I’m an engineer who likes building things that survive real conditions, whether it’s space hardware, embedded systems, or bionic tech. I studied Electrical and Computer Systems Engineering at Monash and now work as a DFT engineer at The Melbourne Bionics Company while pushing my venture ReviveTech to extend the digital life of electronics. My background spans FPGA design, embedded hardware, sensing systems, and startup work. As a former SMEE President and Startmate Fellow, I blend technical depth with an entrepreneurial mindset. I care about deep tech, circular systems, and creating tools that make life more accessible and more resilient.

I’m interested in deeper work at the intersection of silicon, sensing, and sustainable systems. I’m looking to learn more about advanced FPGA design, low power architectures, circular electronics, and scalable refurbishment pipelines. I’m also diving into biomechanical sensing, compression tech, and AI on the edge. I’d like to connect with people building deep tech products, climate focused digital infrastructure, or hardware heavy startups. If you work in embedded systems, bionics, repair ecosy

I’m exploring the future of circular electronics through ReviveTech, where I’m building systems that extend the life of devices instead of sending them to landfill. I’m tinkering with sensing hardware, real time compression control, and small scale refurbishment pipelines that can scale.

Computer Science graduate (Advanced CS, minor in Finance) from Monash. I’ve worked across software and engineering roles at ReeceTech, Nova Rover, and Chemist Warehouse. I enjoy building tools that blend algorithms, data, and user experience. Currently exploring ML, optimisation, and systems engineering while working part-time.

Machine learning, optimisation, robotics/IoT, fintech, and developer tools. Looking to collaborate with engineers interested in search algorithms, ML systems, or real-world automation.

Working on a stock-market prediction model using historical charts and technical patterns, and a sports-betting arbitrage bot that scans price discrepancies between Sportsbet and Bet365.

Samin is a co-founder and CEO of Melbourne Bionics. He holds degrees in Electrical Engineering and Biomedical Science, with a background in developing human-centred health technologies. Samin has worked on concussion detection systems for athletes and at-home lung-health monitoring tools, and was a HISS Scholar for creating an eating-companion platform supporting patients with eating disorders. Samin and his team are currently building a next-generation wearable compression sleeve - originally designed for lymphoedema management - that has evolved into the first lower-leg device capable of real-time prediction of injuries such as ACL rupture for elite athletes. A regulated medical-device version of the technology is planned for future clinical use.

Medical technology, bionics, electromagnetic fields.

The Melbourne Bionics Company - www.melbournebionics.com A lower-leg sleeve that monitors load on muscle groups and ligaments, and predicts likelihood of injuries, such as ACL rupture.

I’m Raghav Sharma, a Master of Artificial Intelligence student at Monash University with a strong focus on building end-to-end AI systems. My experience spans deep learning, LLMs, RAG pipelines, and agentic workflows, as well as deploying models on cloud platforms. I’ve led technical projects ranging from IoT analytics dashboards to investor–client matching engines, and I’m currently building an AI automation stack for real businesses. I enjoy turning messy, real-world data into usable products, and I’m especially interested in applications at the intersection of healthcare, operations, and productivity. At this hackathon, I’m keen to prototype something that’s both technically solid and actually shippable.

Interested in building AI-native backend systems: LLM agents, agentic orchestrators, RAG pipelines, and vector DB search powering real products. I want to go deeper on evaluation, monitoring, experiment tracking, and cost-efficient cloud deployment. Looking to connect with people strong in AI engineering or data engineering who enjoy building batch and streaming data pipelines and turning messy workflows into reliable, scalable automation.

Building an intelligent multi-timezone meeting scheduler: a Flask-based API that reads participants’ Google/Outlook calendars, intersects availability in 15-minute slots, and ranks options with a NumPy scoring engine (preferences, roles, urgency, buffers). It supports soft/confirmed holds, robust timezone handling, OAuth token refresh, and is tested on 30+ edge-case scenarios for production use.