THINK Lab Research
The work at the THINK (Transportation-Human Interaction-and-Network Knowledge) Lab is at the intersection of human behavior (in particular mobility patterns), the built environment and the larger infrastructure systems, and data science. Our work answers to some of the most important questions for this society relating to sustainability and resilience at different scales. More specifically, THINK lab's research activities center on unpacking complexities across scales, from micro-level individual mobility behaviors, to meso-scale interactions formed as the result of individual behaviors (whether the subject of interest is human or physical assets), to macro-level system behaviors that propagate through a single network or multiple networks.
1
What underlying mechanisms characterize behaviors across different scales?
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How can we best leverage interactions within and between infrastructure systems and social systems to support sustainability and resilience?
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How do people, communities and cities evolve over time and reemerge from a disaster
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How do individual mobility patterns play a role in forming networks, which then facilitate cascading spreading patterns of socially desirable or undesirable outcomes?
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How can we design the most effective interventions toward healthier behaviors, sustainable behaviors or achieving one or multiple societal outcomes?
TH (Transportation-Human)
We deal with infrastructure systems (transportation for example) and humans at the same time, not one or the other. This is because infrastructure systems provide services to humans, whose behaviors then influence the performance of infrastructure systems.
I (Interaction)
Interaction is considered in our research everywhere! Another, similar word we often use is "interdependency." We consider interdependency between different physical systems (eg. transportation and power) and between physical and social systems (eg. transportation and humans).
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N (Network)
We approach from a network perspective, meaning that we consider a set of entities that are inter-connected with each other, or formally GRAPHS. These entities may be social entities, including people, families, groups, agencies and organizations etc. or physical components such as facilities that attract people's activities whether they are indoor or outdoors, or locations that transfer flows (intersections or substations or cell towers). Depending on our specific studies of interest, our networks may be static or dynamic, in equilibrium or chaos.
K (Knowledge)
We create both knowledge and novel methodologies.
Balancing Between Science and Broader Impacts
The work at THINK lab contributes in two important dimensions: science and broader impacts. The former means an intellectual contribution to one or more scientific disciplines (e.g., statistical methods, optimization, network science, data science etc.) and the latter can mean different things, for example, providing a solution to a real-world problem that will result in significant societal benefits (e.g., designing an intervention that will lead to more people to switch to healthier behaviors). One can make a significant contribution to either or both.
If you imagine a graph with these two dimensions: science and broader impacts, the research conducted at THINK lab is all over. Where your research will be in this two-dimensional space depends what really excites you and what you want to pursue after your education.
Katie Idziorek
One project (by Katie Idziorek) that the lab is working is on community resilience, i.e., how to leverage community residents' own resources for enhancing their own adaptive capacity for a disaster? We are in the first stage of the research during which we have conducted many focus groups, workshops and is carrying out a community-based survey in a number of neighborhoods. At this stage, I would say that the research that has been conducted so far has significant broader impacts (through many interactions with different communities in the region) and has not yet made a significant contribution in science yet. As we move this research further, we will expect to make scientific contributions in a number of areas including resource allocation (optimization) and information learning and sharing.
Dr. Xiangyang Guan
Dr. Xiangyang Guan completed his PhD dissertation on successfully developing a backward approach that makes no assumption on the network structure, simply takes failure outcome data (e.g., congestion, power blackout) as inputs to infer the underlying failure propagation process that gives rise to the observed phenomenon. The developed methodology was tested and validated in four independent cases involving transportation, power, epidemic and an interdependent system of transportation and power. The developed backward approach is a stark departure from the vast majority of the works in the area, which always start with a pre-determined network structure and propagation mechanisms and then simulate the propagation outcomes. The paper was published in PNAS. In this case, Dr. Guan's dissertation makes a solid contribution to the underlying science that is used to answer the critical question of how to infer propagation patterns with only failure outcome data. More specifically, he developed a model by integrating the state of the art from Bayesian statistics, network science and optimization.
Research Philosophy
We conduct research for the purposes of knowledge discovery, methodological innovation, self-enlightenment and contribution to the society. We believe that when we conduct research truly for the purpose of uncovering the unknown, our potentials are boundless. I note that this does not mean publications are not important: they are absolutely important for your career! A truly fruitful research career can be both joyful and painful—joyful, because when new insight is learned, that moment of joy is indescribable and embarking a research career means that we take on a lifelong journey dotted with moments of joy; painful, because often times, we can also walk on a path that takes long hours, and is lonely and full of struggles. It is exactly this combination of loneness, struggles and moments of joy that defines our research life and makes our work fun.
Having walked a longer journey than most of you, I have a few suggestions for you:
Settle your heart deep, so that you can engage in deep thinking, having your own time, and are not constantly bothered by what and how many papers you can publish.
Keep writing. Writing helps clarify and deepen our thinking. Writing is also a lifelong learning journey.
Read broadly, which may include non-technical papers and stories and papers that do not directly fall into your research area. You will find that many phenomena in life are well-connected and have commonalities. By doing this, you are trying to build yourself with well-rounded qualities, as opposed to being only good in certain technical aspects (e.g., data mining…).
Enjoy life. Enjoy your surroundings (people, nature, and everything else), engage in socially meaningful activities and conversations and contribute to the society.
What you would learn by working with me: aside from learning the technical skills that will facilitate you to perform some sophisticated research tasks, some of the most important capabilities you shall learn (which will benefit you for life, even if you decide not to conduct research or move out of the field of transportation) are below:
