Research
I am currently conducting research in digital mental illness screening. Previously, I have also worked on projects including asexual citizenship, image accessibility, longitudinal antibiogram modeling, medical named entity recogntion, environmental sustainability, and combinatorics.
Digital Mental Illness Screening Research
Journal Articles
ML Tlachac, M Heinz, M Reisch, SS Ogden, “Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety Screening”, ACM Proceedings on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol 8 (1), 2024
AC Bryan, MV Heinz, AJ Salzhauer, GD Price, ML Tlachac, NC Jacobson, “Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment”, Biomedical Materials & Devices, Springer, 2024
ML Tlachac, A Shrestha, M Shah, B Litterer, and E Rundensteiner, “Automated Construction of Lexicons to Improve Depression Screening with Text Messages”, IEEE Journal of Biomedical and Health Informatics (J-BHI) Special Issue on Advancing Biomedical Discovery & Healthcare Delivery Through Digital Technologies, 2022
ML Tlachac, M Reisch, B Lewis, R Flores, L Harrison, and E Rundensteiner, “Impact Assessment of Stereotype Threat on Mobile Depression Screening using Bayesian Estimation”, Healthcare Analytics, Elsevier, 2022
ML Tlachac, R Flores, M Reisch, K Housekeeper, E Rundensteiner, “DepreST-CAT: Retrospective Smartphone Call and Text Logs Collected During the COVID-19 Pandemic to Screen for Mental Illnesses”, ACM Proceedings on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol 6 (2), 2022
ML Tlachac, R Flores, M Reisch, R Kayastha, N Taurich, V Melican, C Bruneau, H Caouette, J Lovering, E Toto, E Rundensteiner, “StudentSADD: Rapid Mobile Depression and Suicidal Ideation Screening of College Students during the Coronavirus Pandemic”, ACM Proceedings on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol 6 (2), 2022
ML Tlachac, E Rundensteiner, “Screening for Depression with Retrospectively Harvested Private versus Public Text”, IEEE Journal of Biomedical and Health Informatics (J-BHI), vol 24 (11), pp 3326-3332, 2020
Book Chapters
ML Tlachac, R Flores, E Toto, E Rundensteiner, “Early Mental Health Uncovering with Short Scripted and Unscripted Voice Recordings”, Deep Learning Applications (DLAV), vol 4, Springer, 2022
R Flores, ML Tlachac, E Toto, E Rundensteiner, “Transfer Learning for Depression Screening from Follow-up Clinical Interview Questions”, Deep Learning Applications (DLAV), vol 4, Springer, 2022
Conference Papers
R Flores, A Shrestha, ML Tlachac, E Rundensteiner, “Multi-Task Learning Using Facial Features for Mental Health Screening”, IEEE International Conference on Big Data (BigData) Special Session on Healthcare Data, pp 4881-4890, 2023
ML Tlachac, M Reisch, M Heinz, “Mobile Communication Log Time Series to Detect Depressive Symptoms”, 45th International Conference of IEEE Engineering in Medicine and Biology Society (EMBC), 2023
ML Tlachac, W Gerych, K Agrawal, B Litterer, N Jurovich, S Thatigotla, J Thadajarassiri, and E Rundensteiner, “Text Generation to Aid Depression Detection: A Comparative Study of Conditional Sequence Generative Adversarial Networks”, 2022 IEEE International Conference on Big Data (Big Data), pp 2804-2813, 2022
A Shrestha, ML Tlachac, R Flores, E Rundensteiner, “BERT Variants for Depression Screening with Typed and Transcribed Responses”, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) Computing for Well-being (WellComp), 2022
R Flores, ML Tlachac, A Shrestha, E Rundensteiner, “Temporal Facial Features for Depression Screening”, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) Mental Health: Sensing and Intervention, 2022
ML Tlachac, SS Ogden, “Left on Read: Anxiety and Depression Screening with Reply Latency”, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) Mental Health: Sensing and Intervention, 2022
R Flores, ML Tlachac, E Toto, E Rundensteiner, “AudiFace: Multimodal Deep Learning for Depression Screening”, Machine Learning for Healthcare, 2022
S Senn, ML Tlachac, R Flores, E Rundensteiner, “Ensembles of BERT for Depression Classification”, 44th International Conference of IEEE Engineering in Medicine and Biology Society (EMBC), pp 4691-4694, 2022
ML Tlachac, E Toto, J Lovering, R Kayastha, N Taurich, E Rundensteiner, “EMU: Early Mental Health Uncovering Framework and Dataset”, 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Special Session Machine Learning in Health, 2021
R Flores, ML Tlachac, E Toto, E Rundensteiner, “Depression Screening Using Deep Learning on Follow-up Questions in Clinical Interviews”, 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021
E Toto, ML Tlachac, E Rundensteiner, “AudiBERT: A Deep Transfer Learning Multimodal Classification Framework for Depression Screening”, 30th ACM International Conference on Information and Knowledge Management (CIKM) Applied Research Track, 2021 (best applied paper; Google Colab)
ML Tlachac, V Melican, M Reisch, E Rundensteiner, “Mobile Depression Screening with Time Series of Text Logs and Call Logs”, 17th IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2021 (poster presentation)
ML Tlachac, K Dixon-Gordon, E Rundensteiner, “Screening for Suicidal Ideation with Text Messages”, 17th IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2021
ML Tlachac, A Sargent, E Toto, R Paffenroth, E Rundensteiner, “Topological Data Analysis to Engineer Features from Audio Signals for Depression Detection”, 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020
E Toto, ML Tlachac, F Stevens, E Rundensteiner, “Audio-based Depression Screening using Sliding Window Sub-clip Pooling”, 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020
ML Tlachac, E Rundensteiner, “Depression Screening from Text Message Reply Latency”, 42nd International Conference of IEEE Engineering in Medicine and Biology Society (EMBC), pp 5490-5493, 2020
ML Tlachac, E Toto, E Rundensteiner, “You’re Making Me Depressed: Leveraging Texts from Contact Subsets to Predict Depression”, 16th IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp 1-4, 2019
Conference Abstracts
M Shah, ML Tlachac, B Litterer, S Thatigotla, N Jurovich, E Rundensteiner, “Improving Lexical Category Features for Depression Screening with Text Messages”, IEEE Conference on Biomedical and Health Informatics (BHI), 2021
ML Tlachac, E Rundensteiner, “The 10 Most Important Features in Predicting Depression from Content of Retrospectively Harvested Text Messages”, IEEE Conference on Biomedical and Health Informatics (BHI), 2019
Women in Data Science (WiDS) Central Massachusetts Abstracts
S Senn, ML Tlachac, R Flores, E Rundensteiner, “Ensembles of BERT for Depression Classification”, 2022
ML Tlachac, M Reisch, R Flores, E Rundensteiner, “StudentSADD versus DepreST: Collecting Data During COVID-19 for Rapid Mental Illness Screening”, 2022
K Houskeeper, M Dzwil, D Amicarella, ML Tlachac, “Extraction of Named Entities from Text Messages”, 2022
A Shrestha, ML Tlachac, M Shah, B Litterer, E Rundensteiner, “Constructing Lexicons to Improve Depression Screening with Texts”, 2022
K Agrawal, ML Tlachac, E Rundensteiner, “Generating Conditional Text Messages based on Depression”, 2021
M Reisch, ML Tlachac, “Stereotype Threat Study on Mobile Application”, 2021
R Kayastha, V Melican, C Bruneau, H Caouette, M Reisch, N Taurich, J Lovering, ML Tlachac, E Toto, E Rundensteiner, “Student Depression Dataset Collection”, 2021
Presentations
ML Tlachac, “Bayesian Estimation instead of t-tests for Comparison of Depression Screening Scores”, Cross-Disciplinary Research Symposium, Bryant University, 2022
ML Tlachac, “Text Reply Latencies for Mental Illness Screening”, Celebrating Academic Excellence, Bryant University, 2022
ML Tlachac, “Stereotype Threat Impact on Mobile Depression Screening”, Celebrating Academic Excellence, Bryant University, 2022
ML Tlachac, “Digital Biomarker Datasets for Mental Illness Screening”, Celebrating Academic Excellence, Bryant University, 2022
ML Tlachac, “StudentSADD: Rapid Mobile Depression and Suicidal Ideation Screening of College Students during the Coronavirus Pandemic”, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2022
ML Tlachac, “DepreST-CAT: Retrospective Smartphone Call and Text Logs Collected During the COVID-19 Pandemic to Screen for Mental Illnesses”, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2022
ML Tlachac, “Emutivo Digital Phenotype Datasets”, Cross-Disciplinary Research Symposium, Bryant University, 2022
ML Tlachac, “Screening for Depression and Anxiety with Text Logs”, 3MT, WPI, 2022
ML Tlachac, “StudentSADD: Student Suicidal Ideation and Depression Detection Dataset”, GRIE, WPI, 2022
ML Tlachac, “Collecting Data to Screen for Student Mental Health”, Graduate Research Lightning Talks, Arts and Sciences Week, WPI, 2021
ML Tlachac, “Mobile Data Collections for Mental Illness Screening”, Annual Sustainability Project Competition, WPI, 2021
ML Tlachac, “Improving Depression Screening with Text Messages”, 3MT, WPI, 2021
ML Tlachac, “Transfer Learning for Depression Screening with Text Messages”, GRIE, WPI, 2021
ML Tlachac, “Depression Screening with Text Messages”, Annual Sustainability Project Competition, WPI, 2021
ML Tlachac, “Text Generation with Signal Retention for Depression Screening”, 3MT, WPI, 2020
ML Tlachac, “Patient Text Message Data Generation”, GRIE, WPI, 2020
ML Tlachac, “Screening for Depression with Retrospectively Harvested Private versus Public Text”, Graduate Colloquium, Florida State Online ACM Computer Club, 2020
ML Tlachac, “Predicting Depression from Retrospectively Harvested Smartphone Data”, 3MT, WPI, 2019
ML Tlachac, “Predicting Mental Health from Smartphone Text Messages”, GRIE, WPI, 2019
Asexual Citizenship Research
Journal Article
- P Hart-Brinson, ML Tlachac, E Lepien, “Contradictions in Experiences of Compulsory Sexuality and Pathways to Asexual Citizenship”, Sexuality & Culture, Springer, 2023
Abstracts
P Hart-Brinson, ML Tlachac, E Lepien, “Paths to Asexual Citizenship Under Conditions of Compulsory Sexuality: An Intersectional Approach”, Midwest Sociological Society Annual Meeting, 2022
ML Tlachac, P Hart-Brinson, “Asexuality: An Emerging Sexual Orientation and Identity”, Celebration of Excellence in Research and Creative Activity, UWEC, 2015
Image Accessibility Research
Conference Papers
M Alkhathlan, ML Tlachac, E Rundensteiner, “Haptic Auditory Feedback for Enhanced Image Description: A Study of User Preferences and Performance”, 19th International Conference promoted by the IFIP Technical Committee 13 on Human–Computer Interaction (INTERACT), Springer, 2023
M Alkhathlan, ML Tlachac, L Harrison, E Rundensteiner, “Improving Image Accessibility by Combining Haptic and Auditory Feedback”, 24th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) Demos, 2022
M Alkhathlan, ML Tlachac, L Harrison, E Rundensteiner, "”Honestly I Never Really Thought About Adding a Description”: Why Highly Engaged Tweets are Inaccessible”, 18th International Conference promoted by the IFIP Technical Committee 13 on Human–Computer Interaction (INTERACT), Springer, 2021
Environmental Sustainability Research
Conference Paper
- R Flores, ML Tlachac, E Rundensteiner, “Measuring the Uncertainty of Environmental Good Preferences with Bayesian Deep Learning”, ACM International Conference on Information Technology for Social Good (GoodIT), 2022
Conference Abstract
- ML Tlachac, “Sociodemographic Factors Influencing Household Energy Efficiency in the United States”, Joint Mathematics Meetings, 2016
Presentations
ML Tlachac, “Sociodemographic Factors Influencing Household Energy Efficiency in the United States”, Colorado Energy Office, 2015
D Levin, L Paukner, ML Tlachac, “The Slippery Slope of the Polar Bears: A Mathematical Model of their Population”, Mathematics Retreat, UWEC, 2015
Longitudinal Antibiogram Modeling Research
Conference Papers
ML Tlachac, E Rundensteiner, K Barton, TS Troppy, K Beaulac, S Doron, “Anomalous Antimicrobial Susceptibility Trend Identification”, 42nd International Conference of IEEE Engineering in Medicine and Biology Society (EMBC), pp 5880-583, 2020
ML Tlachac, E Rundensteiner, K Barton, S Troppy, K Beaulac, S Doron, J Zou, “CASSIA: An assistant for identifying clinically and statistically significant decreases in antimicrobial susceptibility”, 15th IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp 389-392, 2018
ML Tlachac, E Rundensteiner, K Barton, S Troppy, K Beaulac, S Doron, “Predicting Future Antibiotic Susceptibility using Regression-based Methods on Longitudinal Massachusetts Antibiogram Data”, 11th International Conference on Health Informatics (HealthInf), pp 103-114, 2018
Book Chapter
- ML Tlachac, E Rundensteiner, TS Troppy, K Beaulac, S Doron, K Barton, “Predictive Modeling of Emerging Antibiotic Resistance Trends”, Biomedical Engineering Systems and Technologies, Communications in Computer and Information Science, Springer, vol 1024, pp 348-366, 2019
Conference Abstract
- K Beaulac, ML Tlachac, E Rundensteiner, K Barton, S Troppy, S Doron, “Utilization of Predictive Modeling to Identify Emerging Statewide Antibiotic Resistance Trends”, SHEA Spring 2018 Conference: Science Guiding Prevention, the Society for Healthcare Epidemiology of America, 2018 (best abstract, 5 awarded)
Presentations
ML Tlachac, “Utilization of Predictive Modeling to Identify Emerging Statewide Antibiotic Resistance Trends in Massachusetts”, Massachusetts Healthcare Associated Infection/Antibiotic Resistance Technical Advisory Group Meeting, 2018
ML Tlachac, “Tackling the Antibiotic Resistant Bacteria Crisis Using Predictive Analytics”, Graduate Colloquium, WPI Data Science REU, 2017
ML Tlachac, “Predicting Depression from Retrospectively Harvested Smartphone Data”, GRIE, WPI, 2019
Medical Named Entity Recognition Research
Book Chapter
- S Wunnava, X Qin, T Kakar, ML Tlachac, X Kong, E Rundensteiner, S Sahoo, S De, “Multi-layered Learning for Information Extraction from Adverse Drug Event Narratives”, Biomedical Engineering Systems and Technologies, Communications in Computer and Information Science, Springer, vol 1024, pp 421-446, 2019
Combinatorics Research
Journal Article
- D Bevan, D Levin, P Nugent, J Pantone, L Pudwell, M Riehl, ML Tlachac, “Pattern Avoidance in Forests of Binary Shrubs”, Discrete Mathematics and Theoretical Computer Science, vol 18(2), pp 1-22, 2016
Conference Abstracts
D Levin, P Nugent, L Pudwell, M Riehl, ML Tlachac, “Pattern Avoidance in Forests”, Joint Mathematics Meetings, 2015
P Nugent, L Pudwell, M Riehl, ML Tlachac, “Pattern Avoidance on Increasing Binary Trees”, Permutation Patterns Conference, 2014
Presentation
- D Levin, L Pudwell, M Riehl, ML Tlachac, “Help the Robots Stack Boxes: Pattern Avoidance in Forests of Binary Trees”, Mathematics Retreat, UWEC, 2015