Research InterestsBackground: Over the last decade the discipline of life sciences has benefited tremendously from new massively parallel and highly quantitative technologies. These technologies have facilitated rapid data acquisition at a consistently improving resolution and throughput across all forms of modalities, from super-resolution microscopy to sequencing technologies.
Transformational advances in information technology have complemented this phenomenal growth in data acquisition capacity, from cloud to high performance computing; large scale data management systems and high bandwidth networks. However, these new opportunities present unique challenges. Researchers now are able to amass datasets that exceed their abilities to collaborate and interpret them in a timely fashion; this is further exasperated by a multitude of software tools for analyzing data, and by continually improving analysis methods and computational infrastructure.
The underlying challenge associated with managing the lifecycle of research data at this rapidly growing scale necessitates interdisciplinary collaborations and team science approaches. In turn, team science requires an agile, responsive computational platform that fosters and facilitates these collaborations, which often span multiple teams, departments, institutes, and even continents.
Over the last two decades my work has focused on developing computational platforms and enabling technologies, primarily directed towards improving research productivity and collaboration for interdisciplinary teams and virtual organizations.
The key thrust areas for my work encompass life cycle management for:
1. High throughput and automated bio sample processing systems
2. Highly scalable data and metadata management systems
3. High throughput and high performance computing systems
My recent work has been directed towards supporting pervasive computing needs for mHealth (mobile health) initiatives and health interventions, with focus on developing study management platforms that leverage cloud based telephony, messaging and video in conjunction with wearable’s and sensors.
Platforms and tools developed by team are actively utilized for:
1. Managing bio samples and data for clinically certified (CAP/CLIA) NGS pipelines
2. Large scale genotyping (million+ samples) platforms with robotic automation
3. National scale Cyberinfrastructure (iPlant) that facilitate global team of researchers to effectively manage their data, computation and collaborations using a cohesive computational platform
4. Health interventions and patient monitoring
Teaching:I firmly believe that measured adoption of emerging computational technologies and methods are essential for life scientist to successfully operate at the scale and complexity of data they are constantly encountering. This can only happen if there is continuing education and practical training focused around the use of Cyberinfrastructure and computational thinking. I have developed and taught workshops, graduate and undergraduate project based learning courses with emphasis on these topics
My team (Bio Computing Facility) engages with the campus community at various levels ranging from multi- institutional collaborative projects, graduate and undergraduate courses for credit and special topic seminars and workshops.
With emphasis on enabling digital discoveries for the life sciences.