Join Date
October 2024
Country
Gender
Not Specified
Age
Not Specified
About Daniel Vlasits
Daniel is the Co-Founder and CTO at Yoneda Labs
Daniel studied Computer Science at the University of Cambridge. He has strong software engineering experience having been a software engineer at Cisco, Optiver and a programming languages researcher at the University of Cambridge.
He won first place at the international robotics competition Pi Wars, and ranked 5th in the Cambridge Computer Science cohort.
Daniel studied Computer Science at the University of Cambridge. He has strong software engineering experience having been a software engineer at Cisco, Optiver and a programming languages researcher at the University of Cambridge.
He won first place at the international robotics competition Pi Wars, and ranked 5th in the Cambridge Computer Science cohort.
Companies & Work
Yoneda Labs
Yoneda Labs provides software to help chemists optimise reactions. When chemists make a drug or a material, we help them figure out the best reaction parameters such as temperature, concentration and catalyst.
When Jan was working at chemical labs, he experienced the struggle of spending weeks guessing reaction conditions. We then started experimenting with ML to speed up the process.
Now, as a team of three friends from the University of Cambridge, we’ve spent the last month combining our domain expertise in Computer Science, Machine Learning and Chemistry to develop state of the art models for reaction optimisation.
Although ML is becoming well established in other fields, current chemical models generalise poorly and require lots of programming experience. We make our models easily accessible to chemists in the lab.
Finding the right conditions quickly allows pharmaceutical companies to test more drugs, and finding better optima makes manufacturing process cheaper and more environmentally friendly.
When Jan was working at chemical labs, he experienced the struggle of spending weeks guessing reaction conditions. We then started experimenting with ML to speed up the process.
Now, as a team of three friends from the University of Cambridge, we’ve spent the last month combining our domain expertise in Computer Science, Machine Learning and Chemistry to develop state of the art models for reaction optimisation.
Although ML is becoming well established in other fields, current chemical models generalise poorly and require lots of programming experience. We make our models easily accessible to chemists in the lab.
Finding the right conditions quickly allows pharmaceutical companies to test more drugs, and finding better optima makes manufacturing process cheaper and more environmentally friendly.
