Bob Vo

Logo

Hi, I am currently a Graduate Machine Learning Student at Ryerson University. Topics of interest: Deep Learning, Computer Vision, Signal Processing.

View My LinkedIn Profile

View My GitHub Profile

Portfolio


Class Presentation:

This is my presentation for a paper reading assignment in the Deep Learning For Computer Vision class.

Paper Title: End to end object detection with transformers. Carion, N. et al. Facebook AI Research. ECCV 2020

https://youtu.be/e6xp4AHLCVs

Transformer Model


Human Activity Recognition using Deep Learning

ABLSTM model for Wifi CSI based Human Activity Recognition - Report

I have attempted to reproduce the results in the WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM using a publicly available dataset. The process involves the re-implementation of the Attention Based Bidirectional Long Short Term Memory method in the original study. My algorithm’s results align with the claims in the original paper.


Reproduce Unsupervised Domain Adaptation for Object Counting

This is my reimplementation of an Adversarial Domain adaption model for the task of object counting. This is a general domain adaptation framework and it could be applicable for other vision tasks as well.

Github

Paper Title: Unsupervised Domain Adaptation for Plant Organ Counting. Ayalew, T.W. et al. ECCV 2020 Workshops

plant

plant


Low Yield Analysis Database

Award: Best Intern Project at the Intel Intern Projects Symposium 🌟🌟🌟

Developed a database application in C# and SQL for Intel Manufacturing Lab to assist engineers to keep track of High Valuable Inventory. Reduced missing items/violations by ~50%


Flicker Stimulator (25 stars - Github)

SSVEP Stimulator using MATLAB and Psychtoolbox

This is a 4 classes (or more) flickering stimulator for Steady State Evoked Potential experiment. The software generates four different target as can be seen in this video. Youtube demo video This tool presents a stable flickering frequencies which are usable for BCI applications.


WEEGEE Project

Master thesis This is the thesis project for my Master in Biomedical Engineering.

WEEG is a 24-bit 8-channel EEG system with integrated hardware and software interface. The device is based a new system-on-a-chip (SoC) to reduce circuitry components and be cost effective. Real-time view of 8-channel EEG data is also displayed.

images/Untitled%202.png plant

Publications

Vo T.T. (2017) Development And Evaluation of WEEG: A Wearable 8-Channel System for Brain Computer Interfaces. In: Goh J., Lim C., Leo H. (eds) The 16th International Conference on Biomedical Engineering. IFMBE Proceedings, vol 61. Springer, Singapore. https://doi.org/10.1007/978-981-10-4220-1_14

Vo T.T., Nguyen N.P., Vo Van T. (2018) WEEGEE: Wireless 8-Channel EEG Recording Device. In: Vo Van T., Nguyen Le T., Nguyen Duc T. BME 2017. IFMBE Proceedings, vol 63. Springer, Singapore. https://doi.org/10.1007/978-981-10-4361-1_106

Nguyen T.C., Huynh TQ., Vo TT., Nguyen P.N., Vo Van T. (2018) An EEG Front-End System Using ADS1299. In: Vo Van T., Nguyen Le T., Nguyen Duc T. BME 2017. IFMBE Proceedings, vol 63. Springer, Singapore. https://doi.org/10.1007/978-981-10-4361-1_123

Certificate

aws machine learning cert

AWS Machine Learning Specialty Certification

plaTF Certnt

TensorFlow Developer Certificate

Event Speaker (Microsoft)

Cloud Platform Online: Modern Business Intelligence

Honors-Awards

Intel Achievement Award, TMG, Divisional Recognition Award.

Intel Best Oral Presentation in Intern Projects symposium.

Intel National Scholarship (Top 3% students in the country)