SEMLA 2021 - Full registration

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June 8th at 12pm Eastern Daylight Time (EDT)Human-Centered AI: What it is and how software engineering can contribute to its successSpeaker: Ben Shneiderman, Emeritus Distinguished University Professor in the Department of Computer Science at the University of MarylandAbstractA new synthesis is emerging that integrates AI technologies with HCI approaches to produce Human-Centered AI (HCAI). Advocates of this new synthesis seek to amplify, augment, and enhance human abilities, so as to empower people, build their self-efficacy, support creativity, recognize responsibility, and promote social connections. These passionate advocates of HCAI are devoted to furthering human values, rights, justice, and dignity, by building reliable, safe, and trustworthy systems.The talk offers three ideas: - HCAI framework, which shows how it is possible to have both high levels of human control AND high levels of automation- Design metaphors emphasizing powerful supertools, active appliances, tele-operated devices, and information abundant displays- Governance structures to guide software engineering teams, safety culture lessons for managers, independent oversight to build trust, and government regulation to accelerate innovationThe talk will emphasize the software engineering practices that will make machine learning more reliable, by increasing audit trails, reducing bias, and supporting explainability. These ideas are drawn from Ben Shneiderman’s forthcoming book (Oxford University Press, January 2022). Further information at: https://hcil.umd.edu/human-centered-ai Join the Human-Centered AI Google Group at: https://groups.google.com/g/human-centered-ai and follow @HumanCenteredAI on Twitter.Biography of the speakerBen Shneiderman (http://www.cs.umd.edu/~ben) is an Emeritus Distinguished University Professor in the Department of Computer Science, Founding Director (1983-2000) of the Human-Computer Interaction Laboratory (http://hcil.umd.edu), and a Member of the UM Institute for Advanced Computer Studies (UMIACS) at the University of Maryland. He is a Fellow of the AAAS, ACM, IEEE, NAI, and the Visualization Academy and a Member of the U.S. National Academy of Engineering, in recognition of his pioneering contributions to human-computer interaction and information visualization. His widely-used contributions include the clickable highlighted web-links, high-precision touchscreen keyboards for mobile devices, and tagging for photos. Shneiderman’s information visualization innovations include dynamic query sliders for Spotfire, development of treemaps for viewing hierarchical data, novel network visualizations for NodeXL, and event sequence analysis for electronic health records.Ben is the lead author of Designing the User Interface: Strategies for Effective Human-Computer Interaction (6th ed., 2016). He co-authored Readings in Information Visualization: Using Vision to Think (1999) and Analyzing Social Media Networks with NodeXL (2nd edition, 2019). His book Leonardo’s Laptop (MIT Press) won the IEEE book award for Distinguished Literary Contribution. The New ABCs of Research: Achieving Breakthrough Collaborations (Oxford, 2016) describes how research can produce higher impacts. His forthcoming book on Human-Centered AI, will be published by Oxford University Press in January 2022.________________________________________________________________________June 15th at 12pm Eastern Daylight Time (EDT)Software Innovation to Enable Broad-Based AI LiteracySpeaker: Nisha Talagala, CEO and Co-founder of Pyxeda AI and AIClubAbstractThe AI market is projected to grow to $190 Billion by 2025. AI is being used in every industry and is projected to be a core skill for the future. While lack of production machine learning (MLOps) was a limiter in the last few years, these limits are starting to be overcome, with MLOps practices now standard in many organizations. More businesses are starting to see positive returns from their AI initiatives. We are shifting to a new phase of AI development, where broad segments of the non-technical workforce are encountering AI in their job roles. This is both exciting and fraught with peril. Instances of AI failures, legal issues, and ethical issues are rising. There is pressure on AI development to accommodate not just data scientists but people from all walks of life. In this talk, we discuss recent AI and Machine Learning technology trends and the role of MLOps in driving AI commercial success. We then discuss what it takes to bring AI knowledge out of the technical domain and into the broader workforce, and technology trends like low-code that enable broad adoption. We will describe a software framework that enables AI Literacy via a number of novel approaches – including automation of data preparation, automated AI compiler/code generation, agile iteration, and optimization of the entire AI lifecycle. We will then discuss the use of this software infrastructure in AWS and GCP to implement a framework for AI Literacy – the Four Cs – and experiences of bringing AI literacy to individuals worldwide.Biography of the speakerNisha Talagala is the CEO and founder of AIClub.World. Nisha has significant experience in bringing AI Literacy to individuals from students to professionals. Previously, Nisha co-founded ParallelM which pioneered the MLOps practice of managing Machine Learning in production for enterprises – acquired by DataRobot. Nisha is a recognized leader in the operational machine learning space, having also driven the USENIX Operational ML Conference, the first industry/academic conference on production AI/ML. Nisha was previously a Fellow at SanDisk and Fellow/Lead Architect at Fusion-io, where she worked on innovation in non-volatile memory technologies and applications. Nisha has more than 20 years of expertise in enterprise software development, distributed systems, technical strategy, and product leadership. She has worked as technology lead for server flash at Intel – where she led server platform non-volatile memory technology development, storage-memory convergence, and partnerships. Prior to Intel, Nisha was the CTO of Gear6, where she designed and built clustered computing caches for high-performance I/O environments. Nisha earned her Ph.D. at UC Berkeley where she did research on clusters and distributed systems. Nisha holds 73 patents in distributed systems and software, over 25 refereed research publications, is a frequent speaker at industry and academic events, and is a contributing writer to Forbes and other publications.________________________________________________________________________June 17th at 12pm Eastern Daylight Time (EDT)Questioning the AI: Towards Human-Centered Explainable AI (XAI)Speaker: Q. Vera Liao, Research Staff Member in IBM T.J. Watson Research CenterAbstractArtificial Intelligence technologies are increasingly used to make decisions and perform autonomous tasks in critical domains such as healthcare, finance, and criminal justice. The needs to understand AI in order to improve, contest, develop appropriate trust and better interact with AI systems have spurred great academic and public interest in Explainable AI (XAI). Recently, open-source toolkits, including IBM Research&#x27;s AI Explainability 360, are making a growing collection of XAI techniques into practitioners’ toolbox. My colleagues and I at IBM Research conduct human-computer interaction (HCI) research that aims to empower AI practitioners to make effective and responsible use of such a toolbox to create good XAI user experiences. Meanwhile, our work provides insights into real-world user needs for AI explainability to inform gaps and opportunities for XAI algorithmic research. Our work follows two complementary paths. First, we conduct HCI research by designing and studying XAI systems of various use cases in the AI lifecycle. Second, we study AI design practices of product teams and engage with the design community to develop and advocate for user-centered design processes for XAI. I will conclude the talk with lessons learned for bridging the process of creating responsible AI systems and empowering people in the process.Biography of the speakerQ. Vera Liao is a Research Staff Member in IBM T.J. Watson Research Center, working in the “Trusted AI” area. Her research background is in human-computer interaction (HCI), with current focuses on human-AI interaction, explainable AI, and conversational agents. Her work received multiple awards at ACM CHI and IUI. She was awarded IBM Outstanding Research Accomplishments for contributions to IBM&#x27;s Watson Assistant and Trusted AI toolkits. She serves on the Editorial Board of International Journal of Human-Computer Studies (IJHCS) and ACM Transactions on Interactive Intelligent Systems (TiiS). She received a Ph.D. in Computer Science and a M.S. in Human Factors from University of Illinois at Urbana-Champaign, and a bachelor’s degree in Industrial Engineering from Tsinghua University.________________________________________________________________________June 22nd at 12pm Eastern Daylight Time (EDT)Continuous Delivery for Machine LearningSpeakers: Arif Wider, professor of software engineering at HTW Berlin and a principal technology consultant with ThoughtWorks Germany and Danilo Sato, Head of Data &amp; AI Services at ThoughtWorks UKAbstractMachine Learning applications are becoming popular in our industry, however the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application. Continuous Delivery for Machine Learning (CD4ML) is the discipline of bringing Continuous Delivery principles and practices to Machine Learning applications. In this talk we will share our industry experiences implementing CD4ML, introduce its technical components, and explore what future challenges need to be solved.Biography of the speakersArif Wider is a professor of software engineering at HTW Berlin and a principal technology consultant with ThoughtWorks Germany, where he served as Head of Data &amp; AI before moving back to academia. As a vital part of research, teaching, and consulting, he is passionate about distilling and distributing great ideas and concepts that emerge in the software engineering community. Arif is a frequent speaker at conferences and loves to bring together people with diverse areas of expertise such as data scientists and developers.Danilo Sato is the Head of Data &amp; AI Services at ThoughtWorks UK. His 20 years technology career combines experiences leading accounts and teams with a breadth of technical expertise in many areas of architecture and engineering: software, data, infrastructure, and machine learning. He is the author of DevOps in Practices: Reliable and Automated Software Delivery, a member of ThoughtWorks&#x27; Technology Advisory Board and Office of the CTO, and is an experienced international conference speaker.________________________________________________________________________June 24th at 12pm Eastern Daylight Time (EDT)AIOps: From Research Innovations to Industrial AdoptionsSpeaker: Yingnong Dang, Principal Data Scientist Manager in Microsoft AzureAbstractThe scale and complexity of cloud computing has been ever-increasing. This brings challenges on effectively building and managing cloud computing systems that are highly efficient and reliable, enable high customer satisfaction, and achieve high engineering productivity. In this talk, I will first share an AIOps vision of infusing AI into the cloud computing platform and DevOps process. I will then share a few AIOps efforts in Microsoft Azure to demonstrate how an AIOps solution can be built and adopted in industrial settings. Specifically, I will share how Azure uses intelligent anomaly detection and correlation for safeguarding the rollouts of hundreds of component payloads to millions of machines spreading in 60+ Azure regions across five continents (project Gandalf safe deployment).I will also share how we built a resilient mechanism for Azure against failures by employing ML-based prediction and an online learning mechanism (project Narya). I will then talk about our learnings on engineering AIOps solutions, and a few open challenges on cloud computing that need more research and innovations in the related areas including software engineering and systems.Biography of the speakerYingnong Dang is is a Principal Data Scientist Manager in Microsoft Azure. Yingnong’s focus is on building analytics and ML solutions for improving Azure Infrastructure availability and capacity, boosting engineering productivity, and increasing customer satisfaction. Yingnong and the team have a close partnership with Microsoft Research and academia. Before joining Azure in December 2013, Yingnong was a researcher in Microsoft Research Asia lab. His research areas include software analytics, data visualization, data mining, and human-compute interaction. As a researcher, he has transferred various technologies to Microsoft product teams including code clone analysis, crash dump analysis, performance trace analysis, etc. He owns 45+ U.S. patents and has published papers in top conferences including ICSE, FSE, VLDB, USENIX ATC, and NSDI.________________________________________________________________________July 1st at 12pm Eastern Daylight Time (EDT)Architecting ML-Enabled SystemsSpeaker: Grace Lewis, Principal Researcher and the lead for the Tactical and AI-Enabled Systems (TAS) Initiative at the Carnegie Mellon Software Engineering Institute (SEI)AbstractDeveloping software systems that contain machine learning (ML) components requires an end-to-end perspective that considers the unique life cycle of these components — from data acquisition to model training to model deployment and evolution. While there is an understanding that ML components in the end are software components, there are some characteristics of ML components that bring challenges to software architecture and design activities, such as data-dependent behavior, drift over time, and timely capture of ground truth to inform retraining. The goal of this talk is to highlight some of these challenges, along with proposed practices and remaining gaps for successfully architecting ML-enabled systems.Biography of the speakerGrace Lewis is a Principal Researcher and the lead for the Tactical and AI-Enabled Systems (TAS) Initiative at the Carnegie Mellon Software Engineering Institute (SEI). She is a Principal Investigator for two projects in the growing field of software engineering for machine-learning (ML) systems: “Characterizing and Detecting Mismatch in ML-Enabled Systems” and “Predicting Inference Degradation in Production ML Systems.” Her current areas of expertise and interest include software engineering for AI/ML systems, software architecture (in particular the development of software architecture practices for systems that integrate emerging technologies), edge computing, and software engineering in society. Grace holds a B.Sc. in Software Systems Engineering and a Post-Graduate Specialization in Business Administration from Icesi University in Cali, Colombia; a Master in Software Engineering from Carnegie Mellon University; and a Ph.D. in Computer Science from Vrije Universiteit Amsterdam. Grace is an IEEE Senior Member and very active in IEEE Computer Society committees and conferences. She is currently the VP for the IEEE Computer Society Technical &amp; Conference Activities (T&amp;C) Board, Member of the Board of Governors, Member of the Diversity and Inclusion (D&amp;I) Committee, Alternate Representative for IEEE-CS on the ABET CSAB Board of Directors, as well as an ABET Evaluator for Computer Science undergraduate programs.

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