Concrete for Martian exploration and environmental sustainability
Fabrication, testing, and data analysis of special types of concrete (and similar materials). Of primary focus this year are in-situ material for exploration of Mars and environmentally-friendly concrete to be used on our planet.
Requirements: GPA > 3.6
Faculty: Masataka Okutsu
3D Scanning–Robotic Arm Integration for Intelligent Systems
This project introduces students to the fundamentals of combining 3D scanning technologies with robotic arm systems for automation and precision tasks. Participants will gain hands-on experience in capturing 3D models and programming robotic movements to interact with scanned objects.
Requirements: Advanced programming skills, Advanced skills in hardware design and circuits, GPA > 3.2, self-motivated, and can work independently.
Faculty: Yi Yang
Bird Window Collisions on the Penn State Abington Campus
This project will investigate the incidences of bird collisions with windows on the Penn State Abington campus. The aim of the project is to estimate the number of bird collisions with windows and campus locations that are at an increased risk of collisions.
Requirements: Science majors with an interest in ecology, conservation, or birds and a minimum GPA of 3.0.
Faculty: Les Murray
Harnessing Large Language Models for Intelligent Computing Systems
This project aims to exploit Large Language Models (LLMs) and natural language processing (NLP) techniques to enhance the quality and efficiency of various data-driven tasks. The key sub-projects include 1. Enhancing Sentiment Analysis: Using LLMs to capture nuanced emotional and contextual signals in textual data. 2. Improving Time Series Analysis: Applying LLMs to extract patterns, forecast trends, and interpret temporal data. 3. Providing Objective Interpretations from Data: Generating unbiased summaries and insights from structured and unstructured datasets. 4. Enabling Automation: Utilizing LLMs to perform routine tasks, decision-making, and workflow orchestration. 5. Supporting Root Cause Analysis (RCA): Integrating LLMs with conventional RCA frameworks to identify underlying causes of anomalies or failures through intelligent reasoning and contextual understanding.
Requirements: Experience in Tensorflow or PyTorch, experience in data preprocessing, strong self-motivation, and commitment to the project.
Faculty: Janghoon Yang
Controlling a Robot Arm using a Large Language Model (LLM)
The objective of this research project is to develop a controller for a low-cost robot arm using a Large Language Model (LLM). Combining LLM abilities for visual object detection along with code generation, an agent-based system is under development to control a low-cost robot arm using natural language commands (ex: “pick up the blue block”, “align the blocks in a horizontal line”). This technology can impact areas such as intelligent manufacturing, robot surgery, 3D printing, assembly/disassembly, construction, art, circuit fabrication, home robotics, healthcare, etc.
Requirements: Students are required to have a GPA > 3.0, strong knowledge of Python (CMPSC 131 & 132) or beyond, and experience with LLM API programming is beneficial. Ability to work independently and good communication skills are required.
Faculty: Robert Avanzato
Effects of derivatized organic compounds on gastrointestinal stromal tumor cells
Cancer remains one of the deadliest diseases in humans. One often understudied form of this disease is gastrointestinal stromal cancer. Previous studies have shown that thiazolidinones can selectively inhibit the growth of cancer cells in culture. We will test the effects of differentially modified thiazolidinones, synthesized by our organic chemists, on the growth and reproduction of two gastrointestinal stromal tumor cells. These molecules have different halogens attached to the at either the meta or para position. This project will allow students to learn techniques in cell culture, microscopy, and cellular quantification.
Requirements: Students are typically selected from the biology or integrative science majors. No prior research experience is required. Students working with me usually have a GPA of at least 3.0. I have already selected my students for the 2024-25 academic year.
Faculty: Eric Ingersoll [email protected]
Pulsar studies in radioastronomy
Pulsars are the remnants of exploding giant stars. Because pulsars rotate rapidly they produce a periodic signals (pulses) that can be detected with large radio telescopes. In our projects, we use a 20-meter diameter radio telescope at the Green Bank Observatory in West Virginia to detect pulsars. Our research projects support three goals: searching for undiscovered pulsars, studying the properties of pulsars, and using collections of pulsars as a galaxy-size gravitational wave detector.
Requirements: Successful completion of courses in Algebra and Trigonometry. Experience with EXCEL and some background in introductory physics is preferred. Advanced projects are aided by an introductory knowledge of python.
Faculty: Ann Schmiedekamp ([email protected]), Carl Schmiedekamp ([email protected])
Underwater Object Detection using Sonar and Deep Learning (AI)
The purpose of the research project is to apply deep learning (AI) techniques to detect and identify important artifacts (shipwrecks, partially buried man-made structures, pipes, animal life, etc.) in side-scan sonar data collected by an underwater robot. Deep learning is a subset of machine learning and artificial intelligence. Deep learning uses a convoluted neural network (CNN) to identify patterns and objects in images (as well as other data, such sound and text). Deep learning has proven very successful in many application areas as tumor detection in x-ray images and CT scans, natural language detection, and face recognition.
Requirements: Advanced programming skills in Python or MATLAB, knowledge of computer vision and AI (preferred), GPA 3.0 or above.
Faculty: Robert Avanzato ([email protected])
AI and Image Segmentation: Advancing SAM for Enhanced Stereo Video and Point Cloud Processing
This project investigates using AI-driven image segmentation, specifically the Segment Anything Model (SAM2), to enhance stereo video and point cloud processing. By integrating SAM2 with 3D data techniques, the research aims to improve segmentation accuracy and efficiency, addressing challenges in depth estimation and object recognition.
Requirements:
- Advanced programming skills
- Advanced skills in hardware design and circuits
- GPA > 3.2
- Self motivated and can work independently
Faculty: Yi Yang ([email protected])
Classification of Japanese Papers Based on Deep Learning of Optical Coherence Tomography Images
This project focuses on classifying Japanese paper types using deep learning techniques applied to Optical Coherence Tomography (OCT) images. By leveraging advanced image analysis, the research aims to accurately differentiate between various types of traditional Japanese papers, which are often challenging to classify due to subtle structural differences. The project will develop and train deep learning models to identify unique characteristics in OCT images, contributing to the preservation and study of cultural heritage materials.
Requirements:
- Advanced programming skills
- Advanced skills in hardware design and circuits
- GPA > 3.2
- Self motivated and can work independently
Faculty: Yi Yang ([email protected])
Transforming Landscapes with Undergraduate Community Engaged Research and the Commonwealth Arboreta Network
As Penn State Abington prepares for construction on a new Academic Building, this project entails making small changes on campus that reflect the University Sustainable Operations Council's goal of more biodiverse landscaping. Throughout fall of 2024 and spring of 2025, students will conduct research to identify locations, species, and numbers of trees, shrubs, and supportive ecosystems to be planted on campus with the goal of acquiring level one Arboretum Accreditation.
Requirements: None.
Faculty: Michele Grinar ([email protected])
Smart Electric Wheelchair
This project aims to build a smart electric wheelchair using an H-Bridge motor controller for efficient drive control. It enables smooth, precise movement, including forward, reverse, and turning. Smart features like obstacle detection and assistive technology integration will enhance safety and user independence.
Requirements: Advanced pogramming skills
Faculty: Vinayak Elangovan ([email protected]) Co-advisor: Yi Yang ([email protected])
Optimizing Large Language Models for Mental Health Illness Detection through Quantization and Prompt Engineering
Apply quantization techniques to optimize memory usage and computational efficiency without compromising the model’s ability to detect mental health symptoms, supported by prompt engineering for enhanced accuracy. Steps: 1. Quantize models to reduce memory and computational requirements, while ensuring that the prompts used guide the models effectively in detecting mental health symptoms. 2. Evaluate the quantized models' ability to deliver accurate and empathetic responses in mental health contexts by testing with various prompts. 3. Measure performance across different quantization levels and assess the model's efficiency, including inference speed, while maintaining a focus on mental health disorder detection.
Requirements: Python programming, Machine Learning and Data Mining, Data Science
Faculty: Iqra Ameer ([email protected]), Vinayak Elangovan ([email protected])