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Fei Xia

feixia at stanford.edu [Github] [Google Scholar]

About me

I am a third year PhD candidate at Department of Electrical Engineering, Stanford University. I am co-advised by Silvio Savarese in SVL and Leo Guibas. I have collborated with James Zou and David Tse on various projects. My research interests lie in Computer Vision (3D Vision in particular) and Applied Machine Learning.
News:

  • 2019.7 AdaFDR was accepted to Nature Communications.

  • 2019.5 Will Shen and I won Qualcomm Innovation Fellowship as a team. Thank you Qualcomm!

  • 2019.5 Gibson V2 will be released soon, stay tuned.

  • 2019.5 AdaFDR won best paper award at RECOMB2019.

  • 2018.4 We will host a demo for Gibson Env at CVPR'18. Come and check it out!

  • 2018.2 One paper accepted to CVPR'18 (spotlight).

Education

Aug. 2016 - Present, Department of Electrical Engineering, Stanford University,

PhD Candidate.

Aug. 2012 - Jul. 2016, Department of Automation Tsinghua University,

Bachelor of Engineering.

Aug. 2014 - Dec. 2014, Department of Electrical and Computer Engineering, Georgia Institute of Technology,

Exchange Student.

July. 2015 - Sept. 2015, Department of Electrical Engineering, Stanford University,

Visiting Researcher.

Selected Publications

  • Noriaki Hirose, Fei Xia, Roberto Martín-Martín, Amir Sadeghian, Silvio Savarese (2019). Deep Visual MPC-Policy Learning for Navigation. IEEE Robotics and Automation Letters . [pdf][project]
  • Hirose, N., Sadeghian, A., Xia, F., Martín-Martín, R., & Savarese, S. (2019). VUNet: Dynamic Scene View Synthesis for Traversability Estimation using an RGB Camera. IEEE Robotics and Automation Letters. [pdf][project]

  • Fei Xia*, Amir R. Zamir*, Zhiyang He*, Alexander Sax, Jitendra Malik, Silvio Savarese. Gibson Env: Real-World Perception for Embodied Agents. CVPR 2018 (spotlight). [pdf] [code] [project]

  • Fei Xia*, Martin Zhang*, James Zou, David Tse. NeuralFDR: learning decision threshold from hypothesis features. NIPS 2017. [pdf] [code]

Previous Computational Biology papers

  • Martin J. Zhang, Fei Xia, James Zou, "AdaFDR: a Fast, Powerful and Covariate-Adaptive Approach to Multiple Hypothesis Testing", Preliminary version selected as Best Paper Award of RECOMB 2019, also in Nature Communications, 2019. [pdf] [software] [code to reproduce the paper]

  • Qiao Liu, Fei Xia, Qijin Yin, Rui Jiang. Chromatin accessibility prediction via a hybrid deep convolutional neural network Bioinformatics. [pdf] [code]

  • Govinda M Kamath*, Ilan Shomorony*, Fei Xia*, Thomas A Courtade, David Tse. HINGE: long-read assembly achieves optimal repeat resolution. [pdf] [code] Genome Research Vol 27 2017.

  • Ilan Shomorony, Govinda M Kamath, Fei Xia, Thomas A Courtade, David Tse. Partial DNA assembly: a rate-distortion perspective. [pdf] ISIT 2016.

(*Equally contributed to the project and alphabetically listed)

Honors and Awards

  • 2019 Qualcomm Innovation Fellowship
  • 2019 RECOMB Best Paper Award
  • 2016 Stanford Graduate Fellowship (Michael J. Flynn Fellow), Stanford University
  • 2015 Chang Jiong Scholarship (Highest honor in Dept. of Automation, 1/560)
  • 2014 Fang Chongzhi Scholarship (Highest honor in Dept. of Automation, 1/560)
  • 2014 China Scholarship Council Excellent Undergraduate Fellowship

Teaching Experiences

Press Coverage