Data Science Retreat Demo Day #41

Data Science Retreat presents 8 Machine Learning prototypes and projects by Batch 41 participants.

Free to Attend! Join us for an engaging evening with fellow data enthusiasts in the city. Explore innovative projects, connect with like-minded professionals, and enjoy networking over pizza and drinks.

Agenda:
17:30 - Drinks and Networking

18:00 - Welcome & Introduction

Followed by Project Presentations

Project Ideas:

1. Jaguar Identification Project
Project by Davide Panza and Shahabeddin Dayani

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.

2. AI-Powered Sustainability Assistant for EPD-Based Product Design
Project by Murilo Polla

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.

3. Speech-based screening of cognitive impairment in Spanish-speaking populations.
Project by Jose Alfonso Hernandez Ramirez

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.

4. PUT ME TO SLEEP! IF YOU CAN...
a Machine Learning Approach to Sleep-Inducing Sound Patterns
Project by Dr. Khashayar Razghandi

This project aims to apply a data science approach to analyze a wide variety of sleep-aid audio content and user engagement metrics from YouTube / Soundcloud, in order to explore this unique soundscape and identify the patterns most strongly associated with effective sleep induction.
The trained model can be embedded in an application (sleep-aid or sleep-tracker) to evaluate the Sleep-aid Score of an audio content or recommend effective potential tracks —even beyond this sound scope— may it be a rainy night, a western classical or a Python lecture.

5. A Diagnostic Tool for Identifying Malignant Skin Lesions Across All Skin Tones
Project by Matthias Schultze-Kraft

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

6. Predicting antiviral properties of small molecules with GNNs
Project by Slav Semerdzhiev

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.

7. Deep Reinforcement Learning for Assortment Modeling + Deep Learning for Customer Classification and Choice Modeling
Project by Mureji Fatunde

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.

8.EasyLens – Smart AI Assistant for Everyday Tasks in Germany
Project by Osama Abuhussein

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.

20:00 - Open for networking

20:30 - Wrap up

We have limited seat so please RSVP soon. See you all at the event.