NJIT Engineering Researchers Study Effect of State Gun Removal Law
Following a long period of diminishing gun violence in New Jersey’s urban areas, researchers from New Jersey Institute of Technology are now applying engineering methods to the data, as they evaluate the effectiveness of red flags laws that can temporarily prevent dangerous people from possessing weapons.
ERPO — Extreme Risk Protection Orders — allow concerned citizens to petition a court about at-risk individuals who shouldn’t possess firearms. Such laws are currently enacted in 22 states and in Washington, D.C. Although New Jersey’s version took effect in 2019, granular data such as how many petitions were filed, by whom and whether they succeeded is difficult to obtain from the court system.
The closest available data, from the Centers for Disease Control, refers to all causes of death by county. Researchers can examine the CDC dataset for trends in accidents, homicides and suicides with firearms, but it’s imperfect for those whose goal is ERPO insights.
Doctoral student Aayush Chitransh is examining Camden, Essex, Hudson, Mercer and Passaic counties, home to the cities of Camden, Newark, Jersey City, Trenton and Paterson, where gun violence occurs disproportionately to their populations.
Engineering Ph.D. student Aayush Chitransh also earned an M.S. in computer science at NJIT
“Due to differences in firearm violence burden and implementation practices, we anticipate that the law’s impact varies across counties, and that counties containing high-crime cities may not have experienced the same benefits,” explained Chitransh, who is seeking a Ph.D. in engineering science, after earning an M.S. in computer science also at NJIT in 2025. “I use quantitative modeling, mathematics, time-series analysis and causal inference to measure how a policy has affected real-world outcomes.”
To measure the real-world impact of the law, Chitransh employs a data modeling technique called the synthetic control method. First he builds a synthetic twin, which is a mathematical model created by combining data from counties in eight geographically proximate states that haven’t implemented ERPO laws — Kentucky, Maine, New Hampshire, North Carolina, Ohio, Pennsylvania, Tennessee and West Virginia — thus forming a control group. He then projects the twin’s data into the future to create a counterfactual -- that's a baseline of what would have happened if the law had not passed. The gap between the predicted baseline and the actual improvement represents the law's impact.
I am doing this work because I want my engineering skills to matter beyond code.
“I am doing this work because I want my engineering skills to matter beyond code. When I started working on gun violence research, I saw how real world problems are complex and messy, but also how much impact data and careful analysis can have in understanding what truly reduces harm...what truly motivates me is knowing that my work can lead to measurable change and contribute to reducing real human suffering caused by guns.
Chitransh’s advisor is Roni Barak Ventura, assistant professor in the School of Applied Engineering and Technology. “With this specific law, it’s interesting, because it’s a tool that I think people are not aware of. We might find that this law did not affect anything, possibly because people just don’t use it. People are not aware that they can petition the court to take weapons away from someone,” she added.
Barak Ventura said that once their research makes headway, she hopes to collaborate with local stakeholders such as the Newark Public Safety Collaborative. Chitransh already has a suicide prevention fellowship sponsored by the Rutgers Gun Violence Center. They’ll also seek research grants that would allow them to develop new kinds of interventions, along with ways to monitor and scientifically determine its causes, toward the ultimate goal of minimizing the occurrences.