- Assistant Professor Yury Dvorkin recently carried out a study on the vulnerability of urban power grids to cyberattacks on electric vehicles, discovering that you only need to hack 1000 Teslas to cause a major power outage and harm the grid. The preprint of “Public Plug-in Electric Vehicles + Grid Data: Is a New Cyberattack Vector Viable?” is available here.
- Assistant Professor Yury Dvorkin also discussed the recent NYC blackout caused by Con Edison with NBC 4 New York and Brooklyn Daily Eagle. “A heat wave is like an invisible Hurricane Sandy. Both push the grid to the limit,” he told the Brooklyn Eagle.
- Congratulations to Associate Professor Dustin Duncan, who was recently awarded the Emerging Public Health Professional Award from Harvard T.H. Chan School of Public Health!
- Postdoctoral Researcher Vincent Lostanlen recently introduced librosa v0.7: a Python library for audio and music analysis. Librosa is an open-source software project whose core maintainers include Brian McFee from NYU’s music and audio research lab (MARL) and Vincent Lostanlen from CUSP, and which attracts over 100 new users every month. The latest version (v0.7), released this month, offers a streaming mode, feature inversion, faster decoding, more efficient spectral transformations, and other enhancements. The full changelog is available at: https://librosa.github.io/librosa/changelog.html#v0-7-0
- Congratulations to Associate Professor Masoud Ghandehari, who was recently awarded a new NSF award with colleagues at NYIT & CUNY for a project titled “City-as-Lab: A Research Coordination Network for the Study of the Food, Energy, and Water Nexus for Sustainable and Resilient Urban Development.” The $750K award supports a Research Coordination Network (RCN) to address the challenges associated with the Food, Energy, and Water (FEW) nexus for sustainable and resilient urban development.
- Professor Debra Laefer recently returned from field work in Wamba, Kenya where she worked with a Samburu tribal community and a group of highly multidisciplinary researchers to document traditional Samburu architecture.
- Assistant Professor Quanyan Zhu recently helped to organize the 5th International Conference on Artificial Intelligence and Security (ICAIS 2019), held at NYU! Over the past four years, ICAIS has become a leading conference on artificial intelligence and security- garnering comprehensive coverage by CCTV 4 China News- and is a highly selective and premier international forum on computer science and engineering research.
- Assistant Professor Chen Feng published and presented a paper in CVPR’2019, named: DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds. The paper is accepted as one of the 288 oral presentations out of 5160 submissions (a 5.6% acceptance rate). Professor Feng proposes a new method that demonstrates, for the first time without any human labeling, the possibility of using deep neural networks to create globally consistent 2D/3D maps accurately from multiple point clouds, while such a geometry problem was considered difficult to be solved accurately by neural networks. This method has a lot of potential for creating geometric models of buildings/cities, with less human interventions, in applications such as robotics, self-driving, urban monitoring, and so on. The source code is released for education and research purpose online: https://github.com/ai4ce/DeepMapping.
- Congratulations to Assistant Professor Anna Choromanska, who was recently awarded a 2019 IBM Faculty Award!
- Congratulations to NYU CUSP’s graduate students from the Class of 2019, who successfully presented their capstone projects last week! NYU CUSP’s Capstone Program is a six-month applied urban analytics project that partners CUSP Urban Informatics graduate student teams lead by a faculty advisor with a public, private sector, or academic organization that is looking to address a critical urban issue or research problem. This year’s projects covered a diverse range of topics, including exploring gentrification and displacement through user-generated geographic information; addressing NYC transit deserts through local self-organized commuter vans; and predicting health code violations in NYC. You can view all the capstone projects on our website here.
- The Class of 2019 will officially graduate from CUSP next Thursday, August 8, 2019. Congratulations!
AV Vo, DF Laefer, A Smolic, SMI Zolanvari (2019). Per-point processing for detailed urban solar estimation with aerial laser scanning and distributed computing. ISPRS Journal of Photogrammetry and Remote Sensing 155, 119-135.
Mohammad E. Shemshadian, Jia-Liang Le, Arturo E. Schultz, Patrick McGetrick, Salam Al-Sabah, Debra F. Laefer, Anthony Martin, Linh Truong-Hong, Minh Phuoc Huynh (2019). Numerical study of the behavior of intermeshed steel connections under mixed-mode loading. Journal of Constructional Steel Research. Volume 160, September 2019, Pages 89-100.
Harith Aljumaily, Dolores Cuadra & Debra F. Laefer (2019). An empirical study to evaluate students’ conceptual modeling skills using UML. Computer Science Education, DOI: 10.1080/08993408.2019.1642699
Rasulkhani, S., & Chow, J. Y. J. (2019). Route-cost-assignment with joint user and operator behavior as a many-to-one stable matching assignment game. Transportation Research Part B: Methodological, 124, 60-81.
- We are investigating new modeling frameworks to evaluate Mobility-as-a-Service and transit systems. An initial model is based on using cooperative game theory, via many-to-one stable matching, to connect travelers with operator routes. We are now advancing this to allow many-to-many stable matching, which can be used to assess the stability of a MaaS market given the entry of a new route, the change in capacity, in pricing mechanism, in user demand, etc. NSF funded.
Ma, T. Y., Rasulkhani, S., Chow, J. Y. J., & Klein, S. (2019). A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers. Transportation Research Part E: Logistics and Transportation Review, 128, 417-442.
- In this collaboration with researchers in Luxembourg (LISER), we developed a dynamic integrated platform for mobility-on-demand systems to incorporate public transit network capacity and found that in certain scenarios (like LIRR access to NYC) the fleets will naturally converge to providing first/last mile service. Support from NSF and Luxembourg National Research Fund.
He, B. Y., & Chow, J. Y. J. (2019). Optimal privacy control for transport network data sharing. Transportation Research Part C: Emerging Technologies, in press.
- We propose a privacy control mechanism to share complex operator network data for the purpose of supporting open data initiatives for smart cities and data interoperability efforts like LA DOT’s Mobility Data Specification. Support from NSF CAREER grant.
The World Economic Forum released a white paper on “Shared, Electric and Automated Mobility (SEAM) Governance Framework: Prototype for North America and Europe”, which includes cases from Assistant Professor Joseph Chow’s research at C2SMART.
Zhou, J., Lai, X., & Chow, J. Y. J. (2019). Multi-armed bandit on-time arrival algorithms for sequential reliable route selection under uncertainty. Transportation Research Record, in press.
- We tested multi-armed bandit algorithms for dynamic shortest path problems with on-time reliability criteria, which can be useful for automated decision-making for mobility services transporting passengers with schedule preferences (e.g. autonomous shuttles for airport access). Support from NSF CAREER grant.
Cartwright, M., Cramer, A., Salamon, J., Bello, J.P. TriCycle: Audio Representation Learning from Sensor Network Data Using Self-Supervision. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2019.
- This paper presents a method for learning audio representations by predicting when the recording was captured (e.g., what time of day, what day of the week, what time of year). It is the first work that we know of that exploits long-term temporal structure for self-supervised representation learning. While we applied this to recordings from an urban acoustic sensor network, it’s possible this approach may be used to learn representations from other large sets of unlabeled, yet timestamped data, e.g., data from non-audio sensor networks, which are becoming ubiquitous with the rise of IoT and smart sensing in cities.
Kim B, Callander D, DiClemente R, Trinh-Shevrin C, Thorpe LE, Duncan DT. Location of Pre-exposure Prophylaxis Services Across New York City Neighborhoods: Do Neighborhood Socio-demographic Characteristics and HIV Incidence Matter? AIDS Behav. 2019 Jul 18. doi: 10.1007/s10461-019-02609-2.
Fernández-Niño JA, Bonilla-Tinoco LJ, Manrique-Espinoza BS, Salinas-Rodríguez A, Santos-Luna R, Román-Pérez S, Morales-Carmona E, Duncan DT. Neighborhood features and depression in Mexican older adults: A longitudinal analysis based on the study on global AGEing and adult health (SAGE), waves 1 and 2 (2009-2014).PLoS One. 2019 Jul 10;14(7):e0219540. doi: 10.1371/journal.pone.0219540. eCollection 2019.
Li Ding, Chen Feng;. DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds.The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 8650-8659. arXiv:1811.11397 [cs.CV]
- Saif Jabari (NYU AD) and Assistant Professor Joseph Chow (NYU Tandon)are looking to hire a postdoc based at NYU Abu Dhabi this fall! If anyone is interested in working in topic areas related to operations research, urban transportation systems, and shared use mobility network analysis, please email email@example.com.