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.

2020-21 Courses

• Advanced Statistical Modeling
  PSYCH 253 (Spr)
• High-level Vision: From Neurons to Deep Neural Networks
  PSYCH 250 (Win)
• NeuroTech Training Seminar
  NSUR 239, STATS 242 (Win)
• Independent Studies (13)
  • Advanced Reading and Research
    CS 499 (Aut, Win, Spr, Sum)
  • Advanced Reading and Research
    CS 499P (Aut, Win, Spr, Sum)
  • Curricular Practical Training
    CS 390A (Aut, Win, 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)
  • Independent Work
    CS 199 (Aut, Win, Spr, Sum)
  • Reading and Special Work
    PSYCH 194 (Aut, Win)
  • Research
    PHYSICS 490 (Aut, Win)
  • Senior Project
    CS 191 (Spr)
  • Special Laboratory Projects
    PSYCH 195 (Aut, Win)
  • Supervised Undergraduate Research
    CS 195 (Aut, Win, Spr, Sum)
• Prior Year Courses

  2019-20 Courses

  • Advanced Statistical Modeling
    PSYCH 253 (Spr)
  • Large-Scale Neural Network Modeling for Neuroscience
    CS 375, PSYCH 249 (Aut)
  • NeuroTech Training Seminar
    NSUR 239, STATS 242 (Win)
  • Theoretical Neuroscience
    APPPHYS 293, PSYCH 242 (Spr)

  2018-19 Courses

  • High-Dimensional Methods for Behavioral and Neural Data
    PSYCH 253 (Spr)
  • High-level Vision: From Neurons to Deep Neural Networks
    CS 431, PSYCH 250 (Spr)
  • Large-Scale Neural Network Modeling for Neuroscience
    CS 375, PSYCH 249 (Aut)
  • Theoretical Neuroscience
    APPPHYS 293, PSYCH 242 (Spr)

  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)

Stanford Advisees

• Doctoral Dissertation Reader (AC)
  Katherine Hermann, Akshay Jagadeesh, tyler bonnen
• Postdoctoral Faculty Sponsor
  Daniel Bear, Jinyao Yan
• Doctoral Dissertation Advisor (AC)
  Kevin Feigelis, Michael Lingelbach, Eshed Margalit, Javier Sagastuy Brena, Eli Wang, Chengxu Zhuang
• Master's Program Advisor
  Eric Chan, Anirudh Jain, Sharan Ramjee, Megumi Sano, Danny Schwartz, Anfal Siddiqui, Julie Wang, David Zhou
• Doctoral Dissertation Co-Advisor (AC)
  Josh Melander, Damian Mrowca, Aran Nayebi
• Postdoctoral Research Mentor
  Daniel Bear, Jinyao Yan
• Doctoral (Program)
  Honglin Chen