I am a graduate student researcher at U of T supervised by Joseph Jay Williams. I am generally interested in statistics and machine learning applied to HCI. Currently, I am working on issues pertaining to statistical inference from data collected by Multi-Armed Bandit algorithms in online educational experiments. In particular, I am developing methods which balance statistical inference properties and reward maximization, and validating these methods in real world online education settings. In the past I have worked on applications of Deep Learning in fall detection as well as agitation detection at IATSL, under the supervision of Shehroz Khan and Alex Mihailidis
DeepFall - Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders, Jacob Nogas, Alex Mihailidis, Shehroz S. Khan, Journal of Health Informatic Research, vol. 4, pages 50–70, 2020 [Code]
Spatio-Temporal Adversarial Learning for Detecting Unseen Falls, Shehroz S. Khan, Jacob Nogas, Alex Mihailidis, Pattern Analysis and Applications, 2020 (impact factor: 1.512)
Agitation Detection in People Living with Dementia using Multimodal Sensors, Shehroz S. Khan, Sofija Spasojevic, Jacob Nogas, Bing Ye, Alex Mihailidis, Andrea Iaboni, Angel Wang, Lori Schindel Martin and Kristine Newman, 41st Engineering in Medicine and Biology Conference, Berlin, 2019
Fall Detection from Thermal Camera Using Convolutional LSTM Autoencoder, Jacob Nogas, Shehroz S. Khan, Alex Mihailidis, 2nd Workshop on AI for Aging, Rehabilitation and Independent Assisted Living at IJCAI, Federated Artificial Intelligence Meeting, Sweden, 2018 [PDF].
Machine Learning Summit 2018 Vector Institute Best Poster Award: DeepFall -- Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders