Nirav Merchant

Research Interests

Background: 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 an increasingly higher resolution and throughput across all forms of modalities, from super-resolution microscopy to DNA sequencing technologies.

Transformational advances in information technology have complemented this phenomenal growth in data acquisition, including cloud and high performance computing, large-scale data management systems, and high-bandwidth networks. However, managing the life cycle of these datasets from acquisition and analysis to publication and archiving often necessitates interdisciplinary collaborations with geographically distributed teams of experts.

A common requirement for these interdisciplinary teams is access to integrated computational platforms that are flexible, scalable, and agile. These platforms must provide access to appropriate hardware and software that support diverse data types, computational scalability needs, and the usage patterns of diverse research communities. This includes access to shared data storage that can reliably transfer large sets of data, ability to annotate and search these data with descriptive metadata, connections to appropriate computational hardware (e.g., high-memory computers, virtual machines) for analysis, and identity management systems to securely share data with collaborators. Research Focus: 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 (CyVerse/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) 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.