Tech + Research Workshop
Date: October 20 - October 22, 2023
Location: University of Maryland, Iribe Center
The Department of Computer Science at the University of Maryland and the Center for Women in Computing are pleased to present the sixth 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.
Technica and Tech + Research will be a hybrid experience in 2023! You can participate either fully in person or fully remote.
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 TECHNICA- RESEARCH TRACK AND COMPLETE THE ADDITONAL TECH + RESEARCH APPLICATION
Purpose
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. If you are interested in hosting a project, please email Kate Atchison, katea@umd.edu.
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.
2023 Projects
In practical machine learning applications, the acquisition of accurate labels for image datasets is often delegated to human annotators, whose judgments may introduce inconsistencies and errors, leading to what is commonly referred to as "noisy labeled data." This phenomenon poses a significant challenge, as it can severely impede the performance of subsequent machine learning models.
The recent advanced AI models, text-to-image diffusion models, exhibit a dual proficiency. Not only can they generate novel and coherent images based on textual prompts, but they also possess the capability to discern and classify the content within images themselves. By supplying the models with the images and their current, potentially noisy labels, one can leverage the model's discriminative ability to reevaluate and rectify these labels. The model's understanding of the image's content enables it to generate more precise annotations, thereby enhancing the overall dataset quality, which can be more reliably used in various machine learning tasks. Such as classification, and object recognition.
Researchers
Assistant Professor, Computer Science, UMIACS
Graduate Student
"Your package is delayed; click here or else!" We have all received SMS phishing ("smishing") messages like these. What makes them so effective is that they can seem convincing, and we don't have widespread solutions like email has spam detection. The goal of this project is to collect a sample of smishing messages from public repositories, identify what makes them work, and develop new AI-based techniques to detect and even generate the next phase in smishing evolution.
Researchers
Associate Professor, Computer Science, UMIACS
Graduate Student
Graduate Student
Many countries around the world censor internet traffic in an effort to control information, suppress political opposition, and even restrict access to basic information about reproductive health. The goal of this project will be to use public datasets to learn more about how censors operate. Researchers will pose questions like: What is blocked where? How are users affected?
Researchers
Associate Professor, Computer Science, UMIACS
The world of home conversational AI (intelligent home voice assistants like Amazon Alexa and Google Home) spans multiple areas. With this project, we hope to design a working VoiceFlow (skill/application) that serves those who are part of the accessibility community and have needs in their home that could be supported by voice technology. This could include supporting the home ecosystem environment through Voice UI, developing a skill that supports a particular population (aging adults, or cognitively impaired adults) or we ask that students bring in their own perspective in what is needed to be developed for the Voice platforms.
Researchers
Computational tools can help scientists analyze biological data and discover patterns that are not easily visible. One way to learn more is through the use of alignment tools which help scientists compare DNA, RNA, or protein sequences and find identical or similar regions. As this is a large area of bioinformatics, computational biologists have created many different alignment tools for various types of data, including microbial sequences. The microbiome is a community of microorganisms that live in a particular environment. In humans, the body has microbiomes that live on the skin or in the gastrointestinal tract, so knowing more about the human microbiome can have medicinal applications. We want to learn more about these components of the microbiome through metagenomics, the study of microorganisms in a specific environment. In this project, we will perform our own metagenomics study and compare various alignment tools on microbial sequences. We will learn about the mechanisms of popular alignment tools and test them on a microbial dataset. We will compare the performance of each algorithm by examining time and memory usage and determine what the benefits and issues for each algorithm are. No prior experience with biology is required.
Researchers
Professor, Computer Science, UMIACS Director
Graduate Student
Graduate Student
Undergraduate Student
Personal data collection is integral to a vast amount of scientific research, but it exposes participants to privacy risks, such as leakage or re-identification of private information. A privacy violation can be embarrassing or even dangerous—for example, an individual living under an autocratic regime who is re-identified in a study on political attitudes could be targeted for their beliefs. These risks in turn have the potential to create a chilling effect on people’s willingness to engage openly and honestly with scientists. Thus, by understanding people’s expectations and preferences regarding the treatment of personal data collected for research, we can help scientists (1) design ethical studies and communicate effectively with participants, and (2) understand and mitigate potential research limitations stemming from participants’ privacy concerns.
In this project, researchers will design and administer a survey online to understand expectations and preferences around the collection, usage, and protection of personal data in scientific research. We envision a survey that includes questions in at least the following three areas:
- What factors do participants take into account when deciding whether to take part in a research study,
- participants’ level of comfort with different kinds of personal data collection in various research contexts (e.g., different types of data, different use cases, different types of protection), and
- past experiences with research studies that raised security or privacy concerns.
We hope that, after analyzing survey results, the researchers will have preliminary recommendations concerning the acceptability and practical implications of different approaches to personal data collection in scientific research.
Researchers
Associate Professor, Computer Science
Graduate Student
In reinforcement learning (RL), agents learn how to navigate a simulated environment by gaining feedback in the form of rewards. RL research has numerous angles of research: exploration versus exploitation, model-based versus model-free, on-policy versus off-policy, and many more. In our research, we examine how to take advantage of gradients from the environment to help learning generalization and convergence of the model-free methods. The presentation will include background information, related works, methodology, results, and a conclusion, similar to a full research project.
Researchers
Professor, Computer Science
A key takeaway of the COVID-19 pandemic was the need for timely, relevant and actionable information to support effective public health messaging that can impact in-real-life (IRL) outcomes. Frontline public health officials often had little insight into the individuals that they wished to serve, e.g., the willingness to wear a mask. The PandEval project will address these challenges by creating data collections and tools to assist public health officials.
PandEval is sponsored by the National Science Foundation Predictive Intelligence for Pandemic Prevention (PIPP) program (NSF Grant CCF 2200256).
PandEval Project Overview: https://go.umd.edu/pandeval
The goal of this project is to analyze a curated collection of pandemic-related messages on social media platform X (Twitter). The collection will include messages that are issued by a SEED group of 500 public health officials (PHOs) or public health experts (PHEs). The collection will also include direct responses such as likes, retweets and replies from participants in the conversations. The profiles of the participants and their historical tweets will also be available. The participants have been labeled as being more / less willing to accept scientific evidence.
Researchers
Professor, UMIACS
Graduate Student
Deep networks have shown strong capabilities for standard vision tasks such as image classification and object detection. More recently, generative networks have become extremely popular. These models are able to create detailed images that can even fool humans into believing that they are human-generated. Recently, diffusion models have become extremely popular in allowing users to create a highly detailed image using just a text prompt. In this project, students will be introduced to how diffusion networks work and explore their properties such as how different prompts can affect image generation quality. They will also have the opportunity to finetune these networks for their own custom images by introducing new concepts to an existing network. Finally, students can exploit these networks to classify unseen images.
Researchers
Assistant Professor
Graduate Student
Schedule of Events
Thursday 7 p.m.- Orientation |
Date October 19 |
Location Zoom |
---|---|---|
Friday 9 a.m. - 5 p.m.- Research Bootcamp |
Date October 20 |
Location Iribe Center & Remote |
Saturday All Day- Project Time & Technica Kickoff |
Date October 21 |
Location The Hotel, Iribe Center & Remote |
Sunday COMING SOON |
Date October 22 |
Location The Hotel & Remote |
Register to Attend
Who Can Attend?
We welcome all undergraduates who identify as underrepresented genders in computing and who are from all colleges and universities to apply. Current Master's students considering a PhD program may apply and will be considered on a case by case basis.
Registration Process and Fees
Participants will either experience this workshop remotely through the Zoom virtual platform or in-person.
Depending on how students choose to attend the workshop (virtual or in-person), all gear (water bottle, pens, stickers) will be either shipped to your personal address or distributed to you in-person. Students attending virtually will be responsible for their own meals but are welcome to share the lunch space via Zoom. Lunch will be provided to students who are able to attend in-person.
REMEMBER: YOU MUST REGISTER FOR BOTH TECHNICA (RESEARCH TRACK) & COMPLETE AN APPLICATION FOR TECH + RESEARCH
Register for the Tech + Research Workshop by filling out the following form:
REGISTRATION FOR TECH + RESEARCH
Register for Technica here:
Participant Information
Logistical Information for Attendees
Tech Requirements and Access
More information coming soon.
General Information
All students participating in the research workshop must join the Zoom meeting by 10am on Friday morning. We have an important bootcamp event to prepare you for the research projects. If you cannot make it by this time, you need to let us know immediately.
You will not be working on your own idea or hack during Technica. During Technica, you will be working with your research team on a specific project you selected as an interest.
You will be assigned your research group the week before Technica. Each team will have their own Slack channel to communicate. Researchers may send out a small amount of pre-reading to help you prepare for the project.
During Technica, projects will either be fully virtual or fully in-person. No project will run as a hybrid project.
Spread the Word
View the Tech + Research Flyer.
Questions about the registration process, workshop, or logistical information can be sent to i4c@umd.edu
Questions about Technica can be addressed to hello@gotechnica.org
About Technica
On October 21-22 2023, the University of Maryland hosted Technica, the eighth annual hackathon. Technica is the largest hackathon for underrepresented genders in the world. In the span of 24 hours, Technica gives our participants the opportunity to create new applications, websites or hardware projects.
Technica is not a typical hackathon—it serves as a place where the brightest thinkers in the country can come together to collaborate and share their innovative ideas. Our focus is on providing a welcoming, engaging and creative environment to support underrepresented genders in tech culture. We pride ourselves in helping both beginners and experienced hackers explore technology and develop their skills.
Over the duration of 24 hours, participants are immersed in tech culture and encouraged to exercise their imagination to create interesting and innovative hacks.
At Technica, we want to challenge our hackers to step out of their comfort zone and try something new, whether that's exploring a new technology, tinkering with hardware hacks, or coding for the very first time! This past year we introduced our beginners and hardware tracks, designed for hackers looking for extra support or interested in trying their hand at electronics. At Technica, we want our hackers to learn, grow, and meet new people, so join us at Technica 2023!
All participants attending Tech + Research must also register for Technica. Register here.