Past Events

Abstract 

For the last two decades professors, non-profits, philanthropists, and national agencies have been working to broaden participation in computing (BPC) in higher-ed.  Progress has been made, but often it is incremental and takes place in small pockets. At the same time, booming enrollments, college budget models, and other institutional factors frequently stand in the way of implementing systemic changes and work at cross purposes to BPC efforts. 

Launched in 2019 with funding from Pivotal Ventures LLC, a company created by Melinda French Gates, the Center for Inclusive Computing (CIC), housed at Northeastern University, is working in partnership with colleges and universities across the country to increase the representation of women – of all races and ethnicities – in computing. A key focus is to identify and remove institutional barriers and create new pathways to study at both the undergraduate and graduate levels.

About the Speaker

Catherine Gill is the executive director for Northeastern’s Center for Inclusive Computing (CIC), where she oversees strategy, programs, partnerships and fundraising. The CIC’s mission is to materially increase the representation of women of all races and ethnicities earning degrees in computing. Prior to this role, Catherine held the positions of associate dean for strategic partnerships and managing director of the Align Program. Align, a Master’s in computer science specifically designed for individuals who did not study computing as undergraduates, is offered at seven Northeastern campuses and has an enrollment of close to 1,500 students. Catherine is also a lecturer at Northeastern’s D’Amore McKim School of Business where she teaches on social innovation and entrepreneurship. 

Before Northeastern, Catherine was executive vice president at Root Capital, a social enterprise that provides financing to agricultural businesses in Latin America and sub-Saharan Africa, and where she helped launch the Women in Agriculture Initiative.

Catherine holds an MBA from the Instituto de Estudios Superiores de la Empresa (IESE) in Barcelona, Spain, and a B.A. in ancient Greek from Wellesley College. She is the board chair of the Criterion Institute, a think tank that uses finance as a tool for social change.

Abstract 

Despite the widespread adoption of gender equality initiatives such as Athena SWAN in STEM academic fields, progress towards gender equality has remained slow. Negative or indifferent attitudes towards such initiatives may be a significant contributing factor. To improve the diversity climate within STEM academic departments, we need to better understand how to frame gender equality initiatives to evoke positive responses and higher engagement among STEM academics. Funded by an EPSRC Inclusion Matters grant, this interdisciplinary project based at Queen’s University Belfast aimed to examine the factors that improve attitudes towards gender equality initiatives. This presentation will provide an overview of the research project and discuss results from our first three experiments which examined factors such as content and framing of gender equality initiatives and their effects on STEM academics’ attitudes towards such initiatives. These studies involved a multiple measurement approaches including self-report, implicit and psychophysiological measures. Practical implications in academic fields are also discussed based on these empirical findings.

About the Speaker

Dr Lynn Farrell is an Assistant Professor in Psychology at National College of Ireland. She graduated with a BA in Psychology from Maynooth University where she was introduced to Relational Frame Theory (RFT) and discovered new ways to explore social psychological phenomena such as stereotypes and bias. Dr Farrell went on to complete her PhD as an Irish Research Council postgraduate scholar at University College Dublin where she explored the nature and malleability of implicit bias towards women in STEM through the lens of RFT as part of the UCD Contextual Behavioural Science lab. After completing her doctoral research, Dr Farrell took up a Research Fellow position at Queen’s University Belfast where she continued to empirically explore how to improve gender equality efforts in STEM as part of the EPSRC funded Inclusion Matters project. Her research interests and publications to date have focused mainly on understanding and influencing implicit and explicit stereotypes and bias particularly related to gender and improving attitudes towards gender equality initiatives.

Abstract 

The talk is based on training Tim delivers to staff and students across the University and looks at the hidden rules of cultural behaviour and how an appreciation of 'deep culture' can help to improve the effectiveness of our communication across cultures.

About the Speaker

Dr Tim Cooper first came to the University in 1990 as a postdoctoral researcher and teacher in the departments of History and Archaeology. For the past fifteen years he has worked in the Student Support Service, most recently as in International Student Support and is currently a manager in Student Experience, Diversity and Inclusion where he leads on the Global Campus programme aimed at promoting student integration.

Abstract 

The BCS Lovelace Colloquium is happening this year in Sheffield! SWiCS are collaborating with Women@DCS to run some abstract writing workshops for those who want to enter the event (details about it can be found on the attached graphic or at this website). Our previous workshop slides are here and in our next workshop, we will be going through some example abstracts and teaching you how to write your own abstract, along with opportunities to speak to academics and staff for advice. This will be on the 25th January and sign-up can be done here. We hope to see you there!

About the Speaker

Sheffield Women in Computer Science society aims to empower and support to all women within the industry. We are open to all genders, age and departments of studies. Whether you are a woman who wants to know how to break the glass ceiling, or a man who values different perspectives, or a student who isn't studying Computer Science but is interested in it, you are all welcome! We host talks from guest speakers, networking events, volunteering opportunities, as well as socials and fun nights out for our members. Check out our linktree here for more information!

Abstract 

With little time in the computer science curriculum for expanding on the already-packed content, and recruitment of computer science specialist teachers failing to meet targets year after year, is it time to look at how the rest of the secondary school curriculum could be used to strengthen computational thinking? This talk will look at the problems with the current system and propose ways in which underrepresented groups could be targeted with an integrated curriculum.

About the Speaker

Tanya Gleadow is a computing education PhD researcher at Loughborough University, and a lecturer in interaction design at Sheffield Hallam University. Before delving into academia she worked in the tech industry developing low cost hardware and delivering outreach activities to all ages. She has developed and delivered sessions for BBC Digital Academy, Microbit:Live, and Deershed Festival and has written for Make and Hackspace magazines. Her research focuses on the use of externally-produced teaching materials in schools and how teachers can be better supported by the tech industry.

Abstract 

Large-scale language models based on transformer architectures, such as GPT-3 or BERT, have advanced the state of the art in Natural Language Understanding and Generation. However, even though these models have shown impressive performance for a variety of tasks, they often struggle to model implicit and/or non-compositional meaning, such as figurative language and argumentative text. In this talk, I will present some of our recent work on text generation models for figurative language and argumentation. There are two main challenges we have to address to make progress in this space: 1) the need to model common sense and/or connotative knowledge required for these tasks; and 2) the lack of large training datasets. I will discuss our proposed theoretically-grounded knowledge-enhanced text generation models for figurative language such as metaphor and simile, as well as for enthymeme reconstruction and if time permits argument reframing. I will conclude by discussing opportunities and remaining challenges for incorporating knowledge in neural text generation systems.

About the Speaker

Smaranda Muresan is a Research Scientist at the Data Science Institute at Columbia University and an Amazon Scholar. Before joining Columbia, she was a faculty member in the School of Communication and Information at Rutgers University where she co-founded the Laboratory for the Study of Applied Language Technologies and Society. At Rutgers, she was the recipient of the Distinguished Achievements in Research Award. Her research interests are in computational semantics and discourse, particularly figurative language understanding and generation, argument mining and generation, and fact-checking. Most recently, she has been interested in applying NLP to education and public health, as well as in building NLP technologies for low resource languages. She received best papers awards at SIGDIAL 2017 and ACL 2018 (short paper). She is currently serving as a Program Co-Chair for ACL 2022.

Abstract 

Text generation is an active area of Natural Language Processing (NLP) research, covering tasks such as dialogue generation, machine translation (MT), summarisation, and story generation, etc. Despite the progress in the current NLP methods (for example, such powerful language generation models as GPT-3), this task remains a challenge when the validity of outputs is crucial.  This talk covers my work on the generation of synthetic medical text to address the data availability bottleneck for Biomedical NLP. I will also talk about my work on the exploration of supervised and unsupervised rewards for text generation with Reinforcement Learning and my work in simultaneous MT, which applies to incomplete source text and where the optimal integration of visual information is crucial to generate adequate outputs.

About the Speaker

Julia Ive is a Lecturer in Natural Language Processing at Queen Mary University of London, UK. She is the author of many mono- and multimodal text generation approaches in Machine Translation and Summarisation. Currently, she is working on the theoretical aspects of style preservation and privacy-safety in artificial text generation.

Abstract 

IN CS Health has become a prominent domain of inquiry. In the UK the EPSRC our main national funder is only broadening HEALTH as a theme for IT research. Fantastic. And yet you might count on one or two hands the computer scientists with a background also in physiology, human performance, medicine. In health, most CS folk work in service to (collaboration it’s sometimes called) to health professionals - typically clinicians, so folks focused on the body as site of disease. But what happens when we have enough insight into that space of how we function as complex systems ourselves to formulate our own questions based on how we see the world and the body, outside the clinic? New ideas may flourish. So how help the willing engineer and designer get up to speed with the massive complexity that is a human to be able to ask such novel questions to support great solutions to help #makeNormalBetter 4all @scale? Inbodied Interaction is one possible path. Come see if you don’t see new questions from just a little bit of inbodied-ness we’ll explore in 15minutes. 

Meanwhile: You’re all invited to our inbodied interaction online seminary too - you can catch up with any past sessions from Tuning to micro randomisation trials to zazen and the brain to discomfort design https://wellthlab.ac.uk/inbodied2s.

About the Speaker

m.c. schraefel holds the post prof. of computer science and human performance and leads the wellthlab at the University of Southampton. More about m.c here (https://www.ecs.soton.ac.uk/~mc) and more about inbodied interaction design here (https://wellthlab.ac.uk/ii-primer-s1a).

About the Speaker

Caroline Dessent is Head of Department and a Professor of Physical and Analytical Chemistry in the Department of Chemistry at York (@ChemistryatYork). Her research group (@DessentLab) works on photoactive molecules and ensembles, with applications in biological systems and human health. She was previously the Chair of the Equality and Diversity Group in Chemistry (2017-2021), where she lead all of the equality, diversity and inclusion work conducted by the department. She led the Chemistry Department’s successful 2019 Gold Athena Swan award. Caroline has been a keen advocate of supporting the careers of women in science for many years, and has also been the Chair of the Science Faculty’s Athena Swan Working Group, and a member of the University’s Athena SWAN steering group. Caroline is leading a number of projects to support the career development of Black and Minority Ethnic students and staff in Chemistry, and has established a working group to Decolonise and Diversify the Curriculum in Chemistry.

Abstract 

Recent advances in Artificial Intelligence and Natural Language Processing have resulted in considerable gains for language technology applications, from machine translation to conversational agents. However, one challenge that still defies current models is handling Multiword Expressions (MWEs), like idioms (e.g. “rock the boat” meaning to cause problems or disturb a situation), compounds nouns (e.g. “monkey business” meaning dishonest behaviour), and light verb constructions (e.g.“take off” as depart),  as they often display idiomaticity, and their accurate processing often requires knowledge that goes beyond the individual words and their literal combinations. However, as MWEs are found in all languages and domains, representing a large part of the vocabulary of speakers, their inaccurate processing may lead to loss of information (e.g. interpreting “kick the bucket” literally as “strike a container with the foot” when it is used with the idiomatic meaning of “die”). In this talk I will discuss some of the challenges for the computational treatment of MWEs, focusing on techniques for identifying their degree of idiomaticity.

About the Speaker

Aline Villavicencio is the Chair in Natural Language Processing at the Department of Computer Science, University of Sheffield (UK) and also affiliated to the Institute of Informatics, Federal University of Rio Grande do Sul (Brazil). She received her PhD from the University of Cambridge (UK) in 2001, and held postdoc positions at the University of Cambridge and University of Essex (UK). She was a Visiting Scholar at the Massachusetts Institute of Technology (USA, 2011-2012 and 2014-2015), at the École Normale Supé­rieure (France, 2014), an Erasmus-Mundus Visting Scholar at Saarland University (Germany in 2012/2013) and at the University of Bath (UK, 2006-2009).  She held a Research Fellowship from the  Brazilian National Council for Scientific and Technological Development (Brazil, 2009-2017). She is a member of the editorial board of Computational Linguistics, TACL and of JNLE, is a PC Co-Chair of ACL-2022, was PC Co-Chair of CoNLL-2019, Senior Area Chair for ACL-2020 and ACL-2019 among others and General co-chair for the 2018 International Conference on Computational Processing of Portuguese. She is also a member of the NAACL board, SIGLEX board and of the program committees of various *ACL and AI conferences, and has co-chaired  several *ACL workshops on Cognitive Aspects of Computational Language Acquisition and on Multiword Expressions. Her research interests include lexical semantics, multilinguality, multiword expressions and cognitively motivated NLP, and has co-edited special issues and books dedicated to these topics.

Abstract 

The shift to virtual conferences could have been a step towards opening the research community to everyone regardless of their ability to travel. But the reality looks different: Prohibitive conference pricing, inaccessible platforms, and the inability of organisers to accommodate for the realities of life during a global crisis uphold old barriers. I will talk about ongoing efforts by grassroot organisations like Queer in AI to improve accessibility both inside and outside of conferences, why it matters and what we as the scientific community can do to help.

About the Speaker

Sabine Weber is a PhD student at the University of Edinburgh, working on multilingual entailment detection. She is a core organiser with Queer in AI and served as Social Chair for NAACL 2021, one of the largest conferences in Natural Language Processing. You can find her on twitter as @multilingual_s or read her blog.

Abstract 

Autonomous vehicles (aerial, ground, underwater, etc) are going to invest human environment and due to their characteristics, they are natural candidates to integrate the Internet of Things (IoT). By including autonomous vehicles in IoT, it will be possible to compose the Internet of Mobile Things (IoMoT), which introduces new opportunities and challenges. IoMoT raises challenges related to scalability, group control, synchronization, power consumption, limited embedded resources, and limited action time so an important turnover. Considering the required multidisciplinary in the development of embedded systems and that all these dimensions introduce manifold security and safety issues, this talk will focus on security considering the global future IoMoT context outlining some research opportunities and challenges brought by the insertion of Unmanned Aerial Vehicles (UAVs) into IoMoT and an overview of efforts in research and innovation in Embedded Systems in Critical Embedded System Laboratory at USP. 

About the Speaker

Kalinka Regina Lucas Jaquie Castelo Branco received the M.Sc. degree and the Ph.D. degree in Computer Science from the Universidade de São Paulo (USP), in 1999 and 2004, respectively. She is currently an Associate Professor with the Department of Computer Systems, Institute of Mathematics and Computer Science (ICMC-USP). She has experience in Computer Science, with emphasis on Unmanned Aerial Vehicles, Computer Networks, Security, Embedded Systems, and distributed computing. Dr. Kalinka is also a member of the Brazilian Computing Society (SBC).

Abstract 

In medicine, imaging modalities such as computed tomography (CT) are vital for the detection of disease and monitoring of progression or treatment response. Medical images contain a wealth of information, some of which is not perceptible to the human eye. Quantitative feature extraction can generate valuable objective data useful for comparing datasets (across subject cohorts, over time etc). This talk focuses on the combination of feature extraction and machine learning for applications in lung cancer diagnosis and treatment outcome prediction.

About the Speaker

Jessica Sieren, Ph.D. is an Associate Professor of Radiology and Biomedical Engineering. As Associate Director of the Iowa Comprehensive Lung Imaging Center (I-CLIC), Director of the Developing Tumor Imaging Shared Research Resource within the Holden Comprehensive Cancer Center (HCCC) and co-Director of the Radiology Core Lab, she is an active and involved member of the biomedical imaging research community. Current areas of focus include: (1) Multivariate machine learning algorithms for efficient lung cancer diagnosis and treatment planning (2) Determination of the impact of technological advancements and sex-specific biological variability on the integrity of quantitative imaging features for lung disease assessment (3) The development and phenotyping of cancer models for advancing and validating medical imaging protocols, technologies and novel interventions.

Abstract 

Large pre-trained language models have enabled rapid progress in natural language generation (NLG). However, existing NLG systems still largely lack control over the content to be generated, and thus suffer from incoherence and unfaithfulness. In this talk, I will first introduce a neural generation framework that separately tackles the challenges of content planning and surface realization, built upon large models.  Experiment results show that the model is more effective in various tasks: constructing persuasive arguments, writing opinion articles, and generating news stories. It alleviates existing models' issue of producing bland and incorrect text, a result of lacking global planning. I then discuss how to extend the model to conduct dynamic content planning with mixed language models. Finally, I present our recent long document summarization work where efficient attentions are designed to handle more than 10k tokens while prior work can only process hundreds of words. 

About the Speaker

Lu Wang is an Assistant Professor in Computer Science and Engineering at University of Michigan, Ann Arbor since 2020. Previously, she was an Assistant Professor in Khoury College of Computer Sciences at Northeastern University from 2015 to 2020. She received her Ph.D. in Computer Science from Cornell University and her bachelor degrees in Intelligent Science and Technology and Economics from Peking University. Her research focuses on designing machine learning models for natural language processing tasks, including language generation, abstractive text summarization, argument mining, discourse analysis, and their applications in computational social science (e.g. detecting media bias and polarization). Lu received an outstanding paper award at ACL 2017 and a best paper nomination award at SIGDIAL 2012. She won the NSF CAREER award in 2021.

Abstract 

In this session, Emily is going to share her lessons learned in her day jobs in IT companies as well as night job in Open Source Community.  The topics of her talk will span from "women in IT" to "work-life balance" and from "open source fashion" to "Microsoft culture".

About the Speaker

Emily Chen is a Principal Product Manager in Microsoft - Search Technology Center Asia (STCA), working on AI vertical solution landing in China, such as Chatbot, Knowledge Graph, and Cloud Solution for Enterprise customers. She was a co-author of O’Reilly “Beautiful Testing” and filed one US Patent in 2019. She has rich open source community experience of 17 years. Her roles include the Board of Director of GNOME Foundation (2009 – 2010), the founder of GNOME.Asia Community, the organizer of GNOME.Asia Summit (2008 - 2018), the co-founder of Kaiyuanshe (China Open Source Alliance in China, founded in 2014), the core leader of China Open Source Conference (COSCon) and China Open Source Annual Report, and the OSS project mentor in OpenAtom Foundation. Read her interviews: Linux Pro Magazine, HyperAI, CSDN, Segmentfault.

Abstract 

In this talk I will briefly cover what autism is and how understanding of autism has developed over time, including the changing diagnostic process. I will cover common strengths and common areas of difficulty that autistic individuals experience. I will discuss how societal understanding of autism, including stigma and stereotypes, can impact autistic individuals and the barriers this often creates. Supporting our students to enable them to access all aspects of the curriculum within our degree programmes is important. I will consider a number of ways in which we can do this.

About the Speaker

I am a senior lecturer in the Psychology Department at the University of Sheffield. My main area of interest is visual attention in Autism Spectrum Disorders. I am currently exploring the cognitive profile of the broader autism phenotype with a view to improving understanding the diagnostic boundaries of ASD. My main collaborator at the University of Sheffield is Dr Elizabeth Milne (director of the Sheffield Autism Research Lab). I am working on a number of projects in this area using the techniques of EEG and eye-tracking. I am also interested in studying social attention in the real-world. I am working on projects relating to this with Dr Tom Foulsham (University of Essex) and Prof Alan Kingstone (University of British Columbia). My third area of interest is investigating cultural differences in the expression of autistic traits. This work is being conducted with Dr Rajani Ramachandran (India) and Dr Elizabeth Sheppard (Malaysia).

Abstract 

Our world is open-ended, non-stationary and constantly evolving; thus what we talk about and how we talk about it changes over time. This inherent dynamic nature of language comes in stark contrast to the current static language modelling paradigm, which constructs training and evaluation sets from overlapping time periods. In this talk, I will describe our set of experiments and results on taking current state-of-the art models and placing them in the realistic scenario of predicting future utterances from beyond the models' training period. Finally, I will discuss ideas around adaptive language models that can remain up-to-date with respect to our ever-changing and non-stationary world.

About the Speaker

Angeliki Lazaridou is a staff research scientist at DeepMind. She obtained her PhD from the University of Trento, where she worked on predictive grounded language learning. Currently, she is working on interactive methods for language learning that rely on multi-agent communication as a means of alleviating the use of supervised language data.

Abstract 

The talk explains concepts for processing unstructured information in natural language with the latest AI technology. The area of argument mining, i.e., the automatic extraction and classification of natural language arguments from large document sets, will be discussed in greater depth as one particular example.

About the Speaker

Iryna Gurevych (PhD 2003, U. Duisburg-Essen, Germany) is a professor of Computer Science and director of the Ubiquitous Knowledge Processing (UKP) Lab at the Technical University (TU) of Darmstadt in Germany. She joined TU Darmstadt in 2005 (tenured as full professor in 2009). Her main research interest is machine learning for large-scale language understanding, including text analysis for social sciences and humanities. She is one of the co-founders of the field of computational argumentation with many applications, such as the identification of fake news and decision-making support. Iryna’s work received numerous awards, e.g. a highly competitive Lichtenberg-Professorship Award from the Volkswagen Foundation and a DFG Emmy-Noether Young Researcher’s Excellence Career Award. Iryna is currently the President of SIGDAT, one of the most important scientific bodies in the ACL community. She was program co-chair of ACL’s most important conference in 2018, the Annual Meeting of the Association for Computational Linguistics, and she served as General Chair of *SEM 2020, the 9th Joint Conference on Lexical and Computational Semantics. Recently, she has been elected as the ACL VP-elect (2021) and selected as one of the ACL 2020 Fellows.

Abstract 

Language is not only about the words, it is also about the people. While much of the work in computational linguistics has focused almost exclusively on words (and their relations), recent research in the emerging field of computational sociolinguistics has shown that we can effectively leverage the close interplay between language and people. In this talk, I will explore this interaction, and show (1) that we can develop cross-cultural language models to identify words that are used in significantly different ways by speakers from different cultures; and (2) that we can effectively use information about the people behind the words to build better natural language understanding.

About the Speaker

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 currently serves as ACL Vice-President. She is the recipient of a Presidential Early Career Award for Scientists and Engineers awarded by President Obama (2009) and an ACM Fellow (2019). In 2013, she was made an honorary citizen of her hometown of Cluj-Napoca, Romania.

Abstract 

Neurodiversity: What is it? What are the benefits and advantages of being neurodiverse? What are some of the challenges? 

About the Speaker

Siena Castellon is a nationally recognised neurodiversity advocate, who is autistic, dyslexic, dyspraxic and has ADHD. She is the founder of Neurodiversity Celebration Week, an international campaign that encourages schools to flip the narrative from focusing on the challenges and drawbacks of being neurodivergent to focusing on their strengths and talents. In 2020, over 500,000 students from over 760 schools took part in Neurodiversity Celebration Week from around the world. Siena is also the bestselling author of “The Spectrum Girl’s Survival Guide: How To Grow Up Awesome and Autistic.” She was recently selected by the United Nations to be a Young Leader for the Sustainable Development Goals. She is using the global UN platform to raise awareness of neurodiversity and recognizing, supporting and harnessing the overlooked strengths and talents of people who think differently and perceive the world differently.

Abstract 

Social media data poses several interesting challenges to information extraction technology. In my group, we have been working on studying how and why we observe lower performance of sequence labelling methods on social media data, compared to performance of the same models on more edited text, such as newswire data. These studies have informed our design choices for models that are more robust to naturalistic data, even data that includes language switching. My goal is to contribute to increasing the coverage of language abilities by NLP technology.

During this talk, I'll briefly discuss the different proposals we have developed that include enhanced versions of ELMo embeddings, and a more flexible subword tokenization approach than what is available in the commonly used byte-pair encoding of language models. I’ll conclude with a discussion of possible research lines for the near future. 

About the Speaker

Thamar Solorio is an Associate Professor of the Department of Computer Science at the University of Houston (UH). She holds graduate degrees in Computer Science from the Instituto Nacional de Astrofísica, Óptica y Electrónica, in Puebla, Mexico. Her research interests include information extraction from social media data, enabling technology for code-switched data, stylistic modeling of text and more recently multimodal approaches to online content understanding. She is the director and founder of the Research in Text Understanding and Language Analysis Lab at UH. She is the recipient of an NSF CAREER award for her work on authorship attribution, and recipient of the 2014 Emerging Leader ABIE Award in Honor of Denice Denton. She is an elected board member of the North American Chapter of the Association of Computational Linguistics (2020-2021). Her research is currently funded by the National Science Foundation and ADOBE, and in the past she has received support from the Office of Naval Research and the Defense Advanced Research Projects Agency (DARPA). 

Abstract 

Robots are increasingly changing how we work and play, but the way software is developed to control these machines is costly, because it involves so much trial and error.  The RoboStar group, involving researchers in York, Sheffield, and several other institutions around the world, is designing a model-based approach for validation, verification, and automatic generation of code and tests. The mathematical underpinning of the techniques provides assurance of soundness of all artefacts. The goal is to enable development of control software for robotics of better quality at a lower cost. In this talk, we will give an overview of our motivation, approach, and current and future work. 

About the Speaker

Ana Cavalcanti is Professor of Software Verification and Royal Academy of Engineering Chair in Emerging Technologies working on Software Engineering for Robotics: modelling, validation, simulation, and testing. She currently leads the RoboStar research group at the University of York. She held a Royal Society-Wolfson Research Merit Award and a Royal Society Industry Fellowship to work with QinetiQ in avionics. She has chaired the Programme Committee of various well-established international conferences, is on the editorial board of four international journals, and is Chair of the board of the Formal Methods Europe Association. She is, and has been, PI on several large research grants. Her current research is on theory and practice of verification and testing for robotics.

Abstract 

Over the years, I have had considerable experience with corpora annotated for discourse phenomena. This experience, and the experience of others who have been involved with annotating NLP corpora, suggests that we should stop thinking about manual annotation as a "gold standard" for training and testing ML methods, but rather as an object whose generalization via ML, might reveal discrepancies and omissions in annotation that might otherwise have gone unnoticed. This in turn might lead to more consistent annotation that leads to better ML performance. 

About the Speaker

Bonnie Webber received her PhD from Harvard University and taught at the University of Pennsylvania in Philadelphia for 20 years before joining the School of Informatics at the University of Edinburgh, where she is now professor emeritus. 

Known for early research on "cooperative question-answering" and extended research on discourse anaphora and discourse relations, she has served as President of the Association for Computational Linguistics (ACL) and Deputy Chair of the European COST action IS1312, "TextLink: Structuring Discourse in Multilingual Europe". Along with Aravind Joshi, Rashmi Prasad, Alan Lee and Eleni Miltsakaki, she is co-developer of the Penn Discourse TreeBank, including the recently released PDTB-3.

She is a Fellow of the Association for Advancement of Artificial Intelligence (AAAI), the Association for Computational Linguistics (ACL) and the Royal Society of Edinburgh (RSE). In July, she was awarded the ACL Life Time Achievement award for 2020. In both the RSE and the ACL, she continues to work towards ensuring that women are recognized for their achievements in the NLP community and in Science and Technology more generally.

Abstract 

Many people shy away from networking because they don’t like awkward small talk or they don't see value from it. Too often It’s viewed as mostly transactional and a waste of time, when in fact, if it’s done right, it’s the opposite. It’s building long-term, mutually beneficial relationships. Also, networking doesn't have to be all about taking. When combined with giving back and being of service it unlocks huge opportunities and deepens the relationships. In this session, Yousra, through her experience, will highlight how to create real relationships, in a fun and authentic way, that pays off for your career. 

About the Speaker

Yousra Magouri (Linkedin|Medium) is an Associate Principal, Technology Architect at Salesforce. She is a lifelong learner and leading voice for women empowerment and mentoring in the technology industry.

Yousra’s passion for giving back, connecting with women in technology, and belief that we all need to lift each other up has led her to serve as a mentor for emerging female professionals through several organizations.

Apart from working at Salesforce, Yousra directs the Open Minds Project — a nonprofit that gives access to best-selling books, e-learning resources, mentorship, and career assistance to young adults in Tunisia.

Abstract 

Over the past decade - driven by deep learning, increased computational power, and increasingly larger datasets - the field of machine learning has boomed. Along with breakthroughs from academic research, there have also been large scale commercial success stories. In the voice technology field, products like Siri and Alexa have proven popular with users and have brought machine learning further into the mainstream. In this talk, I’ll discuss some of the ways that voice and language technology is used commercially, some of the challenges that companies face, the problems that scientists working in industry tackle, and some of the directions the technology is moving in.

About the Speaker

Catherine is a machine learning scientist and consultant based in Cambridge UK. Since completing her PhD at the University of Cambridge in 2008, she has commercial and academic experience of automatic speech recognition, natural language understanding and human-computer dialogue systems, at Cambridge University, Toshiba Research, Amazon Alexa, and now currently Cobalt Speech. Her interest is centred around the application of research to real-world problems involving speech and language at scale.

Abstract 

Disparities in authorship and citations across gender can have substantial adverse consequences not just on the disadvantaged genders, but also on the field of study as a whole. Measuring gender gaps is a crucial step towards addressing them. In this work, we examine female first author percentages and the citations to their papers in Natural Language Processing (1965 to 2019). We determine aggregate-level statistics using existing manually curated author--gender lists as well as first names strongly associated with a gender. We find that only about 29% of first authors are female and only about 25% of last authors are female. Notably, this percentage has not improved since the mid 2000s. We also show that, on average, female first authors are cited less than male first authors, even when controlling for experience and area of research. Finally, we discuss the ethical consideration involved in automatic demographic analysis. 

About the Speaker

Dr. Saif M. Mohammad is Senior Research Scientist at the National Research Council Canada (NRC). He received his Ph.D. in Computer Science from the University of Toronto. Before joining NRC, he was a Research Associate at the Institute of Advanced Computer Studies at the University of Maryland, College Park. His research interests are in Computational Linguistics and Natural Language Processing (NLP), especially Lexical Semantics, Emotions in Language, Sentiment Analysis, Computational Creativity, Fairness in NLP, Psycholinguistics, and Information Visualization. He has served in various capacities at prominent journals and conferences, including: action editor for Computational Linguistics, chair of the Canada--UK symposium on Ethics in AI, co-chair of SemEval 2017-19 (the largest platform for semantic evaluations), workshops co-chair for ACL 2020, co-organizer of WASSA 2017 and 2018 (a sentiment analysis workshop), and area chair for ACL, NAACL, and EMNLP (for sentiment analysis, lexical semantics, and fairness in NLP). His word--emotion resources, such as the NRC Emotion Lexicon, are widely used for analyzing affect in text. His work has garnered media attention, including articles in Time, SlashDot, LiveScience, io9, The Physics arXiv Blog, PC World, and Popular Science. 

Abstract 

This presentation will introduce and discuss manned space exploration, which is conducted in very unique environments, such as on space-based platforms in low-Earth orbit and onboard spacecraft, and will in the future include habitats located on the surface of the Moon and Mars. The characteristics of such settings, considering structures required to support human life, together with the difficulties and costs involved in travelling from Earth to far-off locations, makes it impossible for medical teams to personally assist astronauts. The space environment also generates a series of medical conditions, as astronauts adapt to hypogravity or microgravity, have to deal with psycho-emotional issues, and are continuously exposed to radiation. Telemedicine and eHealth, which involve the use of telecommunications and technological tools for the acquisition, storage and transmission of health data, and allowing virtual meetings between professionals located in different regions, countries, or beyond, have been used to resolve this problem, providing a solution for overcoming the physical distance between medical doctor and patient.

About the Speaker

Prof Thais Russomano, MD, MSc, PhD is an academic in the areas of Space Physiology, Aviation Medicine, Telemedicine & Digital Health in Universities in Europe and Brazil, as well as the Co-Founder & CEO, InnovaSpace UK. She graduated in medicine from the Federal University of Pelotas, Brazil (1985) and specialised in internal and emergency medicine. She also has a Master’s Degree in Aerospace Medicine - Wright State University, USA (1991), and a PhD in Space Physiology - King's College London (1998). She worked for 3 years as a Space Scientist at the Institute of Aerospace Medicine, German Aerospace Centre (DLR) in Cologne, Germany, before founding the Microgravity Centre, PUCRS, a unique international reference centre in the study of human space physiology and space biomedical engineering, which she coordinated for 18 years. Thais has been linked to King's College London in different academic positions since her PhD in 1994, having acted at CHAPS, Faculty of Life Sciences and Medicine, King’s College London, as the Deputy Course Director/ Senior Lecturer of the Space Physiology & Health MSc degree course (2009-2020). In addition, she is a visiting professor at Aalto University, Finland, in Space & Design, and Deggendorf Institute of Technology, European Campus, Germany, contributing to the MSc in Medical Informatics, UFCSPA in Porto Alegre, Brazil, in the areas of human phys in extreme environments & digital health, and the Faculty of Medicine at the Univ of Lisbon in space phys & aviation medicine. She is also a Director of two space-related private companies, InnovaSpace Ltd (UK) and the International Space Medicine Consortium Inc. (USA). Thais has more than 30 years of experience teaching and researching in the fields of Aerospace Medicine, Space Physiology, Aerospace Biomedicine, Aerospace Biomedical Engineering and TeleHealth, including participation in 200+ scientific events with 300+ scientific papers presented. She has also authored books, book chapters and numerous papers in her areas of expertise. Thais is an Elected Academician of the International Academy of Aviation and Space Medicine (IAASM), and the International Academy of Astronautics (IAA), for which she serves on the Board of Trustees. She is also a member of international associations and societies in space, aviation and telehealth (ISfTeH), a Board Member of companies, and further holds patents related to Space Life Sciences and Aerospace Biomedical Engineering. Thais is currently acting as a voluntary Mentor for Space4Women, an initiative of the United Nations Office for Outer Space Affairs (UNOOSA).  

Abstract 

To those outside the field, search seems like a solved problem. You type a few keywords into Google and you usually get exactly what you want in the top results, no scrolling required. However, when searching over private document collections such as your own files, over non-textual data like images or tables, or in languages other than English, the quality of search results degrades rapidly. In addition, we expect increasingly more from search: we want answers to our questions and recommendations of what to view next. In this talk I discuss why search is hard (and correspondingly fascinating) and how these challenges are being addressed in the industry. 

About the Speaker

Tracy trained in linguistics at Stanford and worked as a researcher and research manager in PARC's Natural Language Theory and Technology group.   She then moved into applied research in Microsoft's Bing search team.  She was a product manager and an engineering manager in the Search Science applied research team at eBay, focusing on eCommerce search across the eBay sites.  At A9 and Amazon, she focused on natural language processing for product search query understanding and then on utilization for sponsored products. Currently she is a principal scientist at Adobe working on search and natural language processing.

Abstract 

Women are underrepresented in exact science,  technology, engineering and mathematics (STEM).  The percentage of women in all fields decreases as  as one advances in the career ladder, a phenomena also known as scissors effect.  In this talk we will  present evidences of this problem and how it impacts negatively the scientific  knowledge.  Some of the obstacles for the participation of women in STEM fields will be identified by scientific studies and potential solutions will be indicated.

About the Speaker

Márcia Barbosa is a Brazilian physicist known for her research on the properties of water, and for her efforts for improving the conditions for women in academia. Graduated (1981), master's (1984) and doctorate (1988) in physics from the Universidade Federal do Rio Grande do Sul. She is member of the Brazilian Academy of Sciences, the World Academy of Sciences (TWAS) and serves as a full professor from the Universidade Federal do Rio Grande do Sul. She has experience in Physics, with an emphasis on Condensed Matter Physics, working mainly in water. 

• For the study of water anomalies, she was awarded the Loreal-Unesco Award for Women in Physical Sciences in 2013.

• In parallel, she works on gender issues for which she won the American Physical Society's Nicholson Medal in 2009.

• For her post-graduate work, she won the Anisio Teixeira da Capes Award in 2016.

• For her work in favour of science, in 2018 she received the Medal of Scientific Merit as a commendator from the presidency of the republic.

• In 2020 she was recognised by UN-Women as one the seven women scientists who have shaped our world.

Abstract 

Human knowledge and use of language is inextricably connected to perception and action. A long-standing, ongoing research challenge has been defining the right datasets and tasks for learning representations of language, relating them to other modalities--especially visual inputs--and taking appropriate actions based on utterances. The recent availability of large multimodal datasets and high-fidelity simulations of the real world is enabling new research on learning grounded representations for complex utterances across the world's languages. In this context, I'll discuss my team's work on multimodal representation learning and vision-and-language navigation, how they relate to one another, and why language grounding matters. 

About the Speaker

Jason is a research scientist at Google, where he works on natural language understanding. He was previously an Associate Professor of Computational Linguistics at the University of Texas at Austin. His main research interests include applied natural language processing, semantics, and language grounding.  Jason received his Ph.D. from the University of Edinburgh in 2002, where his doctoral dissertation on Multimodal Combinatory Categorial Grammar was awarded the 2003 Beth Dissertation Prize from the European Association for Logic, Language and Information.

Abstract 

One hears much about the incredible results of recent neural nets methods in NLP. In particular much has been made of the results on the Natural Language Inference task using the huge new corpora SNLI, MultiNLI, SciTail, etc, constructed since 2015. Wanting to join in the fun, we decided to check the results on the corpus SICK (Sentences Involving Compositional Knowledge), which is two orders of magnitude smaller than SLNI and presumably easier to deal with. 

We discovered that there were many results that did not agree with our intuitions. As a result, we have written so far five papers on the subject (with another one submitted to COLING2020). I want to show you a potted summary of this work, to explain why we think this work is not near completion yet and how we're planning to tackle it. This is work with Katerina Kalouli, Livy Real, Annebeth Buis and Martha Palmer. The papers are: 

• Explaining Simple Natural Language Inference. Proceedings of the 13th Linguistic Annotation Workshop (LAW 2019), 01 August 2019. ACL 2019,

• WordNet for “Easy” Textual Inferences. Proceedings of the Globalex Workshop, associated with LREC 2018

• Graph Knowledge Representations for SICK. informal Proc of 5th Workshop on Natural Language and Computer Science, Oxford, UK, 08 July 2018

• Textual Inference: getting logic from humans. Proc of the 12th International Conference on Computational Semantics (IWCS), 22 September 2017

• Correcting Contradictions. Proc of  Computing Natural Language Inference Workshop (CONLI 2017) @IWCS 2017

About the Speaker

Valeria de Paiva is a mathematician and AI scientist based on the Bay Area, CA, interested in NLP and Inference and all kinds of semantics. She works for a not very-stealthy start-up project, called the Topos Institute. Before that she was at Samsung Research America, Nuance, Deem, Cuil and for many years at Xerox PARC. She is very keen on making sure that women are not short-changed in their professional lives. For that she maintains a facebook group Women in Logic, a blog  Women in Logic, helps with the ACM-W Scholarship program ACM-W Scholarships and initiated and helps to organize the Workshop Women in Logic, now in its 4th year.