Sammy Sidhu
Senior Staff Engineer, Woven Planet Level 5


Graduate Student Researcher
University of California, Berkeley
  • Performed research on the intersection of Deep Learning and High Performance Computing (HPC) under Kurt Keutzer in the ASPIRE Lab.
  • Performed research under Stuart Russell (BAIR Lab) on Markov Chains for Medical AI in collaboration with UCSF.
Graduate Student Instructor / Assistant
University of California, Berkeley
  • CS186 / CS286: Project TA for an class on databases and distributed systems. Wrote a multi-thousand line Database project suited for education in Java. Project is still being used 5 years later to teach database design fundamentals for over 1000 students per semester. Topics taught include: B+ tree indices creation/maintenance, query optimization, transaction concurrency and locking and recovery.
  • CS61A: TA for the introduction to computer science course. I wrote a lab that would teach some fundamentals of functional programming in a fun way, which was computing sentiments for restaurants using Apache Spark and the Yelp dataset. Collaborating with Databricks, we were able to create a lab that ~2000 students / semester used to learn core concepts like map and reduce in a fun yet practical manner.
  • CS61B: Joined for the founding semester of Computer Science Mentors (CSM) and mentored/tutored small groups of students for the data structures and algorithms course. During this semester, I wrote much of the material and handouts that were used to teach across all groups.
  • EE40: Worked as a lab assistant for 2 semesters for the introduction to microelectronics course where it was often the first exposure many students had to hands on hardware. During labs, I taught the fundamentals of Analog HW such as filter, op-amps, etc and lab equipment such as signal generators, oscilloscopes, etc.
B.S. Electrical Engineering and Computer Sciences (EECS)
University of California, Berkeley
Focused in Machine Learning and Distributed Systems. Spent the majority of the 3rd and 4th year either teaching or in the research lab.

Selected Publications

See my google scholar for the full list
Scalable Primitives for Generalized Sensor Fusion in Autonomous Vehicles, 2021, NeurIPS ML4AV
How Lyft Uses PyTorch to Power Machine Learning for Their Self-Driving Cars, 2020, PyTorch Blog
SqueezeNAS: Fast neural architecture search for faster semantic segmentation, 2019, International Conference on Computer Vision
Dscnet: Replicating lidar point clouds with deep sensor cloning, 2019, Conference on Computer Vision and Pattern Recognition
Model based probabilistic inference for intensive care medicine, 2015, Meaningful Use of Complex Medical Data


I worked on the majority of these patents while I was Chief Architect at DeepScale which was acquired by Tesla in 2019
Multi-channel sensor simulation for autonomous control systems, 2021, Tesla Inc.
Systems and methods for training machine models with augmented data, 2020, Tesla Inc.
Neural networks for embedded devices, 2020, Tesla Inc.
Optimizing neural network structures for embedded systems, 2020, Tesla Inc.
Data synthesis for autonomous control systems, 2018, Tesla Inc.


Selected Talks that I have given
AI Experience with Forrest Iandola & Sammy Sidhu, Reflections|Projections 2018
Gave a tech talk on the fundementals of Deep Learning at University of Illinois at Urbana-Champaign to students and faculty. The talk was well received and I had my email inbox full from students for the rest of the week.


News articles that my work has been featured in