Title: Title Politicians and the Policy Agenda: Does U.S. Congress Twitter Use Direct New York Times Content?
Abstract:
Traditionally we've understood that mainstream media hold
a near monopoly on information about the policy agenda and that politicians
influence the policy agenda by propagating policy problems through the media.
What happens when politicians take their appeals to social media? In this talk,
I'll discuss findings from a study in which we compared a year of content in the
U.S Congress Twitter stream with the New York Times to understand whether and
how Congress influence mainstream media through social media use. We found that
for many policy areas, social media is an effective tool for gaining mainstream
media attention. These findings confirm that Twitter is a legitimate political
communication tool, at least for Congress, and provide updates to indexing
theory to account for the diversified media landscape.
Title: Recommendation for Multiple Stakeholders
Abstract:
Recommender systems are typically evaluated on their ability to provide items
that satisfy the needs and interests of the end user. However, in many real
world applications, users are not the only stakeholders involved. There may be a
variety of individuals or organizations that benefit in different ways from the
delivery of recommendations. Broadening our perspective to include these
stakeholders raises a number of new research questions in personalization. This
talk is intended as a preliminary foray into this problem, discussing the
implications of re-defining the recommender system in a multi-stakeholder
environment, including representation of stakeholder preferences, evaluation of
outcomes, and algorithm design.
Professor Burke earned his PhD in 1993 from Northwestern University, working with Professor Roger Schank, one of the founders of the field of cognitive science. He worked in post-doctoral positions at the University of Chicago, and then in 1998, left academic employment to help found a 'dot-com' startup. He returned to academic work in 2000 first at the University of California, Irvine and then at California State University, Fullerton. In the Fall of 2002, he began his current position at DePaul University.
Title: The Marketing Power of First Party Data In the Big Data Era
Abstract:
Although people are talking about digital data more than anything else in the
Big Data Era, the first party data is the key of success for marketing
strategies. The first party data is the major source of marketing power for the
brands. The 2nd and 3rd party data enhances the marketing power of the 1st party
data. Only if the data is integrated around the first part data, the marketing
power of the data would be greatly released. Platform marketing is a very
critical infrastructure and applications in the Big Data Era. DMP that is used
to manage non-PII data and CRM that is used to manage PII-based customer data
are two major platforms. However, DMP must be integrated with CRM to optimize
the marketing power of a data platform. Several of BlueFocus practices will be
presented to demonstrate the marketing power of the first party data, and a
brief introduction to BlueFocus and its Big Data function will be introduced.
Title: Figurative Speech and the Errors that Novice Programmers Make
Abstract:
A reference-point error occurs when a programmer
writes code that mistakenly refers to one element when the intention is to refer
to an element structurally related to it. I review these errors and their
relation to the use of metonymy in human communication. As an example of
metonymy, consider this human-to-human instruction: "Open the ice cream and
serve two scoops." Human listeners effortlessly infer that it's the container,
not the ice cream, that should be opened. Drawing upon the use of metonymy and
cognitive theories of human communication and problem-solving, I explore three
accounts of why reference-point errors occur in novice programming. The first
account involves a deficient mental model, the second assumes a misconception of
the notional machine, and the third considers implicit, proceduralized habits of
communication. I conclude with learning objectives for students that address
these sources of difficulty.
Title: What They Don’t Tell You About Being a Data Scientist - 10 Day-to-Day Challenges
Abstract: There is no shortage of articles, courses and white papers describing the process of building predictive models and developing innovative algorithms. However, many of the day-to-day challenges facing Data Scientists working in the business world are not widely discussed. This talk will shed some light on these challenges, which include working with dirty data, acquiring domain knowledge and aligning on business objectives, and provide insight on how professional Data Scientists overcome these obstacles.
Biography: Cyril Nigg is a Senior Director of Data Science at Catalina Marketing, where he leads personalization and consumer targeting analytical capabilities, working with top drug and grocery retailers. Cyril was previously part of Local Offer Network, a Chicago start-up specializing in machine learning and personalization technology, which was acquired by Catalina in 2013. He began his career in market research, working at Synovate and Ipsos.
Title: Database Forensic Analysis with DBCarver
Abstract:
The increasing use of databases in the storage of critical and sensitive
information in many organizations has lead to an increase in the rate at which
databases are exploited in computer crimes. While there are several techniques
and tools available for database forensics, they mostly assume apriori database
preparation, such as relying on tamper-detection software to be in place or use
of detailed logging. Investigators, alternatively, need forensic tools and
techniques that work on poorly-con figured databases and make no assumptions
about the extent of damage in a database.
In this talk, we present
DBCarver, a tool for reconstructing database content from a database image
without using any log or system metadata. The tool uses page carving to
reconstruct both query-able data and non-queryable data (deleted data). We
describe how the two kinds of data can be combined to enable a variety of
forensic analysis questions hitherto unavailable to forensic investigators. We
show the generality and efficiency of our tool across several databases through
a set of robust experiments.
Biography: James is a third year Ph.D. student at DePaul University. He received his Master's in Computer Science from DePaul University and his Bachelor's in Chemistry from Marquette University. His research focuses on database forensics, security, and optimization.
Title: The Role of Technology in Understanding Perspectives on Aging and Health
Abstract: As the population ages, research has increasingly focused on conditions associated with growing older, such as dementia. Technology is often presented as a solution for managing or treating these various health conditions. This framing can position health conditions as problems to address through design and can neglect the complexity and positive aspects of older adulthood. In this talk, I draw on critical perspectives from Human Computer Interaction and Gerontology. I describe three ways in which technology can help us understand and challenge stereotypes around aging and dementia. I will argue for a view of aging that takes into account the ways that technologies position older individuals and, in turn, the way that this view can inform the design of new technologies to enrich the experience of growing older..
Biography: Amanda Lazar is a postdoctoral fellow affiliated with the Technology and Social Behavior program at Northwestern University. Her research focuses on the ways that technologies designed for health and wellbeing position and support individuals as they age. She completed her PhD in Biomedical and Health Informatics at the University of Washington in 2015 and her undergraduate degree in Electrical Engineering at the University of California, San Diego. Her research has received Best Paper and Honorable Mention awards at the ACM DIS and UbiComp conferences and was supported by the NSF Graduate Research Fellowship and the NLM Training Grant. She has published her work in CHI, CSCW, DIS, AMIA, and Health Education & Behavior.
Title: The 1% Effect: Social Differentiation in Social Media Groups
Abstract: If Wikipedia were a country and its content was its income, the wildly uneven distribution of wealth among its contributor-citizens would be unheard of. The wealth distribution of the U.S., for instance, which some see as quite unfair, pales by comparison, as the top 1% American earners account for “only” 15% of the national income compared to the 77% of content “owned” by the top 1% of Wikipedians. Our book explains how this uneven distribution matters. We believe that far from being an abnormality, it is a sign that Wikipedia has developed organically and naturally. The unbalance in fact shows that the site is structurally differentiated and that functional leaders have emerged, which are essential for the good functioning of this project in particular and of other voluntary project in general.
Biography: Sorin Adam Matei - Professor of Communication, Brian Lamb School of Communication, Purdue University - studies the relationship between information technology, social media, and social behavior. He published papers and articles in Journal of Communication, Communication Research, Information Society, and Foreign Policy. He is the editor of Ethical Reasoning in Big Data,Transparency in social media, Roles, Trust, and Reputation in Social Media Knowledge Markets: Theory and Methods (Computational Social Sciences) . His work was funded by the National Science Foundation, Mellon Foundation, Kettering Foundation, Motorola and other organizations. Dr. Matei is also known for his media work. He is a former BBC World Service, has published in Esquire and Foreign Policy. His publishes frequent research updates on his blog, http://matei.org/ithink. In Romania he is known for his books Boierii Mintii (The Mind Boyars), Idolii forului (Idols of the forum), and Idei de schimb (Spare ideas).
Shan Lu (04/01)
Title: Fighting Software Inefficiency Through Automated Bug Detection
Abstract:
Developers frequently use inefficient code sequences that could be fixed by
simple patches. These inefficient code sequences can cause significant
performance degradation and resource waste, referred to as performance bugs.
Meager increases in single threaded performance in the multi-core era and
increasing emphasis on energy efficiency call for more effort in tackling
performance bugs.
This talk will present our group’s work in understanding,
detecting, and fixing performance bugs. I will first present an empirical study
we did for more than 100 real-world performance bugs. I will then describe
several dynamic and static program analysis tools that we have designed to
automatically detect and fix performance bugs. Our tools have detect hundreds of
performance bugs in the latest versions of widely used open-source applications.
I will conclude the talk by discussing challenges and opportunities for future
rsearc fighting performance bugs.
Shan has won Alfred P. Sloan Research Fellow in 2014,
Distinguished Alumni Educator Award from Department of Computer Science at
University of Illinois in 2013, and NSF Career Award in 2010. Her co-authored
papers won two ACM-SIGSOFT Distinguished Paper Awards at ICSE 2015 and FSE 2014,
one Best Paper Award at USENIX FAST in 2013, an ACM-SIGPLAN Research Highlight
Award in 2011, and an IEEE Micro Top Picks in 2006. Shan currently serves as the
Vice Chair of ACM-SIGOPS.
Anton Oleinik (04/08)
Title: Approaches towards mixing quantitative and qualitative content analysis
Öykü Işık (04/07, at 1:30pm)
Title: Big Data Capabilities: An Organizational Information Processing Perspective
Abstract: Big data is at the pinnacle of its hype cycle, offering big promise. Everyone wants a piece of the pie, yet not many know how to start and get the most out of their big data initiatives. We suggest that realizing benefits with big data depends on having the right capabilities for the right problems. When there is a discrepancy between these, organizations struggle to make sense of their data. Based on information processing theory, in this research-in-progress we suggest that there needs to be a fit between big data processing requirements and big data processing capabilities, so that organizations can realize value from their big data initiative.
Biography:
Öykü Işık is an Assistant Professor in Information Systems Management at Vlerick
Business School in Belgium. She holds a PhD degree in Business Computer
Information Systems (2010) from the University of North Texas. Her research
interests include business intelligence and analytics, business transformation
with IT, and privacy in the age of big data. Her recent work appeared in
Information & Management and Journal of Information Technology Teaching Cases.
Samuel G. Armato III (04/15)
Title: Public Databases and Grand Challenges in Thoracic Imaging
Abstract:
Essential to the conduct of research in the
field of computer-aided diagnosis (CAD) is a collection of clinical
images that capture the disease under investigation.
Such an image collection, known as a “database,” preferably
requires metadata, supplied by a domain expert radiologist, on the
nature, extent, and/or location of the abnormality.
Herein lies a major impediment to investigators wishing to
contribute to CAD research: the collection of image databases is a
laborious and expensive task.
Issues of patient confidentiality mandate strict anonymization
schemes. The time required
for radiologists to review, assess, and annotate images as required by
the research may be quite onerous.
Furthermore, some institutions not affiliated with a medical
center may not even have access to clinical data of any kind.
A complication with image databases, once collected, is their
limited utility. Research
groups that expend the time and money to create a database will likely
use it for a single project and typically are reluctant to share their
database with other groups.
A further consequence of this isolation of databases is the inability to
compare directly the performance of CAD methods reported in the
literature, and since database composition is a known source of
variability in CAD performance, such disparate databases could be
expected to slow down commercialization of successful CAD technologies.
Two solutions that are emerging in an attempt to overcome these
difficulties, especially for lung-image-based CAD research, are the
creation of public databases and the conduct of “grand challenges”
within the medical imaging research community.
Biography:
Samuel
G. Armato III, Ph.D. is an Associate Professor in the Department of Radiology
and the Committee on Medical Physics at The University of Chicago.
Dr. Armato received his undergraduate degree
in physics from The University of Chicago in 1987.
He returned to The University of Chicago in 1991 to
pursue graduate studies in medical physics and earned a Ph.D. in 1997.
Since that time, Dr. Armato has gained recognition
for his work in computer-aided diagnosis (CAD),
which combines physics, mathematics, computer science, and statistics to analyze
medical images for the early detection, diagnosis, and quantitative evaluation
of disease.
Dr. Armato has been developing and investigating CAD
methods in chest radiology for the automated detection of lung nodules in
thoracic computed tomography (CT) scans, the automated volumetric assessment of
pleural mesothelioma in CT scans, the quantitative assessment of mesothelioma
response to therapy, the analysis of temporal subtraction image quality in chest
radiography, the quantitative assessment of normal tissue complications in lung
cancer patients undergoing radiation therapy, and the response of chronic
sinusitis patients to therapy.
He also is interested in the issue of interobserver
variability in diagnostic image interpretation, especially in the context of
establishing “truth” for CAD studies.
Hamed Qahri Saremi (04/15, at 2:10pm)
Title: Ambivalence and Its Responses in Information System Use: An Integrative Framework and Empirical Examination
Abstract: Information Systems (IS) research has largely conceptualized users’ orientation toward using an IS as a unidimensional, linear concept, where users are implicitly assumed to hold a neutral, positive, or negative orientation toward using an IS. However, recent studies show that individuals typically hold simultaneous negative and positive orientations toward behaviors, including possibly IS use, and this results in a sense of ambivalence. Hence, users’ mindsets are possibly more complex than previously assumed, and a bi-dimensional view regarding their orientations toward using an IS, as well as their responses to different combinations of opposing orientations is warranted. In order to address this complexity, this study first elaborates on the concepts of ambivalence and responses to ambivalence in the context of IS use. It then relies on ambivalence theories to suggest an integrative framework of IS users’ five typical states of ambivalence and possible responses to them. The framework is validated with Latent Profile Analysis applied to data collected from 442 Facebook users. The results show that all users hold some degree of simultaneous positive and negative orientations toward the use of an IS, and that each user consequently experiences one of the five hypothesized states of ambivalence. Furthermore, analyses of variance tests demonstrate that each state of ambivalence is more strongly associated with certain plausible responses than other states. Ultimately, this study extends our understanding of users’ mindsets, makes important theoretical and practical contributions to the IS use literature, and paves the way for important future research in this area.
Biography: Hamed Qahri Saremi is an assistant professor of management information systems at University of Illinois at Springfield. He received his PhD in business administrations with concentration in information systems from DeGroote School of Business at McMaster University in November 2014. Dr. Saremi's research has been mainly focused on two areas: patterns of use of information systems and electronic word of mouth. His papers have appeared in high quality research outlets in information systems such as Journal of Strategic Information Systems, Information & Management, and Expert Systems with Applications. Dr. Saremi has served as guest editor, reviewer, and session chair for different research journals and conferences within information systems and management fields. As a certified SAP Associate, Dr. Saremi has taught several different online and face to face courses at the undergraduate and graduate (MBA and Master of MIS) levels in information systems curriculum, including data mining and business process management areas.
Massimo
Di Pierro
(05/13)
Title:
A quick tour of numerical algorithms in python
Abstract:
This talk is quick and fast excursion over the Python language and some of its libraries. Specifically we will discuss sympy, numpy, nlib, nlib, and pyopencl. A particular focus will be given to linear regression, solvers and optimizers, and Python to C compilation.
Biography:
Massimo Di Pierro is an Associate Professor of Computer Science at the
School of Computing of DePaul University. Massimo holds a Ph.D. in High Energy
Theoretical Physics from the University of Southampton in UK. Before that he
worked as Associate Researcher at the Fermi National Accelerator Laboratory. His
current main research areas are Computational Finance and Visualization for
Lattice Quantum Chromodynamics (http://bit.ly/2Jp9dM). His work in the latter
field is supported the Department of Energy under a Scientific Discovery though
Advanced Computing (SciDAC) Grant. The software he writes as part of his
research runs on the biggest supercomputers in the US.
Massimo Di Pierro
is author of more than 60 publications. A partial list can be found at the
Stanford SPIRES Database (http://bit.ly/3VxAPV). He has given multiple talks and
presentations at Physics and Finance conference worldwide. Since 2003 Massimo
has been very active in the Python community and a member of the Chicago Python
Users Group (chipy). He also uses Python in many programming classes and created
Pyhton libraries in python (http://code.google.com/u/massimo.dipierro/). He is
an editor of "Computing in Science and Engineering", a publication of the IEEE
Computer Society.
Massimo is the lead developer of web2py
(http://web2py.com), an Open Source Web Framework based on Python. It counts
more then 100 contributors, 3200 registered users (from google group), and ~180
downloads/day (in average from distinct IPs). In 2008 has published a book on
web2py and a fourth edition will be released on Nov 2011. Web2py was voted by
Inforword best full stack Python framework in 2011 and was the Bossie award in
2011 for Best Source Development Platform.
William N.
Frost
(05/20)
Title:
Imaging brain networks during behavior and
learning
Abstract: Dr. Frost uses fast voltage sensitive dyes to record the firing of up to 200 individual neurons during behavioral motor programs and learning, using invertebrate model systems in which the functions of individual neurons can be determined. Topics to be covered include the degree to which network neurons participate reliably in behavior both moment-to-moment and trial-to-trial, how networks change as memories form, and the development of useful computational tools to assist such studies.
Biography:
Dr.
William N. Frost received his B.A. in Biology at Reed College an M.A. and Ph.D.
in Physiology at Columbia University (Eric Kandel Lab). He had academic
appointments at the University of Texas Medical School at Houston and is
currently Professor and Chair at the Chicago Medical School, Rosalind Franklin
University of Medicine and Science.
His largest scientific contributions
have addressed the following four areas: A) Self-modulation by neural networks
(Intrinsic neuromodulation) (5 papers), B) How neural networks generate behavior
(12 papers), C) Tool development: Use of large-scale imaging to study neural
networks (10 papers) and D) Experience-dependent modification of behavior and
learning (15 papers)
Peter M. Hastings
(05/27)
Title:
Identifying causal structure in student essays
Abstract: Educational standards put a renewed focus on strengthening students' abilities to construct scientific explanations and engage in scientific arguments. Because evaluating student writing is so time-intensive, people often turn to automatic essay evaluation programs, but these generally rely on rough measures of word choice and coverage of a topic. In contrast, our focus is on determining the causal structure in students' explanations of scientific phenomena, so that we can (eventually) provide effective feedback to help them improve their explanations. Starting with hand-annotated essays from hundreds of students in the Chicago area, we developed machine learning techniques to identify both the conceptual elements in student essays and the causal connections between them.
Biography: Dr. Hastings came to DePaul in 2001 from the University of Edinburgh where he was a Lecturer. Prior to his move to Scotland, he worked as a post-doctoral research assistant at the University of Memphis. Before that, he was awarded a postdoctoral fellowship at the University of Michigan in Ann Arbor, where he earned his Ph.D. in computer science. He completed his masters degree at the Johns Hopkins University and his bachelors degree at Michigan State University, both in computer science. Dr. Hastings's research interests include natural language processing, cognitive science, intelligent tutoring systems, educational games, and artificial intelligence.
Denise C. Nacu
(06/03)
Title:
Beyond the Like
Button: Encouraging Productive Contributions in an Online Social Learning
Network
Abstract: Research has revealed that youth participation in online communities can help build 21st century skills such as contributing ideas, creating and refining work, communicating and collaborating with others, and taking initiative to direct one's own learning. However, recent studies have also shown that contributors of online content are a small subset of the population using technical systems, and that this subset is not representative of the larger population. This trend highlights an inequity both in terms of who takes advantage of opportunities to develop technological competencies necessary for participation in the 21st century, and in terms of who is authoring content that informs public opinion and knowledge. In this presentation, I consider how Latino youth interact around online digital artifacts and how we can design features to better support their contributions of communication and critique. I focus on a collaboration with a seventh grade teacher using an online platform in a predominantly Latino middle school, and present the design and results of two design iterations of a software feature that intended to encourage online communication and contribution, using qualitative ethnographic case studies, co-design activities, and quantitative log data. Findings suggest important cultural and pedagogical design considerations for online social learning network interfaces aiming to build learning community and engage diverse youth populations to contribute.
Biography: Denise C. Nacu, Ph.D. is a designer and researcher working at the intersection of technology, learning, and design in the College of Computing and Digital Media School of Design at DePaul University. She co-directs the Technology for Social Good Research and Design Lab and is a researcher with the Digital Youth Network. Before joining DePaul, she was Director of Design at the University of Chicago Urban Education Institute where she designed educational technology tools. She received her Ph.D. in Education (Learning Technologies) from the University of Michigan in 2004, and a B.A. in Art History, with a minor in Psychology from Columbia University in 1995.