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Resume

Work
Experience

Sep 2023 - Current

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Technical Intern | Intelligent and Connected Systems Lab, Columbia University

Working on radio-based FMCW millimetre wave radar array data and mobile UAV platforms for person identification and tracking with ramifications in search-and-rescue. Implementing signal processing techniques hand-in-hand with higher-level ML algorithms to recognize human presence and movement behind different material obstructions. Working in an academic laboratory environment on a part-time basis taking part in both lab paper and proposal presentations.

Dec 2019

Extern | CICC US Securities

Spent time observing the day-to-day activities of a joint-venture investment firm including, among other things, teleconferences with analysts, stock reports, and the formulation of daily briefs for clients, the experience culminating in a presentation to the CEO regarding future market trends.  Learned a great deal, not just about the internal workings of investment banking, equity sales, and stock analysis but also about larger financial concepts, reading hundreds of pages of research regarding macroeconomic patterns in the Chinese mainland, industry trends, and the effects of political tension on the markets along with other topics.

Jun - Sep 2021

Technical Intern | Simplicita

Restructured a PostgreSQL database to increase overall query performance by 95% on average by both improving table structure and query creation increasing the site’s primary search engine performance by over 95%. Implemented PDF and Word document scraping code in R to extract information from over 100 legal contracts in minutes using both simple search and OCR text recognition in addition to exploring ML-assisted solutions such as AWS Textract. Lastly, wrote a near thousand-line python web spider to scrape detailed information from a dozen different websites interacting with webpages through both their source-code and JavaScript elements (with the use of Selenium) to extract not only readily available details but also download links and the contents of their associated files for a full data pipeline

Jun - Sep 2019

Technical Intern | Sulzer Lab, Columbia University

Created code to interpret videos of experimental data and aided in the development and streamlining of code meant to visualize and extrapolate variables from experimental neurochemical readings. Experienced working in an Agile environment and with Test-Driven Development providing a fast-paced industry-like development cycle.  Reference Letter available on request

Education

2022-2023

Columbia University | Master’s Degree

Computer Science (MS), GPA 3.45

Expected December 2023

Machine Learning Track

2018-2022

University of Chicago | Bachelor's Degree

Computer Science (BA), GPA 3.66/4.0

Dean's List 2020-2021

Bachelor of Arts in Computer Science, Neuroscience Minor.

Relevant Coursework

  • COMS 6998 High-Performance Machine Learning Spring 2023

    • The aim of this course was to explore the ways in which AI models can be implemented in high-performance computing clusters. Utilizing AWS and Habanero resources to execute varying models on a variety of datasets the complexities of multi-GPU computation and the ways in which code can be optimized for execution on such platforms

  • COMS 4775 Causal Inference Fall 2022

    • This course was designed to reveal the statistical backdrop to contemporary AI & ML probabilistic way of thinking by better understanding the nature of the statistical ties which can exist between even the most removed of variables, introducing concepts, principles, and algorithms necessary to solve modern, large-scale problems in scientific inferences, business, and engineering with extra emphasis on the tradeoff between assumptions (delineated by current scientific knowledge) and conclusions for standard types of queries, including associational, causal, and counterfactual.

  • COMS 4152 Engineering Software As a Service Fall 2022

    • A radical departure from most theory-centered courses, this class is intended to not only explain the nature of development in the industry from project management and the Agile workflow, Behavior & Test-Driven Development, and the mechanics behind SaaS within the realm of large-scale corporate deliverables but also worked to develop a full SaaS product in Ruby on Rails as a final class deliverable.

  • COMS 4771 Machine Learning Fall 2022

    • Hitting the ground running in ML, this course intended for students with extensive priors mathematical and programming experience taught material including least squares methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines, and kernel methods. 

  • COMS 4995 Applied Deep Learning Fall 2023

    • A course designed to teach practical applications of artificial intelligence. We elucidated the workings of many machine learning models ranging from RNNs, CNNs, BERT, ELMo, and GPT. Coursework included rebuilding such models in PyTorch, analyzing their performance, and implementing various train and test sets to explore the various strengths and weaknesses of different models. 

  • CMSC 20900 Computers For Learning Autumn 2020

    • Learning about learning is perhaps the best way to describe this class. An analysis of the inherent biases programmers build in to their products and how allowing for accessible computing tailored to a wider and more diverse audience can improve the quality of user interaction in addition to overall user satisfaction and information/skill retention.  Final Project for this class also appears on the “Projects” page.  Proceeded to serve as sole teaching assistant (TA) for the 40-person class in Autumn of 2021 holding office hours twice a week and overseeing lab sections with glowing reviews by students at semester’s end.

  • CMSC 25610 Computational Linguistics Autumn 2020

    • ​Explored the applications of machine learning and neural networks to the field of language creation, processing, and comprehension exploring semantic vector representation and psycholinguistics and applying the intuitions therein into learning models from n-grams to ELMo & BeRT and culminating into the final project discussed more in depth in the “Projects” page studying the nature of language evolution through the eye of an NLP computational model.

  • CMSC 25300 Mathematical Foundations for Machine Learning Autumn 2021

    • A wide-ranging yet in-depth introduction to the mathematical underpinnings to machine learning where, per the courses own description: “Graduate and undergraduate students will be expected to perform at the graduate level and will be evaluated equally”. Taught a profound amount on a range of topics including, but not limited to:  linear equations, regression, regularization, the singular value decomposition, iterative optimization algorithms, and probabilistic models, the LASSO, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning

Skills
& Expertise

  • Proficient in:

    • C, C++, C#, R, Standard ML, PostgreSQL, HTML, CSS, Java, Python, Ruby, Django, Go

  • Fluent in Italian and French

  • Cognizant Virtual Experience Program Participant - 2021

    • Worked through the open access Cognizant Virtual Experience Program with Forage Ready, Set, Agile.

    • Completed tasks including Waterfall vs Agile, User Stories, Role Assignments and Ceremonies, Agile Tools and Concepts, Agile, Curve Balls, Agile Methodology Review and Additional Resources.

  • Experience in the following tools:

    • AWS​

    • UNIX

    • Microsoft Office

    • Adobe Creative Suite

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