#14 Susan Murphy about Mastering AI with Statistics and Computer Science

In this episode of The Journeys of Scholars, we sit down with Susan Murphy, Mallinckrodt Professor of Statistics and of Computer Science, and Radcliffe Alumnae Professor at Harvard University. Renowned for her pioneering work in statistical reinforcement learning, mobile health interventions, and interdisciplinary research, Prof. Murphy offers invaluable perspectives on building a fulfilling and impactful academic career.
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Episode Highlights
1. Merging Statistics and Computer Science
o How Susan unites data modeling and algorithmic modeling cultures, referencing the legacy of Leo Breiman.
o Why she chose statistics and computer science over pure mathematics, and where her love of math still shines.
2. Reinforcement Learning for Mobile Health and Beyond
o A deep dive into RL-driven, just-in-time adaptive interventions (JITAIs).
o Speculations on using RL to inform public policy, including potential “self-driving” governments.
3. Ethics, Bias, and the Social Good
o Concerns about AI’s rapid advancement and corporate dominance.
o The flip side: regulation, stifling development, and forging collaborations with social sciences.
4. Future Directions in AI
o The next big breakthroughs: from causal discovery to new frontiers in RL and statistics.
o Open questions in causal inference and the interplay with RL.
5. Macro Strategies: Balancing Time, Energy, and Commitments
o Principles for saying “no” to protect deep work.
o Navigating the balancing act between research, teaching, and family life.
6. Micro Strategies: Day-to-Day Workflow
o Susan’s routines for writing, coding, and reading.
o How she handles emails, organizes teams, and fosters creativity in her lab.
7. Collaboration and Leadership
o When to lead and when to let others steer the ship.
o Building interdisciplinary teams across statistics, computer science, and health.
o Styles of collaboration: Takers, Matchers, and Givers—insights inspired by Adam Grant’s Give and Take.
8. Career Trajectory and Lessons Learned
o Susan’s move from LSU to UNC, then to Michigan and Harvard.
o Her stints in psychiatry, social research, and the decisions she’s proud of—and those she regrets.
o Advice for aspiring scholars across all career stages, from undergraduates to full professors.
9. Entrepreneurship in Academia
o The potential for commercializing just-in-time adaptive interventions for mobile health.
o Balancing scholarly pursuits with entrepreneurial interests.
10. Legacy and Motivation
• What drives Susan to continue innovating and contributing at this stage in her career.
• Insights on setting new goals, cultivating a meaningful legacy, and redefining success.
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Call to Action
Join us as we uncover how Prof. Murphy orchestrates her research, time, and team dynamics, all while pushing the boundaries of reinforcement learning and mobile health. Whether you’re an aspiring data scientist, an academic leader, or simply curious about cutting-edge AI and statistical methods, this conversation will leave you with fresh perspectives and actionable strategies.
Be sure to subscribe for more engaging discussions with leading scholars who share their journeys, successes, and hard-earned wisdom.
#SusanMurphy #ReinforcementLearning #MobileHealth #Statistics #ComputerScience #AcademicLeadership #Podcast #TheJourneysOfScholars


I hope you enjoy it!

Adel

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About me, the interviewer. At the time of producing this interview, I was an Associate Professor at the Institute for Analytical Sociology, Linköping University, and an
Affiliated Associate Professor in Data Science and Artificial Intelligence for the Social Sciences, Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden. Previously he held positions at Harvard University, University of Cambridge, Max Planck Institute for the Studies of Societies, and the Alan Turing Institute.

My research has both a social scientific and methodological orientation. For the social sciences, I research the effects of economic, political, and natural shocks on living standards and health, globally and locally. I implement and develop novel methodologies in machine learning and causal inference to analyze the causes and consequences of poverty and health. I have published in journals such as PNAS, Science Advances, World Development, International J of Epidemiology, American J of Epidemiology, and Ecological Economics, and in machine-learning conferences as Association for the Advancement of Artificial Intelligence (AAAI) and The North American Chapter of the Association for Computational Linguistics (NAACL). More information at www.adeldaoud.se.