I am a fourth year PhD student in computer science at Boston University,
where I am part of the BU LISP group under the supervision of Dr Sang Chin.
I am also a Draper Fellow working in the Machine Intelligence group.
My thesis research focuses on Artificial Neural Network approaches for handling sequences of data, such as time-series and natural language. I have developed methods for better prediction and generation of sequences utilizing tree based models and Generative Adversarial networks.
I earned my B.S in Computer Science and Electrical Engineering from Duke University, and my M.S. in Computer Engineering from N.C. State where I focused on computer architecture. After my masters I worked as a Digital Designer for Allegro Microsystems designing sensors.
Rangamani, A. Harer, J. Chin, S. Modeling Local Field Potentials with Recurrent Neural Networks. Workshop on Statistical Methods for Understanding Neural Systems. Neural Information Processing Systems (Nips), Dec 2015. (PDF)
Kim, L. Harer, J. Rangamani, A. Moran, J. Parks, P. Widge, A. Eskandar, E. Dougherty, D. Chin, S. Predicting Local Field Potentials with Recurrent Neural Networks. IEEE Engineering in Medicine and Biology Society (EMBC), Aug 2016. (PDF)
Chin, S. Cohen, J. Albin, A. Hayvanovych, M. Reilly, E. Brown, G. Harer, J. A Mathematical Analysis of Network Controllability Through Driver Nodes. IEEE Transactions on Comutational Social Systems. Volume: 4. Issue:2. June 2017. (Link)
Harer, J. Kim, L. Russell, R. Ozdemir, O. Kosta, L. Rangamani, A. Hamilton, L. Centeno, G. Key, J. Elingwood, P. Antelman, E. Mackay, A. McConnley, M. Opper, J. Chin, P. Laxovich, T. Automated software vulnerability detection with machine learning. Feb 2018. (PDF) (arXiv)
X. Wang, Q. Zhou, J. Harer, G. Brown, S. Qiu, Z. Dou, J. Wang, A. Hinton, C. A. Gonzalez, S. P. Chin. Deep learning-based classification and anomaly detection of side-channel signals. SPIE Cyber Sensing. 2018. (PDF)
Harer, J. Ozdemir, O. Lazovich, T. Reale, C. Russell, R. Kim, L. Chin, P. Learning to Repair Software Vulnerabilities with Generative Adversarial Networks. Neural Imformation Processing Systems (NIPS) Dec 2018. (PDF) (arXiv)
Russell, R. Kim, L. Hamilton, L. Lazovich, T. Harer, J. Ozdemir, O. Elingwood, P. McConley, M. Automated Vulnerability Detection in Source Code Using Deep Representation Learning. IEEE International Conference on Machine Learning and Applications (ICMLA) Dec 2018. (PDF) (arXiv)