Network Systems Science and Advanced Computing

Latest Highlights

Achla Marathe and Anil Vullikanti

Researchers publish paper on opioid hotspots

Anil Vullikanti, Achla Marathe, and their students published a paper in the JMIR Public Health Surveillance journal titled, “Detection of Spatiotemporal Prescription Opioid Hot Spots With Network Scan Statistics: A Multistate Analysis.”

Congratulations Arindam Fadikar

Congratulations Arindam Fadikar

Congratulations to former graduate research assistant Arindam Fadikar on passing his final dissertation defense. Argonne National Laboratory is lucky to have you!

ICML 2019

Paper by NSSAC researchers presented at ICML 2019

Anil Vullikanti presented a paper by NSSAC researchers titled “PAC learnability of node functions in networked dynamical systems” at the International Conference on Machine Learning 2019.

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Our Approach

NSSAC is pursuing an advanced research and development program for interaction-based modeling, simulation and associated analysis, experimental design, and decision support tools for understanding large biological, information, social, and technological (BIST) systems. Extremely detailed, multi-scale computer simulations allow formal and experimental investigation of these systems. The need for such simulations is derived from questions posed by scientists, policymakers, and planners involved with very large complex systems. Our team is comprised of a diverse group of researchers from various domains including computer science, cognitive science, mathematics, biology, health science, electrical engineering, and economics.

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Focus Areas

Social Cognitive and Behavioral Sciences

Social, Cognitive, and Behavioral Sciences

When will a social media protest transition to a civil crisis? What is the tipping point for rhetoric to become action? How do we design effective, massive health messaging campaigns to make the public’s health more robust? Effective analysis and understanding of such questions must do more than compile data; it should be driven by theoretical knowledge and first principles of what motivates human behavior. This implies multiple levels of scale that coincide with key disciplines of human behavior—the cognitive, behavioral and social sciences—each of which offer unique insights into understanding and prediction. Our sophisticated computational simulations are designed to account for such theoretical first principles, allowing us to provide decision-makers with robust predictions of the influence of policies and the specifics of their implementation on human conduct.

 

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Interdependent Infrastructures

Interdependent Infrastructures

Networks are ubiquitous in our modern vocabulary. From social media to transportation and resource distribution networks, they are the fabric of the interdependent infrastructures we rely on to navigate the complexities of our daily lives. Our work streamlines network analysis, offering effective tools to assist policymakers as they grapple with the most pressing issues: How does a city recover from a natural disaster? How does climate change impact society? How can we best fight infectious disease outbreaks? The versatile tools we have developed interpret, forecast, and explain the dynamics of massively interacting systems.

 

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Systems Biology and Informatics

Systems Biology and Informatics

Revolutions in medical technology that promise patient-centered, personalized medicine are producing an unfathomable amount of data. New techniques that identify previously unknown organisms in our environment, and even in our bodies, are expanding our knowledge of the complexities of ecosystems and living organisms. As the data grows exponentially, our user-friendly tools help research scientists, healthcare professionals, and policymakers distill this information down to manageable and actionable relationships.

 

 

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Public Health Policy Planning

Health Sciences

In any public health crisis, timely knowledge is power. Our transdisciplinary team leverages predictive modeling tools to inform and support public health policymakers as they identify and deploy essential response measures. Our distinguished record of success is visible in the support we provided key government agencies as they tackled H1N1 in the United States and cholera in Haiti, and in the data and analysis we are provided to help contain the Ebola outbreak in western Africa.

 

 

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Additional Areas of Expertise

Researchers at the Biocomplexity Institute have a long history of project expertise. See here for a list of additional contributions to science.

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Computing for Global Challenges Program

Computing for Global Challenges is a summer undergraduate research program that trains undergraduates in the use of computational methods to address real-world problems. Students work with faculty mentors on a range of ongoing research projects, and learn about cutting-edge methods in machine learning, data science, computational biology, and more.

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Join Our Team

Please see our UVA job listings for opportunities with the Biocomplexity Institute and Initiative. Use query term "biocomplexity".

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Publications

PAC learnability of node functions in networked dynamical systems.
Proceedings of the 36th International Conference on Machine Learning, PMLR 97. (2019) A. Adiga, C. Kuhlman, M. Marathe, S.S. Ravi, A. Vullikanti

 

The Matrix: An Agent-Based Modeling Framework for Data Intensive Simulations. ABS
Proceedings of the 18th International Conference On Autonomous Agents And Multiagent Systems, 1635-43. (2019) P. Bhattacharya, S. Ekanayake, C. Kuhlman, C. Lebiere, D. Morrison, S. Swarup, M. Wilson, M. Orr

 

Discovery of under immunized spatial clusters using network scan statistics. ABS
BMC Medical Informatics And Decision Making, 19(1):28. (2019) J. Cadena, D. Falcone, A. Marathe, A. Vullikanti

 

Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation: Application Background. ABS
Indiana University Technical Report. (2019) G. Fox, J. Glazier, J. Kadupitiya, V. Jadhao, M. Kim, J. Qiu, J. Sluka, E. Somogyi, M. Marathe, A. Adiga, J. Chen

 

Approaches for Assigning Offsets to Signals for Improving Frame Packing in CAN-FD. ABS
IEEE Transactions On Computer-Aided Design Of Integrated Circuits And Systems. (2019) P. Joshi, S.S. Ravi, Q. Liu, U. Bordoloi, S. Samii, S. Shukla, H. Zeng

 

Attractor Stability in Finite Asynchronous Biological System Models. ABS
Bulletin Of Mathematical Biology, 81(5):1442-1460. (2019) H. Mortveit, R. Pederson

 

Efficiently Encoding Complex Biochemical Models with the Multistate Model Builder (MSMB). ABS
Modeling Biomolecular Site Dynamics, 1945:119-139. (2019) A. Palmisano, S. Hoops, L. Watson, T. Jones, J. Tyson, C. Shaffer

 

Guy1, a Y-linked embryonic signal, regulates dosage compensation in Anopheles stephensi by increasing X gene expression. ABS
eLIFE, 8:E43570. (2019) Y. Qi, Y. Wu, R. Saunders, Xg. Chen, C. Mao, J. Biedler, Z. Tu

 

Impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States. ABS
BMC Infectious Diseases, 19(1):221. (2019) M. Singh, P. Sarkhel, G. Kang, A. Marathe, K. Boyle, P. Murray-Tuite, K. Abbas, S. Swarup

 

Modeling commodity flow in the context of invasive species spread: Study of Tuta absoluta in Nepal. ABS
Crop Protection. (2019) S. Venkatramanan, S. Wu, B. Shi, A. Marathe, M. Marathe, S. Eubank, Lp. Sah, A. Giri, L. Colavito, K. Nitin, V. Sridhar

 

Influence of the Spatial Resolution of the Exposure Estimate in Determining the Association between Heat Waves and Adverse Health Outcomes. ABS
Annals of the American Association of Geographers, 109(3):875-886. (2019) C. Wu, B. Zaitchik, S. Swarup, J. Gohlke

 

Previous Publications

 

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