MLConf 2019 New York City Agenda

Registration

Opening Remarks

Emily Pitler: Representations from natural language data: successes and challenges

Anna Choromanska: Data-driven challenges in AI: scale, information selection, and safety:

Neel Sundaresan: Teaching a Machine to Code:

Esperanza Lopéz Aguilera: Using a Bayesian Neural Network in the Detection of Exoplanets:

Coffee & Networking Break *Coffee Sponsored by SparkBeyond

Soumith Chintala: Increasing the Impact of AI Through Better Software:

Amir Sadoughi: Developing large-scale machine learning algorithms on Amazon SageMaker:

Renaud Bourassa: Building Machine Learning Models with Strict Privacy Boundaries

Rishabh Mehrotra: Recommendations in a Marketplace: Personalizing Explainable Recommendations with Multi-objective Contextual Bandits

Lunch Break & Networking

Alexandra Johnson: Reducing Operational Barriers to Model Training

Roy Lowrance: Predicting Bond Prices: Regime Changes

Madalina Fiterau: Hybrid Machine Learning Methods for the Interpretation and Integration of Heterogeneous Multimodal Data:

Sandhya Prabhakaran: A Bayesian Approach To Model Overlapping Objects Available As Distance Data:

Liliana Cruz-Lopez: Deep Reinforcement Learning based Insulin Controller for Effective Type-1 Diabetic Care:

Kerry Weinberg: Putting the Tech in Biotech: Challenges and Opportunities in Application of AI in Healthcare

Coffee & Networking Break **Coffee Sponsored by SparkBeyond

Nitin Sharma: Deep Learning Applications to Online Payment Fraud Detection

Janani Kalyanam: Machine Learning to Detect Illegal Online Sales of Prescription Opioids

LeAnna Kent: Using Network Analysis to Detect Kickback Schemes Among Medical Providers

Niels Bantilan: Augmenting Mental Health Care in the Digital Age: Machine Learning as a Therapist Assistant

Event Conclusion