A confirmation bias on how humans actively sample sensory information, Neuromatch, 2020 [Link]
FairyTED: A fair rating predictor for TED talk data, AAAI, 2020 [slides]
To be or not to be, that is the question, UpStat, 2018 [slides]
Inference by binary sampling as model for V1 spiking responses, 3rd year and 4th year lunch talks, Brain and Cognitive Sciences, University of Rochester, 2017 and 2018 [slides]
Bayesian decision making in biological motion, Summer School in Computational Sensory-MotorNeuroscience (CoSMo), University of Minnesota, 2017 [slides]
Removing racial bias in TED talk ratings by awareness of verbal and gesture quality, Responsible AI ICLR 2021 Workshop [Poster]
Understanding diversity based neural networks pruning in teacher-student setup, Neural Compression ICLR 2021 Workshop [Poster]
Diversity based edge pruning of neural networks using determinantal point process, Neural Compression ICLR 2021 Workshop [Poster]
A confirmation bias in how humans actively sample sensory information, VSS 2020 [VSS 2020 poster]
Using perceptual confirmation-bias to study learning and feedback in fovea and periphery, VSS 2020, CCN 2019 [VSS 2020 poster][CCN 2019 poster]
Inference by binary sampling as model for V1 spiking responses, BERNSTEIN 2019, Neuroscience Retreat, University of Rochester 2018 [BERNSTEIN 2019 poster][NSC Retreat 2018 poster]
A probabilistic population code based on neural sampling, NeurIPS 2018, COSYNE 2018 [NeurIPS 2018 poster][COSYNE 2018 poster]
A perceptual confirmation bias from approximate online inference, CCN 2018, COSYNE 2017 [CCN 2018 poster][COSYNE 2017 poster]
![[Poster]](posters/TED_HEM_Racial.png){kind=link}