With over nine years of experience in research and agile application development, I am recognized as an expert in human-centered information systems focused on making the user experience more engaging, transforming concepts into work products and reaching target deliverables. As a human factors and visualization researcher, my focus has been directed toward big data fusion and human performance gain in 3D environments using innovative display techniques. I have researched and published papers in leading conferences in collaboration with Carnegie Mellon University (CMU) Robotics Institute and at Department of Neurobiology at the University of Pittsburgh. As a practitioner, I have led and designed application prototype at IBM Medical Sieve Grand Challenge that filters essential clinical and diagnostic imaging information to form anomaly-driven summaries and recommendations for rediologists. I have also designed and developed several application prototypes to facilitate MRI research using machine-learning algorithms. My expertise includes: ● Visualization & analysis of big data and a thorough understanding of visualization research methods to analyze and contextualize scalable information ● Human factors analysis of human-in-the-loop systems ● Cognitively compliant user interface design and application development to enhance user experience with big data ● Virtual Environment (VE) development, analysis of end-user knowledge elicitation and measure situation awareness (SA)
Current and Prior Positions
Software Engineering Researcher
5/2016 - Present
• Working on Medical Sieve Grand Challenge. Medical Sieve is an ambitious long-term exploratory grand challenge project to build a next generation cognitive assistant with advanced multimodal analytics, clinical knowledge and reasoning capabilities to assist in clinical decision making. It will exhibit a deep understanding of diseases and their interpretation in multiple modalities (X-ray, Ultrasound, CT, MRI, PET, Clinical text) covering various specialties such as radiology and cardiology. The project aims at producing a “Medical Sieve” that filters essential clinical and diagnostic imaging information to form anomaly-driven summaries and recommendations that tremendously reduce viewing load of clinicians without negatively impacting diagnosis results. • Successfully led and developed the user interface (in Angular) for Eyes of Watson - RSNA 2016 application. The Eyes of Watson user interface allowed the participants to select a case from various subspecialties, attempt to make a diagnosis, and see how a work-in-progress including 3D anatomical surface analysis. • Develop MedNet 3D visualization component for visualizing segmented image volume in 3D. Started with surface generation of the anatomical volume as first goal and expanded to fully segmented volume generation by end of MedNet product cycle. • Currently working on brain trauma data analysis in 3D. The goal is to implement a 3D visualizer framework based on exploratory data analysis.