Amy Louise Cochran

Amy Louise Cochran is an Assistant Professor at the University of Wisconsin–Madison with a joint appointment in the Department of Mathematics and the Department of Population Health Sciences. She specializes in computational psychiatry, using mathematical approaches to study the interplay between brain and behavior from a 'top-down' perspective that starts with behavior. Cochran serves as a Statistical Editor at the American Journal of Psychiatry and has developed innovative research methodologies across multiple domains of mental health and medical decision-making.

Her research spans four primary areas: mathematical representations of human learning, mood dynamics in bipolar disorder, mobile mental health therapy frameworks, and causal inference methods for evaluating interventions in stochastic systems. Cochran's work on human learning analyzes algorithms designed to solve inductive problems to elucidate computations humans perform, linking these algorithms to brain activity to identify underlying neural processes. In bipolar disorder research, she developed and validated formal frameworks for mood dynamics, which earned her a K01 Career Development Award from the National Institute of Mental Health. Her mobile therapy work includes creating the digiBP survey for tracking mood in bipolar disorder, which has gained national and international interest. Her causal inference research addresses unique technical challenges including confounding by indication, interference, non-iid data, and the random occurrence and sequencing of events in emergency department settings.

Cochran's recent publications demonstrate a strong trend toward methodological innovation in statistical approaches to mental health and emergency medicine. Her work increasingly integrates computational modeling with clinical applications, particularly in developing tools for evidence-based decision-making in complex healthcare settings. She has made significant contributions to understanding mood dynamics in bipolar disorder, developing mobile interventions for mental health conditions, and creating robust statistical methods for causal inference in emergency department operations.

Her scientific recognition includes:

• K01 Career Development Award from the National Institute of Mental Health
• Statistical Editor position at the American Journal of Psychiatry

Cochran has secured research funding supporting her work on mood dynamics in bipolar disorder and causal inference methods. She has developed multiple clinical tools including the digiBP survey and mobile acceptance and commitment therapy frameworks. Her research program demonstrates strong interdisciplinary collaboration across mathematics, psychiatry, emergency medicine, and public health disciplines. Cochran has also contributed to education through her lecture notes on causal inference and her role as Graduate Non-Math Minor Advisor.

Her laboratory work focuses on computational modeling of psychiatric conditions and developing digital tools for mental health assessment and intervention. She leads research teams investigating the mathematical foundations of human learning processes, mood regulation in bipolar disorder, and causal inference methods for emergency department decision-making. Her work bridges theoretical mathematics with practical clinical applications, creating tools that have real-world impact in mental health care and emergency medicine.