DSI Research Networking Roundtables
September 19, 2017, 12:00 - 3:15pm
How does it work?
The roundtable event provides a focused opportunity for researchers at Brown to meet colleagues from across the university and learn about their activities, resources and expertise in data-driven research. Roundtables represent a broad range of academic domains at Brown, including arts, humanities, social sciences, physical sciences, and life sciences.
There are two sessions, each divided into two 30-minute time blocks for discussion.
Each session will feature around 10 tables staffed by leaders from various institutes, centers and departments at Brown. At the beginning of each 30-minute time block, you will select a table to join. To start the discussion, the roundtable leaders will provide an overview of research, resources, expertise, collaborative opportunities, and potential for student engagement. After the first 30 minutes, a new round of discussions begins, and you can join another table.
Thirty-minute blocks of time will allow for more in-depth discussion than is usually possible at seminars, workshops, or lightning talks, giving attendees and roundtable leaders a chance to learn about particular research programs in some detail and make new connections.
Who should attend?
Anyone interested to learn more about the range and scope of data-related research at Brown and to explore potential collaborations in the various domains of data science. Examples include graduate students wanting to learn about interesting data sources for dissertations and theses; researchers with complex data seeking to learn about new analytic methodologies; faculty and students wishing to learn how to access technical expertise and infrastructure such as high-speed computing or data visualization software; and methodologists wanting to explore the vast range of data-driven research at Brown, including in the arts and humanities.
12:00 – 1:00 Meet the Team / Lunch
- DSI Director Jeff Brock, Professor of Mathematics, will provide an overview of the DSI, introduce new master's students, and describe collaborative opportunities.
- Registered attendees will receive a box lunch
1:00 – 2:00 FIRST ROUNDTABLE SESSION
- 1:00 – 1:30 Round 1
- 1:30 – 2:00 Round 2
2:00 – 2:15 Coffee
2:15 – 3:15 SECOND ROUNDTABLE SESSION
- 2:15 – 2:45 Round 1
- 2:45 – 3:15 Round 2
TABLES FOR FIRST SESSION (1:00 - 2:00)
Massimo Riva, Evelyn Lincoln, Patrick Rashleigh
The Center for Digital Scholarship in the Brown University Library supports scholarly and academic activities conducted or enhanced through the use of digital technology. CDS staff teach workshops in tools and methods that are used in the Digital Humanities, support a variety of DH faculty projects, provide support for writing grants and conducting grant work, and have been invited to contribute to courses that incorporate DH assignments. CDS seeks out new collaborations with researchers in humanities (and other) disciplines that do not traditionally make use of digital methods. For example we might develop class modules on data mining, text encoding or data discovery and collection, or we might collaborate on the information architecture of a new research project.
The Digital Scholarship Lab will be hosting a digital epigraphy workshop conference, Oct. 5-7. Click here for more information.
The Virtual Humanities Lab at Brown University, or VHL, was created in 2004 thanks to a two-year grant from the National Endowment for the Humanities: in its original configuration, the VHL included a platform for the collaborative online editing and publishing of a variety of classic texts in Italian and Latin. The VHL is an international endeavor: contributing scholars are located in the U.S., Europe (Italy and the U.K.), Latin America (Mexico and Argentina), and Australia. More recently, in collaboration with the Brown Library, the VHL has included projects which focus on the development of library collections and archives, such as the Garibaldi Panorama & the Risorgimento Archive, and the Theater that Was Rome. Data-driven projects are a potentially new area of development for the VHL: we are interested in exploring collaborations in the area of text analysis and “distant reading,” including such methodologies as topic modeling, text clustering, sentiment analysis, network analysis.
Faculty, students and staff in the Department of Biostatistics and Center for Statistical Sciences conduct research on the development and application of new statistical methods for the analysis of data related to public health, biology and clinical medicine, including massive and complex data such as from brain images and social networks. Our faculty collaborate with researchers from across the University, and offer both undergraduate and graduate level classes on statistical methodology. We are interested to explore new collaborations on projects that demand new and innovative statistical approaches to address substantive problems in all areas of empirical research, especially health-related research.
Center for Evidence Synthesis in Health
Christopher Schmid, Thomas Trikalinos
At the Center for Evidence Synthesis in Health, we use research and analytic tools to transform vast quantities of evidence into essential knowledge. Our work helps policy experts and clinicians make informed and rational decisions. We conduct research in, and teach the principles of, research synthesis and evidence contextualization. The center includes statisticians, clinicians, epidemiologists, librarians, computer scientists, and health services researchers. Examples of our past work include systematic reviews of cancer care, chronic kidney disease, bariatric surgery and limb prostheses. Our team offers several free software programs to simplify collecting, organizing and analyzing the data.
Department of Computer Science
Ugur Cetintemel, David Laidlaw
The Computer Science Department conducts research and offers courses on a wide array of data science-related topics including data management and engineering, machine learning, artificial intelligence and natural language processing, interactive analytics and data mining, information privacy and security, big data algorithms and visualization. We are interested in pursuing collaborations to advance fundamental computational and data-centric methods as well as explore their innovative applications to challenging real-world problems.
Department of Education
Faculty in the Department of Education engage in a wide range of quantitative research projects such as conducting randomized field trials, working with large scale administrative data from school districts, and analyzing nationally representative secondary datasets. Education provides an exciting field in which to apply new data analytic methods given the newly available longitudinal data captured in student information systems that can be linked to data from other governmental agencies.
Department of Physics
Meenakshi Narain, Robert Pelcovits, Brad Marston, Jonathan Pober
Physicists are working with some of the largest sets of data in existence. From using cutting-edge pattern recognition algorithms to identify particles in high-energy collisions, to analyzing surveys of galaxies and the large-scale structure of the universe, to elucidating teleconnections in climate data, faculty, staff and students in the Department of Physics are advancing data science on several diverse fronts.
Brown Institute for Brain Science
Stephanie Jones, Thomas Serre
The mission of the Brown Institute for Brain Science is to discover and innovate in brain science; apply knowledge to benefit society; and promote a community of diverse opinions, open minds, and disruptive ideas. BIBS supports collaborative research, training and outreach in brain science to benefit 130+ faculty from a range of academic and clinical departments at Brown University. Computation has always been at the core of neuroscience and is an existing strength at Brown, as researchers strive to understand how the brain computes and stores information. Opportunities around data science include: human and animal brain imaging; analysis of neural data, including high-bandwidth multi-channel data; machine learning; behavioral analysis; genomics; brain modeling and theory. Applications include understanding healthy brain function; improving diagnosis, assessment, and treatment of neurologic and psychiatric disorders including Parkinson’s disease, depression, epilepsy, chronic pain, and autism; neural prosthetics; and brain-like machines computing.
Humanity Centered Robotics Initiative
Michael Littman, Bertram Malle
The Humanity Centered Robotics Initiative is focused on studying and designing robotic systems that work with people for the benefit of people. We are interested in the use of data to understand the society-wide impacts of automation and human-robot interaction. We are also interested in the use of data in driving the interactions themselves, for example, by using data to create better sensing and acting systems, to learn social norms, and to optimize the development of robots that people feel comfortable working with.
Rhode Island Innovative Policy Lab
Mark Howison, Mintaka Angell
The Rhode Island Innovative Policy Lab (RIIPL) is a foundation-funded partnership between Brown University and the Rhode Island state government. We are a team of more than 30 behavioral economists, data scientists, and policy analysts devoted to unlocking the power of data and science to improve policies, alleviate poverty, and increase economic opportunity. We have created a 360° view of family well-being and social program impact in Rhode Island by combining all administrative data across the state using state-of-the-art techniques to anonymize and secure data records. Using this database, we apply scientific best practices to the most accurate data to propose solutions to challenging social and policy problems in Rhode Island.
Joukowsky Institute for Archaeology and the Ancient World
Peter van Dommelen, Katherine Brunson, Miguel Ángel Cau Ontiveros, Catalina Mas-Florit
The Joukowsky Institute for Archaeology and the Ancient World is dedicated to the academic study and public promotion of the archaeology and art of the ancient Mediterranean, Egypt, and the Near East. Our principal research interests lie in the complex societies of the pre-modern era and our mandate is to promote research, fieldwork, teaching, and public outreach. We're interested in data management and sharing, digital publication and dissemination, new methods for studying spatial and temporal trends, and digital models of both objects and places (through 3D modeling and VR spaces like The Cave).
Center for Computation & Visualization (CIS)
Gurcharan S. Khanna, John Huffman, Helen Kershaw
The Center for Computation & Visualization (CCV) partners with faculty, students, and researchers to support their research with advanced computational and visualization technologies. Our Ph.D. level application scientists work with research groups to create optimal tools and processes for their computational research goals. Staff may be embedded into their teams or simply consult for them.
The CCV provides on-campus resources including an optimized high performance computing cluster with thousands of cores, almost a petabyte of storage, and very high speed network interconnects. Researchers may buy into the cluster or simply pay as you go. We also provide a range of visualization resources including a CAVE, the YURT, and a visualization lab. We also support external supercomputer access with our Campus Champions.
TABLES FOR SECOND SESSION (2:15 - 3:15)
School of Engineering
Research in the School of Engineering is broad and has a strong range of data-centric projects in several disciplines. Examples include the use of machine-learning regression to accelerate atomic-scale calculations to design improved materials and devices, computer vision algorithm development to produce numeric or symbolic information, and deep learning techniques to analyze large-scale neuroengineering data.
Center for Computational Molecular Biology and COBRE Center for Computational Biology of Human Disease
David Rand, William Fairbrother
The Center for Computational Molecular Biology (CCMB) promotes the development, implementation and application of analytical and computational methods to foundational questions in the biological and medical sciences. The research programs of the Core Faculty in CCMB lie fundamentally at the intersection of computer science, evolutionary biology, mathematics, and molecular and cellular biology. Biological questions that currently unite the CCMB Core and Associate Faculty are: How do genotypes and genes interact to produce phenotypes, and how does this happen from womb to tomb? What drives the formation, maintenance and evolutionary transformations of communities of organisms over time? Quantitative questions that currently unite the CCMB faculty are: how can we design powerful algorithms to make sense of the sea of data produced in the genomic era? What principles are required for a theoretical framework to completely model cellular systems?
The COBRE Center for Computational Biology of Human Disease embraces the age of genomics medicine from an explicitly data-driven, computational perspective. By building a collaborative Center of empirical and computational scientists, we advance new discoveries, algorithms, and genomic screening approaches with direct relevance to several human diseases. The Computational Biology Core (CBC) of the COBRE Center is a group of scientists, data analysts, and software engineers that support data-intensive research. The near-term, primary mission of the CBC is to support Junior Investigators in the analysis and interpretation of high-throughput DNA/RNA sequencing datasets, including data generated in their laboratories and large, publicly-available datasets. The long-term goal of the CBC is to provide a sustainable resource to support data analysis challenges faced by genomic studies across Brown University and our Affiliated Hospitals.
Center for Gerontology and Healthcare Research
Theresa Shireman, Jeff Hiris, Alison Fisher
The Center for Gerontology and Healthcare Research is a nationally prominent research center that studies the diverse health and social service needs of elderly and other persons with chronic illnesses. The Center for Gerontology and Health Care Research maintains a state-of-the-art "dry lab" research environment, complete with computer equipment, software, programming environments, systems programming and data processing staff. We have extensive holdings of publicly available surveys and DUA-protected administrative claims data. Our analysts are experienced at dealing with the complex issues arising from analyzing multi-level and complex-sample data; generating graphical as well as statistical output from these large and complex datasets; and displaying utilization maps, using GIS software. We have analytic expertise including, but not limited to, a broad array of research skills including health economics, pharmacoepidemiology, clinical trials, implementation science, clinical epidemiology, causal inference, and policy analysis, and our investigators are enthusiastic about collaborating within the Brown community and with colleagues around the country.
Advance Clinical and Translational Research (Advance-CTR) supports clinical and translational researchers in Rhode Island through funding, research services and resources, and professional development opportunities. Advance-CTR is comprised of two award cores and three service cores, which include Clinical Research Design, Biostatistics and Epidemiology, and Biomedical Informatics and Cyberinfrastructure. Faculty have access to research support services through these cores and may participate in associated workshops, seminars, and trainings.
The Brown Center for Biomedical Informatics (BCBI) was founded in July 2015 to lead the development and application of informatics approaches in biomedicine and health care. The three-fold mission of BCBI is to: (1) innovate how electronic biomedical and health data are used, (2) implement solutions for improving biomedical research and healthcare delivery, and (3) inspire the next generation of biomedical researchers and clinicians in partnership with collaborators in existing areas of excellence at Brown, its healthcare affiliates, and statewide healthcare organizations.
The CIS Data Science Practice collaborates closely with several research labs and PIs at Brown to build informatics pipelines and integrated data resources, and to apply, evaluate, and improve computational methods for data-intensive research. Our full-time staff of 10 have expertise in machine learning, statistics, informatics, data visualization, text mining, and software engineering. In addition to working across disciplinary boundaries on research projects in the physical sciences, social sciences, life sciences, and the humanities, we also collaborate with senior administrators to support data-informed decision making using Brown's institutional data.
The Mathematics Department at Brown balances a lively interest in students and teaching with a distinguished research reputation. Our several strong research groups, Analysis, Algebraic Geometry, Geometry and Topology, and Number Theory, all have active weekly seminars that draw speakers ranging from the local to the international.
Faculty, postdocs, and graduate students in the Division of Applied Mathematics conduct research in computational and mathematical biology, dynamical systems and differential equations, probability theory and stochastic processes, numerical analysis and scientific computing, and pattern theory and Bayesian statistics. Among the current research directions that pertain to data science are the development of new statistical techniques to understand large data sets in genomics and time series in neuroscience and paleontology, the application of topological data analysis to the classification of spatiotemporal patterns, and the theoretical investigation of randomized algorithms with a view towards assessing the impact of cpu failures in exascale computing. We offer undergraduate graduate-level courses in our core research areas, including statistics. Our faculty collaborate with researchers across Brown and are interested in projects that require the development of new mathematical theories or techniques.
The Institute for Computational and Experimental Research in Mathematics (ICERM) is a research institute in the mathematical sciences at Brown, with core funding from the National Science Foundation. We host programs in areas where computation and computer experiments drive research progress, including several connecting with data sciences and supporting large databases of mathematical objects. We are interested in ideas for future activities where Brown leadership can have an impact on the national (and international) research community.
The PSTC is an internationally respected demography research and training center that seeks to create a dynamic environment in which the Center's associates can contribute to the resolution of human population issues in the 21st century through interdisciplinary research with a global perspective. PSTC faculty, graduate students, and postdoctoral fellows conduct research and fieldwork in more than 20 nations, focusing on issues from health and development to environment and spatial structures (S4). The primary mission of the PSTC is to produce and disseminate innovative research on the causes and consequences of population size, composition, distribution, and well-being in the U.S. and worldwide.
PSTC's spatial core, Spatial Structures in the Social Sciences (S4), provides is an intellectual home for faculty and students working on issues of geography, networks, and context in myriad settings. Project may range from simple map-making, through GIS applications, to innovative research with the latest concepts and methods in spatial analysis. S4's principal focus is to develop, support, and extend spatial research at Brown. To this end, it initiates and consults on a wide variety of research projects and proposals with the Brown community.
Center to Advance Predictive Biology
Beth Leary, Derek Merck
Faculty, students and staff in the Center to Advance Predictive Biology are modernizing toxicity testing and drug discovery and are working to develop cheaper, faster and more predictive methods that also reduce the use of animals in research. Funded by the NIH and NSF, we have miniaturized living human microtissues (heart, brain, lung, ovary, breast, prostate, etc) and are using high throughput confocal microscopy to acquire tens of thousands of 3D images of these microtissues. From this pipeline of images (TBs), we are assessing the form and function of normal and diseased human tissues and their responses to drugs and environmental toxicants. We are interested in new collaborations and students to mine these large data sets for valuable quantitative information about the complex biological responses of these human microtissues.
Andrew Creamer, Bruce Boucek, Harriette Hemmasi
The University Library supports the digital research of students and faculty by partnering with them on their projects, by offering workshops on data management, analysis, and visualization, and by providing them with spaces, resources, and tools for carrying out their data-intensive research. The Library is interested in collaborating with members of Brown’s research community and supporting its grant, research, and data and publication life cycles. We invite student and faculty researchers to seek out partnerships with the Library and explore how we can 1) help them to pursue their projects and grant opportunities 2) help them to preserve, disseminate, and share their digital research products with other researchers and the public and 3) help them to make their digital works discoverable, accessible, and citable.
Brown Arts Initiative (tentative)