The M.S. in Data Science at the University of Pennsylvania blends computational techniques, statistics, and ethics. These research topics help students push the boundaries of analytics, AI, and scalable systems for impactful results across industries.
Developing Interpretable Machine Learning Models for Healthcare Diagnostics
Large Language Models for Automated Legal Document Summarization
Bias Mitigation Techniques in Facial Recognition Datasets
Multi-Class Classification of Consumer Behavior Using Social Media Data
Real-Time Fraud Detection with Anomaly Detection Algorithms
Graph-Based Deep Learning for Recommendation Systems
Predicting Economic Indicators from News Sentiment Analysis
Federated Learning Approaches in Privacy-Sensitive Applications
Big Data Architecture for IoT-Based Smart Cities
Deep Reinforcement Learning for Dynamic Pricing Models
Explainable AI (XAI) Models for Credit Scoring Systems
Unsupervised Clustering of Genomic Sequences Using t-SNE and DBSCAN
NLP Techniques for Mental Health Detection in Social Media
Scalable Data Pipelines for Multimodal Data Fusion
Energy Consumption Forecasting Using Time Series Analysis
Collexa helps UPenn data science students with model building, scalable pipeline architecture, ethics documentation, and thesis writing with real datasets.
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