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I am an assistant professor at UW Madison with a joint appointment in the Department of Mathematics and the Department of Population Health Sciences. I specialize in applied mathematics with a focus on computational psychiatry. This new field uses mathematics to study the interplay between brain and behavior. It adopts a "top-down" perspective that starts with behavior. Currently, I serve as a Statistical Editor at the American Journal of Psychiatry.
Find a complete list of my publications on Google scholar. Below provides a short overview of some areas of interest. Also take a look at my lecture notes on causal inference.
Models of human learning
One area of our research is devoted to mathematical representations of human learning. Our strategy is to analyze algorithms designed to solve different inductive problems, and then use what we learn to elucidate the computation humans perform to tackle such problems. By linking these algorithms to brain activity, we identify the underlying neural processes at play.
Selected publications:
Enkhtaivan, E, Nishimura, J, Cochran, AL (to appear). Placing approach-avoidance conflict within the framework of multi-objective reinforcement learning. Bulletin of Mathematical Biology.
Letkiewicz, AM, Cochran, AL, Privatsky, AA, James, GA, Cisler, JM (2022). Value Estimation and Latent-State Update-Related Neural Activity During Fear Conditioning Predict Posttraumatic Stress Disorder Symptom Severity. Cognitive, Affective, and Behavioral Neuroscience. 22(1), 199-213
Letkiewicz, AM, Cochran AL, Cisler, JM (2022). Severe sexual abuse reduces frontoparietal network activity during model-based reinforcement learning updates. Journal of Psychiatric Research. 145, 256-262
Enkhtaivan, E, Nishimura, J, Ly, C, Cochran, AL (2021). A Competition of critics in human decision-making. Computational Psychiatry. 5(1).
Nishimura, J, Cochran AL (2020). Rescorla-Wagner model with sparse dynamic attention. Bulletin in Math Biology. 82, 69.
Cochran AL, Cisler JM (2019). A flexible and generalizable model of online latent-state learning. PLoS Computational Biology. 15 (9), e1007331.
Mood dynamics in bipolar disorder
Clinicians were puzzled by the way in which the mood of a person with bipolar disorder would fluctuate over time. Through a series of papers, we developed and validated a formal framework for mood's complicated dynamics. I was awarded a K01 Career Development Award from the National Institute of Mental Health based on this work.
Selected publications:
Yee, MA, Yocum, AK, McInnis, MG, Cochran, AL (2021). Dynamics of Data-Driven Microstates in Bipolar Disorder. Journal of Psychiatric Research. 141, 370-377.
Cochran AL, Schultz A, McInnis MG, Forger DB (2018). Testing frameworks for personalizing bipolar disorder. Translational Psychiatry. 8.
Cochran AL, Schultz A, McInnis MG, Forger DB (2017). A comparison of mathematical models of mood in bipolar disorder. In: Érdi P, Sen Bhattacharya B, Cochran A (eds) Computational Neurology and Psychiatry. Springer Series in Bio-/Neuroinformatics, vol 6. Springer.
Érdi P, Bhattacharya B, Cochran, AL (Eds.) (2017). Computational neurology and psychiatry. Springer Series in Bio-/Neuroinformatics, vol 6. Springer.
Cochran AL, McInnis MG, Forger DB (2016). Data-driven classification of bipolar I from longitudinal course of mood. Translational Psychiatry. 6(10), e912.
Mobile therapy
As the lead developer and designer, I have created mobile frameworks for delivering mental health therapy. Our mobile frameworks have undergone successful (and unsuccessful) clinical trials. A notable byproduct of this work is the digiBP survey, designed to track mood in bipolar disorder and gaining national and international interest.
As a side note. Sustaining this work is challenging. As a researcher, there is little incentive to complete app projects, leaving them often unfinished. I would love to work with individuals interested in maintaining these projects.
Selected publications:
Cochran, AL, Maronge, JM, Victory, A, Hoel, S, McInnis, MG, Thomas, EBK (2023). Mobile acceptance and commitment therapy in bipolar disorder: a micro-randomized trial. JMIR Mental Health. 10(1), e43164.
Thomas, EBK, Sagorac Gruichich, T, Maronge, JM, Hoel, S, Victory, A, Stowe, ZN, Cochran, AL (2023). Mobile acceptance and commitment therapy with distressed first-generation college students: a micro-randomized trial. JMIR Mental Health. 10, e43065.
Hoel, S, Victory, A, Sagorac Gruichich, T, Stowe, ZN, McInnis, MG, Cochran, AL, Thomas, EBK (2022). A mixed-methods analysis of mobile ACT responses from two cohorts. Frontiers Digital Health. 4: 869143.
Sagorac Gruichich T, David Gomez, JC, Zayas-Cabán, G, McInnis, M, Cochran AL (2021). A digital self-report survey of mood for bipolar disorder. Bipolar disorders.
Van Til, K, McInnis, M, Cochran AL (2020). A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder. Bipolar disorders.
Kroska EB, Hoel S, Victory A, Murphy SA, McInnis MG, Stowe ZN, Cochran AL (2020). Optimizing acceptance and commitment therapy microintervention via a mobile app with two cohorts: Protocol for micro-randomized trials. JMIR Research Protocols. 9(9): e17086.
Cochran, AL, Belman-Wells, L, McInnis, M (2018) Engagement Strategies for Self-Monitoring Symptoms of Bipolar Disorder with Mobile and Wearable Technology: Protocol for a Randomized Control Trial. JMIR Research Protocols. 7(5):e130.
Causal inference
What started as a side project in 2016 has grown into a major focus of my research: creating causal inference methods to evaluate interventions in stochastic systems. Our motivation is to offer evidence-based guidelines for providers making decisions in the Emergency Department. Beyond its practical importance, our work addresses unique technical challenges, including confounding by indication, interference, non-iid data, and the random occurrence and sequencing of events, and random number and order of events. In 2022, I taught a topics course on causal inference; you can find my lecture notes here.
Selected publications:
David Gomez, JC, Cochran, AL, Zayas-Cabán, G (submitted). Unveiling bias in sequential decision making: A causal inference approach for stochastic service systems.
David Gomez, JC, Cochran, AL, Patterson, B, Zayas-Cabán, G (submitted). Evaluation of a Split Flow Model for the Emergency Department.
Nieser, KJ, Cochran, AL (to appear). Quantifying and reducing inequity in average treatment effect estimation. BMC Medical Research Methodology.
Alverez, S, Cochran, A, Patterson, B, Shah, M, Smith, M, Zayas-Cabán (2020). The average effect of ED admissions on readmissions and mortality for older adults with chest pain. Medical Care. 58(10): 881-888.
Cochran AL, Rathouz PJ, Kocher K, Zayas-Cabán G (2019). A latent variable approach to potential outcomes for emergency department admission decisions. Statistics in Medicine. 38: 3911-3935.
Other work of note
Nieser, K, Stowe ZN, Newport JN, Coker JL, Cochran AL (2023) Detection of differential depressive symptom patterns in a cohort of perinatal women: an exploratory factor analysis using a robust statistics approach. eClinicalMedicine. 57.
Nieser, K, Cochran AL (2021) Addressing heterogeneous populations in latent variable settings with robust estimation. Psychological Methods. 28(1), 39-60.
Cochran, AL, Nieser, K, Forger, DB, Zollner, S, McInnis, MG (2020). Gene-set enrichment with mathematical biology (GEMB). GigaScience. 9(10), giaa091