Damien Lekkas

Data Scientist in Digital Mental Health

Biography

Damien Lekkas is a data scientist with a doctorate in Quantitative Biomedical Sciences from Dartmouth College. Damien has diverse academic and research training in the natural and social sciences, as well as in the application of technology and computation to the biomedical domain. His work frequently leverages online and passively collected longitudinal data to digitally operationalize and analyze mental health constructs and outcomes. Working broadly at the interface of data science and psychology, he has published on a wide array of topics including, but not limited to, emotional affect, personality, eating disorders, depression, anxiety, trauma, and suicide. To contribute to an improved understanding and treatment of psychopathology in the digital era, Damien actively collaborates with researchers, clinicians, and business entities on data-driven projects and solutions within the mental health domain.

Interests

Mental Health & Psychiatry
Human Behavior
Digital Phenotyping
Applied Machine Learning
Network Analysis
Natural Language Processing
Statistical Modeling

Education

PhD in Quantitative Biomedical Sciences, 2024

Dartmouth College
MS in Bioinformatics, 2019

University of the Sciences in Philadelphia
MS in Anthropology, 2014

University of Pennsylvania
BA in Anthropology & Biology, 2014

University of Pennsylvania

Research Appointments

Pre-Doctoral Researcher (AI and Mental Health)

Center for Technology and Behavioral Health, Dartmouth College

March 2020 – December 2024 Lebanon, NH

Research Specialist (Circadian Rhythm and Metabolism)

Institute for Translational Medicine and Therapeutics, University of Pennsylvania

December 2016 – June 2019 Philadelphia, PA

Research Specialist (Ecology and Evolution of Disease Systems)

Department of Biology, University of Pennsylvania

May 2014 – July 2016 Philadelphia, PA

Current Research

Acute suicidal ideation in context: Highlighting sentiment-based markers through the diary entries of a clinically depressed sample

Given the demonstrated utility of smartphone-based EMA and NLP-based applications in mental health and suicide research, as well as a demonstrated need to develop more temporally sensitive models of acute suicidal ideation (SI), the current work aimed to apply a sentiment analysis approach to explore how language reflected in diary entries is tied to acute changes in self-report SI severity.

ChatGPT as Therapy: A statistical and network-based thematic profiling of shared experiences, attitudes, and beliefs on Reddit

Large language models (LLMs), including ChatGPT have exponentially grown in their utility over the past few years. For example, LLMs have frequently been endorsed as a suitable alternative or adjunct for traditional therapies for persons who experience mental health problems and are seeking help.

Automatic descriptive classification of self-reported traumatic experiences: A primer and example in applying a pretrained large language model to study psychological phenomenology.

A large cohort of N=1,473 individuals recruited through Google Ads for pre-screening as part of a larger study on major depressive disorder (MDD) is being used. Written responses to an open-ended prompt regarding the worst event experienced are descriptively classified via a novel 31-item inventory which allows for structured qualitative coding.

Publications

Michael R. Gallagher, Amanda C. Collins, Damien Lekkas, Matthew D. Nemesure, Tess Z Griffin, George D. Price, Michael V. Heinz, Arvind Pillai, Subigya Nepal, Daniel M. Mackin, Andrew T. Campbell, E. Samuel Winer, Nicholas C. Jacobson (2024). Anhedonia in flux: Understanding the associations of emotion regulation and anxiety with anhedonia dynamics in a sample with major depressive disorder. Journal of Affective Disorders, 372.

PDF DOI

Damien Lekkas, Nicholas C. Jacobson (2024). Breaking the silence: Leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning. Scientific Reports, 14.

PDF DOI

Joseph A. Gyorda, Damien Lekkas, Nicholas C. Jacobson (2024). Detecting longitudinal trends between passively collected phone use and anxiety among college students. Digital Biomarkers, 8(1).

PDF DOI

Michael V. Heinz, Damien Lekkas, Veronica Abreu, Camilla Lee, Lisa A. Marsch, Nicholas C. Jacobson (2024). Evaluating a mobile app’s effects on depression and anxiety in medication-treated opioid use disorder. npj Mental Health Research, 3.

PDF DOI

Amanda C. Collins, Damien Lekkas, Cara A. Struble, Brianna M. Trudeau, Abi D. Jewett, Tess Z Griffin, Matthew D. Nemesure, George D. Price, Michael V. Heinz, Subigya Nepal, Arvind Pillai, Daniel M. Mackin, Andrew T. Campbell, Alan T. Budney, Nicholas C. Jacobson (2024). From mood to use: Using ecological momentary assessments to examine how anhedonia and depressed mood impact cannabis use in a depressed sample. Psychiatry Research, 339.

PDF DOI

See all publications

Contact

If you are interested in a potential academic research collaboration, feel free to contact me via the email provided. For those outside of academia, I am also available as an independent contractor for your data science needs. I have 7+ years of experience in quantitative-focused research and am always looking for opportunities to help others solve interesting problems with data. My rate is typically $75-$125/hour; however, this may be negotiable depending on the scope and nature of the work. Whether you have a startup, own a small business, or are embarking on a personal project in need of methodological planning and consulting, automation, data wrangling and introspection, statistical modeling (including machine learning), and/or clear scientific writing, reach out to me via email or on LinkedIn–I would love to work with you!

dlekkas90@gmail.com