I have my academic background in Electronics and communications engineering with a PhD in computer vision and deep learning applied to horticultural applications. My expertise and experience is in design, development, and deployment of machine vision systems on broad areas including horticulture, fisheries, automotive and medical imaging. I have delivered several short-term projects for engineering consulting firm and industries (e.g., AusIndustry, Enterprise Connect and DFAT/AIC).
Over the past six years I am involved with IFFS at CQUniversity in delivering the project funded by DAWR R&D4Profit program and managed through Hort Innovation where I
was involved (during and post PhD) with data collection on farms in Qld and NT, and interaction with growers, Department of Agriculture staff and others using the prototype imaging system which is awaiting licensing on the IP.

1. Central Queensland University, Rockhampton, Australia
       Degree: Doctor of Philosophy (PhD)
       Doctoral Thesis: "Precision Agriculture: Exploration of machine learning approaches for assessing mangocrop quantity"
2. University of Southern Queensland, Toowoomba, Australia
       Degree: Master of Engineering Science major in Electrical and Electronic Engineering
       Masters Thesis: "Feasibility study of incorporating barometric pressure transducers in miniature UAV forobstacle avoidance in an indoor environment"
3. Tribhuwan University, Kathmandu, Nepal
       Degree: Bachelor’s Degree in Electronics & Communications Engineering
       Dissertation: "Home Security System"

School of Health, Medical and Applied Sciences, Institute for Future Farming Systems (IFFS), Central Queensland University

Regional Universities Network (RUN) scholarship for PhD, Central Queensland University

• Completed 'Adult Learning in Practice (OLTC20005)’
• Completed Research Accelerate program- ‘Supervising Doctoral Studies (SDS_10)’

• Demonstrated ability to conduct research and development activities either individually or in a team environment.
• Proven ability to drive research projects and provide postgraduate supervision.
• Proven record of timely publication and reviews on state-of-the-art in the field of related research.
• Highly developed problem-solving skills and proven ability to build integrated solutions and evolve those as needed.
• Demonstrated capability to engage with stakeholders and industry partners to meet the project expectations.
• Demonstrated cross-departmental and cross-institutional collaboration and proven ability to work in diverse projects across different industries.

1. Professional Engineer, ANZSCO Skill level 1, Electronics Engineer (ANZSCO Code 233411), Engineers Australia (EA id: 5028926).
2. Registered member of Nepal Engineering Council (Regd. No. 2858 “Electronics and Communication Engineer”).

• Design and develop machine vision solution and decision support systems for horticultural applications
• Orchard imaging system hardware design and software programming
• Assist on the development of vision system for auto harvester
• Provide supervision to postgraduate students and interns
• Assist in the preparation of grant applications
• Preparation and publication of journal articles, conference papers and scientific reports

• Developed Orchard imaging system- consisting of cameras, GPS receiver and LED lights.
• Developed processing pipeline for automated spatial registration and processing of image data from orchard blocks for visualization in web maps.
• Designed and developed machine learning models for flower/fruit detection, tracking and yield estimation.
• Developed software apps for image acquisition in fruit sizing applications.
• Developed user interface for auto-harvester’s vision system and actuator controls.

• Computer vision and image processing R&D experience with solid knowledge of deep learning object detection and ML algorithms, techniques, and packages.
• Experience with deploying deep learning object detection models in Amazon AWS (EC2instance and S3 bucket) cloud platform.
• Experience with deep learning CNN object detection frameworks- R2CNN, Faster R-CNN,YOLO, and SSD.
• Experience with researching and developing AI models using OpenCV and deep learning libraries- Keras and TensorFlow.
• Python programming experience with a little touch on Java and C++.
• Experience with Microsoft Windows and Linux (Ubuntu) operating systems.
• Experience with edge computing devices and optimization libraries like OpenVino.

1. Senior Computer Vision Engineer
   School of Information Technology and Systems,University of Canberra(www.canberra.edu.au)
   Developed deep learning AI model for detection of malaria parasite in microscope images of bloodsmears
2. Senior Computer Vision Engineer
   Corematic Engineering (www.corematic.com.au)
   Developed deep learning AI model for vehicle hail damage assessment on camera images.
3. Senior Computer Vision Engineer
   Easy Skill Australia Pty Ltd (www.easy-skill.com)
   Developed deep learning AI model for defect assessment of macadamia nuts in a sorter.
4. IT support administrator
   Ignite (www.igniteco.com/) talent business.
   Conducted IT support and desktop rollout activities representing Lenovo.