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Articulated pose estimation is one of the fundamental challenges in computer vision. Progress in this area can immediately be applied to important vision tasks such as human tracking action recognition and video analysis. This talk will discuss papers and progress of computer vision and deep learning towards human pose estimation and its applications. Some papers that we can discuss here: Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., and Schiele, B. Deepercut: A deeper, stronger, and faster multi-person pose estimation model. CoRR abs/[masked] (2016). Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields, Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh CVPR 2017 I will lead the discussion and would encourage everyone else to think about applications and future research/ development ideas in this domain. Bio: Srujana Gattupalli is a Deep Learning Software Engineer at Intel Corporation. She received a PhD degree in Computer Science from the University of Texas at Arlington in 2018. Her research interests are focused on Machine Learning, Computer Vision, Human-Computer interaction and their applications for human body motion estimation and pose tracking in assistive technology. Her academic work experience includes a role as a research assistant at the Vision Learning Mining lab and teaching assistant for graduate courses. She has been a Graduate Intern at Intel Corporation in 2017, working towards research and development for autonomous driving and machine learning algorithms. In addition to this, she has worked as a Software Engineer at Cerner Corporation in 2014. Ms. Gattupalli is an active member of Upsilon Pi Epsilon (UPE) honor society in computing. She has published 7 peer reviewed papers, received 2 international awards and has served as a reviewer in many others. In her spare time, she enjoys painting, philately, reading books, travel and to seek outdoor adventures.