Old lab teaching topics
posted on August 29, 2018


Old lab teaching topics

Every Friday, we get together (over pizza, sometimes) for lab teachings. On a rotating basis, each member of the lab speaks and teaches about something they know. Anything, really. Relevant and interesting topics, good skills to know, nice Python packages, neuroscientific princples, new findings and literature reviews… whatever!

Click here for current (as of spring 2021) topics

Spring 2021

Date Name Topic
Feb. 12    
Feb. 19    
Feb. 26 Tony Asymptotic theory towards double ML
Mar. 5 Richard MCMC sampling
Mar. 12    
Mar. 19 Roozbeh AlphaFold
Mar. 26 Ilenna The New Problem of Induction
Apr. 2    
Apr. 9 Ben B. A Philosophical Understanding of Representations for Neruoscience
Apr. 16 Joshua Glaser Interpretable Machine Learning for Neuroscience
Apr. 23 Richard Kernels and Gaussian Processes
Apr. 30 Ilenna Normal, Extraordinary, and Revolutionary Science
May. 7 Lab break  
May. 14 Lab break  
May. 21 Lab break  
May. 28 Lab break  

Fall 2020

Date Name Topic
Aug. 19 Ben B Natural Information & Intentionality
Aug. 26 Ben Convex optimization
Sept. 2 Gene Recent methods in NLP
Sept. 9 Ilenna A history of transhumanist thought
Sept. 18 Roozbeh TBD
Sept. 25   No lab teaching due to Interlab Teatime
Oct. 2 Amandeep In-Memory Compute
Oct. 9 Justin TBD, maybe scaling laws in biology
Nov. 6    
Nov. 13 Tony Interpretable ML
Nov. 20 Richard Discussion of “Why Philosophers Should Care About Computational Complexity”

Spring 2020

Date Name Topic
Jan. 15 Ari Variational Inference
Jan. 22 Roozbeh Why overparameterized deep networks generalize well?
Jan. 29 Pedro Canonical Correlation Analysis + Update on my research on dimensionality of populations of neurons
Feb. 5    
Feb. 12    
Feb. 19 Brad Wyble TBA
Feb. 26 Tony The deconfounder: blessing or curse?
Mar. 4 Jaan Altosaar Postdoc Candidate Talk
Mar. 11 Titipat Reinforcement Learning (policy based, actor-critic, …) - continue
Mar. 18 Ben Convex Optimization
Mar. 25 Sebastien Tremblay TBA
Apr. 1    
Apr. 8    
Apr. 15 Rachit Data Visualization
Apr. 22 Ben Lansdell TBA
Apr. 29 Ilenna TBA
May 6 Nachi Stern Design and learning in physical networks
May 13 Roozbeh TBA

Fall 2019

Date Name Topic
October 9 David Rolnick Climate change
October 16 Ari Benjamin TBD (plasticity & learning in the brain)
October 23 Ethan Blackwood Neural models of indirection and abstraction
October 30 Ben Lansdell Invariance and causality
November 6 Nidhi Seethapathi Inferring Dynamics from Data
November 13 Tony Liu Theory of Computation
November 20 Ilenna Jones Ion Channel Kinetics
November 27 Shaofei Wang Differentiable Structured Inference and Attention
December 4 Rachit Saluja Compressed sensing and deep learning
December 18 Titipat Achakulvisut Reinforcement Learning (introduction)

Spring 2019

Date Name Topic
Jan. 9 Netanel Ofer Automated Analysis of Interneuron Axonal Tree Morphology and Activity Patterns
Jan. 18 Nidhi Dynamic Time Warping
Jan. 25 Ben Bandit problems
Feb. 11 David Autoencoders & Information Bottleneck
Feb. 27 Adrian Radillo Perfecting the research process [dropbox doc from the teaching] (https://paper.dropbox.com/doc/Kordings-lab-teaching-on-IT-for-scientists–AYUMIhaJvifuArh1uCfm6BivAQ-wXXjZyfix7HiGu9lcroyR)
Mar. 6 Ari Biologically plausible backprop
Mar. 13 Greg Corder (http://www.corderlab.com/) emotional processing of pain in the amygdala
Mar. 20 Ilenna Topics in the Philosophy of Science
Mar. 27 Tony Code Workflow for Research
May 1 Edgar Dobriban Data augmentation
May 15 Ben Baker (Miracchi lab) Representation and information in neuroscience
May 29 Sebastien Tremblay (Platt Lab) The limits of neurophys and why we need your help
June 5 Zhihao (Princeton University) TBA

Fall 2018

Date Name Topic
Sept. 28 Ilenna Capacity of Neural Networks
Oct. 5 Tung Pham GANs for EEG
Oct. 12 Ben GPUs – beneath the heatsink Slides
Oct. 19 Rachit Graph Convolution Networks
Oct. 26 Tony Docker for science
Nov. 2 Titipat AllenNLP library and a little bit of Pytorch
Nov. 9 Roozbeh Multiple Hypothesis Testing
Nov. 16 David Reinforcement learning and catastrophic forgetting
Dec. 3 Ari Independent Component Analysis

Older:

  1. Generalization in neural networks (Ari)
  2. Synaptic learning rules (Ari)
  3. How to science (debugging strategies etc.) (Konrad)
  4. Reinforcement learning and causal inference (Ben)
  5. DAGs and causal inference (Ben)
  6. Neuron firing dynamics and bifurcations (Ilenna)
  7. Submodular functions (Roozbeh)
  8. Recommendation systems (Rachit)

Previous lab teaching