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<!DOCTYPE HTML>
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<span class="position">she/her <p><a href="https://ai.google/research/"> Research Scientist, Google</a></p></span>
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<h1> </h1>
<h2 class="colorlib-heading">Sunipa Dev</h2>
<p> <strong>Hi I'm Sunipa Dev
</strong> I am a PhD student advised by <a href = "https://www.cs.utah.edu/~jeffp/"> Dr. Jeff Phillips </a> at the <a href = "http://www.cs.utah.edu"> School of Computing</a> at the <a href="https://www.utah.edu"> University of Utah</a>. Currently my research involves word embeddings and their geometry and the pursuit to make them more robust, interpretable and fair. My research interests broadly encompass various modules of machine learning, NLP and data mining..</p>
<p>Before joining my PhD position in 2016, I completed my integrated BS - MS in Mathematics at the <a href="https://www.iiserkol.ac.in">Indian Institute of Science Research and Education, Kolkata </a> where I did research on graph theory and brain mapping with <a href ="https://www.iiserkol.ac.in/~anirban.banerjee/"> Dr. Anirban Banerjee </a>.</p>
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<p> <!--<strong>Hi I'm Sunipa Dev-->
</strong>
I am a Senior Research Scientist at <a href = 'https://ai.google/research/'>Google Research</a> working at the Technology, AI, Society, and Culture (TASC) team at the Responsible AI and Human Centered Technologies organization. My research is at the intersection of language, AI, and society. I work towards the goal of safe, inclusive, robust, and trustworthy AI. Some of my current objectives are grouding evaluations of generative models in real world experiences, and contextualizing them with diverse global perspectives and socio-cultural differences. I investigate and develop complementary socio-tecnhonal approaches to imbue an understanding of fairness across different fluid, and intersectional identities of people into robust, scalable measurements, and harm mitigations.<!-- --></p>
<p>Prior to this, I was an <a href = "https://cifellows2020.org"> NSF Computing Innovation Fellow </a> at <a href = "http://web.cs.ucla.edu/~kwchang/"> Dr. Kai-Wei Chang </a> 's lab at <a href = "https://samueli.ucla.edu/"> UCLA</a>, before which I completed my PhD under the guidance of <a href = "https://www.cs.utah.edu/~jeffp/"> Dr. Jeff Phillips </a> at the <a href = "http://www.cs.utah.edu"> School of Computing</a> in <a href="https://www.utah.edu"> University of Utah</a>.
<!-- My dissertation work investigated some fundamental questions about the interpretability and fairness of high-dimensional, distributed representations with a focus on language representations. Among some other challenges, it focussed on understanding the structure and alignment between these embeddings and isolating different concept subspaces within them. As a primary application, it helps identify, disentangle and mitigate biased associations and unfair representations of people of different demographics that the embeddings learn from underlying data and also enhance the interpretability of distributed representations. Using different fundamental NLU tasks, my work also builds different robust evaluation paradigms for problematic and undesirable associations or properties learnt by representations.--></p>
<!--<p>During this time, I was fortunate to intern with Microsoft and Visa Research, where I worked with noisy, high-dimensional embeddings of merchant and user based on transaction data. Multiple patents have been filed for this work in conjunction with Visa Research, who also funded me for a year of my PhD. </p>
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<!--<p>Prior to joining my PhD position in 2016, I completed my integrated BS - MS in Mathematics at the <a href="https://www.iiserkol.ac.in">Indian Institute of Science Research and Education, Kolkata </a> where I researched correlations between different brain functionalities (such as vision and taste) based on FMRI data using network theory with <a href ="https://www.iiserkol.ac.in/~anirban.banerjee/"> Dr. Anirban Banerjee </a>.</p>-->
<p> A full list of my publications can be found <a href="https://scholar.google.com/citations?user=EV1DgP0AAAAJ&hl=en">here.</font></a> The best way to reach me is at sunipadev [AT] google [DOT] com.</p>
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<li> I will be in Singapore for EMNLP 2023 where I will be organizing <a href = "www.winlp.org"> WiNLP </a>. Come say hi! </li>
<li> I have been promoted to Senior Research Scientist at Google Research in October 2023.</li>
<li> We will be organizing the 2nd Workshop on Cross Cultural Considerations in NLP at ACL 2024 in August at Bangkok, and the 3rd Workshop on HCI + NLP at NAACL 2024 in May at Mexico City. More detials about both to follow! </li>
<li> Our paper on community engaged stereotype evaluation resources has been accepted at <a href = "https://arxiv.org/abs/2307.10514"> NeurIPS 2023 </a>. </li>
<li>We had three papers accepted at ACL 2023. One investigated <a href = "https://aclanthology.org/2023.acl-short.118/"> robustness of bias measurements in NLP </a>(won Outstanding Paper Award), second built a <a href = "https://aclanthology.org/2023.acl-long.548/"> scalable stereotype evaluation resource </a> by leveraging LLMs, and the third investigated the <a href = "https://aclanthology.org/2023.acl-long.293/"> extent of misgendering </a> in language models.</li>
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<p>The Big Oxmox advised her not to do so, because there were thousands of bad Commas, wild Question Marks and devious Semikoli, but the Little Blind Text didn’t listen. She packed her seven versalia, put her initial into the belt and made herself on the way.</p>
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<h3>Photoshop</h3>
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<span>75%</span>
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<h3>jQuery</h3>
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<h3>HTML5</h3>
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<div class="progress-bar color-3" role="progressbar" aria-valuenow="85"
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<span>85%</span>
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<h3>CSS3</h3>
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<span>90%</span>
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<span>80%</span>
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<h2>Absolute Orientation of Word Embedding Alignment </h2>
<p>We explore ways to align high dimensional word embeddings extracted by different methods and from different datasets with each other. We also look at the different applications of this technique such as boosting embedding performance and language translation. It is possible to extend these methods to other sets of high dimensional vectors as well.</p>
<p> The full paper can be found <a href = "http://arxiv.org/abs/1806.01330">here</a>. --
My presentation slides from ICDM 2019 can be found <a href="https://github.com/sunipa/sunipa.github.io/blob/master/absOr.pdf">here</a>.</p>
<p> Code can be found <a href = "https://github.com/sunipa/Abs-Orientation">here</a> </p>
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<h2>Attenuating Bias in Word Vectors ></h2>
<p>In this project, we explore and compare ways to detect and reduce different social biases significantly from word embeddings. We perform the geometric operation of linear projection uniformly on all word vectors to correct the bias. We also devise two quantitative methods to uniformly adjudge the gender bias and quality of the embeddings. Finally, we demonstrate the use names as a powerful tool in the detection of bias directions of different types of biases.</p>
<p> The full paper can be found <a href = "https://arxiv.org/abs/1901.07656">here</a>.
My presentation slides from AIStats 2019 can be found <a href="https://github.com/sunipa/sunipa.github.io/blob/master/Attenuating%20Bias%20Presentation.pdf">here</a>.</p>--
<p> Code can be found <a href = "https://github.com/sunipa/Attenuating-Bias-in-Word-Vec">here</a> </p>
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<h2>Combatting Biases in Context-Free and Contextualized Word Embeddings </h2>
<p>We extend out technique of debiaisng embeddings to contextualized embeddings such as ELMo and BERT. We compare the bias content in the different types of embeddings and analyze diffeent types of social biases expressed by them : gender, religion and nationality related biases. We facilitate this analysis by developing a schema of methodical tests based on the task of textual inference such that a deviation of prediction probabilities in the task indicates the amount of bias encoded by the embeddings. </p>
<p> More details can be found <a href = "https://arxiv.org/abs/1901.07656">here</a>. My presentation slides from AAAI 2020 can be found <a href="https://github.com/sunipa/sunipa.github.io/blob/master/AAAI%20Oral.pdf">here</a>.</p>--
<p> Code for developing the test and for debiasing the embeddings can be found <a href = "https://github.com/sunipa/On-Measuring-and-Mitigating-Biased-Inferences-of-Word-Embeddings">here</a> </p>
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<p> PhD in Computing (Data Management and Analysis Track) at the University of Utah, graduated in Fall 2020. </p>
<!--<p>Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.</p>
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<p> Completed a dual BS - MS degree majoring in Mathematics at Indian Institute of Science Education ad Research, Kolkata. </p>
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<!--<div class="panel panel-default">
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<p>Far far away, behind the word <strong>mountains</strong>, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.</p>
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<p>Far far away, behind the word <strong>mountains</strong>, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.</p>
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<p>Far far away, behind the word <strong>mountains</strong>, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast of the Semantics, a large language ocean.</p>
</div>
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<h2 class="colorlib-heading">CV</h2>
<p>Here's a downloadable version of my <a href="https://github.com/sunipa/sunipa.github.io/blob/master/Resume_SunipaDev_Academic (1).pdf">CV</a>!</p>
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