Welcome!

I am a Visiting Assistant Professor of Econometrics and Statistics at the University of Chicago Booth School of Business and the founder and elected chair of the SPEC RG Predictive Data Analytics Working Group.

The overarching goal of my research is to expand the potential of data science in scientific computing by designing robust, efficient, and sustainable system solutions tailored to the evolving needs of data-driven science. As scientific progress increasingly depends on the effective use of data science ecosystems, the diversity of hardware architectures, application demands, and usage patterns poses significant challenges. My work addresses these complexities through a focus on systems and performance engineering, leveraging interdisciplinary expertise to optimize and adapt scientific computing infrastructures for emerging data science applications.

In particular, I see the following key challenges that need to be addressed:

• Resource Optimization: Efficiently allocating and managing resources across diverse hardware platforms to accommodate changing, data-intensive workloads.
• Adaptive Systems: Developing systems that can dynamically adapt to evolving data and model requirements.
• Data Security and Privacy: Safeguarding sensitive data while enabling collaborative data science.
• System-Level Optimization: Optimizing the complex interplay of components within data science ecosystems for maximum performance.
• Sustainable Computing: Minimizing the environmental impact of data science practices.