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.
Duration: Fall 2024, Spring 2025
Faculty: Eric Ingersoll [email protected]
Synthesis of 2,3-diarylsuccinates
We are investigating the synthesis of 2,3-diarylsuccinates by oxidative homocoupling reactions under mild reaction conditions. These organic molecules are intermediates in the preparation of gamma-butyrolactones that have interesting biological activities including neuroprotective properties. Current objectives include determining the effect of alkoxide bases and metal cations on reaction outcomes including reaction kinetics and diastereoselectivity. Students will use routine and advanced organic chemistry techniques (such as air-free reactions, recrystallization, and thin-layer and flash column chromatography) and instruments (NMR, IR, and MS) to study these reactions.
Requirements: 1) Curious, motivated, and independent learners 2) Preferred - students who have completed CHEM 210, 212, and 213
Duration: Fall 2024, Spring 2025
Faculty: Ahmed Nuriye ([email protected])
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.
Duration: Spring 2024 to Fall 2025
Faculty: Les Murray ([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.
Duration: Spring 2025 to Spring 2026
Faculty: Ann Schmiedekamp ([email protected]), Carl Schmiedekamp ([email protected])
What is Justice?
One of famous Youtube lectures is Justice by Michael Sandel. This project is to build up a justice AI engine in the perspective of his Youtube lectures. The main purpose of this project is not to simply build up an inference engine. Rather, it will be used for evaluating both political parties from the consistent point of view. Ultimately, the legitimacy of this engine will be evaluated from the common sense and the law if time allows.
Requirements: Experience in Tensorflow or pytorch, Experience in data preprocessing, Experience in text manipulation, self-motivated person
Duration: Spring 2024, Fall 2024, Spring 2025 (no requirement to present in fair in Apr 2024)
Faculty: Janghoon Yang ([email protected])
Large Language Models (LLMs) for computing
This project will exploit a large language model or natural language processing to enhance the quality of some tasks given as follows. 1. Enhancing Sentiment Analysis with Large Language Models (LLMs) 2. Enhancing Time Series Analysis with Large Language Models (LLMs) 3. Providing objective view from data with Large Language Models (LLMs)
Requirements: Experience in Tensorflow or pytorch, Experience in data preprocessing, Experience in text manipulation, self-motivated person.
Duration: Fall 2024, Spring 2025
Faculty: Janghoon Yang ([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.
Duration: Fall 2024, Spring 2025
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
Duration: Fall 2024, Spring 2025
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
Duration: Fall 2024, Spring 2025
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.
Duration: Fall 2024, Spring 2025
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
Duration: Fall 2024, Spring 2025
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
Duration: Fall 2024, Spring 2025
Faculty: Iqra Ameer ([email protected]), Vinayak Elangovan ([email protected])