From 614b29520b69326e6f1cbfbee826361d646269c7 Mon Sep 17 00:00:00 2001 From: Ian Tang Date: Sun, 22 Sep 2024 20:07:31 +0100 Subject: [PATCH] 2024-09-22 Aesthetics Update --- css/styles.css | 15 +- index.html | 747 +++++++++++++++++++++++++------------------------ sitemap.xml | 2 +- 3 files changed, 388 insertions(+), 376 deletions(-) diff --git a/css/styles.css b/css/styles.css index d5aae1a..b336c79 100644 --- a/css/styles.css +++ b/css/styles.css @@ -27,12 +27,18 @@ html { bottom: 0; width: 100%; background-color: #ebf5ff; + box-shadow: 0px -2px 6px #00000018; } .jumbotron { margin: 0px auto; padding: 40px 0px; background: #D6EBFF; + box-shadow: 0px 2px 4px #00000020; +} + +#profile_photo { + box-shadow: 2px 2px 4px #00000030; } address { @@ -44,7 +50,7 @@ address { body { position: relative; padding: 50px 0px 0px 0px; - z-index: 0; + z-index: 0; margin-bottom: 60px; } @@ -69,7 +75,8 @@ hr.row-item { } .navbar-dark { - background-color: #003e74; + background-color: #003e74; + box-shadow: 0px 2px 4px #00000060; } .navbar-nav > li{ @@ -105,6 +112,10 @@ hr.row-item { .col-12 { margin-top:10px; } + + #main_content { + margin: 0px 5px 0px 5px; + } } :target{ diff --git a/index.html b/index.html index 2ae0e00..8af2433 100644 --- a/index.html +++ b/index.html @@ -44,7 +44,7 @@
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- I am a Research Scientist in AI/ML in Mobile Systems at Nokia Bell Labs Cambridge and a former PhD student at the Mobile Systems Research Lab, University of Cambridge. - - My work focuses on developing data-efficient and scalable machine learning algorithms, particularly for mobile systems. I specialize in developing robust AI models that can handle complex, real-world scenarios by leveraging data-efficient approaches, including semi-supervised and self-supervised learning. -

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- My key research areas include: -

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  • - Scalable and Data-efficient Machine Learning for Human Activity Recognition: I develop novel training algorithms that reduce data dependence while ensuring robust recognition in mobile applications. My work leverages semi-supervised and self-supervised learning techniques for human activity recognition, utilizing methods such as contrastive learning [HCRL @ AAAI 2024, ML4MH @ NeurIPS 2020], self-training [IWMUT 2021], and multi-device collaboration [IWMUT 2022]. -
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  • - Overcoming Catastrophic Forgetting in Continual Learning: I investigate strategies to address the challenges of catastrophic forgetting in continual learning, where models must learn from evolving data streams without losing previously acquired knowledge. My work focuses on approaches that balance stability and plasticity, ensuring models can generalize effectively across new tasks while retaining performance on past tasks [WACV 2024, ICASSP 2022]. -
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  • - Federated Learning for Scalable Learning Algorithms: I explore decentralized machine learning paradigms that prioritize data privacy and enable collaborative learning across distributed devices [ICML 2022]. -
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  • - AI for Health-related Applications: I apply machine learning techniques to health-related challenges, leveraging AI to enhance the accuracy and scalability of health monitoring applications [ML4H 2021, Nat. Mach. Intell. 2020]. -
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- I am passionate about leveraging AI to advance human-centric applications, from healthcare to mobile systems. By developing robust and scalable solutions, I aim to contribute to the future of ubiquitous computing, creating technologies that seamlessly integrate into and enhance our daily lives. -

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- September 2024 - I will be running the second version of the UbiComp SOAR Tutorial on solving the activity recognition problem from 1:00 PM to 5:00 PM on October 6 at Melbourne Australia. Come and join us for an exciting discussion! -

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