Tim Sit

Tim is interested in the intersection between machine learning and neuroscience. In particular, he is interested in the neural basis of learning and inference at the algorithmic level. He is also interested in the analysis of neurophysiological data. 

Tim studied Natural Sciences at the University of Cambridge. For his undergraduate project, he developed new methods for analysing electrophysiological data from in-vitro micro-electrode arrays that is used to study a mice model of autism, supervised by Dr. Susanna Mierau (https://www.pdn.cam.ac.uk/directory/susanna-mierau) and Dr. Guillaume Hennequin (https://ghennequin.github.io/). He also worked with Dr. Susanna Mierau and Mr. Stefano Giandomenico from the Lancaster lab (https://www2.mrc-lmb.cam.ac.uk/groups/lancaster/) on the analysis of electrical activity from brain organoids generated using a novel air-liquid interface method (https://www.biorxiv.org/content/early/2018/06/22/353151). 

Tim worked on brain-computer interfaces at the Chinese University of Hong Kong with Professor Raymond Tong (http://www.bme.cuhk.edu.hk/kytong/), where he applied machine learning methods to decode EEG data. He also applied machine learning methods to decode exploratory behaviour from electrode implants, and social behavioural response from single-unit data with Dr. Yang Zhan (http://bcbdi.siat.ac.cn/index.php/member/showMember/nid/9.shtml) at the Shenzhen Institute of Advanced Technology.

You can find the code for Tim's projects on his github page: https://github.com/Timothysit