Meet Dr. Alex Razim – Inclusive Collaboration Awardee


Meet Alex Razim, the Principle Investigator of a funded LSST-DA Inclusive Collaboration Project. The Inclusive Collaboration Projects are designed to develop, document, and disseminate policies and best practices related to inclusive collaboration. LSST-DA selected projects from an international, open call for proposals that built on the From Data to Software to Science (DS2) workshop held in 2022 at the Flatiron Institute. These projects are funded with support from the Heising-Simons Foundation.

Dr. Razim received her BSc and MSc in astronomy at the V.N. Karazin University in her hometown, Kharkiv, Ukraine. The focus of her master thesis was studying how the probes that land on the Moon surface disturb the regolith stratification, and during this time she was mainly analyzing Lunar Reconnaissance Orbiter images. After graduation from the university, she spent a couple of years working in the Kharkiv Institute of Astronomy as a junior software engineer, and later she was accepted as a EU Marie Curie PhD fellow in the University of Naples. There, her research area changed, as she worked on the usage of supervised and unsupervised Machine Learning methods for improving photometric redshift catalogues. Her PhD position was a part of the SUNDIAL International Training Network dedicated to bringing together computer scientists and astronomers, with the goal of training the astronomers in advanced Machine Learning and Data Science methods. As such, she had the opportunity to learn a number of Machine Learning (ML) techniques and experiment with applying them to many astronomical problems, from faint structure detection to galaxy classification.

Currently, Dr. Razim is a postdoc at the Ruder Bošković Institute in Zagreb, Croatia. She is a member of the LSST Transient and Variable Sky Science Collaboration (TVS SC) and an LSST in-kind contributor. Dr. Razim is working on creating synthetic LSST photometry for testing the methodology that will be later used on the real observations, and takes a great interest in using unsupervised ML methods for astronomical anomaly detection.

What is the focus of your Inclusive Collaboration Project? 

A few years ago the TVS SC organized a Carpentries software development workshop that revealed a great interest and need of the community in programming training that would stretch beyond the typical entry level university courses. Following this discovery, Dr. Razim and few other TVS SC members decided to develop an astronomy-specific intermediate-level software development workshop. This idea aligned nicely with the LSST-DA Inclusive Collaboration initiative, since very few astronomy students and early career stage researchers have an opportunity to get training in software development tools and best practices beyond the basic Python syntax. It’s especially true for the researchers from developing countries and underfunded institutions. Thus, the focus of LSST-DA proposal was the development and organization of a short, but intensive software development workshop. At the moment, Alex and her team have organized one such workshop and are planning the second one in summer 2024, with a possibility of developing additional workshops (e.g. on advanced visualization methods or Agile methodology) in the future.

What drew you to the LSST DA Inclusive Collaboration initiative program

TVS SC leaders. The idea of the workshop development appeared well before the LSST -DA initiative, but when the call was announced, we simply decided to hop aboard.

What are some of your accomplishments as an awardee of the Inclusive Collaboration award?

So far, we have fulfilled two main obligations of our proposal: we developed the first version of the astronomy- and LSST-specific Intermediate Python Software Development workshop materials and organized the first workshop using these lessons. The first workshop was organized for the TVS SC members only, and we had ~20 attendees, mostly PhD and young postdocs, from all over the world. After the workshop, to find out how we can improve the curriculum, we launched an LSST-wide poll to discover what software development tools and best practices are widely used by the astronomers and should be taught to the early career stage researchers. Following the results of this poll, we are currently developing the second version of the materials, which will be used for the second workshop this summer.

Please see Dr. Razim’s presentation – “Intermediate Python for Astronomical Software Development Workshop” aka TVS SC InterPython located here.