Daksh Jain

aboutMe.js

> const DAKSH_JAIN = ;
        
> var interests = ["Software Engineering", "Machine Learning", "Fullstack Development"];

Experience

Google

Title: Software Engineer III
Technologies: Go, Python
Currently, I am working on tooling that enables Google Cloud to track and mitigate risks within its system. My team also works to help Google automatically identify risks through the power of LLM. Furthermore, I work on a team that helps engineers ensure that we meet SLOs on customer issues.

Amazon

Title: Software Development Engineer
Technologies: Java
Working on developing new features and applications within the Amazon marketplace. Provide users with a better experience in searching for various products and deals within the retail store.

Air Products

Title: Process Engineering Intern
Worked on creating Excel tools for analyzing temperature profiles within Liquid Natural Gas Heat Exchangers. Worked in VBA to automate the process of these calculations. Also designed a tool for determining heat exchanger costs for 5+ designs.

Endevor

Title: Software Engineering Intern
Technologies: .NET Framework, SQL, CSS, JS, C#
Worked on developing Illuminate Suite, a software to improve accessibility and security of nuclear plant security. I desigend Forward Access Point, the primary application for security personnel to determine if a person is granted or denied access to the plant.

Projects

Gambit

Technologies: MongoDB, Express, React, Node
Languages: JavaScript, HTML, CSS
About: A React application that I used to teach myself more about modern day frameworks and how to create my own fullstack applications. This project has features including user authentication and gambling games including BlackJack, Roulette, and Slots. I learned how to use hook based React to manage contexts and other state variables within the application. I also implemented JWT authentication in cookies to persist a user login throughout their time on the site. This website is deployed here. Feel free to register and try out the features that I have added so far!
Note: The link to the Heroku deployment may take several minutes to activate due to Heroku's idling state which redeploys the app after long periods of inactivity. To speed up the process, please also go to this to start up the back-end express server also hosted on Heroku.

TF-IDF

Languages: Python
About: This project implements the Term Frequency - Inverse Document Frequency search algorithm on provided text documents. This algorithm weights words based on the number of times it appears in the document with the inverse of the times the word appears in the total collection of documents. Although this search algorithm is not the bleeding edge of today's search algorithms such as PageRank, it provides a simple, yet effective technique for determining the most relevant document/pages from a user's search.

WebNote

Languages: JavaScript
About: This project is a chrome extension that allows users to save "sticky notes" based on the domain they are currently on. It is also enabled with Google Chrome Sync, meaning a sticky note saved on the user's desktop will also appear on their laptop or other computers where the user is signed in. For example, it may be helpful for jotting notes from Wikipedia in your browswer without having to switch to another application like Microsoft Word. Also, the user can simply come back to www.wikpedia.org on the original or different computer, and use or continue taking more notes.

Disbiome Analysis

Technologies: SciKit-Learn or TensorFlow
Languages: MATLAB, Python
About: During a statistics course called Random Variability in Chemical Processes, I learned about the Disbiome Database which stores data about "microbial composition changes in different kinds of disease." After speaking with a professor about this topic more, I learned more about the possible correlation that may exist between the microbiome of a person and the likeliness/chance they may have a disease. So far, I have used MATLAB to create a prelimenary identifier that detects if there is a positive, negative, or no correlation between the data. From these results, there seems to be some trends towards certain microbes having a strong correlation between the probability the person will acquire an illness. I have taken a course on Machine Learning and continuing to learn more to determine the best selection of tools for this task.

Contact