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Background
- Assistant Professor in the Department of Computer Science at Quinnipiac University.
- Broad research interests in machine learning, deep learning, data mining, and AI for smart and connected health.
- Focuses on multimodal data analysis, time series modeling, missing data imputation, and clinical outcome prediction, particularly in mental health.
- Develops sequential and longitudinal prediction models for depression treatment outcomes using heterogeneous data (e.g., mobile sensing, daily surveys, medication data).
- Advances domain adaptation and representation learning to address platform variability (e.g., Android vs. iOS) in mobile health data.
- Recent work explores deep learning architectures such as GRU-D, BRITS, and transformer-based models, with regularized domain-adaptation approaches for robust cross-platform healthcare prediction.
- Collaborates closely with clinicians to integrate AI-driven insights into clinical decision-making; predictive models outperform traditional assessment tools.