How to collaborate

We are excited to announce the list of collaboration opportunities in our Mon(IoT)r IoT lab. These can be interesting opportunities to get exposed to cutting-edge IoT technologies and understand how they work.

If you are interested in any of these projects, you are a current active undergraduate or graduate student at Northeastern University, and you satisfy the prerequisites for the project you are interested in, please send an email using the contact information of the project of your choice with subject “Fall 2026 Mon(IoT)r Lab collaboration PROJECT NAME” (mandatory to receive an answer!) and a recent resume attached (with GPA), specifying the following:

  • What project you are interested in, specifying (a) why you are interested in that particular project, (b) why you are a fit for that project, (c) how you plan to use your existing experience to contribute to that project, (d) how collaborating to the project aligns with your career goals. If you are interested in more than one project, please rank them starting from the one you are interested the most.
  • The preferred start date and end date for the collaboration, and the total number of average hours you plan to spend per week on this project;
  • Your expected course load for the semester (list of classes and credits);
  • Any other time commitments you have during the semester, for example TA, RA, a co-op, side jobs, on campus / off campus activities, clubs, both paid and unpaid;
  • Your availability for a volunteer (unpaid) position. Note that we do not currently have any available paid or for-credits positions;
  • The Northeastern University campus location you are based on and the possibility or preference to work on site in Boston, remotely, or both.

Please note that, in general, we have a preference for projects that are on-site, that last a whole semester (15 weeks), and for a minimum of 10 hours per week.

We will start reviewing applications for Fall 2026 as soon as we receive them and will be back to you within two weeks, so please be patient if you do not hear back by then. Also, for some projects, we may give you a take-home exercise related to the project before starting working with us.

If you are interested in applying for a later term (e.g., Spring 2027), we cannot guarantee that this list of projects will still be valid. Therefore, we suggest waiting for the projects to be updated before applying, which typically happens within two weeks after the Spring or Fall semester starts. We are not currently offering summer projects.

Fall 2026 projects


If no project interested you, please come back in September. More projects will be added during the first two weeks of September 2026.

Project “Surveillance Pricing”: Understanding Personalization and Dynamic Pricing Patterns on Consumer Platforms

A recent Consumer Reports investigation found that Instacart showed different prices for the same grocery items to different shoppers: nearly three-quarters of tested items varied, with some prices differing by as much as 23% per item. When such prices are based on information gathered about consumers, known as “surveillance pricing,” people can easily get ripped off. There are important fairness concerns when prices are based on sensitive traits – like income, age, zip code, and family status. However, little is publicly known about companies’ surveillance pricing practices.
The goal of this project is to empirically measure how consumer platforms personalize prices and price-adjacent experiences using controlled online experiments. Rather than relying on platform disclosures, we will build a measurement framework that can (1) simulate diverse user “personas,” (2) collect time-series snapshots of prices/fees/discounts across treatments, and (3) analyze whether observed differences are consistent, explainable (e.g., surge in demand, inventory, current events), or suggestive of differential treatment.
This project will focus on consumer e-commerce environments where personalization is plausible and highly impactful. Our current targets are food and grocery delivery platforms (i.e., Grubhub, UberEats, DoorDash, Instacart), where users may see differences in categories such as delivery and service fees, recommended items, or promotion eligibility. The resulting evidence can help inform debates around algorithmic fairness and consumer protection.
In the Spring 2026 semester, this project will focus on (a) building reliable, cross-platform scraping and data-collection infrastructure, (b) defining and validating a set of behavioral and contextual signals that personas can express (e.g., filtering by deals, searching for expensive options, time-of-day ordering patterns), and (c) executing pilot studies across a curated set of services (i.e., restaurant ordering, grocery delivery) to test for systematic differences across treatments.
This project will have several outcomes, including published source code and data, published research papers in academic venues, and press articles about our findings through our journalist partners.
This research can be performed on site and/or remotely.

Prerequisites:

  1. Broad interest in consumer protection research, marketplace integrity, and the societal impacts of algorithmic decision-making
  2. Able to contribute in at least one of the following areas: experimental design, web automation, data collection, and analysis
  3. Programming experience (JavaScript experience highly recommended; familiarity with browser automation tools like Puppeteer/Selenium is a plus)
  4. Comfort working with structured datasets (CSV/JSON) and evolving codebases with many moving parts
  5. Willingness to learn new technical skills.

Contact information: Elaine Ly (ly.el@northeastern.edu), specifying “Fall 2026 Mon(IoT)r Lab collaboration SURVEILLANCE PRICING” in the subject line.