Enric Boix

Enric Boix
  • Assistant Professor of Statistics and Data Science

Contact Information

  • office Address:

    433 Academic Research Building
    265 South 37th Street
    Philadelphia, PA 19104

Research Interests: theory of AI (including distillation, reasoning models, steering, monitoring, training dynamics, inductive bias of architectures, adversarial examples, feature learning, and AI safety)

Links: Personal Website

Research

Teaching

Past Courses

  • AMCS9950 - Dissertation

    Allows for a PhD student to be enrolled full-time to work exclusively on research, writing and preparing his/her doctoral thesis and defense. All required coursework (20 CUs) must be completed, and the student must have passed his/her thesis proposal/oral candidacy examination prior to being enrolled.

  • AMCS9999 - Ind Study & Research

    Study under the direction of a faculty member.

  • STAT4850 - Foundations of Deep Learning

    This course serves as a first, conceptual introduction to Deep Learning, which is the technology at the heart of modern AI. Topics include: what is a neural network and how to train it, generative AI, failure modes and safety of deep learning, and efficient deep learning.

  • STAT5850 - Foundations of Deep Learning

    This course serves as a first, conceptual introduction to Deep Learning, which is the technology at the heart of modern AI. Topics include: what is a neural network and how to train it, generative AI, failure modes and safety of deep learning, and efficient deep learning. Prerequisites: Calculus, beginner programming experience with Python. Highly recommended: basic linear algebra (matrices and matrix multiplication).

  • STAT8990 - Independent Study

    Written permission of instructor, the department MBA advisor and course coordinator required to enroll.

  • STAT9910 - Sem in Adv Appl of Stat

    This seminar is for graduate students who wish to learn about current research frontiers. It covers advanced topics in probability, statistical theory and methods, applied statistics, data science and artificial intelligence. Specific topics vary from year to year and emphasize both theoretical foundations and applications.

In the News

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Activity

Latest Research

Daniel Beaglehole, Adityanarayanan Radhakrishnan, Enric Boix, Mikhail Belkin (2026), Toward universal steering and monitoring of AI models, Science, 391 (6787), pp. 787-792.
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In the News

The Way of Excellence: Lead for Sustainable Performance

In this Nano Tool for Leaders, author Brad Stulberg explains why you should stop thinking of excellence as a destination and more as a process of growth.…Read More

Knowledge @ Wharton - 2026/08/17
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