Sha (Stanley) Lai
lais823-AT-bu.edu
CV
Github

Biography

I am a PhD student in the department of Computer Science (CS) at Boston University (BU) and my advisors are Margrit Betke and Prakash Ishwar from the ECE department.
I obtained a Bachelor of Science degree in Mathematics at the University of Washington, Seattle, in 2016 and a Master of Science degree in Computer Science at BU in 2019. Currently, I am finishing my PhD thesis on methods for tackling limited data in ML.

Education

Boston University - PhD candidate in CS: January 2019 - May 2025 (expected)
Boston University - Master of Science in Computer Science: September 2017 - January 2019
University of Washington (Seattle, WA) - Bachelor of Science in Mathematics: September 2012 - December 2016

Research

My research interests lie in the intersection of theoretical machine learning and its practical applications. I am particularly interested in addressing challenges posed by datasets that are not well-suited for conventional machine learning techniques. This includes areas like communication and journalism, where human biases can significantly influence data annotation, and medical fields, where datasets are often limited in size.

Teaching

Teaching Fellow - CS 440(Undergraduate) / CS 640 (Graduate): Artificial Intelligence
Boston University | Every fall semester since 2019
This used to be a combined undergraduate/graduate course where I led weekly recitation sessions, managed assignments and discussions, and provided support to students in their course projects. When the course was later splitted into two independent undergraduate and graduate levels, I continued my work in the graduate-level AI course (CS 640).

Teaching Fellow - CS 131 (Undergraduate): Combinatoric Structures
Boston University | Summer 2024
I led weekly discussion sessions and graded exercises and exams for this online course.

Mentorship

Mentor for Master student Saurav Chennuri - Boston University | 2022 - 2023
I mentored the master student Saurav Chennuri on a collaborative project in studying aphasia severity. During this time period, I assessed the validity and feasibility of the mentee's proposed approaches, helped them construct and articulate the rationale behind a complex feature selection and classification pipeline. This project eventually led to a publication (see publication #8 below).

Affiliation

Research groups in Boston University:
  1. Image and Video Computing (IVC)
  2. Artificial Intelligence and Emerging Media (AIEM)
  3. The Artificial Intelligence Research (AIR) initiative
  4. Aphasia Research Lab

Selected Publications

  1. Jiang, Yanru, Lai, Sha, Guo, Lei, Ishwar, Prakash, Wijaya, Derry, and Betke, Margrit (2022). Community Detection of the Framing Element Network: Proposing and Assessing a New Computational Framing Analysis Approach. Paper presented at the annual conference of the Association for Education in Journalism and Mass Communication (AEJMC), Detroit, MI. August 3, 2022.. [Link]
  2. Sha Lai, Yanru Jiang, Lei Guo, Margrit Betke, Prakash Ishwar, and Derry Tanti Wijaya. An Unsupervised Approach to Discover Media Frames. Proceedings of The LREC 2022 Workshop on Natural Language Processing for Political Science, Marseille, France, June 20-25, 2022, ISBN: 979-10-95546-88-7. Pages 22-31 pdf.
  3. Billot, Anne, Sha Lai, Maria Varkanitsa, Emily J. Braun, Brenda Rapp, Todd B. Parrish, James Higgins et al. "Multimodal neural and behavioral data predict response to rehabilitation in chronic poststroke aphasia." Stroke 53, no. 5 (2022): 1606-1614. [Link]
  4. Isidora Tourni, Lei Guo, Taufiq Husad Daryanto, Fabian Zhafransyah, Edward Edberg Halim, Mona Jalal, Boqi Chen, Sha Lai, Hengchang Hu, Margrit Betke, Prakash Ishwar, and Derry Tanti Wijaya. Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage. In Findings of the Assocation for Computational Linguistics: 2021 Conference on Empirical Methods in Natural Language Processing. November 2021, pages 4037-4050, Punta Cana, Dominican Republic. DOI: 10.18653/v1/2021.findings-emnlp.339
  5. Sha Lai, Anne Billot, Maria Varkanitsa, Emily Braun, Brenda Rapp, Todd Parrish, Ajay Kurani, James Higgins, David Caplan, Cynthia Thompson, Swathi Kiran, Margrit Betke, and Prakash Ishwar. An Exploration of Machine Learning Methods for Predicting Post-stroke Aphasia Recovery. PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference. June 2021. Pages 556-564. [Link]
  6. L. Guo, K. Mays, S. Lai, M. Jalal, P. Ishwar, and M. Betke. Accurate, Fast, But Not Always Cheap: Evaluating "Crowdcoding" as an Alternative Approach to Analyze Social Media Data. Journalism & Mass Communication Quarterly, 97(3): 811-834. September 2020. DOI: 10.1177/1077699019891437
  7. Y. Gu, M. Bahrani, A. Billot, S. Lai, E. J. Braun, M. Varkanitsa, J. Bighetto, B. Rapp, T. B. Parrish, D. Caplan, C. K. Thompson, S. Kiran, M. Betke. A machine learning approach for predicting post-stroke Aphasia recovery: A pilot study. Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, June 2020. pages 161-169. DOI: 10.1145/3389189.3389204
  8. Saurav Chennuri, Sha Lai, Anne Billot, Maria Varkanitsa, Emily J. Braun, Swathi Kiran, Archana Venkataraman, Janusz Konrad, Prakash Ishwar, and Margrit Betke. Fusion Approaches to Predict Post-stroke Aphasia Severity from Multimodal Neuroimaging Data. Proceedings of the International Conference on Computer Vision Workshop on Computer vision for Automated Medical Diagnosis (ICCV CVAMD 2023). Paris, France, October 2, 2023. Pages 2636-2645. pdf.