Hersh Cover

Hersh Godse

Maker, Learner, Educator

About Me

I’m a maker. My strengths are my determination, curiosity, and collaborative nature.

I’m an EECS student at UC Berkeley, passionate about applying Computer Vision, Machine Learning, and Artificial Intelligence techniques towards recognizing data patterns and solving problems like self-driving cars and disease detection.

My expertise is in robotics, sensor fusion, and CAD design. I also help teach UC Berkeley’s EE 16A (Information Systems/Devices), EECS 70 (Discrete Math + Probability), and CS 61C (Computer Architecture) courses.

I’m currently interning at Okta, on the Core/Infrastructure Sphere team. I was previously at Intel, where I worked on accelerating autonomous driving via GPGPUs and FPGAs.

A Portland (OR) native, I love biking, hiking, and avidly following Trail Blazers basketball. I’m also a huge comics and Star Wars nerd 🙂

Check out my experience, projects, course notes, and resources below!

Course Notes | Resources

CS 61B: Data Structures
EE 16B: Information Systems and Devices (TBD)
EE 105: Microelectronic Devices and Circuits (TBD)
EECS 126: Probability and Random Processes (TBD)
EE 120: Signals and Systems (TBD)
CS 61C: Computer Architecture (TBD)

CS 61B: Data Structures


Summer 2018Software Engineer Intern - Okta

Intern on Core Sphere, working on optimizing backend infrastructure to gracefully handle requests asynchronously.

Spring 2017 - PresentCourse Staff - UC Berkeley EECS Dept.

Hold weekly office hours, lead group tutoring sessions, proctor exams, and grade hundreds of students for UC Berkeley’s EE 16A (Information Systems/Devices), EECS 70 (Discrete Math + Probability), and CS 61C (Computer Architecture) courses.

Fall 2017Autonomous Driving Research - Berkeley DeepDrive

Research on developing a drive control system for fully automated vehicles using feedback control filters, and integrating with machine learning, sensor fusion, computer vision, and robotics techniques/components of DeepDrive’s autonomous driving stack.

Summer 2017 Software Engineer Intern - Intel

Automated Driving Assistance Systems (ADAS) software optimization for various hardware. Gained thorough understanding of inner workings of cutting-edge self-driving car SW/HW stacks. Experienced deploying software on multiple hardwares and accelerators (CPUs, GPGPUs, FPGAs), and optimized OpenCL/Nvidia CUDA kernels (vectorization, OpenMP workload parallelization, hyper-threading, compiler, algorithm efficiency). Extensive experience studying and modifying OpenCV Computer Vision library for FPGA-enablement.

View OpenCV FPGA Prototype

Summer 2016Software Engineer Intern - Digimarc

Extensive experience developing with Digimarc’s Digital Watermarking (DW) API. Created SmartContact, an app using DW-enabled business cards for automatic contact info exchange. Assisted development and testing of security app for validation of advanced DW-enabled licenses/ID cards. Conducted Computer Vision tests on DW-media to determine DW robustness and imperceptibility. Constructed dataset and analyzed accuracy of DW-enabled barcode scanner on grocery products.



Computer Vision Algorithm for Health Research

Incrementally designed and developed a computer vision (CV) algorithm using image processing and machine learning techniques towards efficiently detecting blood disease like sickle-cell anemia from patient blood samples. Initially implemented open-source in MATLAB, then in Python using OpenCV library. Explored CV/ML techniques such as edge/feature detection, K-means clustering, L*a*b segmentation, and area filters.

View Code
</br>Computer Vision Algorithm for Health Research

Quadcopter Robotics

Planned, organized, documented, and led a club centered around learning the physics governing quadcopter drones, and attempting to build and program one.

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Quadcopter Robotics


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