I am a third-year undergraduate at the University of California: San Diego. I am majoring in Cognitive Science and minoring in Mathematics because I want
to develop machine learning language models that are ethical and human-centered. I enjoy the challenges of trying to model human comprehension of language,
a dynamical phenomenom rife with uncertainty. Moreover, for me, it's incredibly important that the intelligent technologies that we're becoming increasingly reliant on are sensitive to the needs of the user.
The best way to reach me is at email@example.com where x = alexliebscher0
I was a data science intern for TextRecruit during the summer of 2018. I discovered usage and performance insights from the company's large collections of customer and user data.
I focused my time on the 40+
million messages the company
had transmitted through their platform. I built a sentiment analysis model which improved F1 scores over 1.7x compared to other out-of-the-box solutions. I wished to model the flow of text message conversations with the company's
artificial intelligence chatbot to
determine retention and confusion rates, along with drop off points. I engineered two classification models to tag inbound user text messages and outbound chatbot messages as one of 30 or 34 classes, respectively. These were applied to
conversations to generalize the structure and present insights on weaknesses in the platform's NLP capabilities. I also provided KPIs about chatbot usage, and customer activity and growth, which helped company leadership validate business
Maintained and upkept the yards and gardens of large residential and commercial properties. Developed a keen sense of detail, an indefatigable work ethic, and the character to place myself in uncomfortable
situations and drive myself
toward the company’s idea of success.
La Jolla, CA | September 2016 - June 2020
Computer Science and Math
Supervised Machine Learning
Race, Gender, and Artificial Intelligence
Unsupervised Machine Learning
Public Rhetoric & Practical Communication
Computational Models of Cognition
Vector & Multivariable Calculus
Data Analysis and Inference
Cognitive Consequences of Technology
Intro to CompSci: Java I & II
Neuroanatomy and Physiology
Sensation and Perception
Research Methods and Statistical Analysis
Functional Role of Metaphor Framing in Cancer-related Crowdfunding Projects
Fall 2018 - Present
Are there real-world observable consequences of using metaphors to discuss cancer patient behavior and outcome? We are analyzing a corpus of crowdfunding campaigns related to cancer to determine the functional
role of metaphor framing
when people discuss their hardships with cancer.
Tools: Python (pandas), R, Github & git
A Revised Empirical Comparison of Supervised Learning Algorithms
For any classification problem, choosing a proper classifier and its parameters is critical for success. This paper is my attempt at pushing for methodical supervised machine learning. I
performance of seven
algorithms across four data sets. For thoroughness, each classifier was tested over three independent trials, where each trial was subject to three partitions of the data. For each partition of the data, cross validation was
each CV fold, an optimal set of hyper-parameters was found for the classifier using Bayesian Search. Contrary to traditional grid search, this method improves performance, takes advantage of the underlying parameter space with specific
priors, and reduces redundant and insignificant searches. Performance overall proved that Random Forests, Gradient Boosted Trees, and RBF-SVM achieve the highest results. K-Nearest Neighbors may also be a viable solution but should be
treated with care and precision.
For this course project, I was interested in building a model with the ability to "fill in the blanks" when presented with a statement such as "Her hair is as red as ____".
For this, I created my own dataset
and annotated certain
components of each simile. These similes were embedded in a vector space with Word2Vec. I then went through a model selection process in which I tried to minimize the model complexity and data complexity, while maximizing the prediction
output entropy. I reduced the word embedding space (for better generalization), tuned the number of components of the Gaussian Mixture Model (representing the number of semantic topic groups), decreased the probability of the model
randomness, and maximized the entropy of the bin counts of predicted GMM components (to prevent most similes from being clustered under one component). Given partial data (an incomplete simile), the model permutates over all possible
combinations of latent components and vocabulary, searching for the maximum log probability solution. This solution is the "blank" in which to fill the simile. Some results are intriguing, creative, and plausible, whereas others are (fun
I love talking about music: learning about new artists and songs, discussing albums, and debating the lyrics of music. I might argue that my taste in music defines my friend-circle to some degree. When I meet
new people, often times one
of the first questions I'll ask is, "So what kind of music do you listen to?" or "Who are your top three artists right now?" Music can tell you a lot about a person, and I take this to heart. Hence, this project is an attempt to
me a little better.