About Me

I'm currently a fifth-year Ph.D. candidate in Professor Peter J. Ramadge's lab in the Department of Electrical Engineering and Princeton Neuroscience Institute at Princeton University. My primary research interest is to perform neuroimaging (fMRI) analysis using machine learning methods.

Experience

Education

• Ph.D.: Electrical Engineering and Neuroscience09/2014 - Current
  Princeton University - Princeton, NJ
  • Expected graduation date: 05/2019
• Bachelor of Science: Electrical Engineering 08/2010 - 05/2014
  Rice University - Houston, TX
  • GPA: 4.13/4.00 (summa cum laude)
  • Minor: Computational and Applied Mathematics

Work History

• Applied Research Scientist Intern06/2018 - 08/2018
  eBay Inc. – San Jose, CA, USA
  • Multi-label classification for queries
• Graduate Technical Intern05/2017 - 09/2017
  Intel Corporation – Hillsboro, OR, USA
  • Neuroimaging analysis techniques design and implementation
• Research Assistant06/2013 - 08/2013
  Capital Medical University – Beijing, China
  • Medical Data Processing and Analysis
• Technical Assistant06/2011 - 07/2011
  Lenovo (Headquarter) – Beijing, China
  • Code Maintenance using Python

Teaching

• ELE 301: Designing Real Systems
• EGR194: An Introduction to Engineering
• ELE488: Image Processing
• ELE535: Machine Learning and Pattern Recognition
• NEU480: fMRI Decoding: Reading Minds
• CAAM 210: Introduction to Engineering Computation
• CAAM 335: Matrix Analysis

Open-source Software

• BrainIAK (Brain Imaging Analysis Kit) developer

Reviewer

• NIPS (Neural Information Processing Systems), 2016
• Program Committee of the Machine Learning for Health Workshop at NIPS, 2017
• Program Committee of the Machine Learning for Health Workshop at NIPS, 2018

Publications and Abstracts

• Boyu Wang, Hejia Zhang, Joelle Pineau, and Kenneth Norman. "Multitask Metric Learning: Theory and Algorithm." Submitted to The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
• Hejia Zhang, Po-Hsuan Chen, and Peter J. Ramadge. "Transfer learning on fMRI datasets." The 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
• Hejia Zhang, Xia Zhu, and Theodore L. Willke. "Segmenting brain tumors with symmetry." NIPS Workshop: Machine learning for health, 2017.
• Hejia Zhang, Po-Hsuan Chen, and Peter J. Ramadge. "Multi-subject fMRI data factor analysis using BrainIAK." Society for Neuroscience (SfN) Annual Meeting, 2017.
• Hejia Zhang, Po-Hsuan Chen, and Peter J. Ramadge. "Learning Factor Models Using Multi-dataset, Multi-subject fMRI Data." Collaborative Research in Computational Neuroscience (CRCNS), 2017.
• Hejia Zhang, Po-Hsuan Chen, Janice Chen, Xia Zhu, Javier S. Turek, Theodore L. Willke, Uri Hasson, and Peter J. Ramadge. "Searchlight Factor Models in Multi-Subject fMRI Analysis." Collaborative Research in Computational Neuroscience (CRCNS), 2016.
• Hejia Zhang, Po-Hsuan Chen, Janice Chen, Xia Zhu, Javier S. Turek, Theodore L. Willke, Uri Hasson, and Peter J. Ramadge. "A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis." NIPS Workshop: Brains and Bits: Neuroscience meets Machine Learning, 2016.
• Po-Hsuan Chen, Xia Zhu, Hejia Zhang, Javier S. Turek, Janice Chen, Theodore L. Willke, Uri Hasson, and Peter J. Ramadge. "A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation." NIPS Workshop: representation learning in artificial and biological neural network, 2016.
• Tao Wu, Jun Liu, Hejia Zhang, Mark Hallett, Zheng Zheng, and Piu Chan. "Attention to automatic movements in Parkinson's disease: Modified automatic mode in the striatum." Cerebral Cortex 25, no. 10 (2015): 3330-3342.
• Rister, Blaine, Daniel Reiter, Hejia Zhang, Daniel Volz, Mark Horowitz, Refaat E. Gabr, and Joseph R. Cavallaro. "Scale-and orientation-invariant keypoints in higher-dimensional data." In Image Processing (ICIP), 2015 IEEE International Conference on, pp. 3490-3494. IEEE, 2015.
• Hejia Zhang, Azalia Mirhoseini, Farinaz Koushanfar, “An efficient Distributed Approach for Processing Sparse Shift- Diagonal Matrices.” Rice Undergraduate Research Symposium, Apr 12, 2013

Affiliations and Awards

Affiliations

• IEEE student member
• IEEE Eta Kappa Nu Honor Society member (Theta Rho Chapter)
• Tau Beta Pi member (Texas Gamma Chapter)
• Phi Beta Kappa member

Awards and Honors

• SEAS Travel Grant 2017
• Graduate with summa cum laude 2014
• President’s Honor Roll 2010 - 2014
• Bill Wilson Senior Design Award 2014
• Rice Undergraduate Research Symposium K2I Price 2014
• Samuel T. Sikes Jr. Scholarship in Engineering 2012 - 2014
• Chevron Scholarship 2012 - 2013
• SIE Premier Academic Scholarship 2012
• AP Scholar with Distinction Award 2010

Contact

Email

zhanghejia2010@gmail.com

Address

Princeton University Equad B204 Electrical Engineering Department, Princeton, NJ, 08544