Dr. Archna Bhatia is a Research Scientist in Natural Language Processing (NLP) and Speech Processing at the Florida Institute for Human and Machine Cognition. Previously she has held postdoctoral researcher positions at the Language Technologies Institute at Carnegie Mellon University, and at the University of Colorado at Boulder. She received her PhD in Linguistics from the University of Illinois at Urbana-Champaign in 2011.
Dr. Bhatia’s research is focused on (1) developing NLP systems that integrate human acquired knowledge, such as linguistics, and machine learning for various applications such as cybersecurity, information extraction and cognitive modeling, (2) developing ontology and lexicon to improve semantic parsing and natural language understanding, and (3) speech processing for health applications such as detection and assessment of conditions such as ALS and stress. For example, she is currently working on extraction of individuals’ beliefs and sentiments, using NLP and social psychology, based on the textual content they produce in social media (Pirolli et al., 2020). Recently, she worked in a team that developed a human language technology pipeline for active defenses against social engineering attacks that makes use of NLP, computational sociolinguistics and metadata analysis (Bhatia et al., 2020; Dalton et al., 2020; Dorr et al., 2020). To compute deep semantic representations of sentences, she has attempted to capture the richness of lexical semantics focusing on verb particle constructions, a type of multiword expressions using lexical resources such as WordNet, and has worked on incorporating the acquired knowledge into an ontology and lexicon to improve semantic parsing (Bhatia et al., 2018; Bhatia et al., 2017b). Finally, in the health domain, she has been working on developing non-invasive techniques for detection and monitoring of physiological, psychological and neurological conditions. For example, she has developed an approach to detect stress and measure individuals’ response to stress based on the speech and language they produce (Bhatia et al., 2021). Previously, she developed a non-invasive speech based method for detection and monitoring of ALS based on divergence from the asymptomatic speech (Bhatia et al., 2017a; Bhatia et al., 2017c).