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-subtitle: <a href="/publications/">Responsible AI</a> for building a resilient, sustainable and equitable future
+subtitle: <a href="/publications/">Responsible AI</a> for a resilient, sustainable and equitable future
 
 
 profile:
@@ -19,13 +19,16 @@ social: false # includes social icons at the bottom of the page
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-I am an Associate Professor of [Electrical and Computer Engineering](https://www.ece.ucr.edu/) at the University of California, Riverside, where I am also a cooperating faculty member in the [Computer Science and Engineering](https://www1.cs.ucr.edu/) department. My research broadly focuses on <a href="/publications/">responsible AI</a> 
-for building a **resilient, sustainable and equitable** future. Towards this goal, I study both algorithmic foundations and empirical methodologies, including:
+I am an Associate Professor of [Electrical and Computer Engineering](https://www.ece.ucr.edu/) at the University of California, Riverside, where I am also a cooperating faculty member in the [Computer Science and Engineering](https://www1.cs.ucr.edu/) department. My research broadly focuses on **<a href="/publications/">responsible AI</a>** 
+for a resilient, sustainable and equitable future. More concretely, I am interested in advancing AI's **social responsibility** to serve and benefit society in an ethical manner, including building AI systems with minimum resource usage and environmental costs
+on society (especially in capacity constrained communities grappling with energy and climate change challenges)
+and safeguarding
+AI for equitable and robust deployment in high-stakes environments. Towards this goal, I study both algorithmic foundations and empirical methodologies, centered on:
 
 
 - **<span style="color:darkred;">Sustainable AI:</span>** Developing principled methodologies to measure and minimize AI's resource usage and lifecyle environmental footprint (**[Communications of the ACM](https://arxiv.org/abs/2304.03271), [e-Energy'24a](https://arxiv.org/abs/2405.17469), [e-Energy'24b](https://arxiv.org/abs/2311.03615), [ASPLOS'24](https://dl.acm.org/doi/abs/10.1145/3620665.3640374), [SIGMETRICS'22a](https://arxiv.org/abs/2111.01203)**)
 
-- **<span style="color:darkred;">Safe decision-making:</span>** Robustifying machine learning predictions in highly dyanmic, uncertain, and/or adversarial environments such as renewable-powered computing systems (**[SIGEMETRICS'24](https://arxiv.org/abs/2401.04340), [NeurIPS'23a](https://arxiv.org/abs/2311.01568), [NeurIPS'23b](https://arxiv.org/abs/2310.20098), [ICML'23](https://arxiv.org/abs/2306.00172), [SIGMETRICS'22b](https://arxiv.org/abs/2204.08572)**) 
+- **<span style="color:darkred;">Safe decision-making:</span>** Robustifying machine learning predictions in highly dyanmic, uncertain, and/or adversarial environments such as renewable-powered computing systems (**[SIGEMETRICS'24](https://arxiv.org/abs/2401.04340), [NeurIPS'23a](https://arxiv.org/abs/2311.01568), [NeurIPS'23b](https://arxiv.org/abs/2310.20098), [ICML'23](https://arxiv.org/abs/2306.00172), [ICLR'24](https://openreview.net/pdf?id=e2YOVTenU9), [SIGMETRICS'22b](https://arxiv.org/abs/2204.08572)**) 
 
 - **<span style="color:darkred;">Algorithmic fairness:</span>** Building equitable AI to tackle societal challenges such as sustainability and climage change (**[ICML'24](https://arxiv.org/abs/2406.02790), [e-Energy'24c](https://arxiv.org/abs/2307.05494), [Harvard Business Review <span style="font-weight:lighter;color:darkred;">(perspective article)</span>](https://hbr.org/2024/07/the-uneven-distribution-of-ais-environmental-impacts)**)