Professional Summary

I am a rising fourth-year at UC Berkeley studying Electrical Engineering and Computer Science; my academic interests lie in deep reinforcement learning and computer vision, specifically perception for autonomous driving. My interests lie in applications of machine learning, whether it be for self-driving cars, the visually-impaired, or microscopic imaging. I have been recognized internationally for social impact and design and have been recognized by UC Berkeley for leadership.

For more, see my resume. Alternatively, see my LinkedIn.


**University of California, Berkeley** (2014-2018)
*Electrical Engineering and Computer Science (BS) . Major GPA: 3.81/4.00*
Graduate Combinatorial Algorithms and Data Structures, Graduate Deep Learning, Convex Optimization, Probability Theory and Random Processes, Artificial Intelligence, Machine Learning, Algorithms, Data Structures, Computer Architecture


  • International Semifinalist for Adobe Design Achievement Awards ('17) *placed in both Social Impact (web/mobile) and Fine Arts (graphic design/print)*
  • International Top 16 in "Web & Mobile", Design ('17) *Semifinalist for "The Rookies Co." design competition with ~9000 entries across 80+ countries, 600+ design schools*
  • SURF Fellowship UC Berkeley ('17) *Independent summer research, 90 recipients/annum*
  • Eta Kappa Nu Engineering Honor Society ('16) *Top 25% in College of Engineering*
  • The Leadership Award ('16) *demonstrated leadership excellence at UC Berkeley*
  • Dean's Honor List ('16) . *Top 10% in College of Engineering*
  • Regents' and Chancellors' Scholarship ('14) *Top 2% of entering undergraduates*


SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
by Bichen Wu, Alvin Wan, Forrest Iandola, Peter H. Jin, Kurt Keutzer
CVPR 2017​ ​.​ ​Winner​ ​of​ ​Best​ ​Paper​ ​Award​ ​at Embedded Vision Workshop . Paper . Code

SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
by Bichen Wu, Alvin Wan, Xiangyu Yue, Kurt Keutzer
Submitted to ICRA 2018 . Paper . Video

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
by Bichen Wu, Alvin Wan, Xiangyu Yue, Peter Jin, Sicheng Zhao, Noah Golmant, Amir Gholaminejad, Joseph Gonzalez, Kurt Keutzer
Submitted to CVPR 2018 . Paper


Object Detection for Autonomous Driving
working under PhD candidate Bichen Wu and Professor Kurt Keutzer in ASPIRE
focused on real-time, small-scale models for object detection and point cloud segmentation for autonomous vehicles

Deep Reinforcement Learning
working under PhD candidate Vaishaal Shaankar and Professor Benjamin Recht in RISE
focused on reductions in training time and complexity in deep reinforcement learning


Semi-Supervised Deep Learning for Molecular Structures (repo, report)
collaboration with Ke Xu lab in UC Berkeley's College of Chemistry, classification of molecular structures found in point cloud datasets known as stochastic optical reconstruction microscopies (STORM)

Memory-Limited Machine Learning (repo)

completed under PhD candidate Vaishaal Shaankar in RISE, Python utility for streaming stochastic gradient descent, employed when either (1) data cannot fit in memory or (2) kernel matrix cannot fit in memory


Computer Science Instructor . Elite Educational Institute . Winter 2017

  • taught institute's first programming course "Game Development with Python"

Software Engineering Intern . Facebook . Summer 2016

  • worked with selendroid, ADM, and IntelliJ for MyDay (Messenger media-focused redesign), Tincan (encrypted threads) and Picheads (native camera integration)
  • updated internal GraphQL dex, halved number of network requests for photo gallery, centralized Tincan media load


Head Student Instructor for Machine Learning
Fa'17 . Sp'17 (non-head) . ~500 students per offering

  • tba

3-time Head Student Instructor for Discrete Mathematics and Probability Theory
Sp'17 . Fa'16 . Sp'16 . 800+ students per offering

  • 4.83/5.00 rating, 0.3 above department average
  • led staff of 70+ student instructors, readers, and academic interns; academically responsible for 70+ students, 20-hour appointment
  • organized 9 exams and over two dozen course events, across all semesters
  • streamlined course organization, wrote 60-page book, published ~50 quizzes Fa'16, Sp'16

Student Instructor for Structure and Interpretation of Computer Programs
Fa'15 . Sp'15 (tutor) . Su'16 (head tutor) . 1200+ students per offering

  • academically responsible for 60+ students, 20-hour appointment
  • released 30-page compilation, released over a dozen quizzes, wrote and administered 250-person mock midterm
  • core developer for the course's Ok autograder: increased test coverage from 37.1% to 90.7%, enhanced API security, use for 1400+ each semester

Tutor for Artificial Intelligence
Su'16 . 200 students

  • wrote calculus worksheets and maintained the contest server


  • I'm among several founding developers (among top 3 contributors) for various essential UC Berkeley Data Science custom-built software, including the datascience Python package, interact server, and the Ok autograder.
  • I serve as Director of Technology for TEDxBerkeley, where I manage 250+ volunteers annually with custom-built software and have launched 3 annual iOS apps ('15, '16, '17).
  • To grant autonomy to the blind using computer vision, I am conducting a case study and built a prototype iOS application with a small team of 2 others.
  • Still a yellow belt in Taekwondo but working on it. Veteran cheesecake-lover though.