Dr. Rada Mihalcea

Professor at the University of Michigan

Rada Mihalcea Rada Mihalcea is a Professor of Computer Science and Engineering at the University of Michigan and the Director of the Michigan Artificial Intelligence Lab. Her research interests are in computational linguistics, with a focus on lexical semantics, multilingual natural language processing, and computational social sciences. She serves or has served on the editorial boards of the Journals of Computational Linguistics, Language Resources and Evaluations, Natural Language Engineering, Journal of Artificial Intelligence Research, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics. She was a program co-chair for EMNLP 2009 and ACL 2011, and a general chair for NAACL 2015 and *SEM 2019. She is an ACM Fellow, a AAAI Fellow, and currently serves as the President of the ACL. She is the recipient of a Sarah Goddard Power award (2019) for her contributions to diversity in science, and the recipient of a Presidential Early Career Award for Scientists and Engineers awarded by President Obama (2009).

Talk title: "The Other Side(s) of Word Embeddings"

Word embeddings have largely been a "success story" in our field. They have enabled progress in numerous language processing applications, and have facilitated the application of large-scale language analyses in other domains, such as social sciences and humanities. While less talked about, word embeddings also have many shortcomings -- instability, lack of transparency, biases, and more. In this talk, I will review the "ups" and "downs" of word embeddings, discuss tradeoffs, and chart potential future research directions to address some of the downsides of these word representations.

Dr. Isabelle Augenstein

Professor at the University of Copenhagen

Isabelle Augenstein Isabelle Augenstein is an associate professor at the University of Copenhagen, where she heads the Copenhagen Natural Language Understanding research group as well as the Natural Language Processing section. Her main research interests are fact checking, low-resource learning and explainability. Prior to this, she was a postdoctoral researcher at University College London and a PhD student at the University of Sheffield. She is the president of the ACL Special Interest Group on Representation Learning (SIGREP) and maintains the BIG Directory of members of underrepresented groups and supporters in Natural Language Processing.

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Dr. Sujith Ravi

Director at Amazon Alexa AI

Sujith Ravi Dr. Sujith Ravi is a Director at Amazon Alexa AI where he is leading efforts to build the future of multimodal conversational AI experiences at scale. Prior to that, he was leading and managing multiple ML and NLP teams and efforts in Google AI. He founded and headed Google’s large-scale graph-based semi-supervised learning platform, deep learning platform for structured and unstructured data as well as on-device machine learning efforts for products used by billions of people in Search, Ads, Assistant, Gmail, Photos, Android, Cloud and YouTube. These technologies power conversational AI (e.g., Smart Reply), Web and Image Search; On-Device predictions in Android and Assistant; and ML platforms like Neural Structured Learning in TensorFlow, Learn2Compress as Google Cloud service, TensorFlow Lite for edge devices.
Dr. Ravi has authored over 100 scientific publications and patents in top-tier machine learning and natural language processing conferences. His work has been featured in press: Wired, Forbes, Forrester, New York Times, TechCrunch, VentureBeat, Engadget, New Scientist, among others, and also won the SIGDIAL Best Paper Award in 2019 and ACM SIGKDD Best Research Paper Award in 2014. For multiple years, he was a mentor for Google Launchpad startups. Dr. Ravi was the Co-Chair (AI and deep learning) for the 2019 National Academy of Engineering (NAE) Frontiers of Engineering symposium. He was also the Co-Chair for ACL 2021, EMNLP 2020, ICML 2019, NAACL 2019, and NeurIPS 2018 ML workshops and regularly serves as Senior/Area Chair and PC of top-tier machine learning and natural language processing conferences like NeurIPS, ICML, ACL, NAACL, AAAI, EMNLP, COLING, KDD, and WSDM.

Talk title: "Powering Deep Learning with Structure"

Dr. Heng Ji

Professor at the University of Illinois at Urbana-Champaign

Heng Ji Heng Ji is a professor at Computer Science Department, and an affiliated faculty member at Electrical and Computer Engineering Department of University of Illinois at Urbana-Champaign. She is an Amazon Scholar. She received her B.A. and M. A. in Computational Linguistics from Tsinghua University, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Multimedia Multilingual Information Extraction, Knowledge Base Population and Knowledge-driven Generation. She was selected as "Young Scientist" and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017. The awards she received include "AI's 10 to Watch" Award by IEEE Intelligent Systems in 2013, NSF CAREER award in 2009, Google Research Award in 2009 and 2014, IBM Watson Faculty Award in 2012 and 2014, Bosch Research Award in 2014-2018, and ACL2020 Best Demo Paper award. She was invited by the Secretary of the U.S. Air Force and AFRL to join Air Force Data Analytics Expert Panel to inform the Air Force Strategy 2030. She is the lead of many multi-institution projects and tasks, including the U.S. ARL projects on information fusion and knowledge networks construction, DARPA DEFT Tinker Bell team and DARPA KAIROS RESIN team. She has coordinated the NIST TAC Knowledge Base Population task since 2010. She has served as the Program Committee Co-Chair of many conferences including NAACL-HLT2018. She is elected as the North American Chapter of the Association for Computational Linguistics (NAACL) secretary 2020-2021. Her research has been widely supported by the U.S. government agencies (DARPA, ARL, IARPA, NSF, AFRL, DHS) and industry (Amazon, Google, Bosch, IBM, Disney).

Talk title: "How to Write a History Book?"

Understanding events and communicating about events are fundamental human activities. However, it's much more difficult to remember event-related information compared to entity-related information. For example, most people in Mexico will be able to answer the question "Which city is Universidad Nacional Autónoma de México is located in?", but very few people can give a complete answer to "Who died from COVID-19?". Human-written history books are often incomplete and highly biased because "History is written by the victors". In this talk I will present a new research direction on event-centric knowledge base construction from multimedia multilingual sources, and then perform consistency checking and reasoning to detect and correct misinformation. Our minds represent events at various levels of granularity and abstraction, which allows us to quickly access and reason about old and new scenarios. Progress in natural language understanding and computer vision has helped automate some parts of event understanding but the current, first-generation, automated event understanding is overly simplistic since it is local, sequential and flat. Real events are hierarchical and probabilistic. Understanding them requires knowledge in the form of a repository of abstracted event schemas (complex event templates), understanding the progress of time, using background knowledge, and performing global inference. Our approach to second-generation event understanding builds on an incidental supervision approach to inducing an event schema repository that is probabilistic, hierarchically organized and semantically coherent. This facilitates inducing higher-level event representations analysts can interact with, and allow them to guide further reasoning and extract events by constructing a novel structured cross-media cross-lingual common semantic space. To understand the many facets of such complex, dynamic situations, we have developed various novel methods to induce hierarchical narrative graph schemas and apply them to enhance end-to-end joint neural Information Extraction, event coreference resolution, event time prediction, and misinformation detection.

Dr. Ted Pedersen

Professor at the University of Minnesota, Duluth

Ted_Pedersen Ted Pedersen is a Professor in the Department of Computer Science at the University of Minnesota, Duluth. His research interests are in Natural Language Processing and most recently are focused on computational humor and identifying hate speech, with a particular focus on Islamophobia. His research has previously been supported by the National Institutes of Health (NIH) and a National Science Foundation (NSF) CAREER award. More details are available at http://www.d.umn.edu/~tpederse ..

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Dr. Pablo Rivas

Assistant Professor of Computer Science, Baylor University

Pablo Rivas My research interests revolve around the problem of computational efficiency and model accuracy on deep and wide machine learning algorithms including the ethical and societal implications of its applications. This involves basic research in the core tenets of machine learning, and large-scale data mining with applications in big data analytics, large-scale multidimensional multispectral signal analysis, natural language processing, computer vision, and health-care imaging. I have worked on support vector machines for regression on massively large datasets and proposed efficient and computationally tractable training algorithms, which provides a focus for my research on large-scale machine learning applications. My interest in deep learning has led me to investigate its accuracy in the detection of leukocoria, which is a symptom of retinal cancer, and producing a software product freely available to the world thanks to our multidisciplinary, collaborative, research team. My research has also explored many problems in the general areas of applied mathematics, numerical optimization, swarm intelligence optimization, evolutionary algorithms, soft computing, fuzzy logic, and neurofuzzy systems.


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Dr. Leticia C. Cagnina

Professor at the Universidad Nacional de San Luis, Argentina

Leticia C. Cagnina Doctora en Ciencias de la Computación en la Universidad Nacional de San Luis (UNSL-Argentina), Magister y Licenciada en Ciencias de la Computación en la misma universidad. Investigadora Adjunta en el Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET-Argentina). Profesora en la UNSL en carreras de grado y posgrado. Actualmente co-dirige el proyecto de investigación denominado "Aprendizaje automático y toma de decisiones en sistemas inteligentes para la Web" (LIDIC-UNSL) y participa en la dirección de trabajos finales de carreras de grado y posgrado. Ha realizado estancias de investigación en el CINVESTAV-Universidad Politécnica Nacional (México), Universidad del Egeo (Grecia) y en la Universidad Politécnica de Valencia (España). Posee más de 40 publicaciones a la fecha, entre las que figuran artículos en revistas con factor de impacto, capítulos en libros y trabajos en congresos. Dentro de los intereses actuales de investigación se encuentra el procesamiento automático del lenguaje natural, el estudio de representaciones para textos, perfilado de autor, detección automática de riesgos en la web (depresión, anorexia, ciberacoso, etc.), spam de opiniones y métricas de calidad para Wikipedia.

Tutorial: “Redes Neuronales: conceptos básicos y aplicaciones”

En este tutorial se pretende dar una visión introductoria al paradigma de las Redes Neuronales Artificiales (RNA). Se describirá brevemente el funcionamiento de las RNA y se mostrará cómo a través de la combinación de unidades simples de procesamiento (neuronas) interconectadas operando de forma paralela, se consigue resolver problemas complejos. Mediante la utilización de las notebooks de Colab, se desarrollarán aplicaciones que implementen RNA para el reconocimiento de formas o patrones, predicción y clasificación, utilizando el lenguaje Python. Como objetivo final se pretende proporcionar al participante, las bases que le permitan discernir cuándo y cómo poder aplicar este modelo computacional, entendiendo la “magia” que hay detrás de las RNA.

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