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Dissertation Defenses

Our Ph.D. students work hard for years honing their research. This effort culminates in an oral dissertation where they present, explain, and defend their ideas. The final defense of the dissertation is conducted by an examination committee recommended by the graduate faculty advisor and approved by the dean of The Graduate School. All final oral examinations are open to members of the graduate faculty.

Below is the published list of upcoming or recent dissertation defenses for Information Systems students.

Upcoming Defenses

Title: Understanding and Supporting Collaboration Between Youth in Technical Workplaces
Student: William Easley
Time: Friday, September 11, 2020, 12 pm – 3 pm
Location: Meeting Link: https://umbc.webex.com/umbc/j.php?MTID=mc3c17c758cbb6d0a640de365bf203ccc
Meeting number (access code): 120 111 8670
Meeting password: 3exRkqNYu34

Committee: 
Dr. Foad Hamidi (Chair/Advisor)
Dr. Helena Mentis (Chair/Advisor)
Dr. Anita Komlodi
Dr. Amy Hurst
Dr. Wayne G. Lutters
Dr. Betsy DiSalvo

Abstract:
Emerging digital technological advances have been a driving force in the evolution of the modern workplace. This influence extends not only to the creation of new industries and types of work, but also to how work is completed. For example, new technologies have made it possible for us to coordinate and communicate over vast geographic distances. In response to these changes, considerable efforts have been made to re-train our existing workforce to have the technical and communi- cation skills necessary to be successful. This shifting landscape presents exciting opportunities to focus on ways to support youth – who have spent their entire lives with access to inter-networked technologies – as they prepare to enter the workplace of the future.

This dissertation describes longitudinal research conducted at the Digital Harbor Foundation 3D Print Shop over a period of more than three years. The print shop offers after-school technical employment to youth from diverse backgrounds. To understand the experiences and challenges faced by this population as they transitioned into this environment, we have utilized a variety of methods including participant and direct observation, interviews, the analysis of chat logs, a focus group, and a participatory elicitation method.

The three studies included in this document all seek to better understand how youth coordinate work in technical settings with a specific focus on their usage of communication technologies. The first documents and unpacks instances where youth successfully and unsuccessfully coordinated their work during asynchronous handoffs. The second study characterizes the youths’ use of Slack, a popular workplace chatting tool and investigates the factors motivating and limiting adoption of this tool. The final study examines the youths’ attitudes and perceptions towards workplace communication technologies. Findings and recommendations from this dissertation offer insight into how we can better prepare and support our future workforce.

Recent Defenses

Title: Interpretable deep learning models for Electronic Health Records
Student: Peichang Shi
Advisor: Dr. Aryya Gangopadhyay
Time: Monday, June 22, 2019, 10 am – 12 pm
Location: Virtual
Abstract:

Deep learning neural networks (DNNs) have been proved to be powerful tools in computer vision and machine learning across robotics, healthcare, and security. However, despite their superior performance to traditional machine learning methods, it remains challenging to understand their inner mechanism and explain their downstream output due to the black box effects.

In this dissertation proposal, we aim to unfold the black-box and provide explanations in a transparent way using Electronic Health Records (EHRs). Our proposed approach is to add a partially connected layer after the output from the DNNs based on a decision tree. This decision tree layer could interpret DNNs through rules from the input vectors. Our method could achieve global interpretability for all the test instances on the hidden layers without sacrificing the accuracy obtained from the original deep learning model. This means our model is faithful to the original deep DNN model, which leads to reliable interpretations. Beyond that, we would also like to address some common issues related to EHRs including irregular time series data, censored data, multitask learning issues and hierarchical structure variables in the model.

The methods proposed in this dissertation will be validated with the End Stage Renal Disease (ESRD) Population Public Use File (PUF). This PUF data contains the current and historical ESRD patient population and associated clinical data, which has about 3.2 million patients including hospital admissions, hospitalizations, chronic conditions and 13 monthly clinical measures.