Daniel Yamins

Current Research and Scholarly Interests

Our lab's research lies at intersection of neuroscience, artificial intelligence, psychology and large-scale data analysis. It is founded on two mutually reinforcing hypotheses:

H1. By studying how the brain solves computational challenges, we can learn to build better artificial intelligence algorithms.

H2. Through improving artificial intelligence algorithms, we'll discover better models of how the brain works.

We investigate these hypotheses using techniques from computational modeling and artificial intelligence, high-throughput neurophysiology, functional brain imaging, behavioral psychophysics, and large-scale data analysis.

2017-18 Courses

• High-Dimensional Methods for Behavioral and Neural Data
  PSYCH 253 (Spr)
• Large-Scale Neural Network Modeling for Neuroscience
  CS 375, PSYCH 249 (Aut)
• Theoretical Neuroscience
  APPPHYS 293, PSYCH 242 (Spr)
• Independent Studies (9)
  • Advanced Reading and Research
    CS 499 (Aut, Win, Spr, Sum)
  • Advanced Reading and Research
    CS 499P (Aut, Win, Spr, Sum)
  • Directed Reading in Neurosciences
    NEPR 299 (Aut, Win, Spr, Sum)
  • Graduate Research
    NEPR 399 (Aut, Win, Spr, Sum)
  • Graduate Research
    PSYCH 275 (Aut, Win, Spr, Sum)
  • Independent Project
    CS 399 (Aut, Win, Spr, Sum)
  • Reading and Special Work
    PSYCH 194 (Aut, Win)
  • Research
    PHYSICS 490 (Aut, Win, Sum)
  • Special Laboratory Projects
    PSYCH 195 (Aut, Win)
• Prior Year Courses

  2016-17 Courses

  • High-level Vision: From Neurons to Deep Neural Networks
    PSYCH 250 (Spr)
  • High-level Vision: From Neurons to Deep Neural Networks
    PSYCH 250A (Spr)

Stanford Advisees

• Doctoral Dissertation Reader (AC)
  Biafra Ahanonu, Mona Rosenke
• Postdoctoral Faculty Sponsor
  Daniel Bear
• Postdoctoral Research Mentor
  Daniel Bear, Nicholas Haber