CV

Data systems Research for Exploration, Analytics, and Modeling Lab (DREAM Lab)

University of Massachusetts Amherst

Predictive machine learning (ML) model maintenance

• Currently working on methods to predict model performance without ground truth by utilizing custom ensemble models and uncertainty measures.
• Creating predictive retraining strategies to retrain models with minimal downtime and improved predictive performance on drifting data.

PhD Research Intern

Dolby Advanced Technology Group (ATG)

Extended and improved predictive ML model maintenance

• Developed a dynamic temperature scaling method for machine learning models that calibrates logits based on input features, improving uncertainty estimation and prediction reliability
• Expanded predictive model maintenance by incorporating granular loss levels and adaptive loss bounds to optimize retraining decisions, while experimenting with extensions to address concept drift.

M.S/PhD Student

University of Massachusetts Amherst

Computer Science

• COMPSCI 683: Artificial Intelligence
• COMPSCI 645: Database Design and Implementation
• COMPSCI 611: Advanced Algorithms
• COMPSCI 682: Neural Networks

Supervised ECG wave segmentation using convolutional LSTM

ICT Express

Malali, A., Hiriyannaiah, S., Siddesh, G. M., Srinivasa, K. G., & Sanjay, N. T. (2020). Supervised ECG wave segmentation using convolutional LSTM. ICT express, 6(3), 166-169.