Tech + Research Workshop

Tech + Research Banner

Date:  November 9 - 11, 2018

Location:  STAMP Student Union, Computer Science Instructional Center, and the Armory

REGISTER FOR TECH+RESEARCH HERE

REGISTER FOR TECHNICA HERE

The Department of Computer Science at the University of Maryland and the Center for Women in Computing are pleased to present the Tech + Research: Welcoming Women to Computing Research, a three day research workshop geared towards engaging undergraduate women in computing. During this workshop, student teams will come together and collaboratively work together to use technology to solve pressing issues.

In collaboration with Technica, the largest all-women hackathon 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 sessions introducing attendees to the basics of computer science research (CSR) and highlight the exciting opportunities that come with pursuing a graduate degree in computer science.

IMPORTANT: YOU MUST REGISTER FOR BOTH TECHNICA AND TECH + RESEARCH

 

Purpose

This workshop hopes to give undergraduate CS women 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 give undergraduate women to meet computing faculty and current graduate students as well as 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 the gender gap the currently exists in CS departments in higher education.

Attendees of this event will not only be expanding on their computer science skills, they will also be given the opportunity to meet and network with many individuals who are present in the computer science 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:

University of Maryland, College Park

  • Department of Computer Science
  • Department of Electrical and Computing Engineering
  • College of Information Systems
  • College of Education

University of Maryland, Baltimore County

  • Department of Information Systems

Projects

This project will develop a Virtual Try-on App that can estimate a 3D model of a human body and its outfit directly from a few photographs with little human interaction. Our current system can capture the global shape and geometry of the clothing, and extract the physical and geometric properties of cloth. Unlike previous methods using full 3D information (i.e. depth, multi-view images, or 3D scanned geometry), our approach can achieve garment recovery from a set of single-view images by using physical, statistical, and geometric priors and physics-based cloth simulation for Virtual Try-on of customized apparel to improve the user's online shopping experience.

Researchers

Photo of Ming Lin
Ming Lin
Chair, Professor, CRA DREU Co-Director
UMD Department of Computer Science
Photo of William Liang
Junbang Liang
Graduate Student
UNC, Chapel Hill

Autonomous driving and deep learning are two trending topics today. Can we use deep learning to power autonomous driving and let a vehicle to drive itself via images from a single front-facing camera? This project involves exploring how to set up a deep learning framework, how to collect training data in Unity, how to train a deep neural network, and eventually using the trained network to steer a vehicle on both training and testing routes. Basically the project will build a deep-learning-based system that learns from a large set of simulation data from a driving simulator and attempts to model a system for a safer navigation for autonomous vehicles or driverless cars.

Researchers

Photo of Ming Lin
Ming Lin
Chair, Professor, CRA DREU Co-Director
UMD Department of Computer Science
Photo of Weizi Li
Weizi Li
Graduate Student
UNC, Chapel Hill

This project will investigate opportunities provided by augmented reality (AR) technology to support learning of foreign languages. AR devices allow users to perceive (see and hear) additional information that is blended seamlessly into their physical environment. This provides many possibilities to enhance the environment of a user with information in a foreign language. For example, objects in the user's environment could be tagged with their names in a foreign language and text-to-speech synthesis could be used to pronounce their names. In addition, the user could interact with virtual objects or even avatars in a foreign language, which will be seamlessly presented in the real environment of the user. This project will leverage AR displays and existing services for object recognition, machine translation, and AR rendering to develop a prototype app that explores the possibilities of these technologies for foreign language learning.

Researchers

Photo of Matthias Zwicker
Matthias Zwicker
Professor, Reginald Allan Hahne Endowed E-nnovate Professorship
UMD Department of Computer Science
Photo of Yiu Jiang
Yui Jiang
Graduate Student
UMD Department of Computer Science

Let say you have a list of 1,000,000 numbers. You want to write a program to find the max of those numbers. That will take roughly 1,000,000 comparisons. Hence it will take roughly 1,000,000 steps. But what if you have TWO computers! You can run both at the same time. Hence you can make two comparisons in one step. You can do better than 1,000,000 steps! How much better? But what if you have THREE computers! You get the idea. More generally: What if you have n numbers and k computers? So you can do k comparisons in one step. How many steps do you need to find the max of the n numbers?

Researchers

Photo of William Gasarch
William Gasarch
Professor
UMD Department of Computer Science

Privacy and cybersecurity are major concerns in modern society. Unlike many other technological concerns, the concept of privacy is directly shaped by individuals who may or may not have any formal instruction in computing or have ever considered the implications of their personal actions with respect to privacy or online security. This project will focus on developing a survey related to measuring privacy attitudes, deploy the survey to a crowdsourcing service such as Amazon's Mechanical Turk, and analyze the resulting data in order to test hypotheses and draw conclusions.

Researchers

Photo of Michelle Mazurek
Michelle Mazurek
Assistant Professor
UMD Department of Computer Science
Photo of Duncan McElfresh
Duncan McElfresh
Graduate Student
UMD Department of Mathematics

In order to realize the promise of precision oncology, doctors need reliable molecular markers that predict a tumor’s drug response. Researchers have recently begun to measure the response of cancer cell lines to catalogues of drugs. These datasets are publicly available, and include molecular measurements such as mutations in DNA sequence. This project will design and train models for drug response from molecular data. There will be discussion surrounding strategies to allow the models to leverage knowledge of molecular and cancer biology to learn patterns of response. The project will also explore methods to take advantage of the multiple “views” of the cancer cells given by the molecular data, and to train a single model that predicts response to multiple drugs. The project will apply open-source Python software for machine learning on preprocessed datasets. Importantly, while the models will take advantage of this biological knowledge, no prior biological expertise is required!

Researchers

Photo of Max Leiserson
Max Leiserson
Assistant Professor
UMD Department of Computer Science
Cindy Li
Graduate Student
UMD CBBG

Technology is being developed to support people with dementia in aging in place. Aging in place involves the ability to live in one's own home and community safely, independently, and comfortably, regardless of age, income, or ability level. Despite technological advances, the adoption and acceptance of these technologies is low, likely in part due to a lack of consideration of the needs of technology users with dementia. In this project, attendees will create design concepts and prototypes for smart home technologies that are based on input from people living with dementia.

Researchers

Amanda Lazar
Assistant Professor
UMD College of Information Studies
Emma Dixon
Graduate Student
UMD College of Information Studies
Alisha Pradhan
Graduate Student
UMD College of Information Studies

Password can be considered as the first line of defense against cyber-attackers. Nowadays many systems have complicated rules to force users to generate "secure" passwords, which are hard to memorize and create a lot of inconvenience. In this project, students will learn how to evaluate the strength of passwords and use a developed hardware system to create "more secure" passwords. They will demonstrate the effectiveness of the given hardware system's ability to enhance the strength of "weak" password. Students will also work on methods to hack the hardware system in order to predict the "more secure" password for a given "weak" one.

Researchers

Gang Qu
Professor
UMD Department of Electrical and Computer Engineering
Qian Wang
Graduate Student
Qian Xu
Graduate Student

With the popularity of Supervisory Information System (SIS), Supervisory Control and Data Acquisition (SCADA) system and Internet of Things (IoT) sensors, we can easily obtain abundant sensor data in manufacturing. We could save manufacturing maintenance costs and prevent further damages if we can 1) accurately predict system anomalies from the sensor data and 2) analyze the possible anomaly casualties among the IoT sensor data. This project will apply different Data Science approaches on anomaly detection/prediction and causality analysis. We expect the project will be implemented as Jupyter Notebooks using programming languages Python, R, or Scala.

Researchers

Jianwu Wang
Assistant Professor
UMBC Department of Information Systems
Pei Guo
Graduate Student
UMBC Department of Information Studies

Amazon, Microsoft, and Google all have widely used cloud computing products that enable anybody to deploy and maintain applications in their respective clouds. Most applications require data storage, and each cloud vendor provides several data management options, such as Amazon RDS, Amazon DynamoDB, and Amazon Aurora, Google Spanner, and Microsoft Azure SQL and Microsoft Cosmos DB.

This research project will attempt to “break” these different cloud data management offerings. Students will read the published papers that describe the architectural approach taken by each of (or a subset of) these offerings and identify the weak points in each offering. Then, the project will write applications on top of each of (or a subset of) these offerings that exploit these weak points, and expose the architectural flaws that we find. For example, some of these offerings assume “partitionable workloads” where transactions do not frequently access data from different partitions of the data. The project will write applications that produce high amounts of multi-partition transactions. Other offerings assume that transactions rarely conflict with each other. The project will write high-conflict applications. In the end, students will have demonstrated the central role that assumptions make in architectural design, and how different systems perform extremely poorly when these assumptions are violated.

Researchers

Daniel Abadi
Professor, Darnell-Kanal Professor of Computer Science
UMD Department of Computer Science

This project will develop a “harry-potter” style wand that understands its user’s (the wizard) hand gestures. We will equip a wooden wand with a Raspberry PI microcontroller, an Inertial Measurement Unit (IMU) sensor, and a WiFi interface. Our approach is to use unsupervised machine learning classifiers to differentiate between different hand gestures. Each hand gesture will be accounted for one spell. The result of the machine learning algorithm will be sent over the wifi interface to a server. By the end of the project, the participants will engage in a competition in which they will test their wands in a wizard duel. The one with the best wand (or machine learning algorithm to be more specific) will win the duel.

Researchers

Photo of Yasser Shoukry
Yasser Shoukry
Assistant Professor
UMD Department of Electrical Engineering
Haitham Khedr
Graduate Student
UMD Department of Electrical Engineering

Schedule of Events

Friday 11/9/18 Location
Registration/ Check-In 7:30am-9:00am Stamp Student Union
Opening Breakfast 8:45am-9:30am Stamp Student Union
Bootcamp 9:30am-12:30pm Stamp Student Union
Lunch 12:30pm-1:15pm Stamp Student Union
Bootcamp 1:30pm-3:30pm Stamp Student Union
Team Breakouts 3:30pm-5:00pm Stamp Student Union
Reception 5:00pm-6:00pm Stamp Student Union
Dinner 6:00pm-7:00pm TBD
Board and Brew Game Night 7:00pm-9:00pm The Board and Brew
 
Saturday 11/10/18 Location
Check-in at Technica
(if not already completed)
8:00am Reckord Armory
Breakfast 9:00am Reckord Armory
Research Track Morning Check-in 10:15am-11:00am Reckord Armory
Technica Fair 9:00am-11:00am Reckord Armory
Opening Session and Speaker 11:00am Reckord Armory
Lunch in Teams 12:15pm-1:00pm CSIC
Research Work Time  1:00pm-6:00pm CSIC/Other Labs
Dinner at Technica 6:00pm-7:00pm Reckord Armory
Research Work Time/ Enjoy Technica 7:00pm-11:00pm Reckord Armory
 
Sunday 11/11/18 Location
Breakfast 7:30am CSIC
Research Work 8:00am-10:00am CSIC/Other Labs
Presentation Prep 10:00am-12:00pm CSIC/Other Labs
Lunch at Technica 12:00pm Reckord Armory
Presentations at Technica 12:30pm-2:00pm Reckord Armory
Closing Ceremony 2:00pm-3:00pm Reckord Armory

Register to Attend

Who Can Attend?

We welcome women undergraduates from 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. Please apply by October 15th for best consideration. Applications will be accepted until October 26th. We will begin rolling confirmations begining October 19th. 

Registration Process and Fees

Participants will have two options to experience this workshop.

  • Students who live more than 15 miles away from College Park, MD will be provided housing. 
  • Students who live on or near the UMD campus  (less than 15 miles away from College Park, MD) will not be provided housing. 

All meals, snacks, and supplies will be provided for both groups from Friday morning breakfast through Sunday lunch. Students are responsible for their own transportation to and from UMCP.

REMEMBER: YOU MUST REGISTER FOR BOTH TECHNICA & TECH + RESEARCH

Register for the Tech + Research Workshop by filling out the following form:

REGISTER FOR TECH+RESEARCH HERE

Register for the Technica here:

REGISTER FOR TECHNICA HERE

Participant Information

Logistical Information for Attendees

Hotel Accommodations:

Hotel TBD (will be close to UMD)

Timing:

Students should arrive by 8am on Friday morning. Hotel accommodations are availibe on Thursday night if requested.

Getting to UMD:

Students are responsible for their transportation to and from UMD. 

Parking:

Parking will be provided for non-UMD students if you request parking at least 2 weeks ahead of time. 

Transportation: The College Park Metro and Greenbelt Metro stations are in the area, but are not considered walkable. Shuttle UMD service is available from College Park Metro. Metrobus and ride share options are available from both stations.

Meals: All meals will be covered between Friday AM and Sunday lunch. Please list any specific dietary restrictions on your registration form. 

Questions about the registration process, workshop, or logistical information can be sent to katea@cs.umd.edu

Questions about Technica can be addressed to hello@gotechnica.org

About Technica

On November 10th-11th, the University of Maryland will host Technica, the fourth annual all-women hackathon. Technica is the largest all-women hackathon in the world, and welcomed over 850 participants from across the country last year. 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 women in tech culture. We pride ourselves in helping both beginners and experienced hackers explore technology and develop their skills.

All participants attending Tech + Research must also register for Technica. Register here.

 

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