Calgary, Alberta, Canada
Innovator and tech enthusiast driven by the belief that technology can change the world for the better. With a masters in computer engineering from UWaterloo and a keen interest in AI, my passion for using advanced technology to improve life on our planet is evident in my recent years of work, particularly in AgTech industry where I co-invented a groundbreaking AI-based Micro-Precision Irrigation (MPI) system. This revolutionary patented solution (US Patent 20240245015A1) offers upto 30% water savings and upto a 12% increase in crop yield (varies with crop type), offering farmers a sustainable solution to the challenges of water management and crop production. With a unique combination of technical & leadership skills, a creative mind, and a passion for problem-solving, I am committed to bring about positive change and help shape a better future for all. If you're interested in exploring the potential of AI-based remote sensing technologies and IoT wireless Lora communication to drive sustainable change, I would love to connect and hear your ideas.
Managing InteliRain’s flagship AI-driven precision irrigation platform, overseeing full delivery from web application development, ML model optimization to corner-irrigation algorithm design for center pivot systems with corner technology. Scaled Micro-Precision Irrigation (MPI) from pilot to commercialization, deploying 50+ systems across Canada (AB, MB, SK, PEI) and the U.S. (ID, WA), standardizing software, data workflows, and deployment protocols. Owned stakeholder engagement and success delivery with 25+ growers, agronomists, and service dealers. I translated field and user feedback into actionable priorities and feature release plans. Leading a high-performing cross-functional team of software developers and ML engineers, defining product roadmaps, performance KPIs, and delivery processes to ensure reliable, production-grade releases. Leading dealer enablement and readiness programs through on-site training for installation and commissioning, improving deployment consistency and reducing setup issues by 50%+ Directed optimization of ML-based soil moisture and irrigation decision models, improving field-level water-use efficiency, crop yield stability, and on-farm operational simplicity. Established and managed the technical issue escalation and triage process, enabling faster root-cause analysis, reducing field downtime, and channeling learnings into iterative product improvements.
Co-invented a fully automated AI-driven Micro-Precision Irrigation (MPI) system (US patent application US20240245015A1) that combines machine learning, robotics, and multi-spectral sensing to optimize irrigation in real time (soil moisture, crop growth, and weather inputs). Delivered measurable field outcomes: up to 30% reduction in water use vs. traditional irrigation methods and up to 10% increase in crop yield across multiple field tests in pilot phase. Built and integrated end-to-end analytics pipelines for real-time sensor data ingestion, processing, and decisioning, enabling precise water delivery and reduced environmental impact. Led a team of PhD researchers (University of Alberta) on the Alberta Innovates–funded AI-CR collaboration; completed 100% of project milestones on schedule. Supervised and mentored an ML engineer, tracking progress and improving the ML model performance used for soil moisture estimation and irrigation optimization. Recruited and managed a remote firmware engineer to develop a low-power wireless soil moisture sensor; delivered the full system within the required timeline while using ~40% of the allocated budget (≈60% under budget). Core strengths / focus areas Image Processing • Sensor Fusion • Python Algorithm Development • Data Analysis & Visualization • Robotics Software Integration • ML-based Soil Moisture Estimation
Completed the MASc program in Electrical and Computer Engineering at UWaterloo. • Work spans across the breadth of acoustic image processing involving the development of a novel adaptive filtering technique for image denoising and use of plane-wave beamforming and machine learning to solve challenging problems. • Secured 2 international conference publications on my research projects. Research Projects Accomplished: • Adaptive Eigen-based Motion Clutter Filtering for Ultrafast Ultrasound Flow Imaging using Unsupervised Machine learning (Multivariate Clustering) in MATLAB. • Adaptive Ultrasound Image Denoising using SVD and multi-angle Plane-Wave Beamforming in MATLAB • Statistical human artery flow detection performance evaluation using ROC and Likelihood Ratio analysis.
Course ECE 290 Responsibilities: Conducting weekly tutorials to a class of 120 students. Leading weekly in-class Practice Quizzes and marking of all scantron based tests and uploading marks to Waterloo Learn. To enhance learning experience through in-class discussions and helping students with subject related queries. Regular coordination with course instructor to aid in conducting weekly quizzes.
* Build new electronic equipment from schematic designs, selection and layout of components, debugging and testing circuits * Construct, install, maintain and repair variety of electronic equipments * Participate in the modification of specialized electronic equipment * Provide technical advice on selection of components
• Lead multiple team projects including design of Electromagnetic Acoustic Transducer (EMAT) for aerospace NDT and design and development of Intelligent Mobile Robots (IMRs) for disaster mitigation and firefighting. • Secured a journal publication and two international IEEE conference publications on my research projects. • Area of focus: Ultrasonic Non-destructive Evaluation, Machine Vision and Embedded systems.
For details of events accomplished visit our Facebook Page: www.facebook.com/ieee.pnec Mission: IEEE PNEC Student Branch aims to progress as a comprehensive society, responsive to technological change. We at IEEE PNEC, envisage exploring, initiating and enlightening the knowledge of theory and practice of all aspects of Electrical Engineering, Electronics Engineering, Computer Engineering and the allied branches of engineering and related sciences. We promote wholesome interaction of the students with the pioneers and renowned leaders of the field both in academic as well as corporate sector. Our basic paradigm is the professional development of the students in accordance to the Constitution and Branch Bylaws of IEEE and the conventions as defined by Pakistan Navy Engineering College. Besides fostering a spirit of technological innovation, we help the students of the College in becoming confident, competent, well-rounded individuals brimming with personal and professional talent and well-equipped to make their mark in the world as members of IEEE PNEC and graduates of the College.