Researchers are developing new mathematical techniques to speed up biomechanical imaging

A researcher in the Rochester Institute of Technology's School of Mathematical Sciences is developing new mathematical techniques that could improve applications
ranging from medical imaging to predicting how quickly ice flows into the ocean.
Assistant Professor Olalekan Babaniyi received a two-year, $250,000 Academic Pathway for Initiating Early Careers in Mathematical and Physical Sciences (LEAPPS - MPS) grant from the National Science Foundation to develop more effective techniques
for solving inverse problems.

Inverse problems are used to make predictions and decisions
about physical systems that are difficult to measure from observable data.
However, they require a lot of computing time and computing power to solve
.
Babaniyi says that by developing ways to effectively solve the inverse problem, it could open the door to
important new applications where speed is critical.

"It's helpful
for medical imaging when you're trying to figure out how hard soft tissue is," Babaniy said.
"This could help non-invasively diagnose different types of diseases that cause tissue hardening, such as cancer
.
" You want to be able to get images of these parameters in real time so doctors can see stiffness
when scanning patients.
It is not possible with the current method, but I believe that this new method can do it
more effectively.
”

The grant will allow Babaniyi to fund two PhD students in mathematical modeling to help them complete the project and develop a new curriculum at RIT that covers all aspects of
solving inverse problems.
Babaniyi said he will redouble his efforts to engage students from underrepresented groups and teach them techniques
for solving inverse problems and working with data.

Joshua, Dean of the School of Mathematical Sciences? "In a world of data, the ability to extrapolate and learn more about the underlying conditions being observed is becoming increasingly important
," Faber said.
"Dr.
Babaniyi's work will be an important step in solving problems that currently require a lot of time and calculation, and to propose effective ways to solve them with important real-world consequences
.
" His work is a model for many in the School of Mathematical Sciences, applying mathematical insights and computational skills to problems
that have broad implications for the scientific community and the world around us.
”

Babaniyi is the second faculty member
in the School of Mathematical Sciences to receive an NSF leap-MPS grant this year.
Huang Chi Ming, assistant professor in the School of Mathematical Sciences, was recently awarded a $180,000 grant to conduct computational modelling research to improve understanding
of future coastal risk uncertainties.
This two-year project will link uncertain geophysical and socioeconomic factors to flood damage and emphasize the participation
of underrepresented students in the mathematical sciences.

The LEAP-MPS program supports research by tenured prefaculty in the math and physical sciences at institutions that have not traditionally received significant NSF funding, such as minority service agencies, major undergraduate agencies, and R2 universities
.