Tracking locations and populations around the world is becoming increasingly important as whales face harm from ship strikes, entanglement of fishing nets and redistribution of prey due to changes in ocean temperature.
To promote these efforts, two Indian-American students at the University of Pennsylvania Chinmeigobind and Niharbaramdi dedicated their summer to Penns Faculty Research Mentoring Program (PURM) project, combining mathematics, signal processing, animal behavior and machine learning.
Their goal: harness whale sound data and artificial intelligence to map whale locations and determine how many people live in a particular target area. In this work, Govind and Ballamudi use National Oceanic and Atmospheric Administration (NOAA) data from the north sound receiver of Cape Cod Bay, but according to Penn today, their research applies everywhere.
The results of this effort could help the duo “get better data on the number of whales' regions or regions' distributions, which can inform policy makers and environmental groups on policies, including whales,” and is a dual major in engineering and applied science and original science department of Artificial Intelligence and Computer Engineering, originally from Mechanicsburg, Pennsylvania. “The results of our study are not just whales, [also] Other sea animals. ”
Read: Three Indian American students selected for Arizona's High Speed Medical Education Program
Provided by the Undergraduate Research & Fellowship Center, Purm immerses students in their first or second year with Pen with 10 weeks of summer research experience under the guidance of faculty experts.
“The University of Arts and Sciences and Computer Science Minor” in Madison, Wisconsin, said: “The University of Arts and Sciences is a great way to help you get to know the best of your life.” “It's really cool to be able to work on a project. [uses math to] If you can have a census, help influence what the policy looks like. ”
Each student led part of the Purm project. Govind focused on the whale's location, and Ballamudi worked on censoring them. In this context, placement involves tracking and counting individual whales. Meanwhile, census involves approximating the size and distribution of whale populations to more effectively monitor movements.
To find whales, Govind utilized acoustic data from NOAA receivers (essentially underwater microphones) to estimate the origin of whales' call. Each receiver detects sound waves from unique whale call at different times. Govind sends recorded audio data to a machine learning model to estimate the “arrival time difference.” This is used to calculate whale coordinates, similar to how mobile phones use GPS to derive locations.
“Time differences separate the sound into a specific curve,” explains Govind. “If you have more receivers (using five), you'll have enough data to locate the whale and generate confidence intervals about where the whale is.”
Using AI to optimize and refine the acoustic data, Govind was able to record the origin point of the whale call with a “median error of 20 milliseconds”. This slight uncertainty says it is more than enough to estimate the location of the whale.
At the same time, Ballamudi used machine learning models and NOAA sound data to simulate marine environments and census whale populations. This AI-driven approach is more effective than relying on data from physical receivers, taking into account the obstacles brought about by ocean noise and multipathing.
Read: Four Indian Americans named Guggenheim Fellows in 2025 (
“We sampled the actual ocean noise and generated the signal according to the literature on what whale signals usually look like,” says Balamdi. “We can use that information to generate as much data as we need.”
This strategy allows Govind and Ballamudi to innovate in learning about individual and group whale behavior. During the PURM project, Ballamudi has accurately predicted the number and distribution of whales between 90 and 95% of the time.
The AI model used in this Purm project also demonstrates the promise of future steps that students can take in this study, continually optimizing and improving accuracy.
“It would be great if you could get a model that recognizes multiple sources at the same time and allows you to see them all in one shot,” Govind said.
Once the pair can record the exact number of whales in their target range, Balamdi says that they can use that data to retrospectively identify the exact location of each whale.
“We want to see if this approach works no matter what, not just in well-controlled software, but also in a world where there are far more confounding variables than the software can explain,” Ballamudi says.
Towards the end of their Purm experience, Govind and Ballamudi were called to present their results to US Navy sponsors. It highlights the impact on policymakers working to protect whales. And we share the opportunity to expand this research.
