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EasyLens is an iOS app that uses AI and computer vision to simplify daily tasks like waste sorting, understanding German documents, and identifying tourist spots. Users simply point their camera and select a feature; the app does the rest using Core ML and OCR.

An AI-powered assistant that helps companies design sustainable products by leveraging Environmental Product Declarations (EPDs) for informed decision-making. The aim of the project is to simplify sustainability analysis and optimize product design for lower environmental impact.

This project aims to develop an intelligent system using computer vision to identify individual jaguars by their unique facial and body patterns. A Vision Transformer (ViT) and advanced self-attention models will be used for segmentation and classification, with fine-tuned embeddings to enhance accuracy. The system will aid zoologists in tracking jaguars, especially after natural disasters, and will be deployed as an API for practical use.

AI-based diagnostic tools for skin cancer have become increasingly popular due to their efficiency and low cost. However, a critical limitation of many existing models is their lack of diversity in training data—particularly the underrepresentation of darker skin tones. As a result, these models tend to perform poorly on non-white patients, increasing the risk of misdiagnosis or delayed diagnosis and contributing to significant health disparities. This project aims to address this issue by developing a diagnostic tool capable of accurately identifying malignant skin lesions across all skin tones

This project focuses on developing a speech-based screening tool for Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) using machine learning models trained on audio recordings from Spanish-speaking individuals. The goal is to create a non-invasive, cost-effective, and easily deployable diagnostic tool for early cognitive impairment detection.

On digital platforms (or in physical settings) where customers arrive repeatedly, platforms must decide which options to offer them, as this influences their subsequent choices as well as the outcomes for both user and platform. This project will 1) classify customers on a digital platform based on their latent motivation (think: whether they're here to learn vs earn, or which out of several options is the primary emotion driving them) using deep mixture models, 2) predict consumer actions by using deep learning to estimate parameters of the consumer choice model, and 3) use reinforcement learning to select customer offerings/recommendations in order to optimize a long-term outcome.

This project leverages graph neural networks (GNNs) to predict the antiviral potency of small molecules by analyzing their molecular structures and interactions. By learning complex patterns in chemical data, GNNs can identify promising drug candidates with high efficacy against viruses. The insights gained from this approach aim to accelerate antiviral drug discovery and development.