Brown Bag Lunch Schedule

The HCIL has an open, semi-organized weekly "brown bag lunch (BBL)" every Thursday from 12:30-1:30pm in HCIL (2105 Hornbake, South Wing). The topics range from someone's work, current interests in the HCIL, software demos/reviews, study design, proposed research topics, introductions to new people, etc. The BBL is the one hour a week where we all come together--thus, it’s a unique time for HCIL members with unique opportunities to help build collaborations, increase awareness of each other’s activities, and generally just have a bit of fun together. There is no RSVP; simply show up!

If you would like to give or suggest a talk, presentation, workshop, etc., send an email to BBL student co-coordinators Joohee Choi (jchoi27@umd.edu) or Pavithra Ramasamy (pavithra.ramasamy94@gmail.com). In the email, briefly describe the topic and preferred dates.

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Spring 2018 Schedule

Date	Leader	Topic
01/25/2018	Kickoff to a new Semester!	Come, network, make introductions, and share what you are working on Please come to our first BBL of the Spring 2018 semester to introduce yourself and share what you're working on in the coming semester. The first BBL will be for us to network with each other and kickoff a great new semester.
02/01/2018	Bahador Saket Georgia Tech, Atlanta	Visualization by Demonstration Abstract: A commonly used interaction paradigm in most visualization tools is manual view specification. Tools implementing manual view specification often require users to manually specify visual properties through GUI operations on collections of visual properties and data attributes that are presented visually on control panels. To interact with tools implementing manual view specification users need to understand the potentially complex system parameters being controlled. Additionally, in such tools, users need to constantly shift their attention from the visual features of interest when interacting. In this talk, I present an alternative interaction paradigm for visualization construction and data exploration called visualization by demonstration. This paradigm advocates for a different process of visualization construction. I will also discuss the trade-offs between these interaction paradigms based on the data collected from an empirical study. I will then discuss applications of the "by demonstration’" paradigm in other areas in data visualization. Bio: Bahador Saket is a third-year Ph.D. student at Georgia Tech, where he works with Dr. Alex Endert. His current research focuses on the design of interaction techniques for visualization construction and visual data exploration. Prior to joining Georgia Tech, Bahador worked at different research labs including Microsoft Research, CNS Research Center, and NUS-HCI Lab. He has published over 12 peer-reviewed articles in the leading journals and conferences in the field of human-computer interaction and data visualization such as IEEE Transactions on Visualization and Computer Graphics (TVCG), Computer Graphics Forum, CSCW, UIST, and MobileHCI.
02/08/2018	Elissa Redmiles University of Maryland, College Park	Dancing Pigs or Security? Measuring the Rationality of End-User Security Behavior Abstract: Accurately modeling human decision-making in security is critical to think about when, why, and how to recommend that users adopt certain secure behaviors. We used behavioral economics experiments to model the rationality of end-user security decision-making in a realistic online experimental system simulating a bank account. We ask participants to make a financially impactful security choice, in the face of transparent risks of account compromise and benefits offered by an optional security behavior (two-factor authentication). We find that more than 50% of our participants made rational (e.g., utility optimal) decisions, and we find that participants are more likely to behave rationally in the face of higher risk. Additionally, we confirm that users are boundedly rational: they make decisions based on some risks and context, but not others, and we can model their behavior well as a function of these factors. Finally, we show that a “one-size-fits-all” emphasis on security can lead to market losses, but that adoption by a subset of users with higher risks or lower costs can lead to market gains. Bio: Elissa Redmiles is a Ph.D. student at the University of Maryland in Computer Science. Her research focuses on using computational and social science methodologies to understand and improve users' privacy and security learning processes, behavior, and perceptions. She is the recipient of an NSF Graduate Research Fellowship, a National Science Defense and Engineering Graduate Fellowship, and a Facebook Fellowship. Prior to pursuing her Ph.D., Elissa held Marketing Management and Software Engineering roles at IBM and was a Data Science for Social Good Fellow at the University of Chicago.
02/15/2018	Erin Peters-Burton George Mason University, Fairfax, VA	Building Student Self-Awareness of Learning to Enhance Diversity in the Sciences Abstract: Many students are being left out of pursuing further studies in science because the current system of science education values students who learn via completion in an isolated, rather than collaborative way (Tobias, 1990). The stereotype of students who excel in science tend to be the ones who can conform to the institutional structure where the teacher is the sole source of knowledge (Friere, 2000). Through the idea of “Education as the Practice of Freedom” (hooks, 1994), the presentation will explain investigations that explore tangible ways to break down that stereotype. This research begins with the assumption that if teachers taught the ways science operates as a discipline, then students gain more power to construct their own scientific knowledge because they understand the “rules” of knowledge validation (Duschl, 1990). Learning how scientific knowledge is constructed and being self-aware of one’s own learning in science can help level the playing field so that students can do inquiry well (NRC, 1996; AAAS, 1993) and the science classroom will be a more inclusive, positive environment rather than relying on isolated competition for teaching. In this presentation, I will present an overview of research I have done over the past 10 years that focuses on helping students to become self-aware of their learning in science and how scientific knowledge is constructed. The work involves 8th grade students, undergraduates, graduate students, and professionals. The studies include constructs such as self-efficacy, motivation, metacognition, self-regulated learning, and visualization. Findings of the studies are synthesized into self-awareness priorities and how those constructs will ultimately impact social justice by providing more opportunities to see alternative perspectives and learn the “rules” of knowledge validation in science. As a result, students develop a sense of agency and an identity where anything is possible because they can learn independently in any situation. Bio: Erin E. Peters-Burton is the Donna R. and David E. Sterling Endowed Professor in the College of Education and Human Development at George Mason University. She has a B.S. in Physics from the University of Illinois, a M.Ed. in Educational Psychology and Social Foundations of Education from the University of Virginia, and a Ph.D. from George Mason University (VA) in Educational Psychology and Educational Research Methods. She has taught middle school and high school science and mathematics for 15 years prior to her academic work and was a National Board Certified Teacher in Early Adolescence Science. She has published in science education, teacher education, educational psychology, marine biology, geology education, history and philosophy of science, technology, educational leadership, and learning disability journals. Her book, Thinking Like Scientists: Using Metacognitive Prompts to Develop Nature of Science Knowledge, and her edited book, The STEM Road Map: A Framework for Integrated STEM Education have led to the curriculum series books from the National Science Teacher Association entitled, STEM Road Map for Elementary School, STEM Road Map for Middle School, and STEM Road Map for High School. In 2016 she was awarded the Association of Science Teacher Educators Outstanding Science Teacher of the Year in recognition of her work with the professional development of secondary science teachers.
02/22/2018	Norman Su Indiana University	The Problem of Designing for Subcultures Abstract: Members of subcultures speak about and act with pervasive technologies in service to their distinct traditions. I will describe how outwardly subcultures maintain a unified front, yet inwardly are rich sites for compromise and confrontation over technology. I will highlight findings from work we have done with subcultures and, in particular, my own fieldwork with Irish traditional musicians. I will close by describing new design opportunities for technologies that acknowledge the remarkable solidarity and discord of subcultures. Bio: Norman Makoto Su is an Assistant Professor in the School of Informatics, Computing, and Engineering at Indiana University Bloomington. His research interests lie in human–computer interaction (HCI) and computer–supported cooperative work (CSCW). His Authentic User Experience (AUX) lab characterizes the relationship of technology with subcultures and designs systems to support their notion of authenticity. He received his Ph.D. in Information and Computer Science from the University of California, Irvine and a B.A. in Computer Science from the University of California, Berkeley. He was a postdoctoral research fellow in the School of Information and Library Studies at University College Dublin, Ireland. He has done internships at IBM, The Aerospace Corporation, and PARC.
03/01/2018	Ya-Wei Li Center for Conservation Innovation, Defenders of Wildlife	Using Data and Technology to Save Endangered Species. Abstract: We will discuss how Defenders of Wildlife, a nonprofit wildlife conservation organization, is expanding its use of technology and data analytics to conserve endangered species. We will summarize our projects involving remote-sensing data to monitor wildlife habitat and compliance with conservation agreements; data mining of federal government decisions to build the largest public repository of text-searchable documents on the U.S. Endangered Species Act; natural language processing of those documents to improve public understanding of how our government conserves endangered species; use of data visualization tools to reveal patterns in large datasets; and other initiatives. We invite the audience to actively engage with us about how we can improve our work and offer ideas for future projects and potential collaborations. Bio: Ya-Wei (Jake) specializes in endangered species law, policy, and science. He leads the Center for Conservation Innovation at Defenders of Wildlife, which focuses on developing innovative and pragmatic strategies to conserve endangered and at-risk species. Before joining Defenders in 2010, Jake practiced environmental law in the private sector. Jake holds a B.S. from Drexel University and a J.D. from Cornell University Law School. At Cornell, Jake also completed graduate coursework in conservation biology and herpetology. Jacob works on linking science to Endangered Species Act policy. He works with others inside and outside of Defenders to make ESA-related data available and easily interpretable, so that policy makers and the public can make informed decisions about conservation. Before joining Defenders, Jacob was a postdoctoral fellow at University of Connecticut, and earned his Ph.D. from the University of Texas at Austin in 2013. From 2000-2008, Jacob was a field biologist with the U.S. Fish and Wildlife Service in New Mexico and Arizona, during which time he completed his Bachelor's degree in Conservation Ecology at Prescott College.
03/08/2018	Deok Gun Park University of Maryland, College Park	Thinking, Autism and AGI Abstract: Despite recent advances in deep learning, we do not know yet how we can combine these application-specific models to build an artificial general intelligence (AGI). Furthermore, the data is becoming the bottleneck to scale these approaches for the multiple tasks. In this talk, I propose a theory of the thinking and a neural algorithm that can bootstrap intelligence with limited computational resources and data. This neural algorithm approximates the O(n3) parameter space of the thinking theory into the O(1) parameters to make learning tractable for the biological intelligent agents. I will explain this proposal by cognitive phenomenons that are observed in a human, such as infant language acquisition, visual and verbal thinking, personality, creativity, exploit-exploration trade off, dreaming, one-shot learning, abstract language. Bio: Deokgun Park is a Ph.D. candidate in the HCILab of the Department of Computer Science at the University of Maryland, College Park, being advised by Prof. Niklas Elmqvist. His research focuses on the computational methods for open-ended tasks. He completed M.S. in Interdisciplinary Engineering at Purdue University and M.S. in Biomedical Engineering at Seoul National University, where he obtained B.S. degree in Electrical Engineering at Seoul National University. He worked at the government research institute, industry research labs, and startups. He has published and licensed his patents to companies including Samsung Electronics.
03/15/2018	Clemens Klokmose Aarhus University, Denmark	TBD TBD
03/22/2017	No Brown Bag, Spring Break.
03/29/2018	Wei Bai University of Maryland, College Park	Understanding User Tradeoffs for Search in Encrypted Communication Abstract: End-to-end message encryption is the only way to achieve absolute message privacy. However, searching over end-to-end encrypted messages is complicated. Several popular instant messaging tools (e.g., WhatsApp, iMessage) circumvent this inconvenience by storing the search index locally on the devices. Another approach, called searchable encryption, allows users to search encrypted messages without storing the search index locally. These approaches have inherent tradeoffs between usability and security properties, yet little is known about how general users value these tradeoffs, especially in the context of email rather than instant messaging. In this paper, we systematize these tradeoffs in order to identify key feature differences. We use these differences as the basis for a choice-based conjoint analysis experiment focused on email (n=160), in which participants make a series of choices between email services with competing features. The results allow us to quantify the relative importance of each feature. We find that users indicate high relative importance for increasing privacy and minimizing local storage requirements. While privacy is more important overall, local storage is more important than adding additional marginal privacy after an initial improvement. These results suggest that local indexing, which provides more privacy, may often be appropriate for encrypted email, but that searchable encryption, which limits local storage, may also hold promise for some users. Bio: Wei Bai is a PhD student in the Department of Electrical and Computer Engineering at the University of Maryland, advised by Prof. Michelle L. Mazurek. His research interests include network security and privacy with an emphasis on human factors, and his dissertation is about user perceptions of and attitudes toward encrypted communication. He obtained his MS in electrical and computer engineering from the University of Maryland. Contact him at wbai@umd.edu.
04/05/2018	Eun-Kyoung Choe University of Maryland, College Park	TBD TBD
04/12/2018	CHI practice talks	Combining smartwatches with large displays for visual data exploration by Karthik Badam and Tom Horak TBD
04/19/2018	Hernisa Kacorri University of Maryland, College Park	TBD TBD
04/26/2018	TBA	TBD TBD
05/03/2018	Amanda Lazar University of Maryland, College Park	TBD TBD
05/10/2018	Joel Chan University of Maryland, College Park	TBD TBD
05/17/2018	TBA	TBD TBD

Past Brown Bags

View the Past Brown Bag Lunch Schedules to learn more about prior talks.