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Farnoush Banaei-Kashani is an Associate Professor (tenured) in the Department of Computer Science and Engineering at the University of Colorado Denver. She is the Founder and Director of the Big Data Lab (BDLab) and Director of the Big Data Science and Engineering Program. Additionally, she holds affiliate faculty positions in the Department of Pharmaceutical Sciences and the Department of Mathematical and Statistical Sciences at CU Denver. Prior to joining CU Denver in 2014 as an Assistant Professor, she served as Associate Director of Research at the IMSC NSF Research Center and Research Staff at the InfoLab, both at the University of Southern California. Banaei-Kashani received her Ph.D. in Computer Science from USC in 2006, M.S. in Electrical Engineering from USC in 2001, and B.S. in Computer Engineering from Sharif University of Technology in 1996.
Her research encompasses Data Science applications addressing real-world challenges with AI-powered solutions across the data lifecycle, including health informatics, personalized medicine, computational biology, intelligent edge computing, IoT, transportation, smart infrastructures, and geospatial analysis, handling diverse data types such as sequences, time series, graphs, spatiotemporal, images, and videos. She also specializes in data management, mining, and modeling, particularly spatiotemporal and graph data management/mining and high-throughput processing. With over 120 refereed publications, notable works include "Efficient Maximal Reverse Skyline Query Processing" (Geoinformatica, 2017), "A Lightweight and Inexpensive In-ear Sensing System For Automatic Whole-night Sleep Stage Monitoring" (ACM SenSys 2016, Best Paper Award), "Voronoi-based geospatial query processing with MapReduce" (IEEE CloudCom 2010, Best Paper Award), and "Janus - Multi Source Event Detection and Collection System for Effective Surveillance of Criminal Activity" (JIPS, 2014). Her research is funded by NSF, NIH, DOE, DOD, NASA, and industry. She serves as a senior program committee member for conferences like KDD, SIGMOD, VLDB, and has received multiple best paper awards.

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