AI & ML Empowerment

Cultivating Conceptual Understanding of How AI/ML Methods work Under the Hood

FOCUS QUESTIONS

Pattern

How can the technical concepts underpinning AI & Machine Learning (ML) be made intuitive and understandable for entrepreneurs and educators from diverse disciplines and backgrounds?

In what ways can we make learning technical AI & ML concepts inclusive and engaging?

What is AI’s potential for analytical and affective competencies?

How can we use AI to enhance—rather than limit—our originality, creativity, and critical thinking?

IMPACTS

  • Conducting interdisciplinary, niche research projects in collaboration with students

  • Investigating the reasoning and affective potential of modern AI

  • Building connections with leaders in AI/ML and bringing that knowledge to Babson through a speaker series

  • Advancing pedagogical innovation through the design and development of educational technology

  • Fostering creative, interdisciplinary collaborations across labs

INITIATIVES

Speaker Series in AI/ML

The event was organized to raise awareness about modern AI, bringing to Babson industry leaders ranging from content creators and best-selling authors to data scientists and machine learning engineers. These experts demystified how various AI and machine learning tools function under the hood, making complex concepts both accessible and engaging. The talks deepened the audience’s understanding of the technology while also highlighting its practical and ethical implications. Attendees had the opportunity to connect directly with the speakers, fostering new relationships and professional growth. As a result, the series helped build both knowledge and professional networks within the community.

Research Internships

The Research Internships initiative is created to provide students with opportunities to engage in cutting-edge, niche research exploring the vast potential of AI. With the rapid advances in AI and the widespread hype around its capabilities, these internships aim to ground student inquiry in rigorous research that explores both the promise and the limitations of AI technologies. One internship focuses on analyzing human dreams, applying modern AI and machine learning tools to explore a timeless human fascination—from ancient civilizations to psychoanalysts like Freud and Jung. Another project challenges students to rigorously test the logical reasoning capabilities of large language models by having them solve classical logical conundrums, pushing the boundaries of AI’s ability to reason beyond surface-level fluency. Additionally, an interdisciplinary internship investigates the complex life and artwork of Vincent van Gogh, applying advanced machine learning methods to analyze the relationship between his mental state throughout his life and the evolving themes in his art. Together, these research opportunities allow students to use AI to explore research problems they are passionate about, while also gaining insight into the technology’s potential and limitations.

LEADER

Davit Khachatryan, Associate Professor, MAST

Davit Khachatryan

Associate Professor, MAST
Leader of The Generator’s AI & ML Empowerment Specialty Lab

“The ML tools underlying AI are technical but can be made intuitive and accessible to users. Gaining an intuitive understanding of what happens under the hood of AI technology will empower humans as AI continues to permeate our daily lives.”

AI research interests

  • AI and Machine Learning
  • AI pedagogy
  • Generative AI

Davit Khachatryan is a data scientist specializing in machine learning and natural language processing. His pedagogical innovations include the development of Playmeans, a web app for inclusive data science education using live data from Spotify; as well as Cases used in teaching time series analysis. Before Babson College, he was a Senior Associate at PricewaterhouseCoopers (PwC), focusing on predictive modeling and advanced data analytics for clients in healthcare, finance, and government sectors. At Babson, he teaches courses in machine learning, data science, and statistics.

Recent publications

Girdharry, K., & Khachatryan, D. (2024). Meaningful Writing in the Age of Generative Artificial Intelligence. Double Helix, 11.

Khachatryan, D. (2023). Playmeans: Inclusive and Engaging Data Science through Music. Journal of Statistics and Data Science Education, 31(2), 151-161.

Eloyan, A., Yue, M. S., & Khachatryan, D. (2020). Tumor heterogeneity estimation for radiomics in cancer. Statistics in medicine, 39(30), 4704-4723.

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