My research focuses on methodological advancements in the field of causal inference, with a particular emphasis on addressing complex, high-stakes challenges prevalent in healthcare and public health. I am currently working with Prof. Elizabeth Stuart (Johns Hopkins University) and Prof. Kara Rudolph (Columbia University). During my PhD, I was advised by Profs. Cynthia Rudin, Alexander Volfovsky, and Sudeepa Roy at Duke University.

Research Summary

I specialize in advancing methodologies for causal inference, particularly within the complex landscape of healthcare and public health. My work develops methodologies that are:

• Accurate - to ensure accurate estimation of heterogeneous causal effects even when confronted with limited data, offering decision-makers a reliable foundation upon which to base their choices.

• Trustworthy - to empower domain experts to comprehend the inner workings of the causal inference process. This not only enables experts to validate the underlying assumptions but also guarantees patients' safety.

• Domain-conscious - to bridge the research-to-practice gap and yield solutions that are readily implementable. I leverage the context and domain knowledge to tailor solutions specific to a subject matter.

I firmly believe that the most impactful and implementable contributions arise when methodological advancements are deeply rooted in the relevant context. This belief is exemplified by my active collaboration with healthcare professionals, including neurologists at Massachusetts General Hospital (MGH) and Beth Israel Deaconess Medical Center (BIDMC), to improve treatment for critically ill patients, and with epidemiologists at Columbia University Medical College (CUMC) to design strategies for managing opioid use disorder.

Work Experience

Meta

Amazon

Amazon

The Urban Institute

IBM India Research Laboratory

Arista Networks

Teaching Summary

My teaching philosophy and pedagogical approach are built upon three fundamental principles. First, I emphasize the cultivation of a Growth and Collaborative Mindset, prioritizing learning over mere performance by employing pre and post-lecture ungraded quizzes to gauge progress and encourage feedback. Collaborative assignments and projects involving students from diverse backgrounds promote teamwork and peer learning rather than competition. Additionally, I empower students to explore independently and utilize web-based resources with proper referencing. Second, I aim to bridge the gap between theory and application in Computer Science and Statistics, using practical examples to help students grasp complex real-world scenarios alongside theoretical foundations. For instance, I elucidate the Bayes theorem through the context of disease probability estimation after a positive test. Assignments integrate theoretical proofs, method implementation, and real-world data applications, offering students tangible insights into the practicality and limitations of concepts like Support Vector Machines. Finally, I am committed to Fostering Inclusivity and Embracing Diversity in today's diverse educational landscape. I achieve this by incorporating storytelling and historical narratives to enhance discussions, such as exploring Alan Turing's biography in conjunction with the Universal Turing Machine concept, celebrating his contributions to society while addressing the challenges he faced due to his sexual orientation. This approach underscores the inseparable connection between science and human culture, enriching STEM education with historical context and providing students with a broader perspective on mathematical concepts, ultimately enhancing their engagement and comprehension.

Teaching Experience

1. Instructor Workshop: Introduction to Causal Inference (Advanced), Duke Datathon, Fall 2019 

2. Teaching Assistant COMPSCI 671D Machine Learning, Spring 2019

3. Instructor, Focus Group: Introduction to Data Science, Duke MEMPDC, Fall 2018.

4. Teaching Assistant COMPSCI 590.2 Computational Microeconomics, Fall 2018 (link)

5. Teaching Assistant COMPSCI 223 Computational Microeconomics, Spring 2018 (link)

6. Teaching Assistant COMPSCI 230, Discrete Mathematics, Fall 2017 (link)

7. Teaching Assistant COMPSCI 230, Discrete Mathematics, Spring 2017 (link)

8. Teaching Assistant COMPSCI 201, Data Structures and Algorithms, Fall 2016 (link)