I am an Assistant Professor at the University of Hong Kong (HKU), jointly appointed in the Institute of Data Science and Department of Urban Planning and Design. I obtained my PhD at the University of Michigan, working with Mark Newman on complex networks and statistical physics, and did my undergraduate studies at the University of Rochester, beginning my studies in complex systems with Gourab Ghoshal.
I am interested in the theory of complex networks and statistical physics, as well as their applications in urban and social systems. My main areas of focus are:
1. Statistical inference and machine learning for complex network data
- Discovering and characterizing latent structure in network data. My research aims to develop new methods to uncover meaningful patterns in network data, which presents new challenges for inference due to its relational, hierarchical, and high (usually infinite!) dimensional nature. These methods are motivated by fundamental concepts in statistical physics, information theory, and Bayesian statistics, which collectively enable principled, interpretable, and (often) completely non-parametric methods for unsupervised learning in the network setting. These methods typically require the development of new learning objectives as well as sampling and optimization techniques to reveal the desired patterns and distinguish structure from noise.
- Quantifying uncertainty in network data. There are many ways in which uncertainty comes into play when analyzing network data: Networks may be noisy due to unavoidable statistical errors in the measurement process or systematic errors from sampling bias; Networks measured in cross-sectional or longitudinal studies may be subject to systematic errors across experimental environments; Samples of networks or their partitions from a generative model or Bayesian posterior distribution are subject to the intrinsic uncertainties of their corresponding latent distribution. My research aims to develop methods to quantify these various forms of uncertainty in network data to enable robust analyses in the presence of heterogeneity and noise.
2. Understanding urban systems through the lens of network science and statistical physics
- Characterizing mobility networks and their impact on city prosperity and resilience. Human mobility patterns play a critical role in determining the social, economic, and physical well-being of cities and their residents. From a structural perspective, street and transportation networks facilitate the distribution of resources as well as the accessibility of jobs, amenities, and services. From a dynamical perspective, networks of human mobility determine the mixing and contact patterns of urban residents, which impacts the spread of ideas and infectious diseases as well as the vibrancy, safety, and congestion of urban spaces. My research aims to uncover the hidden, large-scale patterns in structural and dynamical mobility networks that impact the propserity and resilience of global cities.
- Extracting structural patterns in spatial urban data through a topological lens. There is an abundance of spatial data available to help understand the structure and function of urban regions, from census data providing demographic information for urban populations, to the precise locations of points of interest such as services or attractions, to satellite imagery capturing large scale land usage patterns. However, due to high levels of heterogeneity in population density and the strong dependence of statistical analyses on spatial scale, topologically motivated methods such as spatial network analysis can often provide a more robust lens through which to study urban spatial data. My research aims to develop new methods based on network science and information theory to characterize urban spatial data, with the goal of providing a set of tools that can be used to understand spatial phenomena such as socioeconomic segregation and political redistricting.
My research involves a balance of mathematical theory, computer simulation, and analysis of empirical data, and draws on ideas from a range of disciplines including network science, statistical physics, information theory, Bayesian statistics, machine learning, and combinatorics. For more info, check out my Publications page.
I am looking for researchers at all levels (Postdoctoral Fellows, PhD students, and Research Assistants) to work on the lines of research listed above. Please send me your CV via email if you are interested. I encourage you to include in your email a paragraph or so stating your research interests/experience and how you feel these interests fit with the research areas outlined above, as this will better facilitate matching you to potential projects. PhD applicants may also be interested in checking out the Hong Kong PhD Fellowship Scheme, which provides an excellent stipend and support for PhD studies in Hong Kong.
I make sure to read all applicant emails, but do not take it personally if I forget to reply since I often won’t be able to process these emails immediately. In general I am always happy to discuss opportunities for supervision, but take note that openings for positions may be subject to university- and department-level admissions criteria as well as project funding. I will try my best to be clear about these sources of variability and how they apply to each interested candidate. I very much appreciate your interest in my research!
- Sebastian Morel-Balbi, Postdoctoral Fellow (2023-).
- Statistical Inference and Machine Learning for Network Data (DATA 8002, Data Science PhD/MPhil Program, Fall 2023).
- Transport Network Analysis and Modelling (MUDT 5010, Urban Design and Transport MSc Program, Fall 2023).
- Science of Cities (URBA 6006, Urban Analytics MSc Program, Fall 2023).
- Network Science
- Statistical Inference
- Urban Science
- Complex Systems
- Machine Learning
- Statistical Physics
PhD in Physics, 2021
University of Michigan
MS in Physics, 2018
University of Michigan
BS in Physics, BA in Mathematics (summa cum laude), 2017
University of Rochester