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.

Samira Khan is an assistant professor at the University of Virginia. Her research focuses on Computer Architecture and Computer systems, especially building new systems by rethinking the traditional abstractions and redesigning system layers to adopt new technologies and enable future applications. Her group, Shiftlab thrives for "Paradigm Shift", and named after Thomas Kuhn's famous book "The Structure of Scientific Revolutions".

Server memory management is an evolving and challenging area. With growing memory capacity installed per machine and increasing memory DIMM costs, data center planners are facing with huge increase in memory TCO. A plausible direction is to categorize memory accesses by required service level objective (SLO) with the end goal to map lower SLO request to cheaper but low quality memory resource, while using expensive, highest quality memory resource for most critical application request. I work in Linux server memory management to categorize memory access transparent to the user space.

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.