PhD Seminar • Artificial Intelligence (Theoretical Neuroscience) — A Biologically Plausible LSTM Cell for Chaotic Time-series Prediction
Aaron Voelker, PhD candidate
David R. Cheriton School of Computer Science
Aaron Voelker, PhD candidate
David R. Cheriton School of Computer Science
Aakar Gupta, Postdoctoral research scientist
Facebook Reality Labs
Computing is increasingly embedded in objects on us and around us. And we are increasingly embedded in digital environments. These computing environments limit old but enable new input-output affordances. Utilizing these affordances requires us to move beyond traditional ways of expressing human intention.
Gramoz Goranci, University of Vienna
Aishwarya Agrawal, PhD candidate
School of Interactive Computing, Georgia Tech
Alexey Karyakin, PhD candidate
David R. Cheriton School of Computer Science
Yuxin Chen, Postdoctoral scholar, Department of Computing and Mathematical Sciences
California Institute of Technology
How can we intelligently acquire information for decision making, when facing a large volume of data?
Khalid Al-Kofahi, Head, Corporate R&D, Center for AI and Cognitive Computing
Thomson Reuters
Nabiha Asghar, PhD candidate
David R. Cheriton School of Computer Science
Andrew Delong, Head of Computational Research
Deep Genomics
Genomics focuses on the sequences in our genomes and how they encode for function in our cells. Predicting how sequences will be interpreted by the cell is important for identifying disease-causing mutations and for designing therapies.
Saba Alimadadi, Postdoctoral Researcher
Northeastern University
Program comprehension is crucial in software engineering, a necessary step for performing many tasks. However, the implicit and intricate relations between program entities hinder comprehension of program behaviour and can easily lead to bugs. It is particularly challenging to understand and debug modern programming languages such as JavaScript, due to their dynamic, asynchronous, and event-driven nature.