Greg Laughlin‘s primary research interests focus on the detection and characterization of extrasolar planets. He also has an interest in event prediction, and has published work dealing with predictions that are resolved within milliseconds (high frequency trading) to extremely long cosmic horizons (future evolution of the Universe). He is a co-founder of Metaculus (www.metaculus.com), a forecasting technology platform that optimally aggregates quantitative predictions of future events, as well as a co-founder of Lucinetic (www.lucinetic.com) an AI-driven language generation startup.
What do you do with data science?
I have worked on a number of problems that draw on large data sets. In Astronomy, I have recently collaborated with Malena Rice (who starts a Yale Faculty position in 2023) on novel techniques to detect Solar System bodies within the photometric data sets obtained by the NASA TESS Mission (https://arxiv.org/abs/2010.13791).
In Academic Finance, I have used large tick data sets from the CME futures exchange and from the Nasdaq, NYSE, and Cboe equity exchanges to determine end-to-end network latencies within the US financial markets (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2227519).
In Network Science, I was Co-I on an NSF-funded project which developed plans for ultra-low-latency communications networks that can serve as overlays to the current Internet (https://www.usenix.org/conference/nsdi22/presentation/bhattacherjee).