into SPARQL queries then queries the ontology to fetch results
- Built scripts to use RASA's internal testing suite and achieved up to 97% keyword accuracy.
DeepLearning.ai Specialization
- 5 course intermediate level deeplearning specialization offered by world renowned Andrew Ng.
- Topics learned include developing and improving neural networks and structuring machine learning projects.
- The specialization involves around 20 assignments using Tensorflow and other Python libraries. Some notable projects include autonomous driving systems, neural art transfer and trigger word detection.
Other Achievements & Certificates
Get In Touch
Thank You
Do You Have Any Relevant openings for
me or any other queries?