Projects 2023

Creating an Organic Chemistry PAL: A Personalized Aide for Student Learning

Information: This IRT project aims to develop a personalized tool for students– a coach for competency-based organic chemistry learning. This is part of our larger goal to improve student learning, experience, and outcomes in undergraduate Organic Chemistry. The project relies and builds upon CARBON - a platform currently in use in our organic chemistry courses, which we built, tested, and validated as part of our 2022-2023 IRT effort. CARBON is a real-time assessment report generator, expandable database, and datamining tool that accepts per-item, per-student, assessment data. It organizes data according to course, assessment, and learning goals and has expandable datamining capabilities to relate assessment data within and across courses.; Dima Bischoff-Hashem (Public Health), Anand Kuchibhatla (Computer Science), Rohit Manjunath(Computer Science), Shreya Pandey (Computer Science), Senuri Rupasinghe (Computer Science), & Anthony Shenouda (Cell Biology and Neuroscience); Kim-Phuong Le, Assistant Research Professor and Lawrence Williams, Professor of Chemistry, Department of Chemistry and Chemical Biology

Characterizing nanoplastic interactions with biological materials

Information: Research on micro- (MP) and nanoplastics (NP) has dramatically increased recently due to the recognition of the widespread and intense contamination posed by waste plastic materials. Since 2019, I have been a "biology consultant" for chemistry faculty member Dr. Julie Peller at Valparaiso University on microplastic research. Previous projects involved quantifying the microplastic waste in watersheds and describing how the microplastics associated with algae in Lake Michigan. Dr. Peller is currently funded by NSF (NSF 21-589) to assess micro- and nano-plastic suspensions, agglomerations, and contaminant adsorptions. The research question for this interdisciplinary project with Rutgers students will begin simply at "how do nanoplastics interact with biological materials?" Recent studies have reported that microplastics are present in human placentas, deep in human lung tissue, and traveling in our bloodstream. Students in this project will generate standard nanoplastics via a technique developed by the Peller lab and study how these nanoplastics interact with chemicals or compounds that are commonly found in the human body. For example, the most abundant blood protein is albumin. If we put nanoplastics and albumin in an aqueous (blood-like) solution, how will they interact? The hope is that students partaking in this project will be able to characterize the interactions of nanoplastics with several different compounds that are characteristic of human blood/body fluids. Research has conclusively shown that we are ingesting and excreting plastics every day. The research now needs to turn to ask the question- what are these tiny plastic particles doing as they float in the human body? This interdisciplinary project aims to begin answering that question.; Emaan Abdelmeguid (Biological Sciences), Rena Chen (Biological Sciences) & Dhruv Patel (Exercise Science); Cassandra Nelson, Assistant Teaching Professor, Department of Exercise and Sport Science

STaT3D - Smart Tactical at point-of-injury Trauma 3D printing Device

Information: In treating trauma injury, the continuum of care starts at the trauma scene or point-of-injury. This point-of-injury treatment by first responders (in civilian case) and medics (in combat) is critical in order to improve patient outcomes in the next treatment phases - the definitive treatment in hospital followed by the rehabilitation in longer term, and advance the field of regenerative medicine.; Hallie Gordon(Mechanical Engineering), Venus Ikinnagbon(undeclared), Adam Moskowitz (Mechanical Engineering) & Abhinav Ramidi (Biomedical Engineering); Kim-Phuong Le, Assistant Research Professor, Department of Chemistry and Sangya S. Varma, Associate Dean for Program Development, Mathematical and Physical Sciences (MPS); Associate Professor of Professional Practice

The Impact and Ethics on the Usage of Artificial Intelligence

Information: Longstanding research has shown that there is a fundamental dissonance that exists between the proliferation of artificial intelligence (AI) and the continued maintenance of human rights, as well as constitutional rights in the American context. AI systems are said to approachintelligence as a way of information processing via machine learning (ML). ML, considered to be the motor of AI, is the process of deducing patterns and learning from examples and experiences. Essentially, AI is ever-increasing in task efficiency because a computer program improves after gaining experience in solving the specific task. The two important subsets of ML is supervised learning and unsupervised learning. Supervised learning is a type where the algorithm learns to deliver the ideal output that is matched with a certain input, known as labelled data. In unsupervised learning, the algorithm is not given an ideal output, it is only given an input. The main aim of unsupervised learning is to discover patterns and structures on its own,known as unlabeled data. This hands-off approach for unsupervised learning is largely responsible for the call for increased ethical regulation of AI to protect individuals’ human and constitutional rights.; Yuvrajsinh Chavda (Information Technology and Informatics), Victoria He (English), Drishti Kanakia (Pre-Business) & Faith Wilson (Journalism and Media Studies; Christine Cahill, Assistant Teaching Professor, Department of Political Science

Hidden Social Cues- Hidden No More Through AI

Information: A major challenge in biology is the need for improved methods to recognize and quantify behaviors. Our ability to detect small and rapid movements, movement sequences, and larger patterns of behavior through observation is limited. Standard methods often consist of training animal subjects to discriminate between two stimuli on a task in which an animal has to emit a response to a correct sensory stimulus and inhibit responding to an incorrect sensory stimulus, or involve laborious hand scoring by human observers using behavioral rubrics. In addition, many animals have sensory capabilities we do not possess (e.g. sensitivity to ultraviolet (UV) light or magnetic fields), and thus can be affected by environmental signals of which we are unaware. Most existing methods for quantifying behavior are subject to observer limitations, methodological confounds, and/or conceptual biases. Fortunately, modern machine learning techniques applied to behavioral data (video, audio, etc.) promise to open up a new field of computational ethology that can precisely characterize the behavior of animals as they navigate in and respond to their environment.; Sarah Benedicto(undeclared), Edward Bershad(Biological Sciences), Amie Choe (Computer Science) &Samika Mehra (undeclared); Mimi Phan, Cognitive Science and Rich Martin, Computer Science

IRT Projects

Projects

Projects 2023 - 2024

Creating an Organic Chemistry PAL: A Personalized Aide for Student Learning

Characterizing nanoplastic interactions with biological materials

STaT3D - Smart Tactical at point-of-injury Trauma 3D printing Device

The Impact and Ethics on the Usage of Artificial Intelligence

Hidden Social Cues- Hidden No More Through AI