Ph.D., founder of KaleidoForge. I develop and deploy machine learning solutions directly on hardware, covering the full ML to hardware stack and IoT ecosystems.
I am Romina Soledad Molina, Ph.D., founder of KaleidoForge, a studio dedicated to connecting machine learning, optimization, and physical hardware architectures. My goal is to solve the pain of technical fragmentation by developing unified solutions that bridge the gap between AI and embedded systems. I develop and deploy ML solutions directly on hardware, from tiny microcontrollers and IoT edge devices to high performance FPGA and SoC platforms.
My work covers the full ML to hardware ecosystem: model design and training, compression, physical deployment, system profiling, and reproducible workflows for scientific and industrial applications. This focus led to the creation of KalEdge, our flagship platform designed to automate and simplify these complex workflows.
I hold a joint Ph.D. in Industrial and Information Engineering (Italy) / Computer Science (Argentina), and I have spent years teaching and conducting research in digital signal processing, artificial vision, and embedded AI systems.
Since 2019, I have been collaborating with the Multidisciplinary Laboratory (MLab) at ICTP in Trieste, working on machine learning for advanced scientific instrumentation and contributing as an instructor in multiple international schools and workshops on FPGA, SoC, and embedded AI.
What I enjoy most is making complex systems clear, structured, and practical, so teams can focus on solving problems instead of struggling with technical fragmentation.
KaleidoForge emerged from a simple idea: modern ML systems do not need to be fragmented and complicated to be powerful. When tools, workflows, and hardware interact coherently through unified platforms like KalEdge, you get systems that are easier to maintain, faster to iterate, and more intuitive to extend into new IoT applications.
It is a space focused on clarity, engineering discipline, and thoughtful design, removing the unnecessary friction of disjointed development processes.
Help people build ML systems that run efficiently, scale sensibly, and remain understandable over time.
Systems and ideas should be explainable. Clarity is the foundation for reliable engineering.
Progress through small, meaningful steps. Each cycle sharpens the solution.
Decisions aligned with constraints and purpose. No unnecessary complexity.
Every project, course, and workflow within KaleidoForge follows principles that keep technology purposeful and human centered.