MathWorks

Enabling the Team:
● Guide weekly discussions on Generative AI's business impact, driving insights and collaboration.
● Organize a hackathon to explore AI ideas, assess risks, and discover opportunities.
● Facilitate monthly knowledge-sharing sessions to share best practices and gather feedback across offices in different time zones (EMEA, APAC).
● Maintain a centralized knowledge base to streamline trainer workflows. Built scripts and automations to improve process efficiency and quality.

Individual Responsibilities:
● Perform needs analysis of skill gaps for teams of engineers and propose learning paths and training programs.
● Design and deliver content and training on data science, computer vision, image processing,  statistics, machine learning, and deep learning related topics.
● Capture post-training feedback from customers to improve product and training delivery.

Senior Training Engineer - Image Processing, Computer Vision, Machine Learning, and Deep Learning

Help engineers and scientists do their best work:
● Deliver courses on IP, CV, ML and DL 
● Work with customers to gather requirements and develop customized curricula 
●  Identify and implement delivery improvements based on student evaluations

Training Engineer - Image Processing, Computer Vision, Machine Learning, and Deep Learning

● Develop user-focused content explaining key concepts and solutions
● Collaborate with cross-functional team members
● Participate in the software design and development process
● Test and Validate capabilities of core MATLAB tools

Technical Content Developer

Rochester Institute of Technology

Advisors : Dr. Eli Saber, Dr. David Messinger, and Dr. Nathan Cahill 

I developed algorithms and conducted research  on the following main projects:
● Improving the Accuracy of an Object-Based Classification System  (Funded by 
   Department of Defense - DoD) - Segmentation and Classification
● Extracting Spatial-Information from Images (Funded by DoD) - Feature Detection
● Panoramic Image Stitching (Funded by Imaging Science Graduate Assistantship) - Computational Geometry

Please see "Projects" section for more details.

Graduate Research Assistant

Relevant Coursework in Imaging Science
● Digital Imaging Mathematics
● Human Visual System
● Optics
● Fourier Methods in Imaging
● Digital Image Processing
● Radiometry
● Remote Sensing and Image Analysis
● Advanced Digital Image Processing
● Multi-View Geometry 
● Computational Methods in Imaging Science

Graduate Student

XEROX CORPORATION

R &D Summer Intern 
● Researched techniques for image stitching
● Developed MATLAB code to simulate images from multiple linear sensor arrays  
● Created algorithm to stitch images from  multiple low cost sensor arrays to enable wide width scanning

Multi-Sensor Image Stitching

R &D Summer Intern 
● Attended training for color device (printer, scanner) characterization and calibration techniques. 
● Researched different methods to reconstruct selective frequency components from spectrum data.
● Implemented edge detection (canny, scale space search) to automatically extract page profiles from reflective density sensor data.
● Stream lined the code  for learning periodic print defect (banding) and correcting it via electronic compensation techniques. 
● Assisted team in analyzing and logging results from a sixteen day experiment (four factor, two level, multiple responses)
● Attended on-site class for color device (printer, scanner) characterization and calibration techniques.

Image Quality and Consistency

R&D Summer Intern 
● Studied the use of centroiding algorithms to perform feature detection within an image.
● Developed image registration algorithms to enable the comparison of images captured at different stages in the printing process.
● Designed a graphic user interface using MATLAB to facilitate the processing of multiple images.
● Analyzed images to measure the amount of rotation & translation introduced by the paper path into the sensed image.

Sensor Modeling and Image Registration

MAGNA POWERTRAIN INC

NVH Engineering Intern
● Processed signals using DSP techniques (FFT) to identify the sources of noise and vibration in parts. 
● Researched Zwicker’s Loudness Model to quantify pleasantness of noise to the human ears.  
● Learned to setup a Data Acquisition System for Noise and Vibration Analysis. 
● Programmed in VBA to automate the data acquisition which improved efficiency  of the process from 10 hrs to 45 minutes.

Noise Vibration and Harshness Testing

Kirtas Inc.

R&D Engineering Co-op
● Learned basics of Digital Image Processing, Optics and Photography. 
● Detected the non-uniformity of brightness in the images.  
● Performed a mathematical analysis on the non-uniformity issue.
● Worked on and learned to operate BookScanAPT1200 (Book Digitizer).

Image Non-Uniformity Characterization

I help improve team readiness and innovation through hackathons, knowledge-sharing sessions, and process automation. I’ve designed and delivered training on Data Science, Machine Learning, and Deep Learning and co-founded public speaking programs at RIT and deliver training for MathWorks.

My focus is on making technical concepts accessible and creating practical learning content for diverse audiences.

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