1Understand the opportunities and challenges of using wearable sensors within the healthcare system through directed reading and experiential learning assignments.

2Dictate the key ethical and professional guidelines when working with healthcare data through directed reading, guest lectures, and self-reflection.

3Process and interpret a variety of physiologic signals and derived data streams across various physiologic and clinically-relevant timescales, especially pertaining to cardiovascular and thermoregulatory systems.

4Cite - from traditional lecture-based learning, directed reading, and knowledge gained from experience working with one's own data - how the brain and autonomic nervous system governs involuntary organ and body functions at the tissue, organ, and system level to maintain homeostasis across different allostatic loads, and how DHT-derived data streams can be processed and analyzed to identify patterns of activity indicative of normal stress physiology, abnormal stress physiology, infection, or disease.

5Apply advanced signal processing and statistical methods comfortably to process noisy and discontinuous DHT-derived data streams to produce meaningful estimates of physiologic activity that account for uncertainty in the measurements through analysis of their own physiologic data streams throughout the semester, assessed through in-class labs, team-based coding competitions, and homework assignments.

6Communicate effectively between mathematical, engineering, biological, and medical disciplines, including with subject matter experts, through knowledge gained from the unique mix of lectures and labs taught by instructors with medical and engineering expertise.