YSP Alumni
Alumni Directory
2026 YSP Program Coordinators:
Coordinators: Ahmed Othman & Kenneth Santizo Carbajal & Frances Corcell
Participating Labs
| Lab | YSP Students | Title | Abstract |
|---|---|---|---|
| Faculty: Alshawabkeh, Akram Mentors: Amanda Thomas & Nima Sakhaee | Spencer Seidel, Chelsea Bateman | Synthesis and Characterization of Biomass-Derived Functional Carbon for Electrochemical Water Treatment Final Presentation Final Poster | Environmental contamination is a prevalent issue in Puerto Rico, and results in a multitude of health complications including preterm births, pregnancy loss, and stunted child growth. Contamination mainly stems from EPA Superfund Sites along the northern coast, with effects sometimes being amplified by extreme weather events. The PROTECT Program seeks to provide cost effective and efficient water filtration to the people of Puerto Rico in order to combat water contamination. Sargassum is an invasive brown algae that is beginning to wash up on Puerto Rican beaches and coastlines, negatively impacting tourism. Bamboo has spread rapidly through forests since its introduction in the early 1900’s and displaces native flora. Creating cathodes from dried sargassum and bamboo provides both contaminated water filtration and the removal of invasive species from Puerto Rico. The research aims to optimize the synthesis of an electrochemical water filtration system consisting of cathodes made from activated sargassum and bamboo. Specifically, the cathodes are created using different acid ratios, temperatures, and particle sizes to determine which formula adsorbs the most emerging contaminants. Methylene Blue were used as emerging contaminants during experimentation. Hydrogen peroxide production indicates the chemical transformation and breakdown of emerging contaminants in water. A UV-Vis Spectrophotometer was used to quantify Methylene Blue and Hydrogen Peroxide concentration during experimentation. |
| Faculty: Amini, Rouzbeh Mentors: Guadalupe Garcia | Kunal Shanbhag, Anjali Misra | Does Freezing Change the Mechanical Behavior of Aortic Tissue? Final Presentation Final Poster | The aorta is the main artery of the body and must stretch and recoil with each heartbeat to function properly. Its mechanical properties, such as strength and flexibility, are critical for maintaining healthy blood flow and preventing conditions like aneurysms or dissections. Researchers often study aortic tissue outside the body to better understand these properties, but because fresh tissue is not always available, samples are frequently frozen for later use. In this project we ask a simple but important question: does freezing alter how aortic tissue behaves mechanically? The answer has broad implications, as many studies rely on stored tissue, and any changes caused by freezing could affect scientific conclusions and clinical insights. To address this, in this project we will compare fresh and frozen aortic tissue using biaxial mechanical testing, a method that stretches tissue in two directions simultaneously to mimic physiological conditions. Paired samples from the same tissue source will be tested to minimize variability, and key measures such as stiffness and stretch will be quantified. Statistical comparisons, including paired tests and models that account for repeated measurements, will be used to determine whether freezing leads to meaningful changes. By identifying whether frozen tissue reliably represents fresh tissue, this study will help improve the accuracy and reproducibility of cardiovascular biomechanics research. |
| Faculty: Bajpayee, Ambika Mentors: Bill Hakim & Andrew Selvadoss | Tejasvi Kalluri, Katherine Sola | Charge-Based Targeted Drug Delivery Final Presentation Final Poster | Students will gain hands on experience in designing cell derived biomaterials as well as peptide based targeting delivery systems. |
| Faculty: Caparco, Adam Mentors: Christopher Castillo | Marcus Klingler, Phoebe Corcell | Bioremediation of Nanoplastics Using Mycelium Paddy Biopods and Plants Final Presentation Final Poster | Students in “What’s the Effect of Nanoparticles on Agriculture?” will investigate how tiny engineered particles (over 1,000 times thinner than a human hair) interact with plants. Are they helpful tools for improving plant health, or could they cause unintended harm? In this project, we will design nanoparticles coated with different plant hormones and study how plants take them up through leaves, roots, and stems. Students will grow and monitor two plant species, use microscopy to image plant cells, and apply techniques from biochemistry and molecular biology to understand how hormone-coated nanoparticles influence plant physiology. By the end of the project, Young Scholars will have hands-on experience designing and performing experiments, analyzing data, reading scientific literature, and presenting their findings. This project introduces students to cutting-edge research at the intersection of agriculture, nanotechnology, and plant biology. |
| Faculty: Copos, Calina Mentors: Samantha Finkbeiner & Ansa Brew-Smith | Zahra Atti, Maiia Vovk | Modeling Tissue Changes During Limb Regeneration Final Presentation: Canva | Pptx Final Poster | Only a few vertebrates, such as salamanders, have retained the ancestral ability to fully regenerate limbs, making them powerful systems for understanding how complex tissues rebuild after injury. Limb regeneration requires cells to coordinate their shape, orientation, and mechanical properties to rebuild tissue. This project investigates how epithelial cells organize during limb regeneration in axolotls. Using the computational modeling platform CompuCell3D, students will test how cellular properties such as adhesion influence tissue organization. This project highlights the power of mathematical modeling to test biological hypotheses, uncover mechanisms that are difficult to measure experimentally, and predict how cellular behaviors combine to drive tissue regeneration. |
| Faculty: Davidow, Juliet Mentors: Brianna Aubrey & Erica Niemiec Skelton | Adil Tigli, Lena Lee | Assessing Quality Control of MRI-based Measurements of Iron in Subcortical Structures for Adolescent Learning Final Presentation Final Poster | Adolescents must learn a lot on the path to becoming adults. This learning often results from the good, or bad, outcomes that they experience. Learning from experienced outcomes can be broadly described as motivated learning. A hallmark of adolescence is a marked shift in the motivations that drive youth in seeking out experiences. As such, adolescence has been described as a life stage characterized by changes in motivated learning. Though motivated learning is central in adolescence, and critical for behaving adaptively in general, we know surprisingly little about the neurocognitive development of motivated learning. This project draws upon learning theory, brain imaging, and computational modeling approaches to substantiate age-differences in cognitive mechanisms of motivated learning and to ask how brain development during adolescence uniquely contributes to development-normative shifts in motivation in this life stage. A series of behavioral and brain imaging studies will measure multiple types of motivated learning to address three levels of gaps in our current knowledge: behavioral, mechanistic, and neural. |
| Faculty: Gallaway, Joshua Mentor: Surafel Mustefa Beyan | Alexander Braga, Tarun Kannan | Vanadium Pentoxide Cathodes in Aqueous Zinc Ion Batteries Final Presentation Final Poster | Rechargeable alkaline batteries are a potentially transformational technology to enable widespread balancing of solar and wind power in the grid. The student researcher in this position will produce a series of alkaline battery cathodes. These batteries will be fabricated and cycled for spectroscopic analysis. The results will be analyzed and summarized in a final report. The students will learn basic techniques in how to make batteries. The students will also learn communications skills to effectively interpret and communicate their work. The students will use copper oxide (CuO) battery material. The students will also use aqueous potassium hydroxide (KOH), which is a hazardous material. They will be trained in safety procedures to use this material. |
| Faculty: Liao, Maijia Mentors: Louison Thorens & Dongdong You | Fennell Wisseh, Gemma Turner | Determining How Shot Proteins Affect Neuronal Morphology Final Presentation Final Poster | We study how neurons acquire and maintain their complex shapes. Using Drosophila as a powerful model system, we combine genetics, live imaging, and quantitative modeling to reveal how cytoskeletal dynamics and cellular geometry shape neuronal architecture. |
| Faculty: Loth, Francis Mentor: Hannah Higgins | Henry Mai, Nymisha Goli | Comparison of Motion in Chiari Type I Malformation Patients Versus Healthy Controls Final Presentation Final Poster | To improve the lives of Chiari I malformation (CM) subjects through biomedical engineering research and the discovery of novel MR imaging protocols combined with engineering analysis. To achieve this, we propose to create a platform for CM patients to obtain detailed magnetic resonance (MR) imaging at Northeastern University (NEU) that is comprised of anatomic, biomechanical, and physiological measures. These images are critical to further elucidate the cause of CM symptomatology as well as guide clinical management. This imaging will be used to quantify the biomechanical environment at the cranio-cervical junction for CM subjects as decompression surgery is a procedure intended to change the biomechanics to a more favorable condition. |
| Faculty: McCleary, Jacqueline Mentor: Sayan Saha | Adam Aoua, Karuna Tarafdar | Creating Dark Matter Mass Maps using Stratospheric Balloon Observations Final Presentation Final Poster | The Super Pressure Balloon-borne Imaging Telescope (SuperBIT) completed its science flight in Spring 2023. Since then, extensive work has been carried out to process the imaging data and measure the shapes of thousands of galaxies, producing a galaxy shape catalog that will be published soon (Saha et al. 2026). In this YSP project, students will use these measurements to construct weak-lensing mass maps of ~30 galaxy clusters. These maps trace the total mass distribution (both baryonic matter and dark matter) by measuring small distortions in the shapes of background galaxies. The mass maps will be generated using SMPy, a publicly available mass-mapping code developed by our group, and will contribute to an upcoming publication presenting the first weak-lensing signals detected from a stratospheric telescope |
| Faculty: Onabajo, Marvin Mentors: Yunfan Gao & Minghan Liu & Thomas Gourousis | Angela Wang, Chris Xu | Interference Signal Power Monitoring within Front-End Circuits of Wireless Receivers Final Presentation Final Poster | With the growing density and variety of wireless devices in modern environments, the design of radio frequency (RF) receivers with improved robustness against unwanted interference signals has become increasingly important. Ongoing research addresses this challenge through the development of novel devices, circuits, and digital algorithms that enhance receiver performance. Within this context, this project supplements existing work on automatic calibration techniques based on signal power detection and digital control of analog RF front-end circuits. The project will include the investigation of how different signal waveforms affect the accuracy of power detection methods, experimental measurements using a power detector as part of a prototyping setup, and the refinement of the current measurement setup to support more reliable and comprehensive characterization of interference suppression capabilities. |
| Faculty: Ostadabbas, Sarah Mentor: Bishoy Galoaa | Arin Shinde, Nyasa Luharuka | Video Language Models for Generic Task Assistance Final Presentation Final Poster | This summer research project introduces two high school students to cutting-edge artificial intelligence focused on understanding long videos in real-world childcare settings. The goal is to help build AI systems that can automatically discover meaningful patterns in children’s behavior—such as movement, interaction, and routines—directly from video, without relying on predefined labels or prompts. Students will work with video data from YouTube and learn how to analyze motion over time to identify activities, transitions, and developmental signals. The project emphasizes dynamics-aware reasoning: instead of treating video as isolated frames, students will explore how motion reveals cause-and-effect relationships and changing states in the physical world. Through hands-on coding, visualization, and experimentation, participants will gain experience in computer vision, machine learning, and ethical data practices, while contributing to research that supports safer, more responsive childcare environments. By the end of the program, students will have built simple motion-based analysis tools, participated in weekly research meetings, and presented their findings, gaining early exposure to interdisciplinary AI research with real-world impact. |
| Faculty: Restuccia, Francesco Mentors: Francesca Meneghello & Shahriar Rifat & Mohammad Abdi | Arihant Rangoli, Matthew Sokoloff | Low-Latency AI Execution Systems for Resource-Constrained Devices Final Poster Evaluating Segment Anything Model 3 Against Adversarial Perturbation Final Poster Final Presentation | Next-generation cyber-physical systems such as autonomous drones and robots, connected vehicles, and smart infrastructure, depend on AI models and wireless links for sensing, coordination, and control. In practice, both are fragile, since channels fluctuate, interference and outages occur, sensors drift, and deployed data often differs from training, leading to unsafe or inefficient behavior. This project develops a resilient co-design of AI and wireless networking for safety-critical CPS. We will build uncertainty-aware perception and control that detect distribution shift, quantify confidence, and trigger safe fallback behaviors when models are likely wrong. In parallel, we will design mission-aware wireless protocols that adapt resource allocation and prioritize critical information under congestion, disruptions, or adversarial interference. We will validate the approach on realistic CPS workloads and testbeds, measuring latency, reliability, energy efficiency, and safety under shift and attack. |
| Faculty: Schindler, Peter Mentor: Emad Rezaei | Dhruvaite Upmanyu, Farah Can | AI Driven Design of Semiconductor Alloys with Tuned Effective Masses Final Presentation Final Poster | Impact ionization affects how semiconductor devices perform under high electric fields. Machine learning can predict materials properties more efficiently than experimental measures such as quantum mechanics as they are difficult, time-consuming, and expensive. Our goal was to combine physics knowledge with experimental and computational data, and implement machine learning methods to predict properties of semiconductor materials. Our objectives were learning the basics of python and materials science while collecting, organizing, and analyzing materials data. We learned how to present scientific results through figures. Our results included the discovery of stable semiconductor alloys with tunable band gaps and effective masses. |
| Faculty: Tan, Xiang Zhi Mentors: Drake Moore & Reina Chan | Ilyas Mohamed, Alina Shi | Leveraging Robot Gaze to Signal Membership in Dynamic Group Settings Final Presentation: Part 1 | Part 2 Final Poster | Humans use gaze to signal different manners such as thinking, paying attention to people and directing attention to other objects around them. Prior work has shown that humans naturally anthropomorphize robots and in order to maintain engagement, both humans and robots must communicate through gaze, dialogue, and movement. We investigate how these three components work together to shape interaction with groups of people, and how they can be implemented to improve the social interaction of a robot. The goal is to utilize the gaze of our robot to increase engagement with dynamic audiences. Our project explores how a tabletop robot can use a local Large Language Model (LLM) with custom prompts and face tracking models to create more natural interaction behaviors. Once completed, we hope to deploy the robot in a building so it can interact with interested visitors and tour groups. In doing so, we will evaluate how different gaze behaviors can influence newcomer permanence and interaction in groups. |
| Faculty: Upmanyu, Moneesh | Soumalya Chatterjee, Srishti Kar | Molecular Computations for Understanding Crystallization Pathways for Amorphous Calcium Carbonate Final Presentation Final Poster | Amorphous calcium carbonate (ACC) is a metastable intermediate in the crystallization of calcium carbonate (CaCO₃), making it central to a range of scientific and technological problems. Understanding ACC is important for: (i) carbon sequestration, where mineralization pathways offer a route to mitigate greenhouse gas emissions driving climate change; (ii) biomineralization, since many marine organisms exploit CaCO₃ crystallization via ACC to construct shells, bones, and exoskeletons; (iii) drug delivery, where its water-soluble character makes it a candidate carrier material; and (iv) sustainable materials development, including next-generation low-carbon cements. The goal of this project is to characterize the structure and stability of ACC and to elucidate the mechanisms governing its crystallization. Our broader aim is to understand this mechanism: What conditions cause it? Can we replicate it, and if so, how? |