Click here for current (as of spring 2021) topics
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 |
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