DINS Seminar Series: 2022-23

Welcome to the DINS Seminar Series for AY 2022-23. This page will be updated as more information about specific talks becomes available.

The Department of Informatics and Networked Systems (DINS) is pleased to invite you to join us for a stimulating series of lecture events, the DINS Seminar Series. Taking place regularly throughout the 2022-2023 Academic Year, the series will feature a variety of experts from academia and industry who will discuss their leading-edge work on topics at the intersections of information and data, organizations and society, and systems and networks. The breadth of topics covered in the series reflects the diversity of research and educational opportunities within the Department. 

Quantifying Political Polarization on a Network Using Generalized Euclidean Distance

Speaker: Michele Coscia, IT University of Copenhagen

October 19, 2022
11.00 a.m. - 12.00 noon
Online (Join Zoom Meeting: https://pitt.zoom.us/j/93599179854  Meeting ID: 935 9917 9854)

Abstract:  An intensely debated topic these days is whether political polarization on social media is on the rise or not. This question can only be investigated if we have a measure for polarization. Such a measure should take into account how extreme the opinions of the people are, how much they organize into echo chambers, and how these echo chambers organize in the network. The most popular ways of estimating polarization are insensitive to at least one of these factors, thus they cannot conclusively clarify the opening question. We propose a measure of ideological polarization which can capture these factors. The measure is based on the Generalized Euclidean (GE) distance, which estimates the distance between two vectors on a network, e.g., vectors representing people’s opinion. This measure can fill the methodological gap left by the current state of the art, and leads to useful insights when applied to real-world debates happening on social media and to data from the US Congress.

Gender disparities and the Glass Ceiling effect in science

Speaker: Kristina Lerman, Principal Scientist at University of Southern California Information Sciences Institute

October 26, 2022
11.00 a.m. - 12.00 noon
Online (https://pitt.zoom.us/j/94529250331)

Meeting ID: 945 2925 0331

Abstract: Gender disparities persist in science, systematically reducing career opportunities for women. As a result, women remain a small minority in many fields, especially in senior positions. The dearth of elite women scientists, in turn, leaves fewer women to serve as mentors and role models for the younger generation. We explore gender disparities in citations, showing that women receive less recognition for their work relative to men, and that this cannot be explained purely by their minority status. Instead, gender disparity arises from biased individual preferences about who to cite, that are amplified by cumulative advantage. We present a model for the growth of citations that captures this mechanism and analyze it to show that its predictions align with real-world observations. In the second part of the talk, I present a study of prominent scholars who were elected to the National Academy of Sciences. I identify gender disparities in the structure of citation networks of these researchers and show that these differences are strong enough to accurately predict the scholar's gender. These results provide further evidence that a scholar's gender plays a role in the mechanisms of success in science.

Resilience of Urban Sociotechnical Systems to Disasters, Pandemics, and Societal Changes 

Takahiro Yabe

Speaker: Takahiro Yabe, Postdoctoral Associate in the Connection Science group, SSRC, MIT

November 2, 2022
11:00 am to 12 noon
Online: https://pitt.zoom.us/j/94231244662

Meeting ID: 942 3124 4662

Abstract: Cities are the central engines of productivity, innovation, and cultural diversity, owing to their ability to foster dense social and economic connections among people and organizations. However, cities are also at the forefront of unprecedented challenges, including increased frequency of climate change induced disasters, aging infrastructure systems, and growing inequality and segregation. To build urban resilience to such challenges, we need to better understand the cascading socioeconomic impacts of shocks, which are undergirded by complex interdependencies between social networks, urban environments, and online systems. Leveraging the increasing availability of large-scale human behavior data collected from mobile devices (e.g., mobile phone GPS, social media, web search) I study the resilience of urban sociotechnical systems using a data-driven complex systems dynamics approach. In this talk, I will particularly focus on research on 1) resilience of cities to disasters, focusing on the impacts of complex interdependencies between social dynamics and infrastructure systems (based on papers in PNAS and Sustainable Cities and Society), and 2) resilience of the diversity of urban social contact networks during the pandemic (arXiv preprint available). 

Bio: Taka is a Postdoctoral Associate at the MIT Institute for Data, Systems, and Society (IDSS) and Media Lab mentored by Alex 'Sandy' Pentland and Esteban Moro. Taka’s research lies in the intersections of civil engineering, computational social science, and urban science. His research develops data-driven urban sociotechnical system models for analyzing large-scale human behavior data, to better understand collective social dynamics during disruptions, and to improve the resilience of communities and cities to urban shocks. He received his Ph.D. from Purdue University, advised by Satish V. Ukkusuri, and his Masters and Bachelor Degrees from the University of Tokyo. Taka is also passionate in transforming research to policy, and previously was a Data Science Consultant for the World Bank, working on human mobility data analytics for urban disaster risk management and transportation resilience modeling. 


Creating Critical Technologists: Shifting Computing Education towards JusticeAngela Stewart U of Pitt

Speaker: Angela Stewart, Assistant Professor, Department of Informatics and Networked Systems, School of Computing and Information, University of Pittsburgh

November 9, 2022
11:00 am to 12 noon
Meeting ID: 971 2860 8499

Abstract: Computing education largely focuses on equipping learners with technical competencies to be part of the future workforce. However, this approach has led to technologists who lack the skills to critically reflect on the ways technology might uphold systems of oppression. In this talk, I will discuss a reimagined vision for justice-oriented computing education. I will present findings from several design-based research studies in after-school computing education for girls. I will discuss how principles from these studies can make for more equitable computing education.

Bio: Angela joined the University of Pittsburgh in 2022, with a joint appointment in the School of Computing and Information and Learning Research and Development Center. Prior to that, she was a Postdoctoral Fellow in the Human-Computer Interaction Institute at Carnegie Mellon University. She received a PhD in Computer Science from the University of Colorado Boulder (2020) and a Bachelor of Software Engineering from Auburn University (2015). Angela conducts research at the intersection of the learning sciences, artificial intelligence, and human-computer interaction. She uses multimodal data to understand students' social and cognitive states, particularly in collaborative STEM learning. She also creates equitable educational spaces by designing technologies that support the agency of students and teachers. Angela applies a culturally-responsive lens to her research, with a particular focus in emboldening Black girls' design of transformative technologies. Angela was named a 2021 - 2022 Emerging Scholar by the International Society of the Learning Sciences.


The Lived Experiences Measured Using Rings Study (LEMURS)

Speaker: Christopher M. Danforth, Professor, Department of Mathematics & Statistics, College of Engineering and Mathematical Sciences,

University of Vermont

November 16, 2022
11:00 am to 12 noon
Virtual -- Join Zoom Meeting   https://pitt.zoom.us/j/92863524224

Meeting ID: 928 6352 4224 

Abstract: Building on a decade of work quantifying mood using social media activity, this talk will describe our group’s new longitudinal wearables study of health and well-being. A cohort of 600 first-year students at the University of Vermont have been recruited to take part in an experiment incentivizing behaviors associated with human flourishing. During the Spring semester of 2023, three groups of 150 students will be randomly assigned to engage in expert guided (a) exercise classes, (b) nature experiences, and (c) group talk therapy. Changes in stress, mental health, and sleep will be assessed through a series of weekly surveys deployed through a dedicated app, as well as continuous physiological heart-rate monitoring through the Oura Ring, and compared to a fourth control group of 150 students.

Bio: Professor Chris Danforth received a B.S. in Mathematics & Physics from Bates College in 2001, and a Ph.D. in Applied Mathematics & Scientific Computation from the University of Maryland, College Park in 2006. His early work applied Chaos Theory to improve forecasts made by numerical weather models, and his current work focuses on sociotechnical systems. He is the co-inventor of http://hedonometer.org, an instrument measuring daily happiness based on social media, and has also developed algorithms to identify predictors of depression from Instagram photos. Along with Peter Sheridan Dodds, Danforth runs the Computational Story Lab research group and the Vermont Complex Systems Center. Danforth is Director of the Vermont Advanced Computing Center, and his work has also been funded by NIH, NASA, NOAA, DARPA, DOE, and the MITRE Corporation. Danforth has co-authored over 100 peer-reviewed publications applying mathematical techniques to many fields including atmospheric science, linguistics, psychology, literature, finance, physics, engineering, and biochemistry. He has advised over 50 research dissertations including 20 PhD students, 20 MS students, and 15 undergraduate thesis students. Descriptions of his projects are available at his website: http://cdanfort.w3.uvm.edu

EVENT CANCELED: Explanationism: A Tonic for all that Ails Machine Learning

Speaker: Zachary Lipton, Assistant Professor of Machine Learning and Operations Research, Carnegie Mellon University


Dr. Zachary Lipton is currently an Assistant Professor of Machine Learning and Operations Research at Carnegie Mellon University (CMU). He holds appointments in the Machine Learning Department in the School of Computer Science (primary), Tepper School of Business (joint), Heinz School of Public Policy (courtesy) and Societal Computing (courtesy). His research spans core ML methods and theory, their applications in healthcare and natural language processing, and critical concerns, both about the mode of inquiry itself, and the impact of the technology it produces on social systems.

At CMU, Dr. Lipton directs the Approximately Correct Machine Intelligence (ACMI) Lab. His lab’s focuses include (i) building robust systems that can cope with a changing world, whether due to natural changes in the environment (so-called natural distribution shifts) or due to the strategic manipulations of other agents keen to influence automated decisions; (ii) understanding the social impacts of machine learning in a philosophically coherent way; (iii) the intersection of representation learning and causality; and (iv) leveraging ML to address impactful questions in clinical medicine. He tends to favor applications involving natural language data, and expect this interest to endure.

Socially and Ethically Responsible AI for Sustainable Development: Bringing Invisible Millions at the Center of the AI Revolution

Speaker: Neil Gaikwad, PhD Candidate, MIT

December 14, 2022
11:00 am to 12 noon

Virtual -- Join Zoom Meeting: https://pitt.zoom.us/j/99308939022

Abstract: How should we design AI/ML technologies to benefit the world's poorest m who are invisible in mainstream datasets and not only experiencing the disproportionate impact of climate change and structural inequalities but also algorithmic harms? AI/ML models, trained and evaluated with highly curated datasets and standard benchmarks, demonstrate remarkable computational efficiency in lab settings or online human-subject studies but are ineffective when deployed in the real world. Designing Ethical AI/ML systems for scientifically informing high-stake policy decisions is unquestionably one of the most difficult challenges. In this talk, I present a research program focused on Socially and Ethically Responsible AI for Sustainable Development. By bringing digitally invisible at-risk communities to the center of human-AI collaboration, the scholarship ensures fairness, accountability, transparency, and ethics as an intrinsic part of human-centered AI rather than afterthought optimization. With real-world deployments, I demonstrate what Responsible AI looks like from the perspective of the most vulnerable (e.g., smallholder farmers, racial minorities, gig workers, etc.). This publicly engaged research program shifts the paradigm of the AI revolution to make a newly realized sociotechnical world more inclusive and sustainable.

Bio: Neil Gaikwad is a Ph.D. candidate at MIT, where he is a Human Rights and Technology Fellow and Social and Ethical Responsibilities of Computing Scholar. His research in computational sustainability straddles the interface of Ethical AI and Policy for promoting global inclusion, equity, and societal development. This work has been published in premier artificial intelligence and human-computer interaction conferences (AAAI, KDD, CHI, CSCW, UIST), journals (PNAS), and featured in venues such as The New York Times, New Scientist, Bloomberg, WIRED, and Wall Street Journal. Neil has been recognized with numerous awards in science and engineering, including Facebook Ph.D. Fellowship, MIT Graduate Teaching Award, and Rising Star in Data Science by the University of Chicago, and the Karl Taylor Compton Prize, MIT’s highest student award. He has mentored more than 20 students who published impactful papers, won prestigious fellowship awards, pursued careers in research, and shifted the discourse on AI fairness and racial equity. Neil holds a master’s degree from the School of Computer Science at Carnegie Mellon University. Before MIT, he was a data scientist on Wall Street. 

Science of science, law of law, and patterns of patents: universal citation dynamics in knowledge systems

Speaker: Yong-Yeol (YY) Ahn, Associate Professor, Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington

January 18, 2023

11:00 am to 12 noon

In-person -- Room 316 IS Building, 135 North Bellefield Avenue, Pittsburgh, PA 15213

Zoom option: Join Zoom Meeting: https://pitt.zoom.us/j/93743886499
Meeting ID: 937 4388 6499


Citation is a fundamental way for humans to acquire and expand on existing knowledge. Although many laws and regularities of citation dynamics have been discovered from scientific citations, it is unclear whether and to what extent these regularities are inherent in how humans seek, use, and create knowledge. We show that, despite many stark differences between these systems, the citation dynamics in science, law, and patents share universal patterns. Given the differences in procedure and incentives that exist between judges, inventors, and scientists, our findings suggest that universal citation dynamics may be innate to any cumulative human knowledge system. Our model demonstrates that the evolution of collective attention and a handful of fundamental mechanisms can produce observed universal patterns of citation dynamics.


Designing a Critical Data Literacy Toolkit: Centering Youth Experiences

Speaker: Aditi Mallavarapu,Technology and Learning Sciences Postdoctoral Researcher, Digital Promise; Visiting Scholar, the Learning Research and Development Center (LRDC) at the University of Pittsburgh

February 1, 2023
11:00 am to 12 noon
In person -- Room 538-539 130 North Bellefield Building (Conference Room)

Zoom information for those who wish to attend remotely: Join Zoom Meeting: https://pitt.zoom.us/j/98850768917

Abstract: Data literacy (DL) competencies can play a critical role in addressing the underrepresentation of Black youth in technology workforce pathways. Designing inclusive data literacy resources and technology to match the expectations and needs of historically marginalized youths is essential for reimagining a more inclusive technological workforce. In this talk, I discuss our partnership with nine Black high school students from a historically marginalized community to reimagine the design of a critical DL toolkit, positioning them as advisors for our research project, DATA (Data Analysis to Action) project. Through youth participatory action research (YPAR) the youth engaged in facilitated data advocacy activities analyzing data via a data analysis technology and creating artifacts for advocacy using community-based data. Our advisors identified refinements for DL technology design and activities that better align with their needs. Further centering critical DL, we analyzed advisors’ interactions during the hands-on activities through the data feminism lens to understand how the activities manifest critical thinking with data. We identified technical affordances that can support the advisors in this endeavor. Our major findings highlight that: engaging youth with data that aligns with their lived experiences can empower the youth; moreover, flexible technology and curricular design that supports critical thinking with data can expose the youth to the potential of using data and technology as an empowering tool for advocacy. I discuss these findings and the implications about the data, learning technology and curricular activities that the youth highlighted.

Bio: Aditi Mallavarapu is a Technology and Learning Sciences Postdoctoral researcher at Digital Promise, working as a part of the Center for Integrated Research in Computing and Learning Sciences (CIRCLS) where she specializes in creating data-driven resources to drive research innovation for researchers in the community. She is also a visiting scholar at the Learning Research and Development Center (LRDC) at the University of Pittsburgh, where she uses participatory design to develop culturally responsive technology for young learners to learn data literacy and advocacy skills. She earned her PhD in Computer Science from University of Illinois at Chicago. Her work uniquely investigates the underexplored space of applying human-centered learning analytics and machine learning techniques for exploration-based learning that takes place in complex open-ended learning environments (e.g., museum exhibits). Combining the human-centered approach working alongside interdisciplinary researchers, educators, learners, and policymakers, with the technical data-driven approaches, Aditi reimagines open-ended learning environments as data-driven systems designed to engage learners with real-world complex problems.

Speaker: Edmond Awad, Senior Lecturer in the Department of Economics and the Institute for Data Science and Artificial Intelligence, University of Exeter

February 8, 2023
11:00 am to 12 noon
Virtual -- more details to come!

Speaker: Isabella Loaiza, PhD student and Research Assistant, Human Dynamics Group, MIT Media Lab

February 15, 2023
11:00 am to 12 noon
Virtual -- more details to come

Speaker: Bedoor Alshebli, Assistant Professor, Computational Social Science, NYU Abu Dhabi

March 15, 2023
11:00 am to 12 noon
In person -- more details to come!

Speaker: Peter Sheridan Dodds, Professor, Department of Computer Science, Director of Vermont Complex Systems Center, University of Vermont 

March 22, 2023
11:00 am to 12 noon
Virtual -- more details to come!

Pathway of Innovation

Speaker: Hyejin Youn, Associate Professor, Management & Organizations

March 29, 2023
11:00 am -- 12 noon
In person.  More details to come.

Abstract: To come

Dr. Youn is an Associate Professor, Management & Organizations, Kellogg School of Management at Northwestern University, and Northwestern Institute on Complex Systems (NICO). She is also an external faculty at Santa Fe Institute. Her research aims to develop a mathematical and computational framework to understand complex systems.