Daniel M. Mackin, Ph.D.

Daniel M. Mackin, Ph.D.

Postdoctoral Fellow in Biomedical Data Science

Center for Technology and Behavioral Health

Geisel School of Medicine at Dartmouth College

Biography

Dan Mackin is a psychologist and postdoctoral fellow in Biomedical Data Science in the Geisel School of Medicine at Dartmouth College. He works in the Artificial Intelligence and Mental Health: Innovation in Technology Guided Healthcare (AIM HIGH) Lab in the Center for Technology and Behavioral Health. He received his Ph.D. in Clinical Psychology from Stony Brook University and completed his clinical fellowship at the Warren Alpert Medical School of Brown University.

Dr. Mackin’s research has used a wide range of methods to study the development and course of depression and anxiety. He has investigated how factors such as reinforcement learning, reward processing, life stress, and personality influence internalizing disorders longitudinally. He has also developed measures assessing subtypes of Major Depressive Disorder.

Most recently, Dr. Mackin’s research has focused on applying machine learning techniques to passive sensor data from smartphones and wearable devices to better assess and predict real-time fluctuations in depressive symptoms and functioning. He is also interested in developing and implementing personalized just-in-time adaptive interventions (JITAIs).

Interests
  • Longitudinal Data Analysis
  • Statistical Modeling
  • Machine Learning
  • Reinforcement Learning
  • Passive Sensing
  • Digital Mental Health
  • Psychopathology
Education
  • PhD in Clinical Psychology, 2023

    Stony Brook University

  • Clinical Fellowship in Psychology, 2023

    Warren Alpert Medical School of Brown University

  • MA in Psychology, 2015

    Stony Brook University

  • BS in Philosophy (summa cum laude), 2014

    Northeastern University

Skills

Python
Statistics
R

Research Appointments

 
 
 
 
 
Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College
Postdoctoral Fellow in Biomedical Data Science
July 2023 – Present Hanover, NH
 
 
 
 
 
The Warren Alpert Medical School, Brown University
Clinical Psychology Resident
July 2022 – June 2023 Providence, RI
 
 
 
 
 
Klein Developmental Psychopathology Lab, Stony Brook University
Doctoral Student Researcher
September 2017 – June 2023 Stony Brook, NY
 
 
 
 
 
Mind-Body Clinical Research Center, Stony Brook University School of Medicine
Research Specialist
June 2015 – August 2017 Stony Brook, NY
 
 
 
 
 
Department of Psychiatry, Stony Brook University School of Medicine
Research Specialist
June 2015 – May 2016 Stony Brook, NY
 
 
 
 
 
Department of Psychology, Stony Brook University
Graduate Student Researcher
May 2014 – May 2015 Stony Brook, NY

Projects

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Neuromelanin, Reinforcement Learning, and Anhedonia: Identifying Associations, Antecedents, and Consequences
Reward processing plays a transdiagnostic role in the etiopathogenesis of psychopathology. Reinforcement learning deficits, driven by dopamine dysregulation, are hypothesized to explain key aspects of such abnormalities. Therefore, this project examines whether dopamine, assessed using neuromelanin-sensitive magnetic resonance imaging (NM-MRI), a novel imaging technique for assessing long-term midbrain dopamine function in the Substantia Nigra (SN) and Ventral Tegmental Area (VTA), contributes to reinforcement learning performance.
Neuromelanin, Reinforcement Learning, and Anhedonia: Identifying Associations, Antecedents, and Consequences
Prediction of Fluctuations in Depression and PTSD symptoms in World Trade Center Responders: A Longitudinal Sensing and Ecological Momentary Assessment Study
Ecological momentary assessment (EMA) and passive sensing data from a large sample of World Trade Center (WTC) first responders experincing depression and PTSD symptoms will be used to explore the utility of traditional machine learning and deep learning models in prediction of real-time symptom change.
Prediction of Fluctuations in Depression and PTSD symptoms in World Trade Center Responders: A Longitudinal Sensing and Ecological Momentary Assessment Study
Using Smartphone Sensing Data and Machine Learning to Predict Trajectories of COVID-19-Related Life Stress
The COVID-19 pandemic disrupted the daily lives of individuals worldwide, contributing to significant increases in life stress. Individuals with mental health disorders are known to be at risk for worsening of symptoms and functioning during periods of increased stress, making vulnerable during the COVID-19 pandemic.
Using Smartphone Sensing Data and Machine Learning to Predict Trajectories of COVID-19-Related Life Stress

Additional Selected Publications

(2023). Identifying the DSM-5 mixed features specifier in depressed patients: A comparison of measures. In Journal of Affective Disorders.

PDF DOI

(2023). Reliability and validity of the difficult to treat depression questionnaire (DTDQ). In Psychiatry Research.

PDF DOI

(2022). Intergenerational Transmission of Depressive and Anxiety Disorders: Mediation Via Youth Personality. In Journal of Psychopathology and Clinical Science.

PDF DOI

(2022). Reward processing and depression: Current findings and future directions. In The Neuroscience of Depression - Genetics, Cell Biology, Neurology, Behaviour, and Diet.

PDF DOI

(2022). Is personality stable and symptoms fleeting? A longitudinal comparison in adolescence. In Journal of Research in Personality.

PDF DOI

Contact

If interested in collaborating, contact me via email.