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Assistant Professor of Information Systems and Analytics
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ML Tlachac is an Assistant Professor in the Information Systems and Analytics Department and Faculty Fellow of the Center for Health and Behavioral Sciences at Â鶹ӰÒô. An ACUE certified instructor, Tlachac teaches data science courses such as data mining and natural language processing. Receiving a Data Science Ph.D. from Worcester Polytechnic Institute in Spring 2022, Tlachac’s dissertation research focused on mental health screening with smartphone text logs. Among other research directions, Tlachac is continuing to conduct research at the intersection of mobile health, human-computer interaction, machine learning, and mental health. Tlachac’s research has been published in venues such as IEEE Journal of Biomedical and Health Informatics (J-BHI), IEEE Transactions on Affective Computing (TAC), and ACM Proceedings on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). Tlachac enjoys mentoring student researchers, writing, solving puzzles, and walking with dog Bumper.
Ph D, Worcester Polytechnic Institute
MS, Worcester Polytechnic Institute
BS, University of Wisconsin- Eau Claire
Zhao, T.,Tlachac, M., Bayesian Optimization with Tree Ensembles to Improve Depression Screening on Textual Datasets, IEEE Transactions on Affective Computing, 2024.
Tlachac, M.,Heinz, M., Mental Health and Mobile Communication Profiles of Crowdsourced Participants, IEEE Journal of Biomedical and Health Informatics, 2024.
Tlachac, M.,Heinz, M.,Reisch, M.,Ogden, S., Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety Screening, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2024.
Tlachac, M.,Flores, R.,Reisch, M.,Houskeeper, K.,Rundensteiner, E. A., DepreST-CAT: Retrospective Smartphone Call and Text Logs Collected during the COVID-19 Pandemic to Screen for Mental Illnesses, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2022.
Tlachac, M.,Flores, R.,Reisch, M.,Kayastha, R.,Taurich, N.,Melican, V.,Bruneau, C.,Caouette, H.,Lovering, J.,Toto, E.,Rundensteiner, E. A., StudentSADD: Rapid Mobile Depression and Suicidal Ideation Screening of College Students during the Coronavirus Pandemic, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2022.
Tlachac, M.,Rundensteiner, E., Screening For Depression With Retrospectively Harvested Private Versus Public Text, IEEE Journal of Biomedical and Health Informatics, 2020.
Bryan, A.,Heinz, M.,Salzhauer, A.,Price, G.,Tlachac, M.,Jacobson, N., Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment, Biomedical Materials and Devices, Springer, 2024.
Hart-Brinson, P.,Tlachac, M.,Lepien, E., Contradictions in the Experience of Compulsory Sexuality and the Pathways to Asexual Citizenship, Sexuality & Culture, 2023.
Tlachac, M.,Shrestha, A.,Shah, M.,Litterer, B.,Rundensteiner, E., Automated Construction of Lexicons to Improve Depression Screening with Text Messages, IEEE Journal of Biomedical and Health Informatics, 2023.
Flores, R.,Tlachac, M.,Toto, E.,Rundensteiner, E., Transfer Learning for Depression Screening from Follow-up Clinical Interview Questions, Deep Learning Applications, Springer, 2022.
Tlachac, M.,Flores, R.,Toto, E.,Rundensteiner, E., Early Mental Health Uncovering with Short Scripted and Unscripted Voice Recordings, Deep Learning Applications, Springer, 2022.
Tlachac, M.,Reisch, M.,Lewis, B.,Flores, R.,Harrison, L.,Rundensteiner, E., Impact Assessment of Stereotype Threat on Mobile Depression Screening using Bayesian Estimation, Healthcare Analytics, Elsevier, 2022.
Toto, E.,Tlachac, M.,Rundensteiner, E. A., AudiBERT: A Deep Transfer Learning Multimodal Classification Framework for Depression Screening, Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021.
Tlachac, M.,Gerych, W.,Agrawal, K.,Litterer, B.,Jurovich, N.,Thatigotla, S.,Thadajarassiri, J.,Rundensteiner, E., Text Generation to Aid Depression Detection: A Comparative Study of Conditional Sequence Generative Adversarial Networks, IEEE International Conference on Big Data, 2023.
Tlachac, M.,Melican, V.,Reisch, M.,Rundensteiner, E., Mobile Depression Screening with Time Series of Text Logs and Call Logs, 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 2021.
Tlachac, M.,Sargent, A.,Toto, E.,Paffenroth, R.,Rundensteiner, E., Topological Data Analysis to Engineer Features from Audio Signals for Depression Detection, 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020.
Tlachac, M.,Rundensteiner, E. A., Depression Screening from Text Message Reply Latency, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020.
Tlachac, M.,Rundensteiner, E.,Barton, K.,Troppy, T. S.,Beaulac, K.,Doron, S., Anomalous Antimicrobial Susceptibility Trend Identification, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020.
Tlachac, M.,Heinz, M. V.,Bryan, A. C.,LaPreay, A.,Dimas, G.,Zhao, T.,Jacobson, N. C.,Ogden, S. S., Datasets of Smartphone Modalities for Depression Assessment: A Scoping Review, Transactions on Affective Computing.
Jeanne and John Rowe Fellowship in Data Science, 2024
Academic Excellence, 2024
Merit, 2024
2024 Research Grant, 2024
CHBS summer funds, 2022
CTE faculty mini grant, 2022
Best Applied Research Paper, 2021
CARE (Courageous, Aware, Responsible, Exceptional) Award, 2021
Association for Computing Machinery
IEEE Engineering in Medicine and Biology Society
Institute of Electrical and Electronics Engineers
Assistant Professor of Information Systems and Analytics