Inderjot Singh Saggu

AI Researcher

Cupertino, California, United States7 yrs 7 mos experience
Most Likely To SwitchHighly Stable

Key Highlights

  • Expert in machine learning and computer vision for autonomous systems.
  • Developed innovative algorithms for perception in self-driving technology.
  • Proven track record in reducing errors in machine learning applications.
Stackforce AI infers this person is a Machine Learning Engineer specializing in autonomous vehicle technology.

Contact

Skills

Core Skills

Machine LearningComputer Vision

Other Skills

Deep LearningData AnalysisID Segmentation AlgorithmPythonC++Artificial Intelligence (AI)TensorFlowData MiningData ModelingData ScienceData AnalyticsStatistical Data AnalysisBig DataBig Data AnalyticsData Engineering

About

Self-driving trucks!

Experience

Plus

4 roles

Staff Research Engineer

Promoted

Jan 2025Present · 1 yr 2 mos

Senior Research Engineer

Promoted

Dec 2022Jan 2025 · 2 yrs 1 mo

Research Engineer (Perception)

Jan 2022Dec 2022 · 11 mos

Machine Learning Engineer (Perception)

Apr 2020Jan 2022 · 1 yr 9 mos

University of california san diego

3 roles

Graduate Teaching Assistant

Jan 2020Mar 2020 · 2 mos

  • MATH 155A - Computer Graphics (Prof. Sam Buss)

Graduate Teaching Assistant

Apr 2019Jun 2019 · 2 mos

  • ECE 285 - Machine Learning for Image Processing (Prof. Chales Deledalle)

Graduate Research Assistant

Nov 2018Apr 2020 · 1 yr 5 mos

  • Deep Learning for collaborative autonomous perception
  • Achieved camera only based 6-DoF relative pose estimation for arbitrary large viewpoint changes by exploiting visual cues and co-visible objects.
  • This would enable NLOS communication between self-driving cars operating in a V2V setting.
Deep LearningComputer VisionData AnalysisMachine Learning

Mitek systems

Machine Learning Intern

Jun 2019Sep 2019 · 3 mos · Greater San Diego Area

  • Worked with the Document Tooling Team on designing an improved ID matching tool for automating document onboarding process and increasing efficiency of modelers.
  • Matching tool reduced FPs by 92% over the legacy matcher while retaining over 80% of TPs.
  • Developed novel ID segmentation algorithm based on a U-Net architecture that achieved IoU of 0.98

National university of singapore

Summer Research Intern (SINAPSE)

May 2017Aug 2017 · 3 mos · Singapore · On-site

  • Neuromorphic Engineering and Robotics Group (Prof. Nitish Thakor):
  • Designed and fabricated high-density desktop tactile array and used event-based encoding of pressure signals for high speed (5.2 kHz) and sparse acquisition.
  • This neuromorphic representation of touch is well suited to rapid identification of critical contact events, making it suitable for dexterous manipulation in robotic and prosthetic applications.
Machine LearningID Segmentation Algorithm

Education

Indian Institute of Technology, Delhi

Bachelor of Technology (B.Tech.) — Electrical Engineering

Jan 2014Jan 2018

UC San Diego

Master of Science - MS — Machine Learning and Data Science

Jan 2018Jan 2020

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