Tech + Research Workshop
The Department of Computer Science at the University of Maryland and the Center for Women in Computing are pleased to present the fourth year of Tech + Research: Welcoming Women to Computing Research, a research workshop geared towards engaging undergraduate women in computing held in collaboration with Technica. During this workshop, student teams will come together and collaboratively work together to use technology to solve pressing issues. The October 2020 workshop is supported in part by explore CSR.
Technica and Tech + Research will be a hybrid experience in 2021!
Parallel to Technica, the largest hackathon for underrepresented genders in the nation, students will participate in the Research track at Technica. The weekend event will bring together computing faculty from institutions across the state of Maryland to serve as mentors on projects in their research areas. Along with providing hands-on research experience in a dynamic hackathon setting, the weekend workshop will include virtual sessions introducing attendees to the basics of computer science research (CSR) and highlighting the exciting opportunities that come with pursuing a graduate degree in computer science.
Note: Please be aware that this event involves separate programming from Technica, and the majority of the programming will take place with the Maryland Center for Women in Computing. However, you will have full access to Technica including the Career Fair and Keynote Speakers.
IMPORTANT: YOU MUST REGISTER FOR BOTH TECHNICA AND TECH + RESEARCH
This workshop hopes to give undergraduate CS students who identify as an underrepresented gender in computing an opportunity to learn about future computer science research opportunities and to provide hands-on experience engaging in CS research in a hackathon setting. Additionally, we plan for this event to allow students to meet computing faculty and current graduate students and to socialize and collaborate with like-minded peers. By providing a positive intellectual, social, and emotional environment for the participants to meaningfully engage in computing research, we hope to directly address gender gaps that currently exists in CS departments in higher education.
Attendees of this event will not only be expanding their CS skills, they will also be given the opportunity to meet and network with many individuals who are a part of the CS community at the University of Maryland.
Workshop participants will:
- Meet others who share their curiosity and interest in computer science.
- Explore the research experience in computing related domains.
- Work hands-on with researchers.
- Work in a team to tackle a research problem.
- Present their research with their team.
- Broaden understanding of the possibilities of graduate school and the application process.
Surrounding area schools and departments were invited to submit research projects. Projects from the following departments have been submitted in previous Tech + Research workshops:
University of Maryland, College Park
- Department of Computer Science
- Department of Electrical and Computing Engineering
- College of Information Systems
- College of Education
Tech + Research 2021 Projects:
Shopping online can often lead to inaccurate purchases in terms of sizing, or unhappy customers in terms of style. One way to improve this online interaction is through Virtual Try-on. Here we can estimate a 3D model of a human body directly from a few photographs with little human interaction. However, the quality of these images can severely influence the 3D model we generate. Current state-of-art body reconstruction may not be robust to factors such as angles, positions, lighting etc; this will be relevant as virtual try-on applications are deployed in industry, where consumers will input images taken using different devices or in different conditions. Our goal would be to create a metric to measure such disparities, as well as a method to minimize them.
Professor, CRA DREU Co-Director, Computer Science, UMIACS
Content moderation in online forums is critical for maintaining a community’s standards and helping participants feel comfortable and safe, but doing it well is complicated. Using methods from human-computer interaction (HCI), students in this project team will develop a survey about content moderation experiences and how content moderation has shaped the online communities that respondents participate in. The project team will implement the survey via a survey platform such as Qualtrics or Google Forms, and then deploy the survey on a crowdsourcing site such as Prolific or Amazon Mechanical Turk to collect data. The project team will use quantitative and qualitative methods to analyze the responses they collect. Based on these responses, the team will make recommendations for the design of content moderation tools to help moderators with online community building.
Associate Professor, Computer Science, UMIACS
Professor, Computer Science, UMIACS, and Language Science
Associate Professor, Information Studies
Research Scholar, Information Studies
The creation of intelligent virtual agents (IVAs) or digital humans is vital for many virtual and augmented reality systems. As the world increasingly uses digital and virtual platforms for everyday communication and interactions, there is a heightened need to create human-like virtual avatars and agents endowed with social and emotional intelligence. Interactions between humans and virtual agents are being used in different areas including, VR, games and story-telling, computer-aided design, social robotics, and healthcare. Designing and building intelligent agents that can communicate and connect with people is necessary but not sufficient. Researchers must also consider how these IVAs will inspire trust and desire among humans. This project will develop a Virtual Human/Avatar which can do human-like gestures and motions, including culture-relevant gestures.
Assistant Research Professor, Computer Science, UMIACS
Modern analytical database systems – especially those running in the cloud – separate storage and compute. In other words, the datasets being analyzed sit on servers that are primarily designed to store the data. A different set of "compute" servers perform the actual analysis of data. For example, in Amazon's cloud, data can sit on S3 instances, and EC2 instances are spun up on the fly to analyze the data sitting in S3. Therefore at query time, data must be transferred from the S3 servers to the EC2 servers in order to perform the analysis. Since modern networks are fast, this transfer is not usually a bottleneck, and the convenience of spinning up and down compute servers on the fly (as needed) make it worth it to separate storage and compute in this way. However, in some cases, the storage layer has some basic (or even advanced) query processing capabilities, and it is worthwhile to push down some query processing to the storage layer. This project involves performing research into such hybrid architectures.
Darnell-Kanal Professor of Computer Science
The most common goal of machine learning, as the name suggests, is to train a machine to correctly label the given data. For example, given an image of a cat, a machine should be able to label the image as a cat with high confidence. To accomplish this, machine learning models are generally trained over very large amounts of carefully labelled data containing thousands of images of various objects, including cats. Unfortunately, the availability of such data is a far-fetched assumption in many real-world scenarios. How do we train machine learning models without explicit labels? The answer is, common sense. Humans learn new skills everyday, largely with the help of observation without requiring a lot of teaching. To incorporate this notion of common sense into AI systems, an emerging solution called Self-Supervised Learning has been developed. A self-supervised model trains by itself, by observing unlabelled data and leveraging its patterns to generate its own labels. Self-supervised models convert images to low-dimensional embeddings such that all embeddings of “cat” images are linearly separable from embeddings of “dog” images. In this project, you will learn to train a simple self-supervised model on images and visualize embeddings to show that they are linearly separable. More Information: https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of...
Assistant Professor, Computer Science, UMIACS
Have you wondered how your smartphone answers questions (and when it sometimes can't)? Computers understand text through representations, including the application of question answering: taking a piece of text and finding the right answer. We'll talk about three kinds of representations: tf-idf, word vectors, and contextual representations. We'll then show how these representations can match questions and answers: you turn both the question and the answer into a representation, find the closest answer to the question representation, and then decide whether you should trust this answer or not. You can see a similar system built by undergraduates take on Jeopardy! champions here.
Associate Professor, Computer Science, UMIACS
Every day, more than 50 million older Americans need to make important financial decisions. Becoming older may affect decision-making abilities that can affect their financial freedom and security. Families that stay connected have a greater chance to thrive and need an effective way to connect with older adults over finances. There are a few factors that contribute to poor decision making-complexity of financial decisions for older people, access to advice, coercion and fraud. With this tool we attempt to enhance the quality of life for older adults and their families by connecting them and their finances.
Internet censorship is a problem that affects billions of people around the world. Nation-states engage in automated, in-network censorship of their citizens, and citizens frequently are not told what is blocked or why. In this project, students will analyze very large open source datasets from tools that monitor the occurrence of network censorship. Students will determine where around the world network censorship is occurring, what content is censored, and when. The goal of this project is to have a mechanism to detect new censorship events as they happen. Students will also be encouraged to ask their own research questions about the data. Examples include: are censorship events correlated with political events at the same time? How long do censorship events usually last (and how does that change between regions of the world?) and are there patterns to what data is censored?
Assistant Professor, Computer Science, UMIACS