Welcome
Thanks for visiting Sai Manasa Vedantam's portfolio. Please check the following sections to know more about me and feel free to reach out to me at my email.
I am a Computer Science Enthusiast and a Masters' graduate from The University of Texas at Dallas (UTD) with Intelligent Systems as my Specialization (Jan 2021 - Dec 2022) where I am honored with a Certificate of Academic Excellence for my academic performance. I worked as a Summer Intern at Goldman Sachs and as a Software Developer at Hexagon Capability Center India where I dealt with end-to-end feature development using trending technologies like Angular, ReactJS, Typescript, CSS etc. in front end, making REST API calls and designing backend using ASP.NET MVC framework, Unit testing the modules confirming to SDLC standards.
Software Developer at Hexagon | Jun 2019 - Jun 2020 |
Student Assistant at UTD | Jun 2021 - Sep 2021 |
Grading Assistant at UTD | Sep 2021 - May 2022 & Aug 2022 - Dec 2022 |
Summer intern at Goldman Sachs | Jun 2022 - Aug 2022 |
Programming | Java, Python, C, C# |
Web & UI Development | HTML, CSS, JavaScript, Angular, ReactJS, JQuery, XML, Bootstrap, WinForms |
Database Technologies | MySQL, MongoDB |
Frameworks & IDEs | Flask, ASP.NET MVC, Eclipse, Visual Studio, Apache Spark, Django |
Intelligent Technologies | Machine Learning, Artificial Intelligence, Natural Language Processing |
I have developed Projects on several technologies focusing on various aspects like good design, strategy and fine approach to problem solving. A few of them are listed in the following section.
Responsive shopping website that sells bridal clothes & accessories which allows user to wish list items, book appointment, filter, search through pages etc. along with special privileges for admins.
By assessing real-time requirements, this project builds a strong database system for a Food-delivery System built completely based on SQL and concepts like Cursors, Triggers and Views.
A system that is built using Natural Language Processing techniques aiming to give most sensible answers to the queries posed by humans, based on a fixed domain of data obtained from SQuAD dataset.
A model that performs Crowd Counting & Detection using Point Annotations on heads by addressing the issues with existing systems that are incapable of performing counting & detection simultaneously.
Utilizes the potential of Big Data Management & Analytics along with ML & NLP to suggest the likeliness of having top 10 diseases using a GUI to read the symptoms a person is experiencing & visualize the results.
Builds an end-to-end pipeline to read tweets, process & perform sentiment analysis and visualize the same in real-time with Apache Kafka, Spark, ElasticSearch & Kibana followed by K-means clustering for analysis.
Implements a Video Player that avoids lags & ensures high quality of service by scheduling tasks in Real-time using the bundle of real-time interfaces provided by Litmus-RT along with FFMPEG and SDL.
This project implements Logistic Regression without using built-in python methods. The goal is to understand, develop & assess how several optimization techniques lead to convergence.
This project implements Linear Regression without using built-in python methods. The goal is to understand, develop & assess how several optimization techniques lead to convergence.
Using predictions of three ML algorithms namely Naïve Bayes, Logistic Regression and Support Vector Machines, it predicts the probability of getting attacked by heart disease in near future.
Detects motif (a short and recurring pattern in DNA) holding critical genetic information using Markov Chain Monte Carlo approach’s Gibbs sampling.
Part-of-Speech (POS) tagging is a crucial step in Natural Language Processing. This Project builds Part of Speech taggers based on Brill's Transformation based tagging & Bayesian methods.