Tamara Silbergleit Lehman is a 5th year PhD candidate at Duke university. Her thesis work focuses on reducing overheads of secure memory. More broadly, her research interests lie on the intersection of computer architecture and security. She is also interested in memory systems, simulation methodologies and emerging technologies. Tamara has a Bachelor's degree from University of Florida in Industrial Engineering and a Masters degree in Computer Engineering from Duke University. Her latest publication on understanding metadata access patterns in secure memory at ISPASS 2018 won the best paper award. Her earlier work on developing a safe speculation mechanism for secure memory published in MICRO 2016 got an honorable mention in Micro Top Picks.

Amna Shahab attended The University of Edinburgh for her doctoral studies and was advised by Boris Grot. Her research interests broadly lie in computer architecture and more specifically on memory system design for emerging datacenter workloads. She also has a passion for teaching and hopes to learn how to effectively translate it into a passion for learning from students.

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.