Computational Modeling / Machine Learning / AI Meet & Speak
On Wednesday, May 6th 2020, The Institute for the Brain and Cognitive Sciences (IBACS) & the School of Engineering co-hosted a Meet & Speak which covered interesting research in computational modeling, machine learning, and AI approaches applied to human cognition, brain function, speech and language, disease, and related areas. The meeting offered an opportunity to foster greater cross-college discourse and collaboration, and may serve as a foundation for greater university investment in computational modeling. The event was recorded – each video segment can be found by clicking on the talk titles below:
Speakers:
1.Gerry Altmann (Director, IBACS; Psychological Sciences): Understanding events during language comprehension: What Recurrent Neural Networks can teach us about human understanding
2. Whit Tabor (Psychological Sciences): Dynamical systems theory insight into the relationship between networks and languages
3. Jim Magnuson (Psychological Sciences): Bridging the gaps between automatic speech recognition and human speech recognition
4. Jay Rueckl (Psychological Sciences): Modeling Individual Differences in Reading
5. Ed Large (Psychological Sciences): Dynamic Responses to Syncopated Rhythms Reveal the Neural Origins of Pulse Perception
6. Ian Stevenson (Psychological Sciences): Detecting and modeling synapses from spiking activity
7. Monty Escabi (Biomedical Engineering): From neurons to machines: combining auditory neurophysiology and a hierarchical spiking neural network to understand natural sound recognition in noise
8. Sabato Santaniello (Biomedical Engineering): Biophysically-principled modeling for brain disorders and neuromodulation
9. Jinbo Bi (Associate Head, Computer Science & Engineering): Machine Learning: empowering complex and big data analysis in health care
10.Derek Aguiar (Computer Science & Engineering): Bayesian machine learning in large models with applications in genomics
11. Caiwen Ding (Computer Science & Engineering): Accelerating Deep Neural Networks using Block Circulant Matrix
12. Ranjan Srivastava (Head of Chemical & Biomolecular Engineering):Evolutionary Algorithms for Solving Inverse Function Problems