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talks.html
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---
layout: default
title:
---
<style type="text/css">
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box-sizing: border-box;
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margin: 0;
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margin-bottom: 36px;
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width: 100%;
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<!-- == = ==== == = == = = == = = == = = = == = = == -->
<!-- upcoming talks -->
<h2>Upcoming Talks</h2>
<div class="wrap">
*** No upcoming talk for now, it will be updated if any.
</div> <!-- wrap_div-->
<!-- Past Talks -->
<h2>Past Talks @ LCS2</h2>
<div class="wrap">
<div class="box">
<div class="box-top">
<img class="box-image" src="img/people/Tanu_Mitra.png">
<div class="title-flex">
<h3 class="box-title">Tanu Mitra</h3>
<p class="user-follow-info">Assistant Professor<br><strong> the University of Washington, Information School.</strong></p>
</div>
<p class="description">
<b>Title: </b>Understanding and Countering Problematic Information on Social Media Platforms</br> <b>Date: </b> 31st October, 2023 </br>Duration: 20:00-21:00 </br>Venue: Online
</p>
</div>
<button type="button" class=" button btn btn-primary" data-toggle="modal" data-target="#Tanu_Mitra">
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<div class="modal fade" id="Tanu_Mitra" tabindex="-1" role="dialog" aria-labelledby="exampleModalLongTitle" aria-hidden="true">
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<h5 class="modal-title" id="exampleModalLongTitle">Abstract & Bio
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<h5>Abstract</h5>
<p> Online social media platforms have brought numerous positive changes, including access to
vast amounts of news and information. Yet, those very opportunities have created new
challenges—our information ecosystem is now rife with problematic content, ranging from
misinformation, conspiracy theories, to hateful and incendiary propaganda. As a social
computing researcher, my work introduces computational methods and systems to understand
and design defenses against such problematic online content. In this talk, I will focus on two
aspects of problematic online information: 1) conspiracy theories and 2) extremist propaganda.
First, leveraging data spanning millions of conspiratorial posts on Reddit, 4chan, and 8chan, I
will present scalable methods to unravel who participates in online conspiratorial discussions,
what causes users to join conspiratorial communities and then potentially abandon them.
Second, I will dive into a special type of problematic content: extremist hate groups. Merging
theories from social movement research with big data analyses, I will discuss the ecosystem of
extremists’ communication and the roles played by them. Finally, I will close by previewing
important new opportunities to address some of these problems, including conducting social
audits to defend against algorithmically generated misinformation and designing socio-technical
interventions to promote meaningful credibility assessment of information.
</p>
<h5>Bio</h5>
<p> anu Mitra is an Assistant Professor at the University of Washington, Information School, where
she leads the Social Computing research group. She and her students study and build large-
scale social computing systems to understand and counter problematic information online. Her
research spans auditing online systems for misinformation and conspiratorial content,
understanding digital misinformation, unraveling narratives of online extremism and hate, and
building technology to foster critical thinking online. Her work employs a range of
interdisciplinary methods from the fields of human computer interaction, data mining, machine
learning, and natural language processing.</br></br>
Dr. Mitra’s work has been supported by grants from the NSF, NIH, DoD, Social Science One,
and other Foundations. Her research has been recognized through multiple awards and honors,
including an NSF-CRII, an early career ONR-YIP, Adamic-Glance Distinguished Young
Researcher award and Virginia Tech College of Engineering Outstanding New Assistant
Professor award, along with several best paper honorable mention awards. Dr. Mitra received
her PhD in Computer Science from Georgia Tech’s School of Interactive Computing.
</p>
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<img class="box-image" src="img/people/Santo_Fortunato.jpg">
<div class="title-flex">
<h3 class="box-title">Santo Fortunato</h3>
<p class="user-follow-info">Professor<br><strong>Luddy School of Informatics, Computing, and Engineering (SICE) Indiana University, Bloomington, USA</strong></p>
</div>
<p class="description">
<b>Title: </b>Network communities, embeddings, and the science of science.</br> <b>Date: </b> May 8th, 2023 </br>Duration: 19:30-20:30 </br>Venue: Online
</p>
</div>
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<h5>Abstract</h5>
<p> In this talk I will highlight some contributions in my focus research areas: network science and the science of science.
We propose a measure based on the concept of robustness, that avoids Newman-Girvan modularity's biases by design: robustness modularity is the probability to find trivial partitions when the structure of the network is randomly perturbed. Also, I will discuss the effectiveness of graph embeddings, specifically node2vec, at discovering community structure. I will also show how an iterative procedure, that alternates embedding and edge weighting, makes clusters more easily detectable.
</br>In the science of science I have focused on the dynamics of impact and the evolution of science. The distributions of citations of papers published in the same discipline and year rescale to a universal curve, by properly normalizing the raw number of cites. Nobel Laureates are an endangered species. Finally I will present evidence of social contagion in science: active authors in a certain field induce their collaborators to work in that field.
</p>
<h5>Bio</h5>
<p> Santo Fortunato is the Director of the Indiana University Network Science Institute (IUNI) and a Professor at Luddy School of Informatics, Computing, and Engineering of Indiana University. Previously he was professor of complex systems at the Department of Computer Science of Aalto University, Finland. Prof. Fortunato got his PhD in Theoretical Particle Physics at the University of Bielefeld In Germany. His focus areas are network science, especially community detection in graphs, computational social science and science of science. His research has been published in leading journals, including Nature, Science, Nature Physics, PNAS, Physical Review Letters, Physical Review X, Reviews of Modern Physics, Physics Reports and has collected over 40,000 citations (Google Scholar). His single-author article Community detection in graphs (Physics Reports 486, 75-174, 2010) is one of the best known and most cited papers in network science. Fortunato received the Young Scientist Award for Socio- and Econophysics 2011, a prize given by the German Physical Society, for his outstanding contributions to the physics of social systems. He is Fellow of the Network Science Society (2022) and of the American Physical Society (2022). He is the Founding Chair of the International Conference of Computational Social Science (IC2S2), which he first organized in Helsinki in June 2015. He was Chair of Networks 2021, the largest ever event on network science, a historical merger of the NetSci and Sunbelt conferences. He is author of the book A First Course in Network Science, by Cambridge University Press (2020), the most accessible textbook on the new science of networks. </p>
</div>
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<img class="box-image" src="https://www.nextcanada.com/wp-content/uploads/2019/09/graham-neubig.jpg">
<div class="title-flex">
<h3 class="box-title">Graham Neubig</h3>
<p class="user-follow-info">Associate Professor<br><strong>Carnegie Mellon University</strong>
</p>
</div>
<p class="description">
<b>Title: </b>Is My NLP Model Working? The Answer is Harder Than You Think <br> <b>Date: </b> April 20, 2023 </br>Duration: 19:00-20:00 </br>Venue: Online
</p>
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<h5>Abstract</h5>
<p> As natural language processing now permeates many different applications, its practical use is unquestionable. However, at the same time NLP is still imperfect, and errors cause everything from minor inconveniences to major PR disasters. Better understanding when our NLP models work and when they fail is critical to the efficient and reliable use of NLP in real-world scenarios. So how can we do so? In this talk I will discuss two issues: automatic evaluation of generated text, and automatic fine-grained analysis of NLP system results, which are some first steps towards a science of NLP model evaluation. </p>
<h5>Bio</h5>
<p>Graham Neubig is an associate professor at the Language Technologies Institute of Carnegie Mellon University and CEO of Inspired Cognition. His research focuses on natural language processing, with a focus on multilingual NLP, natural language interfaces to computers, and machine learning methods for NLP system building and evaluation. His final goal is that every person in the world should be able to communicate with each-other, and with computers in their own language. He also contributes to making NLP research more accessible through open publishing of research papers, advanced NLP course materials and video lectures, and open-source software, all of which are available on his <a href="http://www.phontron.com/" target="_blank" style="color:rgb(0,172,255);">website</a></p>
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<!--New speaker-->
<div class="box">
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<img class="box-image" src="img/people/Tom_Hartvigsen.jpg">
<div class="title-flex">
<h3 class="box-title">Tom Hartvigsen</h3>
<p class="user-follow-info">Postdoctoral Associate under Prof. <strong>Marzyeh Ghassemi,</strong> </br> at MIT CSAIL.
</p>
</div>
<p class="description">
<b>Title: </b>Model Editing <br> <b>Date: </b> April 11th, 2023 </br>Duration: 3:00 – 4:00 PM</br>Venue: Online
</p>
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<p>TBD</p>
<h5>Bio</h5>
<p> Tom Hartvigsen is a Postdoctoral Associate at MIT's Computer Science and Artificial Intelligence Laboratory, where he works with Marzyeh Ghassemi. Tom focuses on core challenges in making machine learning and data mining systems responsibly deployable in healthcare settings, mostly for time series and text. His work has appeared at top venues such as KDD, ACL, NeurIPS, and AAAI. He also ran the 2022 NeurIPS workshop on Learning from Time Series for Health and is the general chair of the 2023 Machine Learning for Health Symposium. Tom received his Ph.D. in Data Science from Worcester Polytechnic Institute in 2021, where he was advised by Elke Rundensteiner and Xiangnan Kong.
</p>
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<h3 class="box-title">Debarghya Ghoshdastidar</h3>
<p class="user-follow-info">Assistant Professor </br><strong>TU Munich School of Computation, Information and Technology</strong>
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<b>Title:</b> The Myths about Overfitting</br> <b>Date: </b> March 22, 2023 </br>Venue: EE committee Room, 3rd Floor, Block III, IIT Delhi
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<p> Overfitting is the practice of using a complex machine-learning model that perfectly fits the training data.
Historically, overfitting has been considered a "bad practice" that is expected to produce predictors which perform poorly on new data. However, the recommendation has changed in recent years, where overfitted neural networks perform surprisingly well in computer vision and natural language processing among others. For instance, in the ImageNet image classification benchmark (with 14 million images), the best architectures have more than a billion parameters and yet achieve 90% accuracy. This naturally raises the question of whether overfitting is a good practice or a bad practice.
</br></br>
In this talk, I will discuss the mathematical foundations behind the
classical and modern views about overfitting. I will start with a brief
introduction to the statistical theory that leads to the conclusion
"overfitting is a bad practice". I will then discuss
some recent theoretical results debunk the following myths:</br>
1. large models with too many parameters always overfit the training
data;</br>
2. models that perfectly fit the training data cannot predict well on
unseen data.</br>
The above results are the basis of two promising research directions in
machine learning theory: Neural Tangent Kernels -- that capture the
training dynamics of wide neural networks -- and Double-Descent
phenomenon -- a precise characterisation of the performance of
overfitted models. We will finally see why the classical and the modern
theories of overfitting are not at odds with each other.
As examples, I will briefly talk about two recent works
(arXiv:2202.09054, arXiv: 2210.09809), where we use how these tools can
be used to get better understanding of modern machine/deep learning
practices.</br>
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<h5>Bio</h5>
<p> Debarghya Ghoshdastidar is an assistant professor at TU
Munich School of Computation, Information and Technology, where he leads
the research group on Theoretical Foundations of Artificial
Intelligence. Previously, he obtained a bachelor's degree in electrical
engineering from Jadavpur University, and a master's degree and PhD from
the Indian Institute of Science. His research is at the intersection of
statistics and machine learning, with a strong focus on understanding
when can we learn from data, particularly beyond supervised learning. He
also collaborates on applications of ML in physics and engineering. He
is also a fellow at the Munich Data Science Institute (MDSI) and a
member of the European Laboratory for Learning and Intelligent Systems
(ELLIS).
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<h3 class="box-title">Ted Briscoe</h3>
<p class="user-follow-info">Professor<br><strong>Mohamed Bin Zayed University of Artificial Intelligence, UAE</strong></p>
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<b>Title: </b>Machine learning and natural language processing in support of interactive automated tutoring for non-native writers</br> <b>Date: </b> March 22, 2023 </br>Venue: A-006, R & D Block, IIIT-Delhi
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<p> Massive on-line open courseware, adaptive learning management systems and the like are well-established, but their impact outside of a few subjects such as computer science and maths has been modest so far. The missing ingredient and essential difference between on-line maths or computer science and language learning courseware is objective automated and meaningful learning-oriented assessment (LOA).
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LOA, to be useful, must include both a summative and formative component, must address the appropriate linguistic tasks and skills, must be interpretable and actionable by the learner, and must be integrated into an adaptive and therefore personalised learning platform.
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I'll describe how we have exploited (deep) machine learning and natural language processing to develop LOA for non-native writers, building accurate automated graders benchmarked to Cambridge English exams as well as pedagogically useful feedback models to support learners interactively and incrementally improve their writing.
</br>
Our writing assessment technology is already in use in Cambridge English courseware such as Empower and Linguaskill, and in Write and Improve (e.g: https://www.youtube.com/watch?v=LpTF_o_eyao).
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<h5>Bio</h5>
<p> Ted Briscoe was Professor of Computational Linguistics at the University of Cambridge until 2023 when he moved to MBZUAI. He has published over 150 research articles and three books in the areas of automated speech and language processing. He is co-founder and was CEO of iLexIR Ltd., a consultancy and technology provider specialising in language processing applications, and was the inaugural director of the Alta Institute which conducts research into automated language teaching and assessment. In 2009, iLexIR spun-out SwiftKey, maker of the world's most popular predictive keyboard for smartphones. In 2014, he co-founded English Language iTutoring Ltd and was its chief scientist until 2019.
</br></br>
https://mbzuai.ac.ae/study/faculty/ted-briscoe/
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<h3 class="box-title">Anirban Dasgupta</h3>
<p class="user-follow-info">Professor<br><strong>IIT Gandhinagar</strong>
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<b>Title: </b>Coresets for Machine Learning <br> <b>Date: </b> February 22, 2023
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<h5>Abstract</h5>
<p> In the face of the data onslaught, smart algorithms have a big role to play. Over the last couple of decades, coresets, a small and efficiently calculable summary of the data, have grown in their popularity, both in theoretical and practical settings. They enable approximating large optimizations while needing only a fraction of the resources. In this talk, we will discuss a few recent results related to creating coresets for tensor factorization and Bregman clustering. Our coresets are online in nature, i.e., for every incoming point, it takes an irrevocable decision whether to include it in the coreset. We will also discuss some in-progress results related to creating coresets with deterministic guarantees. </p>
<h5>Bio</h5>
<p>Anirban Dasgupta is currently the N. Rama Rao Chair Professor of Computer Science & Engineering at IIT Gandhinagar. Prior to being at IIT Gandhinagar, he was a Senior Scientist at Yahoo Labs Sunnyvale. Anirban works on algorithmic problems for massive data sets, large-scale machine learning, analysis of large social networks, and randomized algorithms in general. He did his undergraduate studies at IIT Kharagpur and doctoral studies at Cornell University. He has received the Google Faculty Research Award (2015), the Cisco University Award (2016), the ICDT Best Newcomer Award (2016), and the Google India AI/ML Award (2020).</p>
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<h3 class="box-title">B. Aditya Prakash</h3>
<p class="user-follow-info">Associate Professor, <strong>Georgia Tech</strong>
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<b>Title: </b>Data Science and AI for Epidemic Response: Time-Series Forecasting and Network Interventions <br> <b>Date: </b> February 17, 2022
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<p> B. Aditya Prakash is an Associate Professor in the College of Computing at the Georgia Institute of Technology (“Georgia Tech”). He received a Ph.D. from the Computer Science Department at Carnegie Mellon University, and a B.Tech. in Computer Science and Engineering from the Indian Institute of Technology (IIT) -- Bombay. He has published one book, more than 90 papers in major venues, holds two U.S. patents and has given several tutorials at leading conferences. His work has also received multiple best-of-conference, best paper and travel awards. His research interests include Data Science, Machine Learning and AI, with emphasis on big-data problems in large real-world networks and time-series, with applications to computational epidemiology/public health, urban computing, security and the Web. Tools developed by his group have been in use in places like ORNL, CDC and Walmart. He has also received several awards such as the Facebook Faculty Award (2015 and 2021), the NSF CAREER award and was named as one of ‘AI Ten to Watch’ by IEEE. His work has won awards in multiple global data science challenges (e.g., the C3AI COVID19 Grand Challenge) and been highlighted by several media outlets/popular press like <a href="https://fivethirtyeight.com/">FiveThirtyEight.com</a> too. He is a member of the infectious diseases modeling MIDAS network and core-faculty at the Center for Machine Learning (ML@GT) and the Institute for Data Engineering and Science (IDEaS) at Georgia Tech. <br><br>Aditya’s Twitter handle is @badityap. </p>
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<h3 class="box-title">Noshir Contractor</h3>
<p class="user-follow-info">Professor, <strong>Northwestern University</strong>
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<b>Title: </b>People Analytics: Using Digital Exhaust from the Web to Leverage Network Insights in the Algorithmically Infused Workplace <br> <b>Date: </b> January 10, 2022
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<p> In order to bring the performance of people analytics in the algorithmically infused workplace up — and in line with the hype — organizations need to do more than analyze data on demographic attributes. We need to focus not only on who people are but also who they know. The potential for social network analysis to identify “high potentials,” who has good ideas, who is influential, what teams will get work done efficiently and effectively is well established based on decades of research. The challenge has been the collection of network data via surveys that are time consuming, elicit low response rates and have a high obsolescence. This talk presents empirical examples ranging from corporate enterprises to simulated long duration space exploration to demonstrate how we can leverage people analytics – and in particular relational analytics - to mine “digital exhaust”— data created by individuals every day in their digital transactions, such as e‐mails, chats, “likes,” “follows,” @mentions, and file collaboration— to address challenges they face with issues such as team conflict, team assembly, diversity and inclusion, succession planning, and post-merger integration. </p>
<h5> Bio</h5>
<p>
Noshir Contractor is the Jane S. & William J. White Professor of Behavioral Sciences in the McCormick School of Engineering & Applied Science, the School of Communication and the Kellogg School of Management and Director of the Science of Networks in Communities (SONIC) Research Group at Northwestern University. He is also the President-Elect of the International Communication Association (ICA).
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<p>
Professor Contractor has been at the forefront of three emerging interdisciplines: network science, computational social science and web science. He is investigating how social and knowledge networks form – and perform – in contexts including business, scientific communities, healthcare and space travel. His research has been funded continuously for over 25 years by the U.S. National Science Foundation with additional funding from the U.S. National Institutes of Health, NASA, DARPA, Army Research Laboratory and the Bill & Melinda Gates Foundation.
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His book Theories of Communication Networks (co-authored with Peter Monge) received the 2003 Book of the Year award from the Organizational Communication Division of the National Communication Association and the 2021 International Communication Association’s Fellows Book Award. He is a Fellow of the American Association for the Advancement of Science (AAAS), the Association for Computing Machinery (ACM), and the International Communication Association (ICA). He also received the Distinguished Scholar Award from the National Communication Association and the Lifetime Service Award from the Organizational Communication & Information Systems Division of the Academy of Management. He was selected as the recipient of the 2022 Simmel Award from the International Network for Social Network Analysis. In 2018 he received the Distinguished Alumnus Award from the Indian Institute of Technology, Madras where he received a Bachelor’s in Electrical Engineering. He received his Ph.D. from the Annenberg School of Communication at the University of Southern California.
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<h3 class="box-title">Luiz GUStavo Martins</h3>
<p class="user-follow-info">Senior Developer Advocate, <strong>Google</strong>
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<b>Title: </b>TensorFlow Hub for the NLP domain <br> <b>Date: </b> November 24, 2021
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<p>Gus is a Developer Advocate in the Google AI team. His main passion is making Machine Learning easier for developers. As of lately he's been working with TensorFlow Hub and helping make ML models more accessible to everyone </p>
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<h3 class="box-title">Neil Shah</h3>
<p class="user-follow-info">Senior Research Scientist, <strong>Snap</strong>
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<b>Title: </b> Machine Learning on Graphs with Scarce Labels <br> <b>Date: </b> October 27, 2021
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<p>ML on graphs is a prominent, and rapidly developing research area in recent years. Notably, graph neural networks (GNNs) have been proposed as a means of learning representations which utilize graph topology and node features, and are optimized toward targeted tasks of interest. Several such tasks deal with the challenging problem of learning with scarce, or nonexistent labeled data, common in domains like online trust and integrity. In this talk, I'll overview two recent works on learning in label-free and label-scarce settings on graph data with GNNs. The first work, published at TNNLS'21, discusses the use of integrating traditional unsupervised and modern GNN-based approaches for graph anomaly detection in a unified framework, demonstrating marked improvements in learned representation quality for anomaly detection tasks over both traditional and modern independent baselines. The second work, published at AAAI'21, discusses an approach for data augmentation for GNNs which shows large improvements in semi-supervised node classification, particularly in label-scarce settings. Together, the two works paint a promising picture for practical use of graph ML methods in settings with limited labels, by incorporating useful priors and stretching available labeled data with augmentation strategies. I'll also very briefly mention a few other exciting works our group has been up to, grounded in practically minded GNN research. </p>
<p>Bio: <a href="http://nshah.net/" target="_blank">Neil Shah</a> is a Lead Research Scientist at Snap Inc, with interests spanning data mining, machine learning and computational social science, specifically in the contexts of graph-based modeling of user behavior and misbehavior. His work has resulted in 35+ conference and journal publications, in top venues such as KDD, WSDM, ICDM, WWW, CIKM, SDM, AAAI, TKDD and more, including several best-paper awards. He has also served as an organizer, chair and senior program committee member at a number of these. He has had previous research experiences at Lawrence Livermore National Laboratory, Microsoft Research, and Twitch.tv. He earned a PhD in Computer Science in 2017 from Carnegie Mellon University's Computer Science Department, funded partially by the NSF Graduate Research Fellowship.</p>
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<h3 class="box-title">Arun Iyer</h3>
<p class="user-follow-info">Principal Researcher, <strong>Microsoft Research</strong>
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<b>Title: </b> HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classification <br> <b>Date: </b> September 29, 2021
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<p>We consider the problem of learning efficient and inductive graph convolutional networks for text classification with a large number of examples and features. Existing state-of-the-art graph embedding based methods such as predictive text embedding (PTE) and TextGCN have shortcomings in terms of predictive performance, scalability and inductive capability. To address these limitations, we propose a heterogeneous graph convolutional network (HeteGCN) modeling approach that unites the best aspects of PTE and TextGCN together. The main idea is to learn feature embeddings and derive document embeddings using a HeteGCN architecture with different graphs used across layers. We simplify TextGCN by dissecting into several HeteGCN models which (a) helps to study the usefulness of individual models and (b) offers flexibility in fusing learned embeddings from different models. In effect, the number of model parameters is reduced significantly, enabling faster training and improving performance in small labeled training set scenario. Our detailed experimental studies demonstrate the efficacy of the proposed approach.</p>
<p>Bio: I did my Bachelors in Computer Science and Engineering at University of Mumbai. I went on to do my Masters at Indian Institute of Technology, Kanpur (IIT Kanpur) where I did my thesis in Reinforcement Learning. To get some experience in building practical AI/ML system, I spent 4 years as a Research Engineer at Yahoo! Research, Bangalore. There I worked on building large scale information tagging, extraction and management systems. Then I went on to do my PhD at Indian Institute of Technology, Bombay (IIT Bombay) under Prof. Sunita Sarawagi and Prof. Saketa Nath. During my PhD, I worked on building machine learning models for predicting aggregate label statistics. Currently, post my PhD, I have joined Microsoft Research, exploring two areas - 1] Learning Representations on Graphs and 2] Intersection of Program Synthesis and Machine Learning.</p>
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<h3 class="box-title">Aditya Grover</h3>
<p class="user-follow-info">Research Scientist, <strong>Facebook AI</strong><br>
Assistant Professor, <strong>UCLA</strong>
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<b>Title: </b> Pretrained Transformers as Universal Computation Engines <br> <b>Date: </b> September 1, 2021
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<p>We investigate the capability of a transformer pretrained on natural language to generalize to other modalities with minimal finetuning -- in particular, without finetuning of the self-attention and feedforward layers of the residual blocks. We consider such a model, which we call a Frozen Pretrained Transformer (FPT), and study finetuning it on a variety of sequence classification tasks spanning numerical computation, vision, and protein fold prediction. In contrast to prior works which investigate finetuning on the same modality as the pretraining dataset, we show that pretraining on natural language can improve performance and compute efficiency on non-language downstream tasks. Additionally, we perform an analysis of the architecture, comparing the performance of a random initialized transformer to a random LSTM. Combining the two insights, we find language-pretrained transformers can obtain strong performance on a variety of non-language tasks.</p>
<p>Bio: Aditya Grover is a research scientist in the Core ML team at Facebook AI Research, a visiting postdoctoral researcher at UC Berkeley, and an incoming assistant professor of computer science at UCLA. His research centers around probabilistic modeling, approximate inference, and sequential decision making, with applications at the intersection of physical sciences and sustainability. His research has been recognized with a best paper award, a best undergraduate thesis award, several research fellowships (Microsoft Research, Lieberman, Google-Simons-Berkeley), and the ACM SIGKDD Doctoral Dissertation Award. Aditya received his PhD and bachelors in computer science from Stanford and IIT Delhi respectively.</p>
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<h3 class="box-title">Sudipta Sarangi</h3>
<p class="user-follow-info">Department Head and Professor of Economics<br> <strong>Virginia Tech</strong></p>
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<b>Title: </b> Multilayer Networks <br> <b>Date: </b> August 6, 2021
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<p>Online hate speech is an important issue that breaks the cohesiveness of online social communities and even raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many traditional machine learning and deep learning methods to detect hate speech in online social platforms automatically. This talk aims to introduce the pressing problem of online hate speeches and discuss the automated hate speech detection methods.</p>
<p>Bio: Roy is an Assistant Professor at the Information Systems Technology and Design Pillar, Singapore University of Technology and Design. He is a faculty of the transformative Design and Artificial Intelligence programme. His research lies in the intersection of data mining, machine learning, social computing, and natural language processing. He is leading the Social AI Studio, a research group that focuses on designing the next-generation social artificial intelligence systems. He has published in top-tier venues in his relevant field, such as WWW, IJCAI, ICDM, TKDE, ACL, COLING, etc. He serves in the program committees of multiple top conferences. He is currently part of the editorial board for the Social Network Analysis and Mining (SNAM) journal.</p>
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<h3 class="box-title">Ashish Ghosh</h3>
<p class="user-follow-info">Professor and Head of Machine Intelligence Unit<br> <strong>ISI Kolkata</strong></p>
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<b>Title: </b> Deep Learning: Basics, Applications and Our Contribution <br> <b>Date: </b> August 5, 2021
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<p>Online hate speech is an important issue that breaks the cohesiveness of online social communities and even raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many traditional machine learning and deep learning methods to detect hate speech in online social platforms automatically. This talk aims to introduce the pressing problem of online hate speeches and discuss the automated hate speech detection methods.</p>
<p>Bio: Roy is an Assistant Professor at the Information Systems Technology and Design Pillar, Singapore University of Technology and Design. He is a faculty of the transformative Design and Artificial Intelligence programme. His research lies in the intersection of data mining, machine learning, social computing, and natural language processing. He is leading the Social AI Studio, a research group that focuses on designing the next-generation social artificial intelligence systems. He has published in top-tier venues in his relevant field, such as WWW, IJCAI, ICDM, TKDE, ACL, COLING, etc. He serves in the program committees of multiple top conferences. He is currently part of the editorial board for the Social Network Analysis and Mining (SNAM) journal.</p>
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<h3 class="box-title">Roy Ka-Wei LEE</h3>
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<b>Title: </b> Automated Hate Speech Detection <br> <b>Date: </b> July 29, 2021
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<p>Online hate speech is an important issue that breaks the cohesiveness of online social communities and even raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many traditional machine learning and deep learning methods to detect hate speech in online social platforms automatically. This talk aims to introduce the pressing problem of online hate speeches and discuss the automated hate speech detection methods.</p>
<p>Bio: Roy is an Assistant Professor at the Information Systems Technology and Design Pillar, Singapore University of Technology and Design. He is a faculty of the transformative Design and Artificial Intelligence programme. His research lies in the intersection of data mining, machine learning, social computing, and natural language processing. He is leading the Social AI Studio, a research group that focuses on designing the next-generation social artificial intelligence systems. He has published in top-tier venues in his relevant field, such as WWW, IJCAI, ICDM, TKDE, ACL, COLING, etc. He serves in the program committees of multiple top conferences. He is currently part of the editorial board for the Social Network Analysis and Mining (SNAM) journal.</p>
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<h3 class="box-title">Ani Nenkova</h3>
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<b>Title: </b> Interpretability Analysis for Named Entity Recognition <br> <b>Date: </b> July 27, 2021
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<p>In this talk I will present a set of experiments designed to help us understand what neural named entity recognition systems learn and why they make the predictions we see. Specifically, we seek to understand if systems learn name strings (Ani, Julia, Kathy) or if they are able to identify textual contexts that constrain the semantics class of whatever word appears in that context ("My name is __ "). I will present evidence that the performance of neural methods is largely driven by their ability to recognize word tokens as belonging to certain semantic classes. In a study with people, we find that in many cases people are indeed able to identify constraining contexts and figure out the class only from the context in the sentence. People's recognition of constraining contexts aligns better with predictions from biLSTM-CRF models than BERT models. I will present compelling evidence that current models do not integrate contextual clues effectively. These results indicate that NER is a challenging yet practical domain for testing machine text comprehension abilities.
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<p>Bio: Ani Nenkova is a Principal Scientist at Adobe Research and associate professor of computer and information science at the University of Pennsylvania (on leave). Her work on summarization, discourse, multi-modal emotion prediction and information extraction in the biomedical domain has been recognized with best paper awards at SIGDIAL 2010, EMNLP-CoNLL 2012, AVEC 2012 and AMIA 2021. Ani was program chair for NAACL in 2016 and currently serves as editor-in-chief for the Transactions of the Association for Computational Linguistics (TACL). </p>