Current Funding
Optimizing the Temporal Resolution in Dynamic Graph Mining
Funded by National Geospatial-Intelligence Agency (NGA), $361k
Duration: 9/20-8/23
Short Summary
Dynamic graph mining can elucidate the activity of in-network processes in diverse application domains from social, mobile and communication networks to infrastructure and biological networks. Compared to static graphs, the temporal information of when graph events occur is an important new dimension for improving the quality, interpretation and utility of mined patterns. However, mining dynamic graphs poses an important, though often overlooked, challenge: observed data must be analyzed at an appropriate temporal resolution (timescale), commensurate with the underlying rate of application-specific processes. The main objective of the project is to improve the quality and interpretability of dynamic graph mining results by bridging the disconnect between timescale selection and the data mining algorithms. Enabling timescale-aware methods is pivotal to improving the utility of dynamic graphs and their increasingly ubiquitous applications. Project website
Collaborative Research: New class of near-infrared fluorophores derived from DNA-templated silver clusters for deep tissue imaging.
Funded by National Science Foundation , $582k (UAlbany’s support: $251k), Award Number: 2025793
Duration: 9/20-8/23
Short Summary
Since near-infrared (NIR) light penetrates much farther into biological tissues than visible light, NIR fluorescence microscopy allows noninvasive imaging deep into tissues and even whole organisms. However, deep tissue imaging is currently hindered by a lack of small, bright, non-toxic biolabels that emit NIR light. This project will develop brightly fluorescent NIR biolabels by harnessing a class of promising tunable nanomaterials called DNA-templated silver clusters. Silver cluster design and discovery will be carried out with novel machine learning algorithms that learn from experimental materials and inform design. The optimized NIR biolabels will be employed to study endocrine hormones central to metabolic and cardiovascular disease and will also be broadly applicable for biomedical research in other areas, such as tumor formation and metastasis. Undergraduate and graduate student researchers participating in this project will receive multidisciplinary training at the unique intersection of biophotonics, nanomaterials, and data science, and research opportunities for undergraduates will focus on California community college students and transfer students. Project website
SCC: Integrating Heterogeneous Wide-Area Networks and Advanced Data Science to Bridge the Digital Divide in Rural Emergency Preparedness and Response.
Funded by National Science Foundation, $1.5m, Award number: 1831547
Duration: 9/18-9/22
Short Summary
The goal of this project is to develop, implement, and systematically analyze a comprehensive framework and a multi-layer platform for timely information collection, integration, exchange and dissemination to support emergency preparedness and response (EPR) in rural communities. This goal will be met through three primary activities. First, the project will develop a heterogeneous network architecture and corresponding protocols that leverage wide-area wireless backhaul over TV white spaces, WiFi and pocket-switching to provide (i) continuous communication to first responders and (ii) delay-tolerant information access to residents. In addition, the project will design a smartphone app which will support the collection of information from different sources and its exchange among first responders, government agencies and residents. Second, the mobility patterns and network availability collected will enable the development of a dynamic probabilistic community network model. Novel graph-theoretic algorithms will identify information-depleted sub-communities and inform optimal information dissemination strategies. Finally, the project will assess adoption and use of the technologies by various community members to maximize the benefits associated with timely, rich and high-quality information, disseminated through technological devices. Continuous community engagement activities for data provisioning, app design, impact co-evaluation and path to sustainability of the project are key factors for success. The framework will be co-designed and piloted in collaboration with the Town of Thurman, NY. Transferability to the broader rural context will be assessed by engaging two additional rural communities in coordination with the AirBand initiative. Project website
Past Funding
STTR Phase 1: Multi-Layer Mapping of Cyberspace using Composite Dynamic Graph Extraction, Mining and Visualization.
Funded by Office of Naval Research [$80k], Duration: 6/18-12/18
The Layered Inference for Cyber Network Knowledge Synthesis (LINKS) system allows cyber defenders and analysts to visualize, explore, and understand normal and anomalous patterns that occur across the multi-layer cyberspace domain. Underlying this capability are novel graph algorithms that can infer multi-layer event graphs from disparate data sources and effectively mine these graphs in the presence of uncertainty
Accurate and Scalable Simulation of Influence in Online Social Networks
Funded by DARPA SocialSim [$1.9 Million], Duration: 10/17-8/19
SimON is a suite of models, methods, and tools for accurate simulation of the spread of information and evolution of influence in online social networks (OSNs) at scale. The project is part of DARPA 4-year SocialSim program 2017-2021. SimON incorporates multiple models into a comprehensive high-fidelity simulation environment, building upon insights from sociocultural and behavioral analysis, message content understanding, crowd psychology and collective beliefs, network topology and agent synchronicity, social network analysis, and information diffusion theories.
FRAP-A: Learning the Clock of Network Processes from Big Data
Funded by University at Albany-SUNY [$9.3K], Duration: 05/17-04/20
Real-life phenomena can often be modeled as processes on networks: electrical grid failures, misinformation spread in social media, gene expression regulation during embryonic development and brain region co-activation during cognitive tasks to name a few. The tremendous advances in sensor technology, life sciences and the Web have allowed for measuring such networks at unprecedented temporal and spatial resolution, giving rise to Big Network Data. At the same time elegant theoretical models for dynamic network processes have been developed for phenomena in different areas. Employing such models for predictions based on real-world data, however, still presents a significant challenge since network processes have widely varying temporal rates and may evolve in stages that may have varying duration. Hence, to make real-world Big Data actionable, we need to be able to learn the internal clock of network processes directly from data.
Cracking the Color Code of DNA-stabilized Metal Nanoclusters with Rapid Optical Array Characterization and Machine Learning
Funded by NSF [$70.7K], Subaward number: 75595
, Duration: 05/16-08/17
DNA-stabilized silver clusters (Ag-DNAs) are novel fluorophores that are finding numerous applications in nanophotonics, chemical sensing, and bioimaging. The fluorescence colors of Ag-DNAs can be tuned from bluegreen into the near-infrared by selecting the sequence of the single-stranded DNA that templates the cluster. Using a training set of DNA template strands and the fluorescence spectrum associated with each strand, we mine discriminative multi-base DNA motifs that correlate with fluorescent cluster brightness. Furthermore, using such motifs to parameterize DNA templates, we develop a machine learning-based tool to design novel DNA templates that stabilize brightly fluorescent Ag-DNAs.
FRAP-A: Detecting and Modeling Bursty Network Processes
Funded by University at Albany-SUNY [$9K], Duration: 05/15-04/17
Dynamic networks arise in a variety of settings: on-line social and information networks, communication and transportation networks, functional brain maps and protein interaction networks. Processes within these networks including social rumor spread, traffic jam evolution, disease contagion growth and brain region co-activation during cognitive tasks can all be modeled as butsty sequences of events within a locality of the underlying network. The goal of this project is to develop efficient models and algorithms to detect, track and model bursty processes within dynamic networks that modify node states, the network topology or both.