CMU Libraries

Christina Akirtava is a PhD candidate in the Biological Sciences Department at CMU. In the McManus lab, her research primarily focuses on understanding gene expression using high-throughput assays, bioinformatics, and computational modeling. Her expertise is primarily in Python data wrangling and visualization. She also has experience in statistics, machine learning, and R programming. As part of the dataCoLAB team, she uses her background in science/data analysis to help others tackle interdisciplinary problems.

Dr. Matthew Lincoln is a research software engineer at Carnegie Mellon University Libraries, where he collaborates with scholars to plan and implement computational approaches to research, particularly in the humanities. He has expertise in R for statistical learning and data visualization, particularly for text and network analysis. He also has experience in high performance computing, database software, building web-based data analysis platforms in both R and Python, and best practices for designing and packaging reusable data analysis software.

Prasun Shrestha is a Master's student in Data Analytics at Heinz College, CMU. His experience is in predictive analytics, statistical modeling, and data visualizations. He has worked with several tech startups, non-profits, and many research projects as a data analyst, leveraging machine learning models to translate disparate data into actionable insights and help solve problems. From prior liberal arts education in Economics and Statistics, he brings communication and interpersonal skills as a consultant at dataCoLAB. Quintilingual and a former teacher, he distills and deconstructs complex ideas into digestible information that his audiences can understand intuitively. At dataCoLAB, he aspires to become immersed in experiential learning, apply his education to a real-world setting, and contribute to the data ecosystem.

Mike Simko is currently an adjunct professor at CMU teaching the R language at the Heinz School, and has also taught data analytics classes remotely for the University of Texas - Austin. He works primarily with R, but also uses Python for data cleaning/wrangling, analysis and visualizations. Much of his background is in technology (specifically metals), but also has experience with process control, business / financial, social science and other types of datasets. He became interested in volunteering at dataCoLab to learn more skills and hopefully help others with their journeys in exploring data.

Carly Sombric is a PostDoc in the Bioengineering Department at the University of Pittsburgh. Carly is experienced at statistical and experimental design for grant and paper preparations involving human behavior and cognition. Her field of expertise is human motor control and adaptation. Currently, Carly has been leading a multi-institutional effort to amass gait intervention data into an open-source database. She is applying machine learning approaches to classify successful gait interventions. In preparation for her career in research and analytics, Carly looks forward to exploring new experimental and analytical methods as she assists with study planning and statistical design, including power analysis, as a dataCoLAB consultant.