Slides

  • Primate Inferotemporal Cortex Neurons Generalize Better than Analogous Deep Neural Network Units. 2022-12-15, MPI Tuebingen
    [pdf]
  • Building System Models of Brain-Like Visual Intelligence with Brain-Score. 2022-10-05, WorldWideNeuro SNUFA
    [pdf]
  • De-supervising neural network models of the primate ventral stream. 2022-09-12, Kornfeld lab MPI
    [pdf]
  • Artificial Neural Networks Converge to Brain-Like Processing in Vision and Language. 2022-04-06, Erlangen AI & ML meetup
    [pdf]
  • The neural architecture of language: Integrative modeling converges on predictive processing. 2022-04-04, DNA Deviants Journal Club
    [Clubhouse recording]
  • The future of Brain-Score. 2022-03-22, Cosyne 2022 Brain-Score workshop
    [pdf]
  • The neural architecture of language: Integrative modeling converges on predictive processing. 2022-03-14, Boston College Hartshorne and Prud’hommeaux labs
    [pdf]
  • BCS interviews. 2022-03-11, MIT
    [pdf]
  • Thesis Defense. 2022-01-31, MIT BCS
    [recording (passcode: [email protected]!), pdf]
  • Topographic ANNs Predict the Behavioral Effects of Causal Perturbations in Primate Visual Ventral Stream IT. 2021-10-15, Champalimaund Research Symposium (CRS21)
    [poster pdf]
  • Deep Topographic Models Predict the Behavioral Effects of Neural Perturbations in  Primate Visual Cortex. 2021-10-05, C-BRIC
    [pdf]
  • Topographic Neural Networks Predict the Behavioral Effects of Causal Perturbations in Primate IT Cortex. 2021-09-16, MIT Fiete Lab
    [pdf]
  • Deep Spatial Models Predict the Behavioral Effects of Pharmacological Perturbations in Primate Visual Cortex. 2021-08-23, Takeda Fellowship Event
    [poster pdf]
  • Topographic Neural Networks Predict the Behavioral Effects of Causal Perturbations in Primate IT Cortex. 2021-08-19, CBMM retreat
    [pdf]
  • Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 2021-07-05, Telluride Workshop
    [pdf, video]
  • Integrative Neurally-Mechanistic Models of Primate Visual Object Recognition. 2021-06-03, MIT Yang Lab
    [pdf]
  • The neural architecture of language: Integrative modeling converges on predictive processing. 2021-05-20, Stanford NLP
    [pdf]
  • The neural architecture of language: Integrative modeling converges on predictive processing. 2021-03-09, MIT BCS Saxe Lab
    [pdf]
  • Engineering an Artificial Biological Intelligence. Guest Lecture, Program for Software Engineering. 2021-01-15, UNA/TUM, LMU
    [pdf]
  • CBMM Panel Discussion: Should models of cortex be falsifiable? 2020-12-07, MIT (with Tomaso Poggio, Gabriel Kreiman, Josh McDermott, Leyla Isik, Susan Epstein, Jenelle Feather)
    [video]
  • The neural architecture of language: Integrative modeling converges on predictive processing. 2020-12-01, France NLP GDR TAL
    [pdf]
  • Integrative Benchmarking to Advance Neural Models of Vision and Language. 2020-11-25, Virtual Computational Neuroscience Journal Club
    [pdf, recording]
  • Integrative Benchmarking and the Value of Quantitative Models. 2020-10-19, CCN GAC linear/nonlinear workshop
    [pdf]
  • Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream. 2020-10-06, C-BRIC Annual Review
    [pdf]
  • Artificial Neural Networks Accurately Predict Language Processing in the Brain. 2020-08-18, MIT BCS Cog Lunch
    [pdf, code]
  • Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 2020-03-31, neuromatch.io contributed talk
    [pdf, video (starts at 22:45)]
  • Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 2019-12-12, NeurIPS Oral
    [talk recording, slides pdf, slides powerpoint, poster pdf, code, 3min-video]
  • Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 2019-10-29, MIT BCS Cog Lunch
    [pdf]
  • Deep Networks and Pytorch. 2019-08-21, Harvard-MIT Computational Neuroscience Journal Club
    [notebook, github]
  • Continual Learning with Self-Organizing Maps. 2018-12-07, NeurIPS Continual Learning Workshop
    [poster pdf]
  • Aligning Deep Neural Networks and Visual Cortex. 2018-03-01, MIT IQ Launch
    [pdf, powerpoint]
  • A Flexible Approach to Automated RNN Architecture Generation. 2018-02-14, Tenenbaum lab
    [pdf, powerpoint]
  • Introduction to Deep Learning for Neuroscientists. 2017-10-27, MIT BCS Peer Lectures
    [powerpoint, pdf]
  • Recurrent computations for pattern completion. 2016-20-10, Harvard Systems Club
    [pdf]