Select projects

Stock2Vec utilizes Word2Vec embeddings to turn stock price fluctuations into high-dimensional vectors, and then uses principal component analysis (PCA) to compress those many dimensions to 2 to visualize the similarities between two different stocks. After "compression", we found that similar stocks share similar traits or locations on the graph. Furthermore, we have created a 4-dimensional vector representation of companies that can be used to predict which of 11 sectors of the economy a company belongs to. By refining this model we can start to do stock-risk prediction with these embeddings.

For small clinical samples, a summary metric can be more useful and reliable for assessments. In this project, we applied both principal component analysis (PCA) and a deep autoencoder to create a  summary metric from 8 standard clinical scores for the improvement in mobility for 9 lower limb amputees using two different prosthetic devices - a CPU-controlled knee and a mechanical knee. The summary metrics using both methods demonstrate high significance and are more reliable than the individual scores. The autoencoder metric captured 83% of the variance and the PCA only captured 67%. The autoencoder composite score represents a single valued, succinct summary which can be useful for holistic assessment of highly variable, individual scores in limited clinical data sets.

Time, as an essential feature for trend detection, is often neglected in topic modeling. By adding a weighted temporal feature, time, to bias a K-means clustering toward articles is a promising way of trend detection. In this project, Latent Dirichlet Allocation (LDA) and Singular Value Decomposition (SVD) were used in the parameterization of finance journal abstracts. A trend score for automatic detection of a trend was created by utilizing the silhouette score for topic interpretability and the standard deviation of years to quantify localization in time. By introducing the role of time in topic clustering, we are more able to identify historical trends, which ultimately enables a better prediction of the direction of academic research going forward. 

Using a combination of Gaussian mixture modeling and hidden Markov models, we have developed an app to automatically cluster speech in recorded audio to isolate and identify each unique speaker. The current version of the app records and analyzes the conversation to identify who is speaking and when. After a conversation the app reports the total time each person spoke and provides a scrollable piano plot to present the timing and interpersonal dynamics between speakers throughout the conversation. For those with android phones, I would encourage a download of our released “Conversation Moderator” by searching the Google Play Store. 

Bruce Gaynes, an ophthalmologist at Loyola medical, has established a biomarker for Parkinson’s disease that relies on the rate of pupil dilation in a particular combination of drug and light exposure. We have create a program for him to automatically measure the amount of pupil dilation in video. The program accepts video of a pupil reflexively constricting and dilating upon exposure to particular frequencies of light, and returns useful parameters for clinicians to judge if the subject has a related degenerative disease. 

Originally, physical therapists brainstormed a project to track counts of instructed motions during their therapy sessions. However, many of their sessions have movements that are nonstandard, possibly unique to the individual. The goal of this project was to create a system to robustly count periodic motions of any type using a wearable device. This project can be applied to perform and evaluate exercises at home with minimal help from a physical therapist.

In this project, a hidden Markov model (HMM) was used to improve clip-based static classifier accuracy in a patient activity recognition task using wearable sensors for subjects with incomplete spinal cord injury. Tailored activity recognition such as this study is important in creating high-accuracy real-world log of patient activity, so that therapies can be targeted to demonstrably improve at-home movement and quality of life.