Teaching Experience

Guest Lecturer

I was responsible in assisting the advisor in delivering lectures to students. I actively contributed to the lecture content by incorporating my expertise and experience, as well as preparing and delivering lectures on my own.

CSDS 600: Machine Learning on Graph

Course Description

Machine learning is a sub-field of Artificial Intelligence that is concerned with the design and analysis of algorithms that “learn” and improve with experience. Machine learning algorithms have traditionally been developed for tabular data. Recent developments in machine learning, including deep learning architectures, have also focused on other types of data, including image, text, time series, and graph data. This course introduces students to machine learning on graph data, which are often used to represent net- works such as social and information networks. This course will cover mathematical representations for graphs, measures and algorithms for analyzing graph data, probabilistic models for random graphs, and graph representation learning, including graph embeddings and graph neural networks.

I have covered the following topics:

CSDS 340: Machine Learning for Big Data

Course Description

Machine learning is a sub-field of Artificial Intelligence that is concerned with the design and analysis of algorithms that “learn” and improve with experience. While the broad aim behind research in this area is to build systems that can simulate or even improve on certain aspects of human intelligence, algorithms de- veloped in this area have become very useful in analyzing and predicting the behavior of complex systems. Machine learning algorithms have been used to guide diagnostic systems in medicine, recommend interesting products to customers in e-commerce, play games at human championship levels, and solve many other very complex problems. This course is an introduction to algorithms for machine learning and their implementation in the context of big data. We will study different learning settings, the different algorithms that have been developed for these settings and learn about how to implement these algorithms and evaluate their behavior in practice. We will also discuss dealing with noise, missing values, scalability properties and talk about tools and libraries available for these methods.

I have covered the following topics:

Senior design Mentor

I was responsible for overseeing and providing guidance for senior design projects assigned to senior undergraduate students. This included monitoring project progress, providing regular feedback, facilitating team meetings, and ensuring that project goals were met within the given time frame. Additionally, I worked closely with the students to identify areas of improvement and provided guidance on how to refine their design skills and project management techniques.

Chess variant application

Team members: Robert Penrod, Michael Flora, Izabella Torrey, Ali Alshaikhahmed

Abstract:

This project implements a chess application that allows users to play either against artificial intelligence or human players. In addition to conventional chess, users can play atomic chess (I.e. chess in which captures remove all non-pawn adjacent pieces regardless of team). The game is built in such a way that other variants can be implemented easily. The game is embedded in a website freely available to any user with an internet connection. As the game’s physical mechanics do not involve dragging or the keyboard, it is suitable to be played on a variety of devices.

Smart mirror project

Team Members: Tony Pellican, Lauren Weinberg, Thomas Payne, Jonathon Ford

Abstract:

For this project, our primary goal is to produce and test a smart mirror that can use facial recognition AI to log you into a profile you can customize to suit your needs. The mirror is able to display a variety of things such as the weather, time and date, a weight tracker, and is easily expandable. We believe this will allow people to take a more phone-free approach to their morning.

"My Gluco Pal": a diabetes management application

Team member: Nathan Loewenthal, Toki-Phillips Oluwafolahan, Hitanshu Dudeja, Bauer Ritter

Abstract:

The goal of this project is to create a mobile application that allows users to track their diet, glucose levels and insulin intake as well as connecting with many different devices and allows easy sharing of information to their doctor. With this application, users will be aided in keeping a healthier diet whether they have diabetes or not. It will do this by allowing users to easily track their diet and view accurate information about different foods such as the sugar, protein, and calorie content. If the user has diabetes, the application will help them to calculate the insulin they need to take based on the diet they are following. If the user has compatible devices, it will also communicate with those devices. In addition to connecting to these devices to control them, it will also receive any information from it. For example, with a CGM the application will support NFT reading and Bluetooth communication between supporting devices. If the phone used has NFT reading capabilities, the user can tap their phone against their CGM, and it will read the information gathered by this device. This will replace the need for an additional reader solely for getting information from the monitor. The application will also support the Bluetooth protocol to communicate with and CGMs that require communication in that median.

Teaching Assistant

I was responsible for a range of tasks including grading assignments and providing constructive feedback to students, leading engaging and productive discussion sections, holding regular office hours to address student questions and concerns, and supervising laboratory sessions to ensure students had a supportive learning environment.

Case Western Reserve University, 08/2022-current

University of Toledo, 08/2018 - 05/2022

Purdue University Northwest, 08/2015 – 05/2018