Robert is a Machine Learning Engineering Lead at Turbine.ai working on unlocking biology by applying engineering disciplines to decrease cost, provide reproducibility, and answer "why?" in drug discovery.
He holds a Ph.D., summa cum laude, in Engineering and Technology from the Budapest University of Technology and Economics. In his dissertation, he broadened the set of mathematical tools used for real-time stochastic portfolio optimization. Robert was awarded the Pro Scientia Gold Medal for his efforts.
His interdisciplinary experience spans from a decade of teaching to EU FP7 research on genomic prediction at the Hungarian Academy of Sciences to the full life-cycle of developing quant financial models for the trading desks globally at Citigroup. At NDVR he headed the efforts to map cash flow and investment needs of high-net-worth individuals to objective functions and risk metrics, and designed heuristics for their wealth optimization.
In his free time, Robert enjoys hiking and is a proud father.
Drug discovery is costly, time consuming, model systems have poor translation rates to patients and do not significantly reduce the risk of failure in the clinic. Before running any wet lab experiments, Turbine computationally simulates tumor cell behavior in patients to understand the complex mechanisms driving the disease. Observing these in silico experiments, our biologists and translational e...