I am a Computer Science Engineering Student from the University of Michigan, who is currently working at Autodesk on a summer internship. I am passionate about creating and sustaining software products with hopes of having a positive impact on the world.

Let's build something great together.



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Client Note Keeper

Client Note Keeper is an app I launched for iOS in July of 2017. I created the app for my Dad as an extra tool to help store his notes on clients for his psychology business. I also implemented an email export feature for sharing notes and password protection to keep notes secure. To my surprise, the app currently (two weeks after release) has over 50 customers from 3 different countries. I am continuously updating the app to improve performance and add new features.

PeriOperative Project Team

PeriOperative is an engineering project team at the University of Michigan that is part of a larger organization called MHEAL. I join Team PeriOperative because I wanted to work on innovative engineering solutions while benefiting others. During my 2 years with the team, I primarily worked on implementing an embedded controls system for a surgical patient-warning device using off-the-self hardware components. I took a leadership role after my first year and began leading weekly meetings with the team and our advisor who was a Director of Product Development from Stryker Corporation. We had gained some traction with out product and were funded by the University of Michigan to go to the Dominican Republic to collect user feedback on a “low-resource” prototype from surgeons of four hospitals. When we got back to Ann Arbor, we presented our progress and results to over 100 students and advisors at the University of Michigan Engineering Design Review.


I actually came to the University of Michigan to study Movement Science at the School of Kinesiology. The summer after my freshman year I used my Movement Science knowledge to assist in a lab that studied long distance running. I expressed interest in the technical side of the lab work, and then began assisting on data collection and processing using MATLAB. Seeing the powerful role of software in the lab is one of the factors that prompted me to transfer to the School of Engineering to study Computer Science.


I am pursuing a B.S.E. in Computer Science and a minor in business. I currently have a GPA of 3.76 and plan to graduate in April of 2019. Through courses and independent study, I have gained knowledge in the following areas : 

Data Structures and Algorithms, Object Oriented Design, Multi-threaded Programming, Web Systems, TCP/IP sockets, Computer Organization and Architecture, Computational Theory, Discrete Mathematics, Calculus (I, II, IV)

Software Engineering Internship at Autodesk

I have loved my internship at Autodesk. I developed new features for an Autodesk product called FeatureCAM which is used to generate instructions for CNC machines. Throughout my internship I gained team experience by working with a scrum team of developers using the Agile approach, as well as, software development experience by shipping new features to a large code base of production-ready software. Specifically, I used C++ and Windows MFC to integrate a new feature which made it to the main page of the 2018 production version of FeatureCAM.

Augmented Reality


I had played around with AR technology over the past year, mostly using the Vuforia SDK with Unity3D, but I was so absolutely ecstatic when Apple released its ARKit. Using Apple's beta for iOS 11 and beta ARKit, I created the AR Swimming Whale app in which users can visualize a virtual whale swimming in their physical environment. The user can also change the size and position of the whale using the buttons along the bottom of the screen. I am planning to launch the app to the app store when iOS 11 become available to the public in the Fall. 

Machine Learning


This past year I took a strong interest in Machine Learning, so when summer hit, I spent the month of May learning Google's Tensor Flow. I began to wonder if we could classify a person based on what they say. Twitter seemed like the perfect data source for this experiment. I used the Twitter API to gather tweets from different authors in order to train a neural net model in TensorFlow to be used to predict the author of a tweet. I utilized Python and TextBlob to form tensors from tweets based on sentiment analysis and phrase frequencies. Everything in the project had gone great but the predictions were not very accurate. This may be because tweets are not as "data-rich" as images or audio files(which are often used in machine learning projects). I am thrilled with what I have learned through this project and have established future plans to make the model more accurate and publicly available for users to form custom models.