Gustavo Malkomes

Gustavo Malkomes serves as a Research Engineer at SigOpt, where he is involved in advancing the application of Bayesian optimization, hyperparameter optimization, and machine learning. He has co-authored several influential papers, including ‘Creating glasswing butterfly-inspired durable antifogging superomniphobic supertransmissive, superclear nanostructured glass through Bayesian learning and optimization’ and ‘Practical Bayesian optimization in the presence of outliers’. His work also includes contributions to ‘Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design’ and the development of ‘A Nonstationary Designer Space-Time Kernel’. Malkomes collaborates extensively with academics and researchers through SigOpt’s academic program, supporting the company’s mission to empower experts and advance academic research. His efforts focus not only on theoretical advancements but also on applying these optimization techniques to solve practical, real-world problems.