I am an incoming Assistant Professor in the ECE department of Princeton University. Currently, I am a Postdoctoral Research Scholar at Stanford Medicine with Prof. Euan Ashley. I completed my Ph.D. in the Electrical Engineering department at Stanford University where I was advised by Prof. Mark Horowitz. I conduct research at the intersection of computer architecture and systems, and, computational genomics for clinical and evolutionary applications.

Previously, I received a Dual Degree (B. Tech. and M. Tech.) in Electrical Engineering from the Indian Institute of Technology, Bombay in 2017 along with the Akshay Dhoke Memorial Award.

I am a trained classical dancer with a Master's in Dance in Bharat Natyam from the Art Society, Mumbai.

Amila received her Bachelor's and Master's Degrees from the University of Sarajevo in Electrical Engineering within Computer Science and Informatics Department in 2006, 2009, respectively. In academic year 2007/2008 she received Fulbright Visiting Student Award and joined Embedded Systems and Architectures Lab at University California, Riverside as Junior Researcher. In 2010, she spent 1 month at Faculty of Electrical Engineering, University of Ljubljana as a visiting academic. Then, she received MEXT scholarship in 2010 and spend 3 and half years in beautiful Tokyo, where she completed her Ph.D. at Keio University in 2013.

Her primary area of interest is Computer Architecture, including Reconfigurable Architectures, High Performance Computing and Heterogeneous Computing. Her past research mainly focused on finding new ways to accelerate compute-intensive parts of an algorithm by means of offloading it to an FPGA. The challenge is to take advantage of knowledge about an architecture and adapt the algorithm to the architecture rather than the other way around. Her PhD research focused on developing architectures and methodologies that help to reduce the execution time of Cyclic Redundancy Check algorithms, particularly those implemented using FPGAs, and iSCSI protocol implementation.

In recent years, she has expanded her research to include Digital Signal Processing, Computer Vision, Image Segmentation, Machine Learning to name a few.

My research interests are in Computer Architecture and Systems and my current research focuses on optimizing GPU architectures for Deep Learning applications, specifically Recurrent Neural Networks. I have been a part of the effort to augment GPGPU-Sim, a widely used GPU simulator, to execute Deep Learning kernels written using NVIDIA's cuDNN and cuBLAS libraries. My research advisor is Prof. Matthew D. Sinclair.

Angeles G. Navarro received her PhD in Computer Science from the University of Malaga (Spain) in 2000. She is a Professor in the Department of Computer Architecture at the University of Malaga. She has been a Research Visiting Scholar in the University of Illinois at Urbana-Champaign, the Technical University of Munich, the EPCC at the University of Edinburgh, the University of Bristol, and a Research Visitor in IBM T.J. Watson Research Center at New York and in Cray Inc at Seattle. She is the author or co-author of more than 80 papers and has served as a program committee member for several High Performance Computing related conferences. Her research interests are in parallel programming models and compilation techniques for heterogeneous and reconfigurable architectures.

Dr. Navarro has been involved in many initiatives to promote women in Computer Science. She is the co-founder of WSARTECO (Women in SARTECO), a community that is part of the Spanish Computer Architecture Scientific Society, which encourages junior and senior women working in the area to increase visibility and to develop a supporting network.