Dravyansh (Dravy) Sharma is a final year doctoral student at Carnegie Mellon University, advised by Nina Balcan. His research interests include machine learning theory and algorithms, focusing on provable hyperparameter tuning, adversarial robustness, and beyond worst-case analysis of algorithms. His recent work develops techniques for tuning fundamental machine learning algorithms to domain-specific data and introduces new, powerful robust learning guarantees. He has published several papers at top venues in the field of machine learning, including NeurIPS, ICML, COLT, JMLR, etc., with multiple papers awarded with Oral presentations, and has interned with Google Research and Microsoft Research.