I go by nikete in everything but the most formal of documents.
Broadly, I am motivated to work towards the improvement of the understanding. Designing, building and understanding the computational engines that mediate collective cognition. Methodologically my tools come mostly from economics and machine learning. I enjoy theory-driven work and opportunistic experimentation. I run a small consultancy, Amurado Research OU, focused on commercial applications of machine learning and economics. I am also a Research Affiliate of the Laboratory for Computational Physiology at MIT, where I mostly help with causal estimates around treatments in Intensive Care Units. In a broad sense, I care about: How can we design better ways of evaluating proposed policies given the existing observational data? Beyond the ICU, I have also studied various markets (betting, cryptocurrencies, Internet advertising) and related institutions.
I have co-authored articles in machine learning conferences (NIPS, WWW, UAI, IJCAI) and medical journals (Critical Care Medicine, Chest, Journal of Clinical Sleep Medicine). Google scholar has a good first approximation to my academic output.
I submitted a PhD thesis, on algorithms and mechanisms for aiding decision making while preserving subjects freedom of choice. There are two main results in it. (1) When you are learning from the rewards received in a bandit setting and the action that is taken is not the one the algorithm chose, by paying attention to both there are situations where you can do arbitrarily better with only a multiplicative worst case cost. (2) When you are getting advice to inform a decision that you want to mantain ultimate control over, allowing the advice givers to self select so one of them receives all information and “owns” the advice can do much better than trying to use “prediction market” mechanisms directly.