I enjoy investigating interdisciplinary research problems especially cutting-edge challenges in the biological, medical, and physical sciences.

Research interests:  astrostatistics, cosmostatistics, detection and characterization of exoplanets with extreme precision radial velocity spectra, topological data analysis, approximate Bayesian computation, generalized fiducial inference

With the continuing rise in data availability and complexity comes the necessity for new statistical and data science methods to address unanswered questions. My research activities address complex statistical challenges found in the natural and physical sciences through the development of innovative and sophisticated statistical methodology.  I pursue interdisciplinary research investigating prevailing problems in the sciences with emphasis on statistical challenges in astronomy, astrophysics, and cosmology.

Most of ongoing research focuses on methodological development for topological data analysis (TDA) and for the detection and characterization of exoplanets in the presence of stellar activity.  I have also worked in other areas such as approximate Bayesian computation (ABC) and generalized fiducial inference (my dissertation work), and more generally on statistical challenges in astronomy.


The field of astrostatistics is at the intersection of astronomy, astro-particle physics, astrophysics, computer science, cosmology, data science, machine learning, solar physics, and statistics.  Astrostatistics has manifold opportunities for statisticians and data scientists. Challenges exist from the very small scale including the nature of dark matter particles or the detection and characterization of exoplanets, to the very large scale such as inference on physical properties of the formation and evolution of our Universe. I have had the opportunity to contribute to several areas of astronomy and cosmology, including the detection and characterization of exoplanets, inference and structure detection of the large-scale structure (LSS), modeling the distant Universe using Lyman-alpha forest data, and inference on the stellar initial mass function. 

My current research is focused on statistical issues in exoplanet astronomy (ExoStatistics), along with topological data analysis methodology for the large-scale structure of the Universe.

If you are interested in learning more about astrostatistics, I recommend subscribing to our newsletter, Astrostatistics News.  It is a newsletter designed to inform, promote, cultivate, and inspire the astrostatistics community.  If you want to read more about the field, we are developing a list of astrostatistics references here.

Topological Data Analysis

Complicated spatial structures are common in many areas of science, but are difficult to quantitatively analyze without losing important information. Examples of spatially complex, but geometrically similar, structures are fibrin clots in biology and the large-scale structure of the Universe. My research includes developing methods using topological summaries of data called persistence diagrams that retrieve and quantify crucial information that is missed in ad-hoc methods by specifically targeting shape-related features. 

For a non-technical introduction to TDA in cosmology, see Green, Mintz, Xu, and Cisewski-Kehe (2019) Topology of our cosmology with persistent homology. Chance, 32(3), pp.6-13. [paper]


An exoplanet is a planet that is not our Sun.  Astronomers have developed a variety of methods for detecting exoplanets, with one of the most successful being the Radial Velocity (RV) method.  The newest instruments (e.g., EXPRES, NEID) used to detect exoplanets with the RV method are highly precise and stable allowing for the possibility of uncovering a population of low-mass exoplanets, such as Earth analogs.  To fully employ these excellent data and detect the tiny signals left by this sought-after population of exoplanets, sophisticated statistical methods are needed.  Serious challenges to the detection and characterization of these low-mass exoplanets include addressing issues related to stellar activity that can hide or mimic exoplanet signals in the spectra.  My research includes developing methods to aid in the detection of low-mass exoplanets, such as Earth analogs, using extreme precision RV spectra in the presence of stellar activity.

For a non-technical introduction overview of this area, see Cisewski (2017) In search of Earth analogues: Detecting exoplanets amid stellar noise. Significance, 14(2), pp.22-25. [paper]