Professional Summary

I am a first-year PhD student at UC Berkeley studying computer science, with a concentration in artificial intelligence. My academic interests lie in efficient machine learning, perception for autonomous driving, and reinforcement learning. Additionally, I have been recognized internationally for social impact and design.

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


University of California, Berkeley (2018-)
Computer Science (PhD) . Artificial Intelligence

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, Abstract Algebra, Real Analysis

International Awards

  • International Semifinalist for Adobe Design Achievement Awards ('18) both Social Impact, Commercial
  • Microsoft Imagine Cup World Finals "Big Data" Top 6 Finalists ('18) ~40,000 entrants across 200+ countries, for an indoor positioning application focused on medical devices
  • International Semifinalist for Adobe Design Achievement Awards ('17) both Social Impact, Fine Arts
  • International Top 16 in "Web & Mobile", Design ('17) Semifinalist for "The Rookies Co." design competition with ~9000 entries across 80+ countries, 600+ design schools


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
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
CVPR 2018 . Paper . Code


Software Engineer . Rex . May 2018 - present

  • building AI scheduling assistant with natural language support for multiple channels--text, email, Slack, Messenger etc.

Machine Learning Research Intern . DeepScale . Summer 2017

  • employed sensor fusion for higher-resolution point cloud prediction with focus on object detection tasks
  • adapted metrics such as Wasserstein distance to quantify performance

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
Su'18 . Sp'18 . Fa'17 . Sp'17 . ~500 students per offering

  • lead staff of 30+ uGSIs, readers, academic interns
  • compiled course notes, quizzes, and crib sheets (
  • helped reorganize course syllabus and write new content--homework, notes, tests

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


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