Hi, I'm Alisa! I'm a 4th year CS major at Trinity College, CT, USA.

I'm interested in the intersections between computer science & neuroscience,

and hope to pursue a Ph.D. in Computer Science with a machine learning focus.

I'm a curious researcher, language enthusiast {both human & computer} and aspiring innovator.

In my spare time, I enjoy reading, traveling, horseback riding and hiking.

Research Experience

Trinity College — Research Assistant January 2018 – Present

I've been working under Professor Taikang Ning in the Biological Signal Processing Lab since freshman year. During Summer 2018, I also stayed on campus as a Summer Research Assistant.

  • Conduct machine learning analysis by training KNN and SVM models in Python for heart murmur detection and classification
  • Adapt MATLAB feature extraction code to improve model classification based on cross-validated
    accuracy and confusion matrices
  • Developed Android app that wirelessly collects, graphs, analyzes and stores heart sound data using Java and Bluetooth

Publications

  • A. Levin, A. Ragazzi, S. Szot, T. Ning, “A Machine Learning Approach to Heart Murmur Detection and Classification,” In 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 521-525. IEEE, 2020. [PDF] [IEEE Xplore]
  • S. Szot*, A. Levin*, A. Ragazzi, T. Ning, “A Wireless Digital Stethoscope Design,” In 14th IEEE International Conference on Signal Processing (ICSP), pp. 74-78. IEEE, 2018. [PDF] [IEEE Xplore]

Conference Presentations

  • Presented on behalf of my lab at the International Congress on Image and Signal Processing, BioMedical Engineering and Informatics remotely on October 18th, 2020.
  • Presented on behalf of my lab at the IEEE International Conference on Signal Processing in Beijing, China on August 13th, 2018.
Work Experience

Citi – Technology Summer Analyst July 2020 – August 2020

  • Blueprinted a Citi chatbot and virtual queuing system to support social distancing using AWS Lex & Lambda
    that is expected to be implemented due to a successful presentation to senior management
  • Collaborated in weekly sprints and initiated a unique data analytics project in Power BI to set our intern team apart

SAS Institute – Software Engineering Intern (AI team in R&D) May 2019 – July 2019

  • Created 4 AI features to improve data preparation & transformation tools for nontechnical SAS Data Studio users by adapting microservices using Java, FreeMarker and Docker
  • Implemented wildcard support to increase speed and efficiency of data preparation AI model provisioning using Java

Trinity College – Teaching Assistant Spring 2019, Spring 2020

  • Lead weekly TA sessions, assist students one-on-one during weekly labs, and grade weekly problem sets and projects for 50 students
    • Spring 2020: Mathematical Foundations of Computing ~ Spring 2020
    • Spring 2019: Introduction to Computing ~ Spring 2019

Projects


Wireless Digital Stethoscope App
Self-studied Android development to create an app that wirelessly collects, graphs, analyzes and stores heart sound data using Java, Bluetooth and Android Studio.
Mentor Match
Spearheading a national community building effort for the Goldwater Scholar Community. Currently writing an algorithm in Python with FlashText NLP library and Monte Carlo Tree Search to match 1300+ mentors and mentees based on profile data.
Opioid Crisis Risk Model
Analyzed the opioid crisis in 5 US states using Python and Jupyter and developed a custom mathematical model based on correlated socioeconomic factors to predict which counties are most at-risk.
8 Puzzle
Created an program in Java that can solve any 8 Puzzle for which the goal state's parity of inversion is the same with both Breadth-First Search and A* Search algorithms.
Heart Murmur Classifier
Trained and analyzed the performance of KNN and SVM models on various non-traditional heart sound feature sets using pandas and scikit-learn to improve current heart murmur detection and classification methods.
Tic Tac Toe
Implemented a game in Java using the Minimax algorithm that consistently beats (or at least ties with) human players.