The vast amount of digital data we create and collect has revolutionized many scientific fields and industrial sectors. Yet, despite our success in harnessing this transformative power of data, computational and societal trends emerging from the current practices of data science necessitate upgrading our toolkit for data analysis. In this talk, we discuss how practical considerations such as privacy and memory limits affect statistical inference tasks. In particular, we focus on two examples: First, we consider hypothesis testing with privacy constraints. More specifically, how one can design an algorithm that tests whether two data features are independent or correlated with a nearly-optimal number of data points while preserving the privacy of the individuals participating in the data set. Second, we study the problem of entropy estimation of a distribution by streaming over i.i.d. samples from it. We determine how bounded memory affects the number of samples we need to solve this problem.    Bio:  Maryam Aliakbarpour is a postdoctoral researcher at Boston University and Northeastern University, where she is hosted by Prof. Adam Smith and Prof. Jonathan Ullman. Before that, she was a postdoctoral research associate at the University of Massachusetts Amherst, hosted by Prof. Andrew McGregor (from Fall 2020-Summer 2021). In Fall 2020, she was a visiting participant in the Probability, Geometry, and Computation in High Dimensions Program at the Simons Institute at Berkeley. Maryam received her Ph.D. in September 2020 from MIT, where she was advised by Prof. Ronitt Rubinfeld. Maryam was selected for the Rising Stars in EECS in 2018 and won the Neekeyfar Award from the Office of Graduate Education, MIT.