I am a doctoral student under the co-supervision of Prof. Mauro Cherubini from UNIL and Prof. Dinesh Babu Jayagopi from IIIT Bangalore . My research interests lie in the intersection of Human-Computer Interaction and Artificial Intelligence. My goal is to help job seekers overcome the challenges of unemployment with a focus on interview training through the techniques of Artificial Intelligence and Natural Language Processing.
I completed my Master's by Research in AI at IIIT Bangalore where my thesis focused on improving and automating asynchronous video interviews using Machine Learning, Natural Language Processing and Generation, Multimodal Processing. My industry background includes stints at Accenture Technology Labs and HP Inc R&D Labs working on different NLP problems. Prior to my Master's, I worked at Fidelity Investments for 2 years.
When not working, I enjoy doing the classical dance form of Kathak and getting to know about zero-waste/ sustainable practices.
- Pooja Rao S. B., Manish Agnihotri and Dinesh B. Jayagopi, “Incorporating Automatic Follow-up Question Generation for Asynchronous Interviews.”
- Pooja Rao S. B., Sowmya Rasipuram, Rahul Das, and Dinesh B. Jayagopi, “Automatic Assessment of Communication Skill in Non-conventional Interview Settings: A Comparative Study.” In Proceedings of the 19th ACM International Conference on Multimodal Interaction (ICMI) 2017
- Sowmya Rasipuram, Rahul Das, Pooja Rao S. B., and Dinesh B. Jayagopi, “Online Peer-to-Peer Discussions: A Platform for Automatic Assessment of Communication Skill.” In Proceedings of Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) 2017
- Sowmya Rasipuram, Pooja Rao S. B., and Dinesh B. Jayagopi, “Automatic Prediction of Fluency in Interface-Based Interviews.” In India Conference (INDICON) 2016 IEEE
- Sowmya Rasipuram, Pooja Rao S. B., and Dinesh B. Jayagopi, “Asynchronous Video Interviews vs. Face-to-Face Interviews for Communication Skill Measurement: A Systematic Study.” In Proceedings of the 18th ACM International Conference on Multimodal Interaction (ICMI) 2016. Nominated for Best Student Paper
Work towards adding automated elements to asynchronous interview systems by solving the problems of interview follow-up question generation and automatic assessment of candidates using various Machine Learning techniques like text generation, text classification and transfer learning techniques for NLP.
- Automatic Follow-up Question Generation – enable a virtual interviewer to generate a follow-up question given a predetermined question and a candidate response using LSTM-based and Transformer models. (View demo)
- Automatic Assessment of Communication Skill – Supervised predictive models to assess the written communication skill scores of interview candidates in various non-conventional interview settings. (View thesis and defense slides)