2:30pm Room A - Data Science Industry

Kelsey Gonzalez

PhD Candidate at the University of Arizona
Senior Data Scientist at IBM


Kelsey Gonzalez is a PhD Candidate in the School of Sociology at the University of Arizona. Her academic research focuses on computational methods, advanced statistical methods, and social networks analysis in connection to interests in social networks, health behavior, diffusion, and communication technologies. Her dissertation focuses on how people search for information and rely on this information to inform their health behaviors and develop social norms, particularly during times of uncertainty.

Kelsey is a Senior Data Scientist Co-op for the Chief Analytics office at IBM. In this role, she produces ML-driven insights to address questions for workforce management using unsupervised recommendation algorithms to provide IBM employees with actionable recommendations for reskilling. Upon defending herdissertation, she will join IBM as a full-time Senior Data Scientist. Kelsey is a Certified RStudio Trainer and a Carpentries instructor. Locally, she is involved with data science education at the University of Arizona and previously was a Senior Data Science Ambassador for the College of Social and Behavioral Sciences and a Steering Committee member for Research Bazaar Arizona.

Jenn Schilling

Senior Research Analyst
University of Arizona

Jenn Schilling is a Senior Research Analyst in the University Analytics & Institutional Research department at the University of Arizona. Prior to joining the University of Arizona, Jenn taught middle school for two and a half years. She also served as an AmeriCorps VISTA, working in college access. Before turning to education, Jenn spent four years working in the private sector as an Operations Research Engineer in the supply chain at Intel and as a Statistician in the market research industry. Jenn has worked on freelance projects in data visualization and is an adjunct faculty member at the College for Creative Studies, where she teaches courses on data visualization and R. Jenn holds a master’s degree in Computational Operations Research and a bachelor’s degree in Applied Mathematics with a minor in Computer Science, both from the College of William and Mary.

Joana M. F. da Trindade

PhD Student

Joana M. F. da Trindade is a PhD student in EECS at MIT, working with Prof. Sam Madden and Prof. Julian Shun at MIT's Data Systems Group. Her research interests revolve around performance aspects of data processing systems, primarily focusing on query optimization and performance techniques for large-scale temporal graph data management. Before MIT she spent some time in industry as a software engineer (Bloomberg and Google) after completing an M.S. in CS from UIUC, and a B.S. in CS from UFRGS, in Brazil. During her PhD she has collaborated with folks at Microsoft Research New York, Microsoft Gray Systems Lab, and is currently collaborating with Intel's Optane Group with the goal of making better use of NVRAM for temporal graph analytics. She's also very fortunate to be supported by an Alfred P. Sloan UCEM PhD Fellowship and a Microsoft Research PhD fellowship.

Sasha Lavrentovich

Alexa AI Language Engineer

Sasha Lavrentovich joined Amazon Alexa AI based in Cambridge, MA as a Language Engineer in July 2019. Her team works on launching Alexa in new languages, and most recently Sasha has supported development of the Portuguese and Arabic speaking Alexas. Sasha completed her PhD in Linguistics at University of Florida in June 2019. Her dissertation focused on cross-linguistic influence in second language acquisition, using computational methods to carry out a Native Language Identification task by automatically identifying the first language of an English learner based solely on their writings in English as a second language. Sasha completed her MA in Applied Linguistics at Teachers College, Columbia University; her research focused on using corpus methods for addressing questions in first language acquisition.

Moderator: Karen De la Rosa

PhD Candidate | Senior Data Science Ambassador
University of Arizona

Karen De la Rosa is a Ph.D. candidate in the new Applied Intercultural Arts Research GIDP (AIAR) at UA. She has explored the relationship between music-related variables and behavioral phenomena using a wide range of procedures such as machine learning, R and Python analytics, sentiment analysis, linear models, content analysis, frequency distributions, mediator/moderator modeling analysis, and structural equations. Her paper “Why do young adults listen to Music?” was presented at the 2016 International Journal of Arts and Sciences (IJAS) Conference in Rome, and later published in that same journal in 2017. Karen is currently working on machine learning models predicting music consumption behaviors and other related outcomes using business, health, media and historical data. Karen is an R enthusiast and her professional interests include applications of machine learning algorithms in industry as well as data science applications in interdisciplinary settings.