Second CITIES Symposium

Bios and Abstracts

• Monica Menendez

  Bio: Monica Menendez is the Associate Dean of Engineering for Graduate Affairs and a Professor of Civil and Urban Engineering at New York University Abu Dhabi (NYUAD). She is also the Director and Lead PI of the CITIES Research Center; and the recipient of the NYUAD Distinguished Research Award for 2021. Before joining NYUAD in 2018, Prof. Menendez was the Director of the research group Traffic Engineering at ETH Zurich. She holds a PhD (2006) and a MSc (2003) in Civil and Environmental Engineering from UC Berkeley, and a dual degree in Civil Engineering and Architectural Engineering (2002) from the University of Miami.

  Her research interests include multimodal transportation systems paying special attention to new technologies and information sources. She is the author of over 90 peer-reviewed journal publications and over 200 conference contributions, book chapters, editorials, and technical reports. In the last five years, five of the papers that she has co-authored, have received best-paper awards.
  
  

  Title: Traffic Capacity of Cities: Are We There Yet?
  Abstract: In this presentation, we will discuss how to bring together concepts from statistical physics and transportation engineering into a single science of traffic networks, with the goal of improving the performance of urban traffic, ultimately making our cities more sustainable. We will show that traffic and the ensuing congestion patterns for any given city are reproducible across days. Hence, it is enough to monitor the traffic performance of only a few roads to classify daily patterns and the resulting congestion patterns, allowing cities to reduce monitoring costs. In fact, road and bus network topology can explain around 90% of the empirically observed variation in network capacity for over 40 cities around the world. Moreover, it is possible to relate the road level dynamics to the network level dynamics by observing the number and size of traffic congestion pockets. This allows us to use concepts from physics (such as percolation) to describe the propagation of congestion, so that we can model it using sparse network-level data. It also gives us insights into the ability of different networks to cope with congestion, and the moment at which such congestion becomes so widespread that the whole network collapses.
  

• Juliana Freire

  Bio: Juliana Freire is a Professor of Computer Science and Data Science at New York University.  She was the elected chair of the ACM Special Interest Group on Management of Data (SIGMOD), served as a council member of the Computing Research Association’s Computing Community Consortium (CCC), and was the NYU lead investigator for the Moore-Sloan Data Science Environment. She develops methods and systems that enable a wide range of users to obtain trustworthy insights from data. These span topics in large-scale data analysis and integration, visualization, machine learning, provenance management, web information discovery, and different application areas, including urban analytics, predictive modeling, and computational reproducibility. Freire has co-authored over 200 technical papers (including 11 award-winning publications), several open-source systems, and is an inventor of 12 U.S. patents. According to Google Scholar, her h-index is 64 and her work has received over 17,000 citations. She is an ACM Fellow, a AAAS Fellow, and recipient of the ACM SIGMOD Contributions Award, an NSF CAREER award, two IBM Faculty awards, a Google Faculty Research award. Her research has been funded by the National Science Foundation, DARPA, Department of Energy, National Institutes of Health, Sloan Foundation, Gordon and Betty Moore Foundation, W. M. Keck Foundation, Google, Amazon, AT&T Research, Microsoft Research, Yahoo! and IBM. She received a BS degree in computer science from the Federal University of Ceara (Brazil), and MSc and PhD degrees in computer science from the State University of New York at Stony Brook.
  

  Title: Democratizing Urban Data Exploration
  Abstract: The ability to collect data from urban environments through a variety of sensors, coupled with a push towards openness and transparency by governments, has resulted in the availability of a plethora of spatio-temporal datasets. By analyzing these data, we have the opportunity to better understand how different urban components behave and interact over space and time, and obtain insights to make city operations more efficient, inform policies and planning, and improve the quality of life for residents. While there have been successful efforts in this direction, they are few and far between. Analyzing urban data often requires a staggering amount of work, from identifying and wrangling relevant data, to performing exploratory analyses and creating predictive models — tasks that are often out of reach for domain experts that lack training in computing and data science. In this talk, I will discuss work we have done at the NYU Visualization, Imaging and Data Analysis (VIDA) Center that aims to democratize data exploration and empower domain experts to crack the code of cities by freely exploring urban data. I will present methods and systems which combine data management, analytics, and visualization to increase the level of interactivity, scalability, and usability for spatio-temporal data analyses.

• Constantinos Antoniou

  Bio: Constantinos Antoniou is a Full Professor in the Chair of Transportation Systems Engineering at the Technical University of Munich (TUM), Germany. He holds a Diploma in Civil Engineering from NTUA (1995), a MS in Transportation (1997) and a PhD in Transportation Systems (2004), both from MIT. His research focuses on modelling and optimization of transportation systems, data analytics and machine learning for transportation systems, and human factors for future mobility systems. He is/has been PI of several research projects (e.g. H2020 iDREAMS, MOMENTUM, Drive2thefuture, DFG DVanPool and Trampa).

  He has authored more than 450 scientific publications, including more than 165 papers in international, peer-reviewed journals, 250 in international conference proceedings, 3 books and 20 book chapters. He is a member of several professional and scientific organizations, editorial boards (Deputy Editor in Chief of IET Intelligent Transportation Systems; Associate Editor of Transportation Research – Part A: Policy and Practice, Transportation Letters; Editor of EURO Journal on Transportation and Logistics; Editorial Board Member of Transportation Research – Part C, Accident Analysis and Prevention, Journal of Intelligent Transportation Systems, Smart Cities).
  

  Title: Open Data and Transport Modeling: Where are we at?
  Abstract: In the last few decades we have moved from an era of data scarcity to an era of abundant data. While this is full of opportunities, there are also a lot of challenges. In this presentation, we look at the landscape of “open” transport data and use a number of case studies to explore the status quo of transport modeling opportunities.
  

• Kinga Reka Makovi

  Bio: Kinga Makovi is an Assistant Professor at New York University Abu Dhabi. She is a co-PI of the Center for Interacting Urban Networks at NYUAD, CITIES. Prior to joining NYUAD, she earned a PhD in Sociology from Columbia University and a MS in Mathematical Economics from Corvinus University of Budapest.

  Makovi's research addresses questions at the intersection of network science, the social determinants of health and environmental behavior, using computational and experimental methods. Her research has been funded by the National Science Foundation and has appeared in Sociological Science, Scientific Reports and PLoS ONE.
  

  Title: Segregated Mobility Patterns Amplify Neighborhood Disparities in the Spread of COVID-19
  Abstract: The global and uneven spread of COVID-19, mirrored at the local scale, reveals stark differences along racial and ethnic lines. We respond to the pressing need to understand these divergent outcomes via neighborhood level analysis of mobility and case count information. Using data from Chicago over 2020, we leverage a metapopulation Susceptible-Exposed-Infectious-Removed model to reconstruct and simulate the spread of SARS-CoV-2 at the ZIP Code level. We demonstrate that exposures are mostly contained within one's own ZIP Code and demographic group. Building on this observation, we illustrate that we can understand epidemic progression using a composite metric combining the volume of mobility and the risk that each trip represents, while separately these factors fail to explain the observed heterogeneity in neighborhood level outcomes. Having established this result, we next uncover how group level differences in these factors give rise to disparities in case rates along racial and ethnic lines. Following this, we ask what-if questions to quantify how segregation impacts COVID-19 case rates via altering mobility patterns. We find that segregation in the mobility network has contributed to inequality in case rates across demographic groups.

• Norman Garrick

  Bio: Norman Garrick is Professor Emeritus of Civil Engineering at the University of Connecticut. He specializes in the planning and design of urban transportation systems, including transit, streets, street networks, parking, as well as transportation safety and sustainability and transportation poverty. His research and writings have reached a wide audience through outlets such as The New York Times, The Washington Post, The Denver Post and The Hartford Courant, The Atlantic (now Blomberg) CityLab, Planetizen, New Urban News, Streetsblog, Streetfilm and Public Radio both in the USA and abroad. In addition to his academic career, Dr. Garrick has worked as transportation consultant on numerous design charrettes including urban revitalization projects with the Prince of Wales Foundation in Kingston, Jamaica and in Freetown, Sierra Leone, and hurricane recovery in Louisiana and Mississippi. Dr. Garrick is a Fulbright Fellow as well as the recipient of the Transportation Research Board’s Wootan Award for Best Research Paper in policy and organization, and numerous awards for teaching at UConn.
  

  Title: Car Dependency, Transportation Equity and Neo-Colonialism
  Abstract: Transportation equity is intrinsically liked to sustainability, environmental justice, social justice and even to neo-colonialism. The discourse around the issue of transportation equity in the USA, often treats the issue as if it is simply a matter of resource allocation that can be fixed by making sure that transportation funds are fairly allocated. This is an important issue but misses the bigger issues of whether these transportation funds are being used to advance environmental and social justice issues. The research relating to sustainability, environment justice and social justice shows that the main drivers of injustice in these realms is autodependency. This is also the case with transportation equity — our research and others show that autodependency acerbates issues such as disparate traffic safety outcomes and transportation poverty. Furthermore, transportation equity is not just a question of fairness between people in the same society, but it is a particular burden to poorer countries all over the planet where transportation spending depletes state and individual budgets and where the toll of transportation deaths is unacceptably high. In this presentation, we will look at a framework for better understanding transportation equity — a first step in understanding the scope of the problem and in crafting real solutions.

• Daniel T. O'Brien

  Bio: Dan O’Brien is an Associate Professor in the School of Public Policy and Urban Affairs and the School of Criminology and Criminal Justice at Northeastern University and Director of the Boston Area Research Initiative, an interuniversity center that advances civically-engaged research to pursue equity in collaboration with local communities. His research focuses on the physical and social conditions of neighborhoods and the citywide systems that serve them, including work on crime, education, transportation, climate resilience, public health, and public infrastructure. His book The Urban Commons (Harvard University Press; 2018) won the American Political Science Association’s Dennis Judd Best Book Award for work on urban and local politics. His new textbook Urban Informatics (Chapman Hall / CRC Press; 2022) is the first designed specifically for this emergent field that integrates data science with community applications. It is freely available online at ui.danourban.com.

  Title: The Pointillistic City: Well-Being and Equity in Communities and their Places
  Abstract: Daily life occurs at multiple scales. We operate in a “community” and the people and institutions therein. We interface with neighbors on our street. We spend the most time in our own homes. This multilayered complexity of social geography has important implications for our experiences and outcomes, from the “neighborhood effects” familiar to social scientists to microspatial inequities arising from disparities from street to street. Understanding the interplay between these scales is newly accessible thanks to the emergent field of urban informatics — that is, the use of cutting-edge data and technology to better understand and serve communities. This talk will leverage work by the Boston Area Research Initiative at Northeastern University to capture this opportunity. It will begin by using the study of problem properties to illustrate the complementarity between communities and their places. It will then extend these lessons to climate resilience. It will conclude by proposing implications for how we might design 21st century communities whose planning, programs, and policies effectively engage this multilayered complexity.

• Marton Karsai

  Bio: Márton Karsai is an Associate Professor at the Department of Network and Data Science at the Central European University in Vienna. He is a researcher at the Rényi Institute of Mathematics in Budapest, Fellow of the ISI Foundation in Torino, director of the Network Science PhD program at CEU, where he leads the Computational Human Dynamics team. He is a network scientist with research interest in human dynamics, computational social science, and data science, especially focusing on heterogeneous temporal dynamics, spatial and temporal networks, socioeconomic systems and social contagion phenomena. He is an expert in analyzing large human behavioral datasets and in developing data-driven models of social phenomena.

  Title: Socioeconomic Networks, Segregation Patterns and Their Dynamics
  Abstract: The uneven distribution of individual economic capacities are among the main forces, which shape modern societies and arguably bias the emerging behavioral patterns of people reflected by their social network and mobility patterns. However, the observation of socioeconomic networks is a major challenge as it requires the combination of behavioral and socioeconomic data at the individual level. In this talk first we will discuss a set of results aiming to infer the socioeconomic status of people and their effects in inducing segregation patterns in social and mobility networks. While these segregation patterns evolve gradually, yet they can change abruptly in response to external shocks. The recent COVID-19 pandemic induced several such interruptions, with consequences that could be followed from human dynamical data. Building on mobile phone call and mobility datasets of the same population, we will demonstrate how lockdown interventions lead to the re-organization of socioeconomic network segregation patterns. We will find that not all socioeconomic groups could adapt equally to the emergency situation, suggesting socioeconomic status as an important determinant of people’s capacity to reflect to global emergencies.

• Azza Abouzied

  Bio: Azza Abouzied’s research work focuses on designing intuitive data querying tools. Today's technologies are helping people collect and produce data at phenomenal rates. Despite the abundance of data, it remains largely inaccessible due to the skill required to explore, query and analyze it in a non-trivial fashion. While many users know exactly what they are looking for, they have trouble expressing sophisticated queries in interfaces that require knowledge of a programming language or a query language. Azza designs novel interfaces, such as example-driven query tools, that simplify data querying and analysis. Her research work combines techniques from various research fields such as UI-design, machine learning, and databases. Azza Abouzied received her doctoral degree from Yale in 2013. She spent a year as a visiting scholar at UC Berkeley. She is also one of the co-founders of Hadapt — a Big Data analytics platform. In 2019, she received a VLDB test of time award. She has received best paper awards and honorable mentions from both human-computer interaction and database systems research communities.

  Title: Tactics, Threats and Targets: Disinformation through a Cybersecurity Lens
  Abstract: Disinformation can be used to sway public opinion toward a certain political or economic direction, adversely impact public health, and mobilize groups to engage in violent disobedience. A major challenge in mitigation is scarcity: disinformation is widespread but its mitigators are few. In this work, we interview fact-checkers, journalists, trust and safety specialists, researchers, and analysts who work in different organizations tackling problematic information across the world. From this interview study, we develop an understanding of the reality of combating disinformation across domains, and we use our findings to derive a cybersecurity-inspired framework to characterize the threat of disinformation. We closely examine the attacker side, their tactics and approaches, and apply our framework on several examples of recent disinformation campaigns.

• Preslav Nakov

  Bio: Preslav Nakov is Professor at Mohamed bin Zayed University of Artificial Intelligence. He received his PhD degree from the University of California at Berkeley, supported by a Fulbright grant. He is President of ACL SIGLEX, Secretary of ACL SIGSLAV, Secretary of the Truth and Trust Online board, PC chair of ACL 2022, and a member of the EACL advisory board. He is also member of the editorial board of several journals including Computational Linguistics, TACL, ACM TOIS, IEEE TASL, IEEE TAC, CS&L, NLE, AI Communications, and Frontiers in AI. He authored a Morgan & Claypool book on Semantic Relations between Nominals, two books on computer algorithms, and 250+ research papers. His research was featured by Forbes, Boston Globe, Aljazeera, DefenseOne, Business Insider, MIT Technology Review, Science Daily, Popular Science, Fast Company, The Register, WIRED, Engadget, etc.

  Title: Detecting the "Fake News" Before It Was Even Written, Media Literacy, and Flattening the Curve of the COVID-19 Infodemic
  Abstract: Recently, there has been growing interest in automatically debunking rumors, false claims, and "fake news." A number of initiatives have been launched, but the whole enterprise remains in a state of crisis: by the time a claim is finally fact-checked, it could have reached millions of users, and the harm could hardly be undone. An arguably more promising direction is to focus on analyzing entire news outlets, which can be done in advance; then, we could fact-check the news before it was even written: by checking how trustworthy the outlet that has published it is.

  Another important observation is that the term "fake news" misleads people to focus exclusively on factuality, and to ignore the other half of the problem: the potential malicious intent. Thus, we detect the use of specific propaganda techniques in text, e.g., appeal to emotions, fear, prejudices, logical fallacies, etc.

  Finally, at the time of COVID-19, the problem of disinformation online got elevated to a whole new level as the first global infodemic. Infodemic is much broader than factuality as malicious content includes not only "fake news," rumors, and conspiracy theories, but also promotion of fake cures, panic, racism, xenophobia, mistrust in the authorities, etc.

• Paolo Papotti

  Bio: Paolo Papotti is an Associate Professor at EURECOM, France, since 2017. He got his PhD from Roma Tre University (Italy) in 2007 and had research positions at the Qatar Computing Research Institute (Qatar) and Arizona State University (USA). His research is focused on data integration and information quality. He has authored more than 100 publications and his work has been recognized with two “Best of the Conference” citations (SIGMOD 2009, VLDB 2016), three best demo award (SIGMOD 2022,2015, DBA 2020), and two Google Faculty Research Award (2016, 2020). He is associate editor for the ACM Journal of Data and Information Quality (JDIQ).

  Title: Crowdsourced Fact-Checking: Analysis of the Birdwatch Program at Twitter Abstract: Fact-checking is one of the effective solutions in fighting online misinformation. However, traditional fact-checking is a process requiring scarce expert human resources, and thus does not scale well on social media because of the continuous flow of new content to be checked. Methods based on crowdsourcing have been proposed to tackle this challenge, as they can scale with a smaller cost, but have always been studied in controlled environments. In this talk, we report our insights from the first large-scale study of crowdsourced fact-checking deployed in practice, started by Twitter with the Birdwatch program. Our analysis shows that crowdsourcing may be an effective fact-checking strategy in some settings, even comparable to results obtained by human experts, but does not lead to consistent, actionable results in others. We processed 11.9k tweets verified by the Birdwatch program and report empirical evidence of i) differences in how the crowd and experts select content to be fact-checked, ii) how the crowd and the experts retrieve different resources to fact-check, and iii) the edge the crowd shows in fact-checking scalability and efficiency as compared to expert checkers.

• Mohsen Mosleh

  Bio: Mohsen Mosleh is a Senior Lecturer (Assistant Professor) at University of Exeter Business School, a Fellow at Alan Turing Institute, and a Research Affiliate at MIT. Mohsen was a postdoctoral fellow in Cognitive Science at the MIT Sloan School of Management and the Department of Psychology at Yale University. Mohsen’s research interests lie at the intersection of data science and cognitive science. In particular, he studies how information and misinformation spread on social media. His work has been published in leading journals such as Nature, Nature Communications, and PNAS and has been featured by news media such as The Washington Post and CNN.

  Title: Understanding and Reducing the Spread of Misinformation Online
  Abstract: There has been a great deal of concern about the negative impacts of online misinformation on democracy and society. In this talk, I provide an overview of my research on understanding why people share misinformation and how to combat spread of low-quality content online. I first focus on the why question and describe a hybrid lab-field study in which Twitter users (N=1,901) complete a cognitive survey. I show that people who rely on intuitive gut responses over analytical thinking share lower quality content. I then build on this observation with a Twitter field experiment (N= 5,379) that uses a subtle intervention to nudge people to think about accuracy. I show the intervention significantly improve the quality of the news sources they shared subsequently. Finally, I will talk about a follow-up study where we directly correct Twitter users (N=2000) who shared misinformation by replying to their false tweets by including a link to the fact-checking website. We show that unlike the subtle accuracy nudge, the direct public correction results in users sharing lower quality content. Our experimental design translates directly into an intervention that social media companies could deploy to fight misinformation online.