# Trung Trinh

trung.trinh [at] aalto [dot] fi

Research interests: Robust Machine Learning, Bayesian Deep Learning, Uncertainty Quantification, Optimization

Aalto University Computer Science Building

Konemiehentie 2

02150 Espoo, Finland

I’m currently a Ph.D. student in the Probablistic Machine Learning group at Aalto University under the supervision of Prof. Samuel Kaski. I’m expected to graduate in 2025, and I can start working from November 2024. I’m currently looking for Data Scientist or Machine Learning engineer roles in the industry.

My primary research interest lies in enhancing the robustness of deep learning models to distribution shifts. Distribution shifts refer to changes in data distribution between the training and deployment phases, which can lead to significant degradation in model performance. Neural networks are especially vulnerable to these shifts, reducing their reliability in practical applications. My goal is to improve the resilience of neural networks, enabling their deployment in safety-critical systems, such as autonomous vehicles and medical diagnostics. My current work focuses on enhancing generalization and uncertainty calibration of neural networks under distribution shifts through efficient Bayesian approaches and advanced optimization methods.