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Nikhil Thorat

Software Engineer at Google

Nikhil Thorat is a prominent software engineer and entrepreneur known for his extensive experience in data visualization and machine learning. He is currently a co-founder of Lilac AI, a company he started in February 2023, after spending nearly eleven years at Google as a software engineer, where he was instrumental in creating significant projects such as TensorFlow.js and Know Your Data.12

Education

Thorat earned his Bachelor of Science degree in Computer Science and Mathematics from the University of Massachusetts, Amherst, graduating in 2012.14

Professional Experience

  • Co-Founder, Lilac AI (February 2023 - Present)
  • Software Engineer, Google (August 2012 - April 2023)
    • Co-Creator of TensorFlow.js
    • Team Lead for Google Image Search UI
  • Web Developer, Schedr (August 2010 - August 2011)
  • Software Engineer, CampusLIVE Inc (November 2009 - August 2010)

During his tenure at Google, Thorat contributed significantly to the development of machine learning technologies and led teams on various projects that enhanced user experience through innovative software solutions.134

Entrepreneurial Journey

In his recent blog, Thorat reflects on his experience as a first-time founder, emphasizing the importance of collaboration with his long-time colleague Daniel. He discusses the challenges and lessons learned while launching Lilac AI, including the necessity of early customer engagement and the iterative nature of product development.2

Thorat's work has been recognized in various conferences, showcasing his contributions to the field of artificial intelligence and machine learning.3 His career trajectory illustrates a blend of technical expertise and entrepreneurial spirit, making him a notable figure in the tech industry.

Highlights

Mar 24 · twitter

🖐️ micro-handpose: browser hand tracking faster than MediaPipe, pure WebGPU compute shaders.

15 WGSL shaders run palm detection + landmark inference on GPU. 74KB JS (17KB gzip) + 7.7MB weights vs MediaPipe's 34MB package. Zero dependencies.

M4 Pro Chrome / iPhone Safari: • micro-handpose — 2.2ms / 72ms (WebGPU) • MediaPipe GPU — 4.0ms / 97ms (WASM+WebGL) • MediaPipe CPU — 4.5ms / 136ms (WASM)

npm install @svenflow/micro-handpose

const handpose = await createHandpose() const hands = await handpose.detect(video) console.log(hands[0].keypoints.thumb_tip)

🖐️ micro-handpose: browser hand tracking faster than MediaPipe, pure WebGPU compute shaders.

15 WG
Jul 11 · twitter

I believe this but only because of the pool of users they chose.

The ones who are massively sped up are not amazing ICs, they’re ones with manager / TL skills where you’re constantly context switching and talking to 10 people about 10 things all day every day.

Related Questions

What inspired Nikhil Thorat to co-create TensorFlow.js?
How did Nikhil Thorat transition from Google to Databricks?
What challenges did Nikhil Thorat face while starting Lilac AI?
Can you tell me more about Nikhil Thorat's role at Google?
What is Know Your Data, and how did Nikhil Thorat contribute to it?
Nikhil Thorat
Nikhil Thorat, photo 1
Nikhil Thorat, photo 2
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Location

Greater Boston Area