I’m a Machine Learning Scientist at Tripadvisor where I work on building data science methods and machine learning models that drive Tripadvisor’s Experiences and “Things To Do” products. I joined Tripadvisor’s Data Science and Machine Learning Team in June 2023.

In August 2023, I graduated with a PhD from Carnegie Mellon University’s School of Computer Science. My PhD research focused on developing machine learning methods and data-driven interfaces for understanding and augmenting human behavior, with the goal of advancing high-impact application areas like health, well-being, computer-mediated communication, and collaboration.

I have extensive experience in analyzing and modeling complex datasets from multiple modalities, such as smartphone and wearable data, web interaction logs, text and speech data, face and body video, and electronic health records by leveraging my diverse skills in machine learning, human-computer interaction, natural language processing, speech processing, and computer vision. To develop data-driven interfaces, I also leverage iterative prototyping and qualitative methods like user interviews and thematic analysis, thereby creating interfaces tailored to the needs of the user.

I’m a strong software developer experienced in various programming languages and platforms, and have advanced knowledge of data structures and algorithms, structured query language (SQL), and core machine learning frameworks.

My work has been published in top-tier computer science venues such as ACM CHI, IMWUT (UbiComp), CSCW, TOCHI, and JMIR. I have been a part of research teams at Microsoft Research and Snap Research, and have collaborated on research projects with clinicians and the University of Pittsburgh Medical Center. I was awarded the Center for Machine Learning and Health Fellowship, and the Snap Research Fellowship.

I also have a MS in Robotics from Carnegie Mellon University, and a B.Eng. in Computer Science from Nanyang Technological University.

If you’d like to contact me, feel free to reach me at pchikersal[@]gmail.com.

Publications

Peer-Reviewed Conference and Journal Papers

Chikersal, P., Bayer, K., Smith, B. A., Nayar, S. K. When Emoji Speak Louder than Words: Personalized Emoji-first Messaging for Enhanced Communication between Partners and Close Friends.. In Preparation for TBD.

Chikersal, P., Venkatesh, S., Masown, K., Walker, E., Quraishi, D., Dey, A., Goel, M., & Xia, Z. Predicting Multiple Sclerosis Outcomes during the COVID-19 Stay-at-Home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping. In JMIR mental health (2022).

Xu, X., Chikersal, P., Dutcher, J. M., Sefidgar, Y. S., Seo, W., Tumminia, M. J., Villalba, D. K., Cohen, S., Creswell, K. G., Creswell, J. D., Doryab, A., Nurius, P. S., Riskin, E., Dey, A. K., & Mankoff, J. Leveraging Collaborative-Filtering for Personalized Behavior Modeling: A Case Study of Depression Detection among College Students. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Ubicomp 2021).

Tomprou, M., Kim, Y. J., Chikersal, P., Woolley, A. W., & Dabbish, L. (2021). Speaking out of turn: How video conferencing reduces vocal synchrony and collective intelligence. PLOS ONE. [Wall Street Journal Article] [Forbes Article] [Harvard Business Review Article].

Chikersal, P., Doryab, A., Tumminia, M., Villalba, D., Dutcher, J., Liu, X., Cohen, S., Creswell, K., Mankoff, J., Creswell, D., Goel, M., & Dey, A. (2020). Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection. ACM Transactions on Computer-Human Interaction (TOCHI 2020). [Link to Slides]

Chikersal, P., Belgrave, D., Doherty, G, Enrique, A., Palacios, J., Richards, D., & Thieme, A. (2020). Understanding Client Support Strategies to Improve Clinical Outcomes in an Online Mental Health Intervention. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI 2020). [Link to Slides] [Link to Presentation Video]

Xu, X., Chikersal, P., Doryab, A., Villalba, D., Dutcher, J. M., Tumminia, M. J., Althoff, T., Cohen, S., Creswell, K., Creswell, D., Mankoff, J., & Dey, A. K. (2019). Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Ubicomp 2019).

Doryab, A., Villalba, D. K., Chikersal, P., Dutcher, J. M., Tumminia, M., Liu, X., Cohen, S., Creswell, K., Mankoff, J., Creswell, D., & Dey, A. K. (2019). Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: A Three-fold Analysis. In Journal of medical Internet research (JMIR 2019).

Chikersal, P., Tomprou, M., Kim, Y. J., Woolley, A. W., & Dabbish, L. (2017). Deep Structures of Collaboration: Physiological Correlates of Collective Intelligence and Group Satisfaction. In Proceedings of the 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017). [Link to Slides]

Chikersal, P., Poria, S., Cambria, E., Gelbukh, A., & Siong, C. E. (2015). Modelling Public Sentiment in Twitter: Using Linguistic Patterns to Enhance Supervised Learning. In International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2015) (pp. 49-65). [Link to Slides]

Peer-Reviewed Workshop Papers

Chikersal, P., Doherty, G, & Thieme, A. (2020). Towards Using AI to Augment Human Support in Digital Mental Healthcare. In Proceedings of the 2020 CHI Workshop on Technology Ecosystems: Rethinking Resources for Mental Health.

Chikersal, P., Poria, S., & Cambria, E. (2015). SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning. In Proceedings of the 4th International Workshop on Semantic Evaluations (pp. 647-651). Association for Computational Linguistics.

Doctoral Thesis

Chikersal, P. (2023, Dec). Multimodal Behavioral Sensing for Precision Mental Health Care. PhD Thesis, Carnegie Mellon University, Pittsburgh, PA, USA. (Thesis Defense Slides Here)

Pre-doctoral Thesis

Chikersal, P. (2017, May). Deep Structures of Collaboration. Masters Thesis, Carnegie Mellon University, Pittsburgh, PA, USA.

Chikersal, P. (2015, May). Modelling Public Sentiment in Twitter. Bachelor Thesis, Nanyang Technological University, Singapore.

Patents

Bayer, K., Chikersal, P., Nayar, S. K., Smith, B. A. Client device processing received emoji-first messages. U.S. Patent 11,593,548, filed Apr 20, 2021, and issued on Feb 28, 2023.

Bayer, K., Chikersal, P., Nayar, S. K., Smith, B. A. Personalized emoji dictionary. U.S. Patent 11,531,406, filed Apr 20, 2021, and issued on Dec 20, 2022.

Bayer, K., Chikersal, P., Nayar, S. K., Smith, B. A. Emoji-First Messaging. U.S. Patent Application Number 17/234,884, filed Apr 20, 2021, and currently pending.