ML Tlachac

ML Tlachac

Assistant Professor of Data Science at Bryant University

Publications

Journal Articles

[9] 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

[8] 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

[7] P Hart-Brinson, ML Tlachac, E Lepien, “Contradictions in Experiences of Compulsory Sexuality and Pathways to Asexual Citizenship”, Sexuality & Culture, vol 28, Springer, 2024

[6] 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, vol 27 (6), 2023

[5] 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, vol 2, Elsevier, 2022

[4] 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

[3] 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

[2] 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

[1] 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

Book Chapters

[4] 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

[3] 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

[2] 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

[1] 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 Papers

[24] 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

[23] 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

[22] 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

[21] 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

[20] 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

[19] 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

[18] 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

[17] 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 (10 min talk)

[16] 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

[15] R Flores, ML Tlachac, E Toto, E Rundensteiner, “AudiFace: Multimodal Deep Learning for Depression Screening”, Machine Learning for Healthcare, 2022

[14] 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

[13] 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 (15 min talk)

[12] 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

[11] 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)

[10] 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)

[9] 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 (poster presentation)

[8] 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

[7] 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 (10 min talk)

[6] 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

[5] 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 (10 min talk)

[4] 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 (10 min talk)

[3] 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 (15 min talk)

[2] 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 (15 min talk)

[1] 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 (30 min talk)

Conference Abstracts

[8] 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

[7] 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

[6] 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 (poster presentation)

[5] 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)

[4] ML Tlachac, MS Tlachac, A Brisban, “Statistical Analysis of Crime in Eau Claire, WI”, Joint Mathematics Meetings, 2016

[3] ML Tlachac, “Sociodemographic Factors Influencing Household Energy Efficiency in the United States”, Joint Mathematics Meetings, 2016 (poster presentation)

[2] D Levin, P Nugent, L Pudwell, M Riehl, ML Tlachac, “Pattern Avoidance in Forests”, Joint Mathematics Meetings, 2015 (poster presentation)

[1] P Nugent, L Pudwell, M Riehl, ML Tlachac, “Pattern Avoidance on Increasing Binary Trees”, Permutation Patterns Conference, 2014 (poster presentation)

Women in Data Science (WiDS) Central Massachusetts Abstracts

[7] S Senn, ML Tlachac, R Flores, E Rundensteiner, “Ensembles of BERT for Depression Classification”, 2022

[6] ML Tlachac, M Reisch, R Flores, E Rundensteiner, “StudentSADD versus DepreST: Collecting Data During COVID-19 for Rapid Mental Illness Screening”, 2022 (poster presentation)

[5] K Houskeeper, M Dzwil, D Amicarella, ML Tlachac, “Extraction of Named Entities from Text Messages”, 2022

[4] A Shrestha, ML Tlachac, M Shah, B Litterer, E Rundensteiner, “Constructing Lexicons to Improve Depression Screening with Texts”, 2022

[3] K Agrawal, ML Tlachac, E Rundensteiner, “Generating Conditional Text Messages based on Depression”, 2021

[2] M Reisch, ML Tlachac, “Stereotype Threat Study on Mobile Application”, 2021

[1] 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

Instruction Guide

[1] ML Tlachac, “A Guide for using Polyglot on Windows”, Medium, 2020