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IS Department Research Seminar Series

 

 

 

Please join us as we bring you a series of talks highlighting department research and topical issues in our field.

Scheduled Seminars

 

Title: Crimes of Omission: The Trouble with Siloing Datasets and Defining Data Narrowly
Time: Thursday, November 21, 12-1 pm
Location: ITE 459
Speaker: Lee Boot, Imaging Research Center (Director), Visual Arts and Computer Science and Engineering (Affiliate Associate Professor), UMBC
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Abstract:
Concerns about data ethics are gaining traction as researchers, policymakers, news organizations, and the general public realize that quantitative data are becoming a virtual proxy for human beings and our world. Embedded in data are biases and facile assumptions; data are decontextualized as they migrate to serve purposes beyond those for which they were intended. Less discussed is the practice overall and what it means when we isolate datasets from one another and from important factors that are not quantified, but could determine outcomes. Lee Boot will discuss the Imaging Research Center’s efforts to envision tools and practices that will allow all relevant stakeholders addressing a challenge to contextualize data in dynamic, collaborative environments.

Bio:
Lee Boot is Director of the Imaging Research Center, and Affiliate Associate Professor of Visual Arts and Computer Science and Engineering at UMBC. Informed by his background in painting and filmmaking, his work of the past two decades is transdisciplinary research to develop novel digital media technologies, forms, and content to improve the capacity of the digital media and datasphere to address longstanding societal problems including health, education, and urban challenges. Currently, he is developing human interfaces for visualizing and interacting with complex problems that emerge from myriad interrelated factors and also observing and measuring human behavior in simulated virtual reality environments. The work has been sponsored by federal agencies such as the National Institutes of Health, foundations including Surdna, and the Robert W. Deutsch Foundation, and has been commissioned by the National Academy of Sciences. His work has been broadcast, screened, and exhibited nationally and internationally at venues including the Johannesburg Biennial in South Africa. His feature film, Euphoria, received the Gold Award for documentary at the Houston International Film Festival in 2005. His findings have been published in journals and presented at conferences on art, education, new media and digital communications.

 

 

Title: Scalable and Reliable Practical Design: A Bayesian Perspective
Time: Monday, November 25, 12:30-1:30pm
Location: ITE 459
Speaker: Seyede Fatemeh Ghoreishi, Institute for Systems Research (ISR), University of Maryland College Park (postdoctoral research fellow)

Abstract:
Design problems are pervasive in scientific and industrial endeavors: scientists design experiments to gain insights into physical and social phenomena, engineers design machines to execute tasks more efficiently, pharmaceutical researchers design new drugs to fight disease, and companies design websites to enhance user experience and increase advertising revenue. All these design problems are fraught with choices, choices that are often complex and high-dimensional, with interactions that make them difficult for individuals to reason about. Despite several advances made in design and decision making in recent years, lack of reliability and lack of scalability have limited their applications to a wide range of practical problems. This talk will focus on Bayesian optimization, in particular, a new Bayesian formulation of model-based and data-driven experimental design for large-scale and reliable design and decision-making.

Bio:
Seyede Fatemeh Ghoreishi is a postdoctoral research fellow at the Institute for Systems Research (ISR) at the University of Maryland. She received her Ph.D. and M.Sc. degrees both in Mechanical Engineering from Texas A&M University in 2019 and 2016 respectively. She holds a minor in Applied Statistics from the department of Statistics at Texas A&M University. She also received a M.Sc. degree in Biomedical Engineering from Iran University of Science and Technology in 2014 and a B.Sc. degree in Mechanical Engineering from the University of Tehran in 2012. Her research interests include Machine Learning, Bayesian Statistics and Design under Uncertainty with a wide range of applications. She is the recipient of several awards including being selected as Rising Stars in Computational and Data Sciences at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin in 2019.

 

 

Past Seminars

Title: Bayesian Modeling of Intersectional Fairness: The Variance of Bias
Time: Thursday, September 19, 12-1 pm
Location: ITE 459
Speaker: Jimmy Foulds
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Abstract:
With the rising influence of machine learning algorithms on many important aspects of our daily lives, there are growing concerns that biases inherent in data can lead the behavior of these algorithms to discriminate against certain populations. Informed by the framework of intersectionality from the Humanities literature, we propose mathematical definitions of AI fairness that aim to ensure protection along overlapping dimensions including gender, race, sexual orientation, class, and disability. We prove that our fairness criteria behave sensibly for any subset of the set of protected attributes, and we illustrate links to differential privacy. Finally, we present a Bayesian probabilistic modeling approach for the reliable, data-efficient estimation of fairness with multi-dimensional protected attributes. Experimental results on criminal justice, census, and synthetic data demonstrate the utility of our methodology, and show that Bayesian methods are valuable for the modeling and measurement of fairness in an intersectional context.

Bio:
Dr. James Foulds (Jimmy) is an Assistant Professor in the Department of Information Systems at UMBC. His research interests are in both applied and foundational machine learning, focusing on probabilistic latent variable models and the inference algorithms to learn them from data. His work aims to promote the practice of probabilistic modeling for computational social science, and to improve AI’s role in society regarding privacy and fairness. He earned his Ph.D. in computer science at the University of California, Irvine, and was a postdoctoral scholar at the University of California, Santa Cruz, followed by the University of California, San Diego. His master’s and bachelor’s degrees were earned with first class honours at the University of Waikato, New Zealand, where he also contributed to the Weka data mining system.

 

 

Title: Center for Accelerated Real Time Analytics (CARTA): Next Generation Research
Time: Thursday, September 26, 10-11 am
Location: ITE 459
Speaker: Karuna Joshi
Speaker’s website: http://karuna.informationsystems.umbc.edu
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Abstract:
CARTA is an NSF sponsored Industry-University Cooperative Research Center (IUCRC) in UMBC focused on cutting edge inter-disciplinary research in real time analytics using next generation accelerated hardware. This center, lead by UMBC, also includes Rutgers University, New Brunswick, North Carolina State University (NCSU) and Rutgers University, Newark. Our international partner is Tel Aviv University. The overall Center Director is Dr. Yelena Yesha and the UMBC Site Director is Dr. Karuna P Joshi. This interactive talk will cover information about CARTA’s resources and research projects.

Bio:
Karuna Pande Joshi is an Assistant Professor of Information Systems at UMBC and UMBC Site Director of CARTA. She also directs the Knowledge Analytics Cognitive and Cloud (KnACC) Lab. Her research focus is in the areas of Data Science, Cloud Computing, Data Security and Privacy and Healthcare IT systems. She has published over 50 papers and her research is supported by ONR, NSF, DoD, GE Research and Cisco. She teaches courses in Big Data, Database Systems Design and Software Engineering. She received her MS and PhD in Computer Science from UMBC, where she was twice awarded the IBM PhD Fellowship, and her Bachelors in Computer Engineering from the University of Mumbai, India. Dr. Joshi also has extensive experience of working in the industry primarily as an IT Program/Project Manager at the International Monetary Fund.

 

 

Title: Information Extraction: From General Domain to Specific Domain
Time: Thursday, October 10, 12-1 pm
Location: ITE 459
Speaker: Arpita Roy
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Abstract:
Information extraction (IE) is the task of automatically extracting structured information from unstructured text. IE is an important NLP task with many practical applications including business intelligence, scientific research, healthcare records management, financial investigation and social media analysis. Due to the difficulty and diversity of the problem, we divide IE into two different categories; general domain IE and domain specific IE. As each of these domains has its’ own unique characteristics, we propose two different noble IE approaches for each domain. For general domain we focus on leveraging knowledge from existing IE systems and build a superior IE model. For specific domain like cyber security we propose using structured domain knowledge for better IE. In both domain we use machine learning based IE techniques.

Bio:
Arpita Roy is a PhD student at the Department of Information Systems at University of Maryland, Baltimore County, working with Dr. Shimei Pan in the Text Mining and Social Media Analytics Lab. Her research interests include Natural Language Processing, Machine Learning and Text Mining. Her current research focus is developing machine based Information Extractions models for different domains.

 

 

Title: Deep Learning Models for Healthcare and Oncology
Time: Thursday, October 17, 12-1 pm
Location: ITE 459
Speaker: Sanjay Purushotham
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Abstract:
It is widely believed that machine learning and artificial intelligence techniques will substantially change healthcare industries. Even though recent developments in machine learning, particularly deep learning, has achieved success in many applications, such as computer vision, natural language processing, speech recognition, and so on, healthcare applications pose many significantly different challenges to existing deep learning models. Examples include but are not limited to interpretations for prediction, heterogeneity in data, missing values, multi-rate multi-resolution data, big and small data, and privacy issues. In this talk, I will discuss a series of problems in healthcare that can benefit from deep learning models, the challenges as well as recent advances in addressing those. I will also present the findings from our recent foray of applying deep learning models to oncology research.

Bio:
Sanjay Purushotham is an Assistant Professor in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC). Before joining UMBC, he was a Postdoctoral Scholar Research Associate in the Department of Computer Science at the University of Southern California (USC). He obtained his M.S and Ph.D. in Electrical Engineering from USC. His research interests are in machine learning, data mining, optimization theory, statistics, computer vision, and its applications to healthcare & bioinformatics, oncology, and multimedia data analytics. Recently, he has been developing deep learning frameworks to model healthcare data and to predict survival outcomes for cancer patients. He has produced more than 30 publications and has won the best paper and best poster awards at international conferences.

 

 

Title: A Surprise Behind Every Door: Research and reflections on making healthcare safer for older adults coming home from the hospital
Time: Wednesday, November 13, 10-11:30 am
Location: ITE 459
Speaker: Alicia Arbaje, Division of Geriatric Medicine and Gerontology, Johns Hopkins University
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Abstract:
Define and describe national patterns of older adults’ care transitions in the U.S. Describe a multi-site, qualitative study to investigate information management during the hospital-to-home care transition. Present challenges healthcare providers, older adults, and caregivers face during care transitions. Discuss practical approaches to implement best practices and improve care transitions supported by informatics.

Bio:
Dr. Arbaje is an internist, geriatric medicine specialist, and health services researcher at Johns Hopkins University School of Medicine. She is Associate Professor of Medicine and Director of Transitional Care Research at the Center for Transformative Geriatrics Research. Dr. Arbaje is interested in the problems older adults face as they navigate through the healthcare system. She is leading several studies that aim to develop performance measures, define best practices, and ultimately improve the quality of care of older adults as they leave the hospital. The focus of her research has been on identifying patient populations at risk of experiencing suboptimal care transitions, identifying care processes and hospital characteristics related to readmissions, and developing clinical interventions to improve care transitions and reduce hospital readmissions. For the past 15 years, she has been investigating risks to older adults’ safety as they receive skilled home healthcare services after hospital discharge.