Shrideep Pallickara   [Professor]


I am a Professor in the Department of Computer Science at Colorado State University. Agencies in the United States and United Kingdom have funded my research. These include the National Science Foundation, the Department of Homeland Security (including the Long Range program), the Environmental Protection Agency, Department of Agriculture, and the U.K's e-Science program. I am a recipient of the Board of Governors Award for Excellence in Undergraduate Teaching, the OLIE award, the National Science Foundation's CAREER award, and a Monfort Professorship.

My research encompasses methodological and algorithmic innovations at the intersection of machine learning and large-scale systems. These bring issues of tractability, numerical stability, convergence, timeliness and throughput to the fore.These investigations have occured in the context of three broad areas: (1) spatiotemporal data management and analytics, (2) extreme-scale storage systems, and (3) stream processing for Internet-of-Things and Cyber Physical Systems settings.

A key research thrust is on pattern extraction and model construction at scale over voluminous datasets. These models are used to understand processes (natural or otherwise) and forecast phenomena. The constructed models are leveraged in a gamut of settings that range from identifying socio-economic/infrastructure vulnerabilities in urban areas to discerning incipient worsening health in age-at-home settings. The datasets used to construct these models have cumulative volumes reaching several Petabytes while also being high-dimensional with the number of dimensions ranging from several thousand to millions. The models we fit over these data are based on deep learning with a large number of parameters (10s of millions).

Fitting deep learning models over the datasets we consider introduces unique challenges encompassing issues of computational tractability, effective resource utilizations, data management, and completion times – all of which inform my research. My lab focuses on the design of novel algorithms that balance several competing pulls – ensuring numerical stability, dispersion, load balancing, data movements, speed differential across the memory hierarchy, GPUs, RAMs, learning rates, convergence, and online updates – to ensure faster construction of models that generalize well.

Systems software resulting from my research efforts have been deployed in domains such as urban sustainability, agriculture, epidemiology, earthquake science, environmental and ecological monitoring, health care systems, high energy physics, defense applications, geosciences, GIS, and commercial internet conferencing systems.


Research Themes:
[Deep Learning] [Mining] [Sketching] [Orchestration] [Planninng Exercises] [Visualization] [Clouds] [File Systems]

  Department of Computer Science
Colorado State University
1100 Center Avenue, Room 364
Fort Collins, CO 80523-1873 USA
Office Computer Science Building, Room 364
Hours 9:00-10:00 am Fridays    [Spring 2020]

Phone 970.492.4209