Biography
As an industrial engineer, I have embarked on a remarkable journey spanning think tanks, academia, and industry. With an unwavering passion for the automotive domain, my expertise shines in the captivating realm of electric, automated, and connected mobility. Harnessing the power of cutting-edge technologies like machine learning, artificial intelligence, Bayesian methods, simulation tools, and the bedrock of engineering and physics, I take great delight in crafting ingenious solutions that propel the boundaries of modern transportation to new heights.
- Electric and Connected Vehicles
- EV Charging Infrastructure
- Lithium-ion Batteries
- Explainable Artificial Intelligence
- Spatio-Temporal Modeling
- Customized Deep Learning
- Active Learning and Bayesian Design
- Autonomous Vehicle Simulation
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Ph.D. in Industrial and Systems Engineering, 2019-2023
University of Michigan - Dearborn, USA
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M.S. Industrial Engineering, 2018
University of Michigan - Dearborn, USA
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B.E. Mechanical Engineering, 2015
University of Pune, India
Experience Highlights
My top research projects include:
- Electric Vehicle (EV) charging infrastructure reliability improvement. More info here.
- EV-EVSE communication using ISO 15118.
- Error reporting using OCPP and OCPI standards.
- Customized DL solutions for SOC/SOH/RUL estimation in Lithium-ion batteries.
- Explainable AI for advanced battery prognostics and diagnostics.
My top research projects include:
- Safety of Autonomous Vehicles (AVs) - SOTIF & ISO 21448.
- Virtual verification and validation of AVs.
- Exploration of Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) technologies for safer trajectory predictions using GAN-based models. More info here.
- Customized DL solutions for SOC/SOH/RUL estimation in Lithium-ion batteries. More info here.
- Development of explainable AI and Bayesian methods for warranty analytics. More info here.
- Developed ROS-based pythonic library for Software-In-Loop (SIL) and Hardware-In-Loop (HIL) testing for L3+ ADAS features.
- Developed and deployed framework for corner-case realization within self-driving stacks.
- Replicated processed sensor fusion (Camera, LiDAR, Radar)outputs for real-time communication with driving policies.
- Developed OpenDrive maps for integration with CARLA simulator.
- Responsible for cost, value, and feasibility analysis for Stellantis, Ford, BMW, and Volvo Laser/LiDAR scanning programs.
- Implemented continuous improvement practices to reduce CAD modeling defects by over 20%.
- Implemented risk mitigation plans and root cause analysis using a 5 Why’s system.
- Assisted in the development and maintenance of DVP&R and APQP activities such as DFMEA, PFMEA, CP, RCA, and GD&T.
Recent Posts
Featured Publications
Artificial intelligence solutions for Autonomous Vehicles (AVs) have been developed using publicly available datasets such as Argoverse, ApolloScape, Level5, and NuScenes. One major limitation of these datasets is the absence of infrastructure and/or pooled vehicle information like lane line type, vehicle speed, traffic signs, and intersections. Such information is necessary and not complementary to eliminating high-risk edge cases. The rapid advancements in Vehicle-to-Infrastructure and Vehicle-to-Vehicle technologies show promise that infrastructure and pooled vehicle information will soon be accessible in near real-time. Taking a leap in the future, we introduce the first comprehensive synthetic dataset with intelligent infrastructure and pooled vehicle information for advancing the next generation of AVs, named VTrackIt. We also introduce the first deep learning model (InfraGAN) for trajectory predictions that considers such information. Our experiments with InfraGAN show that the comprehensive information offered by VTrackIt reduces the number of high-risk edge cases. The VTrackIt dataset is available upon request under the Creative Commons CC BY-NC-SA 4.0 license at http://vtrackit.irda.club. LinkedIn Post here.
Recent Publications
Recent & Upcoming Talks
Contact
- mayuresh@umich.edu
- 1955 N. Fremont Ave., Idaho Falls, ID 84301
- DM Me!
- Let’s Connect!