Annetta Burger, Computational Social Science PhD candidate, Graduate Research Assistant with the Center of Social Complexity and member of the team working on a DTRA-funded project led by Dr. William Kennedy entitled “Response to a WMD in a Mega-City,” recently attended and presented at the Society for Applied Anthropology (SfAA) 2018 Annual Meeting: Sustainable Futures in Philadelphia. Ms. Burger’s presentation, From Networks to Recovery: An Agent-based Model of Community Resilience, suggested that the community resilience and social capital that lead to improved local recovery from disasters are derived from social connections and social network structures that provide informational, financial and other in-kind resources, and that by modeling social network representations for a simulated society it is possible to explore how households recover from disaster.
Dr. Andrew Crooks, Associate Professor, Computational Social Sciences Program in the Department of Computational and Data Sciences and a member of the Center for Social Complexity, presented Innovations in Urban Analytics at the Association of American Geographers’ annual meeting in New Orleans. Innovations in Urban Analyticsis a look at new forms of data about people and cities that are fostering research that is disrupting many traditional fields. These new forms of micro-level data have led to new methodological approaches in order to better understand how urban systems behave. Increasingly, these approaches and data are being used to ask questions about how cities can be made more sustainable and efficient in the future.
Dr. Claudio Cioffi, Director of the Center for Social Complexity and Professor, Computational Social Science Program within the Department of Computational and Data Sciences along with Niloofar Bagheri-Jebelli, Computational Social Science PhD student, presented a paper, “A Computational Approach to Initial Social Complexity: Göbekli Tepe and Neolithic Polities in Urfa Region, Upper Mesopotamia, Tenth Millennium BC,” at the 83rd Annual Meeting of the SAA (Society for American Archaeology held in Washington, DC in April.
Due to excellent student evaluations – 4.75 out of 5 under “My overall rating of teaching” for Mason Core Info Tech course – Computing for Scientists, Sections 001 and 007, Dr. Joseph Marr was recently recognized by the Associate Provost and Mason Core Committee for his teaching excellence. These evaluations highlight the value the Committee places on students’ perception of teaching in the Mason Core. In its communication to Dr. Marr, the Committee stated “Excellent teaching isn’t easy, or often highly rewarded, but we wanted to recognize your efforts.” Congratulations Dr. Marr!
We currently have one open PhD Positions at Institut Pascal (France). Please find the announcements below:
For the Innovative Training Network (ITN) project ACHIEVE ( http://www.achieve-itn.eu/ ) – AdvanCed Hardware/Software components for Integrated/Embedded Vision systEms (H2020-MSCA-ITN-2017), we are looking for one motivated early stage researcher in Deep learning methods and digital systems (hardware description languages, FPGA and GPU architectures…).
The researcher fellow will be hosted at the Institut Pascal in the DREAM research group of Université Clermont-Auvergne (UCA), for a period of 36 months with the aim of obtaining a PhD. The DREAM group is working on hardware and software development of advanced computer vision architectures. It was formed in 2008 and until now we have been lucky to work with six visiting professors/researchers and more than 15 graduate students. The group is performing research in the following areas:
- Design of smart cameras
- Image processing architectures
- Software methods and tools for embedded systems
The PhD training includes a second internship in the company NVIDIA ( http://www.nvidia.com ),
the famous producer of GPU. Nvidia is located in Paris.
You will receive a PhD scholarship according to the general conditions at Université Blaise Pascal. Tax-fee scholarship includes full social security coverage (net monthly amount starting at ± 2.300 EUR/month + 250 EUR/month mobility allowance + (if applicable) family allowance of 500 EUR). The initial contract will be for a period of 1 year and will start in the third quarter of 2018; this contract should be extended for a total of 3 years, subject to good performance. You will work at the Institut Pascal research group. Information about research group can be found on the web: http://dream-lab.fr
The new researchers will therefore work as a team with existing researchers. They will also cooperate with the other researchers in the ACHIEVE network and participate in the ACHIEVE’s training program.
Deep learning (DL)  methods are currently adopted to solve an ever-greater number of problems in computer vision ranging from image classification to semantic segmentation and object detection . Nevertheless, the execution of such algorithms involves a high computational load and requires a large amount of processing, which calls for dedicated and tailored hardware support .
For years, Graphical Processing Units (GPUs) have demonstrated state-of-the-art computational performance in DL acceleration. However, this task is shortly moving towards custom dedicated hardware implementations on FPGAs and ASICs, especially in embedded systems . In fact, platforms like FPGAs and ASICs are known to deliver better computation/watt performances than GPUs. Moreover, recent trends in DL development demonstrate the efficiency of using extreme compact data types, . These trends promote custom implementations in FPGAs that are designed to handle irregular parallelism and custom data types. Nonetheless, GPUs remain a target of choice to accelerate deep learning thanks to their growing computational capabilities  and ease of programmability.
In this context, the proposed PhD aims to investigate heterogenous embedded accelerators for deep learning with GPU/FPGA co-design. Within the DREAM research group, the main tasks are:
- Study state-of-the-art algorithms to accelerate deep learning inference and training , and tune them for embedded systems/smart cams.
- Portioning the deep learning processing pipeline on the resources of an heterogenous hardware platform.
- Derive efficient data-paths and hardware accelerators on GPU/FPGA-based embedded systems/smart cams.
Bibliography Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.  J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, L. Wang, G. Wang, J. Cai, and T. Chen, “Recent Advances in Convolutional Neural Networks,” Pattern Recognit., 2017.  A. Canziani, A. Paszke, and E. Culurciello, “An Analysis of Deep Neural Network Models for Practical Applications,” arXiv e-print, May 2016.  E. Nurvitadhi et al. “Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?,” in Proceedings of the ACM/SIGDA International Symposium on Field-Programmable Gate Arrays – FPGA ’17, 2017, pp. 5–14.  M. Courbariaux, Y. Bengio, and J.-P. David, “BinaryConnect: Training Deep Neural Networks with binary weights during propagations,” in Advances in Neural Information Processing Systems – NIPS’15, 2015, pp. 3123–3131.  I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio, “Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations,” J. Mach. Learn. Res., Sep. 2018.  Nvidia, “NVIDIA Volta: The New GPU Architecture, Designed to Bring AI to Every Industry.,” Webpage, 2018. [Online]. Available:https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/. [Accessed: 06-Apr-2018].  S. Winograd, Arithmetic complexity of computations, vol. 33. Siam, 1980.
Profile of the candidate
You have a Master of Science degree (at the start of the PhD) and a strong background in Digital design. Candidates with an MsC in another discipline but with good skills in programming languages, signal/image processing may also be considered.
You have a strong interest in image processing, embedded systems for computer vision, a good knowledge of mathematics, signal or image processing, and good programming skills. As stated in the preamble, you must have a working experience in digital system design (VHDL) and/or programming language implementation (C, C++, Cuda).
You function well in a team. You have good or excellent English and scientific writing skills. You combine a strong interest in scientific research with a desire to see your work applied in industry. Due to EC funding rules, only candidates with less than 4 years of research experience can be considered. Candidates MUST not have carried out their main activity (work-studies …) in France for more than 12 months in the past 3 years and must have carried out less than 4-year research experience starting on the date of achieving master degree. Université Clermont Auvergne implements gender-neutral recruitment and selection procedures. Female candidates are especially encouraged to apply.
How to apply
Please submit your application by email to Prof. Francois BERRY at firstname.lastname@example.org
In your email, please include the following:
- A brief motivation of your application: what do you consider the best facts in your CV, which demonstrate your academic excellence in BsC and/or Msc. education? What are your reasons to pursue a PhD? Why would you like to work at Université Clermont-Auvergne? …
- A detailed CV, describing your earlier experience and studies;
- A list of publications (if available);
- A transcript of your educational record (list of courses per year, number of obtained credits, obtained marks) if available. This need not be official document at this stage;
- A (rough) indication or estimate of your rank among other students (e.g., top 10% among 35 students in my master);
- If available: 1-3 English language documents describing your earlier research (e.g., scientific papers, master thesis, report on project work, etc.). These documents need not be on the topic of the positions.
Master Systemes embarques pour le son et l’image: setsis.eupi.uca.fr
tel: +33 (0)4 73 40 72 52
Due to their successful developed of Sugarscape, Drs. Axtell and Epstein were mentioned in a Science Magazine article written by M. Mitchell Waldrop entitled “Free Agents – Monumentally complex models are gaming out disaster scenarios with millions of simulated people.” Sugarscape is an ‘artificially intelligent agent-based social simulation’.
William Lamberti and James Andrews, Computational Sciences and Informatics PhD students along with Matthew Oldham, Computational Social Science PhD candidate, were awarded the 2018 Summer Presidential Scholar Fellowship so that they can continue their progress towards the PhD degrees. The Scholarship Review Committee reviewed dozens of excellent applications, all from Presidential Scholars conducting cutting-edge, innovative and inspiring research. It was a very competitive review process and selection of recipients was a difficult task. The committee was deeply impressed with the quality and breadth of research being conducted at Mason.
Congratulations to Billie, Matt, James and all other recipients of this prestigious award.
Congratulations to Computational Social Science Ph.D. alumnus, Jose Manuel Magallanes Reyes, on his new book “Introduction to Data Science for Social and Policy Research: Collecting and Organizing Data with R and Python.”
Jose Manuel Magallanes, professor of political science and public policy in the Department of Social Sciences at Pontificia Universidad Catolica del Peru, is a visiting professor at the University of Washington’s Evans School of Public Policy and Governance. Jose Manuel is funded by the University of Washington’s eScience Institute thanks to the Washington Research Fund, the Alfred P. Sloan Foundation, and the Gordon and Betty Moore Foundation, where he is a Senior Data Science Fellow.
Jose Manuel’s expertise is on computational approaches to public policy analysis and political decision-making. During his stay at UW, he is conducting interdisciplinary research and proposing feasible applications of Big Data Analytics and Computational Social Science techniques to public sector data in Peru.
Jose Manuel graduated as a Computer Scientist from Universidad Nacional Mayor de San Marcos (UNMSM), and holds a Master degree in Political Science from Pontificia Universidad Catolica del Peru and a Ph.D. in Psychology from UNMSM. He is an affiliate researcher at the Center for Social Complexity at George Mason University, where he completed his second Ph.D. in Computational Social Science. During the last 15 years, Jose Manuel has served as a public officer at the central and local level, and has been involved in several initiatives in the public sector of his country to make better use of the data for better policy and political research and decision-making. His main goal is to keep assisting key Peruvian institutions in the process towards open government.
On July 26-30, 2017, the 1st North American Social Networks (NASN) Conference, a regional conference of the International Network of Social Network Analysis (INSNA), hosted a conference in Washington, D.C. The NASN conference provides an interdisciplinary venue for social scientists, mathematicians, computer scientists, ethnologists, epidemiologists, organizational theorists, public health experts, and others to present current work in the area of social networks.
The conference’s prominent keynote speaker was David Lazer, Distinguished Professor of Political Science and Computer and Information Science, Northeastern University, and Co-Director, NULab for Texts, Maps, and Networks.
Joe Shaheen, Computational Social Science PhD student within the Department of Computational and Data Sciences, was invited to present his paper “Combining Social Network Analysis with Agent-based Modeling to Reproduce an ISIS Social Media Network” at this conference. The paper focused on using social effects to reproduce a collected friend and follow network from social media using bottom-up growth.
An agent-based model is developed to understand the behaviors and rule-sets that generate social media networks. Simple rules are used to synthetically generate a backcloth (friend/follow) network collected using the Twitter API. The Twitter network was collected using seed accounts for known terrorist propaganda accounts. Model parameter adjustments were made to reproduce the collected net-work’s summary statistics and stylized specifics such as average degree, clustering, community size and distribution, as well as general structural metrics. An approximate network was produced in line with the general properties of our collected data. In this paper, we present our findings on the reproduction of a social media network with a focus on testing similarity of summary statistics and structural properties. We find that it is possible to generate a social media network utilizing a few simple rules and a unique time-rule which sets varying interaction rates on entry of new nodes in comparison to existing node activity. We call this time rule mechanism a coupled rule-set. We also present weaknesses in our reproduction and propose an extension of the model for future work which could better reproduce more exact network properties.