Michtom School of Computer Science

Spring 2025 Reports

• “Enabling and Evaluating LLMs specific to a higher ed environment: high data privacy requirements combined with low computational resources,” by Gabriel Abreu ‘25. 
  Beginning with the application of LLMs to supporting the university investment office, my research concluded with the design and evaluation of AI techniques for information retrieval in environments of low computational resources and high privacy requirements.

•  “A User Friendly Tool for Analysis of Student Wellness Survey Data: Merging Data Architecture Best Practices with LLM-based Natural Language Search,” by Yingxuan (Bill) Dong ‘26. 
  I focused on industry standards and emerging technologies most relevant and useful to dashboard analytics, and applied it to support Student Affairs in their data-informed decision-making based on student wellness survey data.

•  “Creating the Data Infrastructure for Analysis of Student Success in Higher Ed: from case study of STEM majors to policy recommendations on data management protocols,” by Jasmine Huang ‘26. 
  My research began with building data scientific analyses of student pathways through STEM majors and resulted in policy recommendations about the collection, integration, and analysis of academic and student data.

•  “Building a low-maintenance dashboard with high user-friendly interactivity for automated data scientific insight into electricity usage on university campuses,” by Kevin Cui '26. 
  My research this semester focused on best practices in dashboard design for data scientific analysis of electricity usage on a university campus, which included the real-time feed of hourly electricity usage data, and the ability to differentiate categories of buildings as well as morning hours from evening hours.

Campus Planning and Operations

• Improving response to daily building alarms: DSII Intern built machine-readable dataset of alarms from all campus buildings, then created analytics to pinpoint faulty alarms, missing sensors, and disproportionately high percentages associated with specific buildings. These analytics help increase the capacity of administrators to rely on data-informed decision-making to allocate scarce staff resources to response and repair.
• Enabling geographic view of utilities infrastructure: DSII Intern designed the schema necessary to create a spatial data platform for viewing all buildings, infrastructure, and utilities components on a campus map. The prototype geographical information system includes precise details of sewer pipes, manhole covers, and electric submeters, all aligned with location-specific measures of deferred maintenance, enabling administrators to more effectively evaluate potential investments (“does it make sense to install a bathroom on this spot in this field?”) and troubleshoot building failures (“which water pipe might be responsible for that manhole spewing so much steam?”)
• Measuring and predicting electricity use: DSII Interns engineered the integration of data from multiple current and historical sources to create a machine-readable dataset of electricity usage by campus building. Interns designed and built a dashboard that allows administrators to analyze usage levels and patterns for each building across seasons (winter, spring, summer, fall) and functions (residence hall, science building, gym, etc). Interns also began building predictive tools for better estimating energy usage by building as well as to highlight relevant anomalies deserving of strategic attention. Read a senior thesis in computer science based on this work.

• Transportation Logistics: DSII Intern created the data scientific platform, including regular feeds of machine-readable data, and user-friendly dashboards, to analyze and visualize ridership data of university campus shuttle service. This data visualization allows administration to identify what day and time vehicles are reaching maximum capacity, as well as identify underutilized stops and times.  This work also generates general trends such as ridership by day, time, and stop as well as highlighting vendor on-time/early/late arrival performance by stop. This is transforming the capacity of administrators to more effectively and efficiently align campus transportation resources to community needs. 

Career Services

• Intern is transforming into machine-readable format all data related to student participation in career services activities, which will enable interactive dashboards, data visualizations, and analyses of trends across student age groups and across career service office offerings. This will support the division as it aims to increase data-informed decision-making on design and delivery of program offerings that provide the most relevant career services resources, in a targeted way, to the student population.

Dean of Arts & Sciences

• Analyzing trends in course enrollments and instructor workloads: DSII Interns engineered and integrated datasets to make it possible to build dashboards of historical and current data on undergraduate course enrollments and instructor workloads. The dashboards now make it possible to disaggregate the data by academic department, instructor, undergraduate major, and individual course. Experimental analytics by Interns based on this new data platform demonstrate the possibility of predicting enrollments in required and high traffic courses, a functionality that, once formally implemented, enables administrators to more effectively plan and allocate academic resources.

Finance

• Intern is identifying multiple streams of data reconciliation that feed into the university’s budget-making, with the aim of streamlining and automating the university-wide process, and documenting the upgrades for all current and future users.

Graduate School of Arts & Sciences

• Analyzing trends in course enrollments for 100+ level courses across the university’s graduate programs: DSII Intern will leverage the data engineering and dashboard design expertise of Interns working in the Dean of Arts & Sciences to build out a dashboard of course enrollment trends and analytics specific to graduate school courses. As with other DSII projects that introduce dashboard functionality to existing work-flows, this will require integration of multiple data sets from various sources. Accomplishment of the goals of this project will bring dramatically increased efficiencies to administrators responsible for assessing and allocating academic resources at the graduate level.

Human Resources

• Launching a Human Capital Management dashboard of  Organizational metrics: DSII Interns began by assembling a prototype HR dashboard, then integrating the data streams and analytics necessary to create metrics showing turnover rates, time-to-fill for open positions, degree of diversity in recruitment pools, and completion rates for performance reviews. Interns also helped collect feedback from a pilot group of users across the university to improve the design and usability of the dashboard. Interns are now creating predictive analytic tools that, once formally implemented through the HR dashboard, will help senior leaders foresee and manage workforce related challenges and opportunities. In addition, current Intern developed a user-friendly encoder for HR admins to use when needing to anonymize sensitive HR data. 

Institutional Advancement

• Building predictive models of charitable giving and of bequests that are customized to Brandeis alumni, donors, and friends: DSII Interns brought data scientific analyses to an entire corpus of Brandeis alumni and fundraising data, including current and historical data dating back to the founding of the university in 1948, enabling fundraising admins to identify key characteristics of those most likely to make major gifts or to leave bequests to the university. Interns are relying on these initial analyses to develop tools for data-informed guidance to administrators making resource allocation decisions regarding the cultivation of potential donors.

• Increasing efficiencies in information retrieval to strengthen donor relations and development of fundraising strategy:  Intern is deploying techniques from machine learning and computational linguistics to dramatically increase the speed and accuracy of searching through 400,000 contact reports of fundraising meetings with past, current, and future donors, with the aim of deepening the capacity of the Institutional Advancement office to nurture donor relations and more effectively align fundraising strategy with the donor base.

Information and Technology Services

• Data Quality Analysis: Intern is designing algorithms to identify and resolve issues in data quality, including missing, incomplete, inaccurate, or duplicated data, across all university data sets. Intern will also help design new protocols and monitoring systems for improving data quality in new data entry going forward.
• Expanding data analytic client services capacity: DSII Interns develop trustworthy and reliable expertise in report generation through Workday (the university’s new cloud-based enterprise management system) which helps administrators broaden data scientific support across the university. For example, DSII Interns in ITS were instrumental in helping to roll out the Human Capital Management dashboard.
• Experimenting with potential improvements to Workday implementation: DSII Interns in ITS help specify potential solutions to data-management challenges that emerge in the Workday implementation process. This has included identifying opportunities for improvement in the Workday Student module, with technical descriptions of functionalities that need to be activated in order to better serve the Brandeis student community.
• Speeding up deployment of data management innovations: DSII Intern was able to create an automated solution to the NSF’s required annual Higher Education Research and Development (HERD) survey thanks to application of innovative data management tool Prism, which is specifically aimed at enabling the Workday ERP to effectively integrate non-native data. Full-time staff resources had not yet been available to deploy Prism for the NSF HERD survey. 
• Security, Access, and Collaboration Among DSII Interns: Intern in ITS is creating new infrastructure for governance check-lists related to on-boarding and off-boarding of DSII projects, including dynamic inventory of datasets being used, software tools being deployed, and details of differentiated access being granted by administrators. This automated system, once implemented, will enable security-conscious distribution and sharing of DSII-generated tools across university divisions.
• Undergraduate Retention: Intern is integrating datasets of relevant student information necessary to evaluate key drivers of student success, as defined by retention until graduation. Intern will help design, implement, and validate the analyses to identify these drivers. This will include developing and refining a predictive model to identify variables most highly correlated with student retention and graduation rates.

Office of Investment Management

• Increasing efficiencies in information retrieval to support the investment decision process: Intern launched experimentation with varieties of language-based data scientific tools to search voluminous and multifaceted documents containing investment-relevant information. The results of this experimentation customized to the Brandeis Investment Office will be used by management to support workflow changes aimed at dramatically cutting down on the number of hours needed to identify key insights, trends, and relationships necessary for investment decision-making. 

Office of the Provost

• Enabling strategic understanding of Leaves of Absence (LOA) taken by undergraduates: Intern engineered and integrated multiple sources of unstructured data into a machine-readable dataset relevant to LOA students, including reasons for the leaves, length, graduation rates, academic majors, and demographic characteristics of the LOA students. Creating a dashboard based on this dataset has now made it possible for administrators to begin analyzing patterns and implications of LOA trends for potential interventions to help improve student outcomes. Intern also automated the process for regularly integrating data and updating analytics based on information from new generations of students.
• Analyzing bias in student course evaluations: Intern created a machine-readable data analytic platform capable of identifying demographic bias driving student responses in course evaluations. This required exploration and application of appropriate statistical techniques for the complex social science analysis of biased behaviors. Preliminary results show surprisingly low to no discrimination from Brandeis students based on the gender of instructors. Results for potential race and ethnicity bias are inconclusive, so far, due to insufficient data. 
• Identifying pathways of student success in undergraduate STEM majors: Intern is engineering, integrating, and analyzing data sets relevant to identifying trends in retention of undergraduates in introductory STEM courses over the past 8 years. Identifying trends and cluster analysis of multiple characteristics of undergraduates as they make their way through these courses will illuminate the most effective policies for improving STEM major retention rates.

Office of Research Administration

• Visualizing trends in sponsored research and predicting likelihood that any one particular grant proposal will be funded: Thanks to the dashboard built by DSII Interns, experts in research funding at Brandeis are now able to more easily share trends with senior academic leaders about the percentage of grant proposals that are successfully funded, with break-outs by department, principal investigator (PI), faculty rank of PI, and individual granting agency. Interns are also working on predictive tools that will help make it possible for administrators to identify not only the likelihood of a proposal’s success, but also the specific characteristics of the proposal that might increase or decrease its likelihood of success.

Office of Technology Licensing

• Identifying key features of federally funded grants that are likely to result in successful development of patents: Intern is engineering, parsing, and linking datasets of federally funded research projects together with US Patent Office data to help specify the profile of grants that result in patents. Once applied to the universe of federally funded projects at Brandeis, this exploration will help highlight those research projects on campus with translational potential. 

Student Affairs

• Creating interactive access to outcomes data and to analytics that answer administrators’ Brandeis-relevant questions from the National College Health Assessment (NCHA) survey: DSII Intern transformed NCHA data into format capable of supporting user friendly dashboard. This now makes it possible for administrators to better understand risks associated with student wellness and to design programs and interventions targeted to address these data-informed insights. DSII Intern automated the process for updating the dashboard with new results from the NCHA. For more detail on this DSII project, see section below on “Measurable Impact.”

We are beginning to explore various ways to measure the impact of the DSII concept. Each DSII Intern-Admin partnership will suggest somewhat different ways of measuring impact.

For example, in the case from Student Affairs, below, the administrator reported a 99% reduction in time needed to access relevant analytics, thanks to the dashboard built with the DSII student. Measuring the amount of time an administrator is dedicating to getting questions answered seems like a meaningful measure of impact.

Another example we see from the Student Affairs case: counting the numbers of program modifications, or new programs and solutions that administrators are able to implement as a result of the data scientific tools and techniques introduced by the DSII, also seems like a path worth pursuing. The case study below describes one such program modification resulting from analytics that would not otherwise have been evident to Student Affairs staff had it not been for the dashboard built through the DSII.

Case Study of Impact: Student Affairs

As part of its responsibility for supporting and improving student well-being on campus, the Division of Student Affairs conducts the National College Health Assessment (NCHA) of the American College Health Association (ACHA) on the Brandeis campus. The value of participating in the survey is that Brandeis Student Affairs is able to receive and analyze completed data to better inform program design and implementation plans regarding student wellness. Unfortunately, student affairs professionals across the country have been constrained in their ability to leverage the NCHA data in support of wellness objectives because of the format of the data: the ACHA manages and delivers the survey through SPSS, a statistical software package introduced 55 years ago which lacks the capability to support quick access to key insights with the greatest relevance to decision-making.

The DSII Intern converted the SPSS data into formats that are machine-readable by modern programming languages like python. This made it possible for the DSII to build a dashboard for easy interactivity with the data. The result is a dramatic reduction in the time needed to access key findings in the data, which in turn dramatically raises the capacity of Brandeis Student Affairs professionals to pull practical relevance out of NCHA data. The application of data scientific techniques to NCHA data at Brandeis has also made it possible to conduct analyses that would not otherwise be possible: these include most importantly the disaggregation of data across multiple groupings, such as race, gender, academic program, family background, and year of graduation. 

Take alcohol consumption as a student wellness issue of obvious importance that needs measuring, monitoring, and guidance on a college campus: student affairs professionals are naturally concerned about risky behaviors involving alcohol among first-year students, since the inexperience of first-years suggests that they stand to benefit most from extra guidance and education. Yet, the data scientific analyses of the NCHA data at Brandeis, which could be disaggregated by class year, demonstrated that it is juniors and seniors who are most engaging in dangerous behaviors involving alcohol. As a result, Brandeis professionals are now redesigning student wellness materials to incorporate the extra support needed by upper-class students.

DSII Intern automated the process by which new rounds of NCHA survey data will be updated into the interactive dashboard. The creation of the NCHA dashboard promises to generate many examples of positive impact.

The DSII grew out of Dr. Liebowitz’s and Professor Tim Hickey’s data analytic research on the higher education workforce.

• Exploring the Impact of Computer Science on the Future of Higher Ed (ScholarWorks)
• Details on the launch and operations of the DSII (Trusteeship Magazine)
• Fostering data-driven change (Brandeis Stories)
• The Internal Internship: Enabling Novel Opportunities for Undergraduate Data Science Experiential Education (Association for Computing Machinery)

Co-Directors

Dr. Jessica Liebowitz is a Research Scientist in Computer Science at Brandeis University, where she is co-founder and co-director of the Data Science Internal Internship (DSII) Lab. This program explores the impact — on university operations and on undergraduate data science education — of student leadership in deploying data science, machine learning, and artificial intelligence into higher education operations. Dr. Liebowitz holds a PhD from Harvard University, BA from Yale University, and Honorary Doctorate from Middlebury College.

Timothy J. Hickey is a Professor of Computer Science whose current work focuses on educational technology, brain-computer interfaces and game-based learning. His specialties include analysis of algorithms, logic programming and parallel processing, symbolic manipulation, and groupware.

DSII Finalist Selection Committee

• Tim Hickey, Professor, Undergraduate Advising Head, Computer Science 
• Jessica Liebowitz, Research Scientist, Computer Science
• Dylan Cashman, Assistant Professor, Computer Science
• Constantine Lignos, Assistant Professor, Computational Linguistics
• Antonella Di Lillo, Associate Professor, Computer Science
• Subhadeep Sarkar, Assistant Professor, Computer Science
• Nianwen Xue, Professor and Chair of Computer Science, ex officio

Undergraduate Curriculum Committee, Integrating DSII into Undergraduate Curriculum

• Antonella Di Lillo, Chair
• Subhadeep Sarkar
• Liuba Shrira
• Constantine Lignos