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class="grid-with-gutters"> <div class="row"> <div class="span8"> <h1 class="page-title"> Graduate Students </h1> <div class="the-content"> <div class="tabbable"> <ul class="nav nav-tabs"> <li class="active"> <a href="#tbs_nav_item_0" data-toggle="tab">A-J</a> </li> <li class=""> <a href="#tbs_nav_item_1" data-toggle="tab">K-Q</a> </li> <li class=""> <a href="#tbs_nav_item_2" data-toggle="tab">R-Z</a> </li> </ul> <div class="tab-content"> <div id="tbs_nav_item_0" class="active tab-pane"> <ul> <li><a href="/cdn-cgi/l/email-protection#45262422302c373771052f2d306b202130">Carlos Aguirre</a> | <a href="https://www.pocaguirre.com/">Website</a> | <a href="https://www.pocaguirre.com/publications">Publications</a></li> <li><a href="/cdn-cgi/l/email-protection#a3cdc1c2c5cdc292e3c9cbd68dc6c7d6">Niyati Bafna</a> | <a href="https://niyatibafna.github.io">Website</a> | <a href="https://scholar.google.com/citations?user=22sfVrEAAAAJ&hl=en&oi=ao">Publications</a></li> <li><a href="/cdn-cgi/l/email-protection#b1d4d3d0c3d0dd80f1dbd99fd4d5c4">Elina Baral</a></li> <li><a href="/cdn-cgi/l/email-protection#98f9faf4f9f1eaeba9d8f2f0edb6fdfced">Andrew Blair-Stanek</a> | <a href="https://www.law.umaryland.edu/Directory/profile.asp?id=893#tab-1">Website</a> | <a href="https://www.law.umaryland.edu/Directory/profile.asp?id=893#tab-1">Publications</a></li> <li><a href="/cdn-cgi/l/email-protection#8ae3e9ebe9e2e5e6ebcae9f9a4e0e2ffa4efeeff">Isabel Cachola</a> | <a href="https://isabelcachola.com/">Website</a> | <a href="https://isabelcachola.com/#publications">Publications</a></li> <li><a href="/cdn-cgi/l/email-protection#0b7f686a643c4b61637e256e6f7e">Tianyu Cao</a> | <a href="https://tianyucao.com/">Website</a> | <a href="https://scholar.google.com/citations?view_op=list_works&hl=en&user=5ZPcT-QAAAAJ">Publications</a></li> <li><a href="ccarpe18@jhu.edu">Cameron Carpenter</a></li> <li><a href="/cdn-cgi/l/email-protection#6d0e0e0518030e055c2d07051843080918">Jonathan Chang</a></li> <li><a href="/cdn-cgi/l/email-protection#016a62696071607330416b69742f646574">Kaavya Chaparal</a></li> <li><a href="/cdn-cgi/l/email-protection#7e1f1f1a1b120b1d171f3e14160b501b1a0b">Alexandra DeLucia</a> | <a href="http://alexandradelucia.com/">Website</a></li> <li><a href="/cdn-cgi/l/email-protection#a3cfc6ccc7d6e3c0d08dc9cbd68dc6c7d6">Leo Du</a> | <a href="https://scholar.google.com/citations?user=efDU43kAAAAJ">Publications</a></li> <li><a href="/cdn-cgi/l/email-protection#740711161a110645341e1c015a111001">Seth Ebner</a> | <a href="http://sebner.com" target="_blank" rel="noopener noreferrer">Website</a></li> <li><a href="/cdn-cgi/l/email-protection#2a4b4c4b5c4b58451b6a40425f044f4e5f">Anna Favaro</a></li> <li><a href="/cdn-cgi/l/email-protection#52253b3e3e7c343e37213a3f333c12383a277c373627">William Fleshman</a></li> <li><a href="/cdn-cgi/l/email-protection#610506000e54210b09144f040514">Dongji Gao</a> | <a href="https://scholar.google.com/citations?user=SNNZ1VAAAAAJ&hl=en">Publications</a></li> <li><a href="/cdn-cgi/l/email-protection#f48d9381959ac5cdb49e9c81da919081"><span data-teams="true"><span class="ui-provider a b c d e f g h i j k l m n o p q r s t u v w x y z ab ac ae af ag ah ai aj ak" dir="ltr">Yaohan Guan</span></span></a></li> <li><a href="/cdn-cgi/l/email-protection#2152494046445313614b49540f444554">Sophia Hager</a></li> <li><a href="/cdn-cgi/l/email-protection#7b10131a0909121c121a153b11130e551e1f0e">Keith Harrigian</a>| <a href="https://kharrigian.github.io/">Website</a> | <a href="https://kharrigian.github.io/publications/">Publications</a></li> <li><a href="/cdn-cgi/l/email-protection#42232a373131272b7402282a376c272637">Amir Hussein</a></li> <li><a href="/cdn-cgi/l/email-protection#543c3c21353a656760143e3c217a313021">Heyuan Huang</a></li> <li><a href="/cdn-cgi/l/email-protection#afdddac6d5c7caefc5c7da81cacbda">Ruizhe Huang</a></li> <li><a href="/cdn-cgi/l/email-protection#bad2c0d3d6d38bfad0d2cf94dfdecf">Zili Huang</a></li> <li><a href="/cdn-cgi/l/email-protection#b1dcdbd0d9d0df83f1dbd9c49fd4d5c4">Maliha Jahan</a> | <a href="https://scholar.google.com/citations?hl=en&user=ds1l9j4AAAAJ">Publications</a></li> <li><a href="/cdn-cgi/l/email-protection#b0cadad9d1ded78381f0dad8c59ed5d4c5">Zhengping Jiang</a> | <a href="https://zipjiang.github.io/">Website</a> | <a href="https://scholar.google.com/citations?user=skgb6DUAAAAJ&hl=en">Publications</a></li> <li><a href="/cdn-cgi/l/email-protection#e390898c908b8ad2d1a3898b96cd868796">Sonal Sunil Joshi</a></li> <li><a href="/cdn-cgi/l/email-protection#57203d2225362e3d66173d3f2279323322">William Jurayj</a></li> </ul> </div> <div id="tbs_nav_item_1" class="tab-pane"> <ul> <li><a href="/cdn-cgi/l/email-protection#21404a49404f101510614b49540f444554">Aleem Khan</a> | <a href="https://aleemkhan62.github.io">Website</a> | <a 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data-sby-flags="resizeDisable" data-postid="4125" data-sby-supports-lightbox="1" data-videocardlayout="vertical" > <div class="sby_items_wrap" style="padding: 5px;"> <div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_z3-AMZ7bdCw" data-date="1739332815" data-video-id="z3-AMZ7bdCw"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=z3-AMZ7bdCw" target="_blank" rel="noopener" data-full-res="https://i3.ytimg.com/vi/z3-AMZ7bdCw/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i3.ytimg.com\/vi\/z3-AMZ7bdCw\/default.jpg","320":"https:\/\/i3.ytimg.com\/vi\/z3-AMZ7bdCw\/mqdefault.jpg","480":"https:\/\/i3.ytimg.com\/vi\/z3-AMZ7bdCw\/hqdefault.jpg","640":"https:\/\/i3.ytimg.com\/vi\/z3-AMZ7bdCw\/sddefault.jpg"}" data-video-id="z3-AMZ7bdCw" data-video-title="Self-(In)Correct: LLMs Struggle with Discriminating Self-Generated Responses --- AAAI 2025"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Related paper: https://arxiv.org/abs/2404.04298 Dongwei Jiang: https://www.jiangdongwei.com/ Abstract: Can LLMs consistently improve their previous outputs for better results? For this to be true, LLMs would need to be better at discriminating among previously-generated alternatives, than generating initial responses. We explore the validity of this hypothesis in practice. We first formulate a unified framework that allows us to compare the generative and discriminative capability of any model on any task. In our resulting experimental analysis of several open-source and industrial LLMs, we observe that models are not reliably better at discriminating among previously-generated alternatives than generating initial responses. This finding challenges the notion that LLMs may be able to enhance their performance only through their own judgment."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Self-(In)Correct: LLMs Struggle with Discriminating Self-Generated Responses --- AAAI 2025</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_lj5-JOZGeUM" data-date="1737336262" data-video-id="lj5-JOZGeUM"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=lj5-JOZGeUM" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/lj5-JOZGeUM/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/lj5-JOZGeUM\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/lj5-JOZGeUM\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/lj5-JOZGeUM\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/lj5-JOZGeUM\/sddefault.jpg"}" data-video-id="lj5-JOZGeUM" data-video-title="CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Title: CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing (NeurIPS 2024) Speaker: Yen-ju Lu https://neillu23.github.io/ Paper: https://arxiv.org/abs/2412.04425 Abstract: We introduce Condition-Aware Self-Supervised Learning Representation (CA-SSLR), a generalist conditioning model broadly applicable to various speech-processing tasks. Compared to standard fine-tuning methods that optimize for downstream models, CA-SSLR integrates language and speaker embeddings from earlier layers, making the SSL model aware of the current language and speaker context. This approach reduces the reliance on input audio features while preserving the integrity of the base SSLR. CA-SSLR improves the model's capabilities and demonstrates its generality on unseen tasks with minimal task-specific tuning. Our method employs linear modulation to dynamically adjust internal representations, enabling fine-grained adaptability without significantly altering the original model behavior. Experiments show that CA-SSLR reduces the number of trainable parameters, mitigates overfitting, and excels in under-resourced and unseen tasks. Specifically, CA-SSLR achieves a 10% relative reduction in LID errors, a 37% improvement in ASR CER on the ML-SUPERB benchmark, and a 27% decrease in SV EER on VoxCeleb-1, demonstrating its effectiveness."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_IlqDv-s82Zs" data-date="1735862629" data-video-id="IlqDv-s82Zs"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=IlqDv-s82Zs" target="_blank" rel="noopener" data-full-res="https://i2.ytimg.com/vi/IlqDv-s82Zs/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i2.ytimg.com\/vi\/IlqDv-s82Zs\/default.jpg","320":"https:\/\/i2.ytimg.com\/vi\/IlqDv-s82Zs\/mqdefault.jpg","480":"https:\/\/i2.ytimg.com\/vi\/IlqDv-s82Zs\/hqdefault.jpg","640":"https:\/\/i2.ytimg.com\/vi\/IlqDv-s82Zs\/sddefault.jpg"}" data-video-id="IlqDv-s82Zs" data-video-title="Beyond Human Preferences: Use Human Goals to Guide AI towards Complementary AI -- Chenhao Tan"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Chenhao Tan (UChicago): https://chenhaot.com/ https://cs.uchicago.edu/people/chenhao-tan/ Abstract Human preferences have been the main component in guiding pretrained AI. In this talk, I argue that human preferences are often insufficient for complementing human intelligence and demonstrate the key role of human goals with two examples. First, hypothesis generation is critical for scientific discoveries. Instead of removing hallucinations, I will leverage data and labels as a guide to lead hallucination towards effective hypotheses. Second, I will use human perception as a guide for developing case-based explanations to support AI-assisted decision making. In both cases, faithfulness is “compromised” for achieving human goals. Finally, I will briefly discuss the case of identifying bias mechanisms in AI, where faithfulness is crucial. I will conclude with future directions towards complementary AI. Bio Tan is an assistant professor of computer science and data science at the University of Chicago, and is also affiliated with the Harris School of Public Policy. He obtained his PhD degree in the Department of Computer Science at Cornell University and bachelor’s degrees in computer science and in economics from Tsinghua University. Prior to joining the University of Chicago, he was an assistant professor at the University of Colorado Boulder and a postdoc at the University of Washington. His research interests include human-centered AI, natural language processing, and computational social science. His work has been covered by many news media outlets, such as the New York Times and the Washington Post. He also won a Sloan research fellowship, an NSF CAREER award, an NSF CRII award, a Google research scholar award, research awards from Amazon, IBM, JP Morgan, and Salesforce, a Facebook fellowship, and a Yahoo! Key Scientific Challenges award."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Beyond Human Preferences: Use Human Goals to Guide AI towards Complementary AI -- Chenhao Tan</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_8eoTc2AJXEo" data-date="1735144518" data-video-id="8eoTc2AJXEo"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=8eoTc2AJXEo" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/8eoTc2AJXEo/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/8eoTc2AJXEo\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/8eoTc2AJXEo\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/8eoTc2AJXEo\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/8eoTc2AJXEo\/sddefault.jpg"}" data-video-id="8eoTc2AJXEo" data-video-title="Trustworthy LLMs — our efforts on mitigating issues regarding social bias, safety and reliability"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Speaker: Jieyu Zhao (USC) https://jyzhao.net/ Abstract: The rapid advancement of large language models (LLMs) has unlocked a myriad of possibilities for positive societal impact, ranging from enhancing accessibility and communication to supporting disaster response and public health initiatives. However, the deployment of these technologies also raises critical concerns regarding accountability, fairness, transparency, and ethical use. In this talk, I will discuss our efforts for auditing models, detecting and mitigating biases, and understanding how LLMs make decisions. We hope to open the conversation to foster a community-wide effort towards more accountable and inclusive LLM practices."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Trustworthy LLMs — our efforts on mitigating issues regarding social bias, safety and reliability</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_dBJK2COTeuk" data-date="1734185376" data-video-id="dBJK2COTeuk"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=dBJK2COTeuk" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/dBJK2COTeuk/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/dBJK2COTeuk\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/dBJK2COTeuk\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/dBJK2COTeuk\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/dBJK2COTeuk\/sddefault.jpg"}" data-video-id="dBJK2COTeuk" data-video-title="MITRA: Beyond Just Machine Translation for Premodern Asian Low Resource Languages-Sebastian Nehrdich"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Abstract Recent years saw the rise of multilingual language models that achieve high levels of performance for a large number of tasks, with some of them handling hundreds of languages at once. Premodern languages are usually underrepresented in such models, leading to poor performance in downstream applications. In my talk, I will introduce the Dharmamitra project, which aims to develop a diverse set of language models to address these shortcomings for the classical Asian low-resource languages Sanskrit, Tibetan, Classical Chinese, and Pali. These models are providing solutions for low-level NLP tasks such as word segmentation, morpho-syntactic tagging etc., as well as high-level tasks such as semantic search, machine translation, and general chatbot interaction. I will talk about the individual challenges and unique characteristics of the data involved, and what strategies we deploy to address these. I will also demonstrate how these different tools can be combined in an application that goes beyond simple sentence-to-sentence machine translation, but instead provides detailed grammatical explanations and corpus-wide search to provide the users with as much relevant information as possible. This application is helpful for early-stage languages learners on the one hand, as well as experienced researchers with high level of language knowledge and very specific demands on the other hand. Biography Sebastian Nehrdich is a research assistant at the University of California, Berkeley, where he is leading the MITRA project together with Prof. Kurt Keutzer. He is also currently completing a PhD degree in computational linguistics at the University of Duesseldorf, Germany. His main research interest is the application of NLP methods to premodern Asian languages with focus on Sanskrit, Pali, Tibetan and Classical Chinese. He holds a master’s degree in Buddhist Studies and worked extensively with literature in premodern languages while also publishing articles on NLP problems for these languages in major conferences. Since 2018, he is the chief developer of the BuddhaNexus platform, a website that provides an interactive and user-friendly interface for the exploration of textual reuse instances in large text collections of the Buddhist tradition with over a hundred million of intertextual links. Since 2023 The MITRA project provides grammatical analyzers, machine translation, semantic search capabilities for classical Asian languages and currently serves between 500-1000 users with primarily academic background on a daily basis."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >MITRA: Beyond Just Machine Translation for Premodern Asian Low Resource Languages-Sebastian Nehrdich</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div> </div> <div class="sby_footer"> <a class="sby_load_btn" href="javascript:void(0);" > <span class="sby_btn_text" >Load More...</span> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> <span class="sby_follow_btn" > <a href="https://www.youtube.com/channel/UCWWiAEF7PMk-OK0-rwGITIg/" target="_blank" rel="noopener" > <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> <span > Subscribe </span> </a> </span> </div> </div> <style type="text/css">body #sb_youtube_sbyUCWWiAEF7PMkOK0rwGITIg5 .sby_caption_wrap .sby_caption { font-size: 13px !important}body #sb_youtube_sbyUCWWiAEF7PMkOK0rwGITIg5 .sby_follow_btn a:hover { box-shadow:inset 0 0 10px 20px rgb(255, 255, 255, .25) !important}body #sb_youtube_sbyUCWWiAEF7PMkOK0rwGITIg5[data-videostyle=boxed] .sby_items_wrap .sby_item .sby_inner_item { background-color: #ffffff !important}</style></div> </div> </div> <div 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