Saswat Padhi
I am a software engineer in the chromeOS performance team at Google. I am primarily interested in software infrastructure: building high-performance systems with strong reliability guarantees.
Prior to joining Google, I was an applied scientist in the Automated Reasoning Group at Amazon Web Services (AWS) in Boston, MA. I have also worked at the Research in Software Engineering (RiSE) group at Microsoft Research (Redmond, WA) and the Systems Research group at Microsoft Research India.
Before joining the industry, I graduated with a Ph.D. in CS from UCLA in 2020, with a focus on synthesizing formally verified software using statistical learning techniques. Prior to that, I earned my B.Tech. in CSE from IIT Bombay in 2014.
My personal homepage: https://saswat.padhi.me.Research Areas
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Predicting User Experience on Laptops from Hardware Specifications
Sunil Bhasin
Udaya Kiran Ammu
Alex Bergman
Allan Knies
NeurIPS 2023 Workshop on Machine Learning for Systems (MLSys) (2023)
Preview abstract
Estimating the overall user experience (UX) on a device is a common challenge faced by manufacturers. Today, device makers primarily rely on microbenchmark scores, such as Geekbench, that stress test specific hardware components, such as CPU or RAM, but do not satisfactorily capture real-life consumer workloads. System designers often rely on domain-specific heuristics and extensive testing of prototypes to reach a desired UX goal, and yet there is often a mismatch between the manufacturers’ performance claims and the consumers’ experience.
We present our initial results on predicting real-life experience on laptops from their hardware specifications. We target web applications that run on Chromebooks (ChromeOS laptops) for a simple and fair aggregation of experience across applications and workloads. On 54 laptops, we track 9 UX metrics on common end-user workloads: web browsing, video playback and audio / video calls. We focus on a subset of high-level metrics exposed by the Chrome browser, that are part of the Web Vitals initiative for measuring user experience on web applications.
With a dataset of 100K UX data points, we train gradient boosted regression trees that predict the metric values from device specifications. Across our 9 metrics, we note a mean R^2 score (goodness-of-fit on our dataset) of 97.8% and a mean MAAPE (percentage error in prediction on unseen data) of 10.1%.
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