Stevens students have exciting opportunities to engage in dynamic research that propels bold ideas, hands-on learning, faculty collaboration and leading-edge technology to drive discovery and progress. Research projects empower students to think creatively, tackle real-world challenges and build skills for their future careers. Learn more about how four undergraduate students are participating in innovative research at Stevens and the impact of their experiences.
Undergraduate research elevates the Stevens experience. From working on campus with professors and graduate students to making discoveries in corporate and federal research labs and more, students have numerous ways to get involved with leading-edge research.
Research is a powerful mode of learning, notes Andrés Mansisidor, director of undergraduate research and fellowships.
“Research rewards doing, rigor and creativity in ways that go beyond the scope of direct instruction or course-based work,” Mansisidor says. “These aspects of research allow students to expand their ideas of knowledge and their fields of study. Being exposed to uncertainty and discovery can bring critical thinking to the next level and better prepare students for tackling hard tasks in their future careers.”
Through pioneering projects exploring AI and pharmaceutics, sustainable jet fuel, the synthesis of bioactive molecules and super-resolution concepts in astrophysics, Stevens student researchers are helping to make an impact for a better future.
Read about the research of four Stevens undergraduates in their own words.
AI, ANTIBODIES AND PHARMACEUTICS
Miles Cabreza, Class of 2026, biomedical engineering major
Faculty: Pin-Kuang Lai, assistant professor, Department of Chemical Engineering and Materials Science
I’ve been working on a project using deep learning AI tools to study pharmaceutics made from organically derived antibodies useful for treating a variety of diseases such as cancer or autoimmune diseases. The goal is to teach the AI to predict how well these drugs can be absorbed and used by the body.
Manually testing the viability of these drugs in labs is highly time consuming and expensive. Developing AI models to screen the drugs for positive or negative traits is important in accelerating and improving the development process. My research is directly applicable in the web app that was created out of it, which is available for any researcher online allowing for early prediction of antibody absorption based on the antibodies sequence data.
I’ve really enjoyed the self-learning and discovery involved in applying language models in a tangible way to medicine. Developing a model that can be useful for other researchers has felt rewarding.
After earning my bachelor’s degree, I plan to work in the biotech industry, with a focus on medicine/pharmaceutics. In a world where AI and computational work are rapidly transforming the discovery process, this research has been a step toward that future and has helped me gain unique skills, culminating in a paper, published in Molecular Pharmaceutics (August 2025): https://doi.org/10.1021/acs.molpharmaceut.5c00523
In the future, I would love to work on cancer research. I believe AI and protein language models will play a significant role in developing patient specific treatments, and it would be exciting to contribute to those developments.
TURNING ALGAE INTO SUSTAINABLE JET FUEL
Eva Baker, Class of 2027, chemical engineering major
Faculty: Adeniyi Lawal, professor and chair, Department of Chemical Engineering and Materials Science
This summer, I was involved in researching the most efficient way to turn algae into sustainable aviation fuel (SAF), or jet fuel. I had the opportunity to independently load and monitor both a bench scale and pilot scale reactor to turn algae into algal oil, prepare a catalyst bed, help run the two separate reactions needed to turn the oil into green diesel and then SAF, and calculate the precent yield of the reaction using a GC-MS (Gas Chromatography-Mass Spectrometry) machine.
Air travel contributes to roughly 2.5% of global emissions due to the overreliance on fossil fuels. Current proposed solutions are inefficient and not financially viable. Algae is a renewable source of energy, and one of its biggest advantages is that algae can grow almost anywhere there is water. The success of this research would mean a big step forward in the fight against climate change.
I loved having the opportunity to become familiar with a lot of machines, equipment and processes that I can use in the future! There are a lot of steps involved.
As for career goals, I hope that I am able to work in sustainability, or more specifically, renewable energy. This research gave me a strong baseline for the process of making renewable energy and also chemical engineering in general.
For a dream research topic, I would love to do research on carbon capture and ways to make the process more viable. Not only is this very interesting to me, but I think it is extremely relevant today.
BIOACTIVE MOLECULES AND DRUG DISCOVERY
Alex Cunney, Class of 2027, chemical biology major
Faculty: Abhishek Sharma, associate professor, Department of Chemistry and Chemical Biology
I'm working in Dr. Abhishek Sharma's lab, which specializes in developing chemical building blocks. Specifically, I’m helping with the preparation of cyclopropane ketone monobpin (CPKMB) compounds, which we are using to study new kinds of organoborane chemistry. The lab is studying the chemistry of the different kinds of these building block molecules, such that they could potentially ease the synthesis of existing drug molecules or help with the synthesis of new drug molecules.
It’s been exciting and interesting to learn how to set up reactions to both make the CPKMB compounds, and to make the precursor compounds to the CPKMBs. Whether it’s been learning about how to keep a reaction under inert conditions or learning how to handle specific reagents, it is during these times that I most feel like I’m living my younger self’s dream of being a scientist and synthesizing new chemicals.
My short-term goals are to pursue advanced degrees (master’s and Ph.D.), after which I hope to enter industry to conduct research in the drug discovery field. A long-term goal is to pivot into education in some capacity, because it has been a dream of mine to help make academic research and information more accessible to the general public. In my experience, chemistry has a reputation for seeming very complex and hard to study. I want to be a part of making chemistry less intimidating and more interesting for those who haven’t studied it.
As for a dream research topic, it would be especially wonderful to be a part of studying compounds that could be used to help patients with Alzheimer’s disease or other neurodegenerative diseases. Alzheimer’s is a disease which has touched my family, and I would love to be a part of making clinical outcomes for patients with the disease more positive.
SUPER-RESOLUTION CONCEPTS IN ASTROPHYSICS
Isabella Lee, Class of 2028, mathematics and physics major
Faculty: Xiaofeng Qian, assistant professor, Department of Physics
I have been part of Professor Xiaofeng Qian’s super-resolution research team since February 2025. Our research focuses on resolving the separation of uncontrollable two-point sources – such as remote astrophysical objects, live cells and unknown quantum emitters – using a novel quantum-inspired parameter-decoupling method enhanced by machine learning.
In simpler terms, we are tasked with separating two very closely spaced light sources that appear as a single blob from any instrument used to view the source. To separate the sources, we use a quantum-inspired technique to untangle the light and separate information about their brightness, then we utilize a machine learning program to analyze the untangled data to accurately measure the separation between the two sources, achieving a level of precision that was unattainable with standardized methods. We are applying this technique to measure the separation between two supermassive black holes in the NGC 4486B system and the orbital distances of binary star systems within the Milky Way!
Currently, there are two applications of our methods: one being astrophysical imaging and the second being biomedical imaging. In astrophysics, when two galaxies merge or when binary stars are in orbit with one another, they are often too close to see separately, but this method can precisely measure their tiny separation. In the biomedical field, the method is revolutionary for studying the fundamental structure of DNA inside a cell's nucleus. It can distinguish between tightly packed, inactive DNA and loosely packed, active DNA by measuring the ultra-close distances between individual fluorescent markers.
Throughout my research experience, I have enjoyed learning more about astrophysics and super-resolution concepts, as it has allowed me to make better progress and build toward my career goals. My current research involvement supports and bolsters my love for learning new abstract subjects within both phenomena.
After completing my undergraduate degree, I plan to pursue a doctoral degree and enter academia as an assistant professor in the field of physics or mathematics as I aim to both teach and conduct my own research in the future.
With my current participation in Professor Qian’s research, this is the first step toward my goal of becoming a professor. Since research is an essential part of a professor's role, it's best to obtain prior experience as early as possible to gain a full understanding of what is to come within the discipline. In the future, I hope to use my experience to conduct research on the application of chaos and control within either nonlinear photonics or astrophysics.
