X-Lab Project Opportunities

Do you have data that needs to be analyzed? A machine learning model that needs to be tested? An app developed? BU Spark! offers a unique opportunity for organizations to partner with Boston University students with computer science and engineering skills on their technology projects. There are several no-cost or low cost options to engage students, see the various options listed below:

  • Free Class Projects
  • Paid On-Campus Externships
  • User Design/User Interaction support

BU Spark! Free Class Projects

Each semester, BU Spark! places computer science and engineering students on data science, machine learning, and software development projects provided by external partner organizations. Projects are approximately 60 hours of work involving teams of three students and require external partner organizations to dedicate approximately 45 minutes per week for the 8 weeks of project implementation. You can Apply To The X-Lab HERE and find FAQs below.

Co-Labs

XCC433: Justice Media Co-Lab Instructors: Brooke Williams and Osama Al-Shaykh

The Justice Media Co-Lab, a collaboration between BU Spark!, based at the Faculty of Computing & Data Sciences, and the Journalism Department at the BU College of Communication, matches interdisciplinary student teams with a background in computer science, statistics, computer engineering, or data science or journalism related disciplines with computational journalism projects provided by external media partners. 

Projects vary in size and scope and range from smaller projects that are approximately 60 hours of work to larger projects that can be as much as 100 hours of work per team member over the course of a semester. Projects are comprised of teams of approximately 3 computer science or engineering students and require partner organizations to dedicate approximately 45 minutes per week for the 8-12 weeks of project implementation.

Data Science Class

Data science projects are designed to help partners answer strategic questions from data analysis conducted on large and multiple data sets. Projects must include data collection (e.g. calls to an API, parsing/ crawling web pages, etc.); data compilation and cleaning (e.g. combining with an internal spreadsheet), and analysis. Students will then analyze the data using methods such as clustering, classification, regression, and network analysis.  Ideally, projects include multiple data sets comprised of a minimum of ~5,000 records.  It is important that the sponsoring organization provide the specific question or questions they are seeking to have answered from the analysis of these data.

Data Science Project Example: In partnership with the ACLU-MA, Spark! students were able to identify patterns, including racial and geographic disparities based on the treatment residents received by the Boston Police Department. Students used three data sets to arrive at their conclusion including stop and frisk data, crime incident report data, and census data.

Data Science Project Example: Using census data and business license database, Spark! mapped residential concentrations and economic development activity among Brasilians in Boston for Digaai.

Machine Learning Class

Machine learning projects aim to build algorithmic models, based on supervised learning approach, that accurately forecast desired outcomes. Projects must include already compiled and cleaned data sets comprised of a minimum of ~5,000 records that includes a portion of “groundtruth” data, i.e. where the result is known. Students will use this groundtruth to build a model that trains the remaining data to achieve forecasts based on a desired threshold of accuracy determined by the partner organization.

Machine Learning Project Example: Working on behalf of an early-stage start up and using data pulled from the Zillow API and RMLS Data, students created an algorithm to estimate real estate sales price. Variables included interior and exterior photos, location, and house features. The model built by the students achieved an 81% accuracy rate.

Machine Learning Project Example: In order to help Converse understand the effectiveness of its product launches BU Spark! students built a model that helps to track social media traffic once a product is launched. Measurements included but are not limited to, demographics and geography of the traffic interested in the product and how the information about the product launch is shared over time.

CS 791: Applied ML For Public Health Instructor: Elaine Nsoesie

Machine learning methods are being used in the analysis of data (e.g., text, image, sound, video and biological data) to understand disease and health trends, and to improve individual and population health. The goal of this course is to provide students with prior knowledge of Machine Learning techniques with the opportunity to gain hands-on experience working on a data analysis project. Throughout the semester, students will work in groups to apply Machine Learning to solve a specific problem submitted by one of our research partners. Weekly lectures will focus on exposing students to (a) applications of ML to public health problems, (b) openly available computational resources, and (c) ongoing research by experts working in ML and health. The course is open to both Masters and PhD students.

Spark! X-Lab Practicum Class

Projects for this class are more open-ended and can include web or mobile app development, data science, data visualization, machine learning and more. The projects accepted will be based on the skills of the students taking the course.

Practicum Project Example: On behalf of an Ed Tech startup, BU Spark! created a prototype of an AR/VR app designed to stimulate children to utilize their immediate environment to explore key early math concepts in geometry.

Practicum Project Example: BU Spark! working in collaboration with a local investigative journalist, built an automated scraper to extract data from public websites into a customized database accompanied by a searchable user interface where reporters or citizens can sort, search and otherwise explore the data.

Paid On-Campus Externship Projects

In addition to the class projects, partners can choose to fund a team of students through an on-campus Spark! Externship. Like the X-Lab Practicum class above, these projects offer greater flexibility in terms of technical scope but they also offer greater flexibility in terms of timing because projects are accepted on a rolling basis.

Here’s how the X-Lab works: Please email buspark@bu.edu if you have any questions about the X-Lab. Applications are accepted on a rolling basis.

  1. Submit A Project Application HERE! 
  2. BU Spark! will schedule a consultation call to discuss the best fulfillment option.
  3. Scope is refined and agreed upon.
  4. Spark! recruits a team of BU students or matches the project to a class.
  5. Student team works directly with the project partner with oversight from Spark!
  6. Project is delivered to the partner along with any relevant code, data, or other agreed-upon deliverables.

User Design/User Interaction support 

In addition to the paid on campus externship, partners can choose to fund a student through an on-campus Spark! Externship, to work on wireframes for your project. The wireframes are often needed before a project can be developed in a technical environment e.g. mobile application, web development  projects.

We have three different project options:
1) UX Prototype and Sketch Wireframe: these are rough sketches of your product concept that reflect your priority functionality for the purposes of testing with users and/or sharing with your development team

2) Style: you have wireframes, you know what you are building or are already building it, but you want to develop a brand identity and a style guide for your product. This includes a logo, color scheme, font selection, etc.

3) Hi-Fidelity Wireframe Designs: you know what you are building or are already building it, you have your style guide and you need help applying the style guide to your wireframes to integrate with your front end development

We offer greater flexibility in terms of timing because projects are accepted on a rolling basis.

Applications are accepted on a rolling basis.

Submit a UXD Project Application HERE!

FAQ’s

Q: What is the BU Spark! X-Lab?
A: BU Spark! is an initiative to support student-driven innovation in computer science and engineering. The X-Lab (Experience Lab) is a program that allows students at Boston University to work on real world projects that are submitted by external partners (you!). Spark! offers two project pathways – curricular (course-lead, semester-long) and co-curricular (internship-style, flexible timeline) – pending what works best for the partner.

 

Q: What types of projects does BU Spark! X-Lab accept?
A: We are interested in all projects from all sectors. We accept projects from non-profits, faculty research, startups, local government, and global companies. Spark! is an experiential learning opportunity for students who are still developing their skills. We prefer exploratory projects that are not mission critical to your organization. The best projects are those that have been thoroughly prepared, if a machine learning or data science project – data collected and/or cleaned , if an app- wireframes developed, etc. and have a clearly defined output. We have growing interest and expertise in machine learning or algorithm development as well.

 

Q: What does the intake process look like?
A:

  • Step 2: Wait to hear back from X-Lab staff, who will schedule an initial call with you to discuss your project and whether it should be channeled to the X-Lab curricular pathway or the co-curricular pathway.
  • Step 3: After the call, Spark! Will draft a project description for your review and approval which can be refined during subsequent conversations if necessary. This is the longest part of the process and be prepared to communicate with us during this time because we want to make sure that your project is as polished as it can be!
  • Step 4: You are all set!

 

Q: What if I need help with part one of the project proposal form?
A: No problem at all; we’re glad to help! If you need assistance or have any questions, you can email the Program Manager (Greta Bruce) at gretab@bu.edu 

 

Q: What happens after my X-Lab Class project is submitted and approved?
A:

  • Step 1: You will be invited to present your project to the class in either September (for the fall semester) or the end of January (for the spring semester). This is known as “Pitch Day”.
  • Step 2: You will have 5 minutes to present your project and 5 minutes for Q & A. If you cannot make the presentation in person, we ask that you send a video or tune in on Skype.
  • Step 3: Shortly after the presentation, you will hear back whether or not your project was chosen by the students for the semester.
  • Step 4: If your project was chosen, you will be introduced to the students via e-mail and asked to schedule a project kick-off meeting and then meet with the team weekly or once every two weeks (preferably in person). The more engaged you are, the better output you will get.
  • Step 5: You will be invited to participate in an end-of-semester presentation event where students present their finished projects to the class and partners. If you cannot make it to the presentation, you may have the students present their final work to you at a different time. You will be responsible for scheduling this with your team by mid-semester. Students will also submit a final report to accompany the end-of-semester presentation.
  • Step 6: Complete an evaluation form for the student team and provide feedback on the Spark! Partner Projects program.

 

Q: What if I am unable to attend the presentations to students or am not based in Boston?
A: If you can’t be there in person, you can present via Zoom or simply record a video of your presentation. The initial presentations are not required, but will greatly enhance your chances of getting your project matched.

 

Q: Do I need to prepare anything for the Poster Presentation Session? What if I won’t be able to attend the Poster/ Final Presentation Session?
A: No, you do not. If you wish, you may have the students present their final work to you at a different time. This will need to be scheduled by mid-semester. Students will also submit a final report to accompany the end-of-semester presentation.

 

Q: Can you guarantee that my X-Lab Class project will be matched with a student team in one of the classes?
A: We can’t guarantee that projects will be matched. We will do our best to help you scope projects that fit within the requirements of the chosen track, fall within the skillset of our student community, and present a compelling opportunity to students. We will also publicize with our student community, but we are unable to make any guarantees that the projects will be adopted. Incentives, access to mentors, and presenting in person always help!

 

Q: What happens after my X-Lab Consulting project is submitted and approved?
A: We will pair you with an individual or a team with the skills to complete your project. You will need to provide them with all of the necessary documentation that you have not included in your proposal form. It is important that you schedule a kick off meeting and communicate with them on a weekly basis or once every two weeks to ensure that work is getting done to your satisfaction.

 

Q: What are the requirements for serving as a project mentor? 
A: At minimum, we need a point person in the partner organization with the context necessary to provide direction to students on the expected project outcomes. The mentor does not necessarily need to be technical, but it is important that this individual can provide  a reasonable amount of time to guide the students through the project.

Q: Who Owns the IP? What about NDAs?
A: Any arrangement can be made between your organization and your student team. We encourage our partners to allow the students to retain ownership or use of the methodologies they develop for your project under an open source licensing agreement – this allows them to show their work and skills to prospective employers. However, we understand that you may want ownership of the specific application or analysis completed on your behalf to remain proprietary or non-disclosed. We also understand you may want to own the IP/ methodologies developed by the students. In short, we encourage your organization to communicate your expected terms for this partnership in advance and we will recruit the student team accordingly.  

For a copy of our standard NDA agreement, or for additional questions, please email gretab@bu.edu 

 

Q: Will BU Spark! provide a legal agreement?
A: Yes, we have template agreements that can be used and adapted based on the required terms. BU Spark! cannot sign agreements on behalf of students, so this agreement will be between your organization and the students, with no legal liability to BU Spark! To review our student services agreement, or for additional questions, please contact gretab@bu.edu

 

Q: Can I get the code the students develop?
A: Yes, we have created a Github repository where the code from all student projects will be uploaded. We can give you access to this repository or create a separate, private repository, particularly if there is sensitive information you would not like to be public.

 

Q: What happens if the student team doesn’t finish my project?
A: We hope that you will always be satisfied with the outputs from the partnership. However, there is always a risk that the volume of work may exceed the capacity of the students from either a time or technical skill perspective. The best way to mitigate against this risk is to work in close collaboration with BU Spark! and your student team to accurately scope the project before you get started. You will need to communicate regularly with students your top priorities for the project.

 

Q: What happens if the quality of the work by the student team does not meet expectations?
A: We will try our best  to make sure this does not happen and we do our best to make sure students are vetted before they are matched to the client project. There will be several mid-project assessments and deadlines during the projects as well in which will allow you to correct for quality. If you are having challenges with the student team – either in terms of regular communications or work quality – please let BU Spark! know. This is an experiential learning opportunity for students and you will have an opportunity to complete a final evaluation so BU Spark! and the students can learn from the engagement.