I am a Junior major in computer engineering in UIUC. I am interested in many applications of machine learning.
I am actively looking for internship
1. Implement a SIFT (scale-invariant feature transform) algorithm in Android studio and can do simple object recognitions
2. Final Group Project for Course Digital Signal Processing Laboratory
3. Algorithm based on the paper "Distinctive Image Features from Scale-Invariant Keypoints" (International Journal of Computer Vision, 2004.)
4. Demo Link: https://youtu.be/AShsPyn6eu4
1. FPGA (field-programmable gate array)
2. Final Group Project for Course Digital System Laboratory
3. Demo Link: https://youtu.be/K5iLkwr95oA
1. Used OCR model tesseract as our transfer learning model for this black-box attack
2. Tried different attacks in perturbing the ID card to attack the model
1. Built a comparative SVM model based on paper "Drebin: Effective and Explainable Detection of Android Malware in Your Pocket" (Copyright 2014 Internet Society, ISBN 1-891562-35-5)
2. Evasion attack on the SVM model based on paper "Evasion Attacks Against Machine Learning at Test Time" (ECML PKDD, Part III, vol. 8190, LNCS, pp. 387--402. Springer, 2013)
1. Apply different machine learning models, including random forests, SVM, linear SVM and so on.
2. Mainly focusing on improving current model's performance in imbalanced dataset.
1. Take an example model from Tensorflow Tutorial (https://www.tensorflow.org/get_started/get_started )
2. Attack the model using Saliency Map, based on the paper "The Limitations of Deep Learning in Adversarial Settings" (the 1st IEEE European Symposium on Security & Privacy, 2016)