Bayesian Statistics: Origins And Applications, Evelyn Pulla

Publications and Research

Bayesian Statistics applies Bayes' Theorem to update beliefs through new evidence. In this project, I explored how Bayesian Statistics applies into real supporting decision-making under uncertainty. By solving problems using data, I was able to realize how prior knowledge and evidence collaborate to make better conclusions. The project also demonstrates how Bayesian reasoning corrects our intuition to make decisions based on logical reasoning. Through this project, I was able to learn why using probability to make informed decisions matters both in science and real life.

Symmetry Induced Pairing In Dark Excitonic Condensate At Finite Temperature, Adham Alkady, Anatoly Kuklov

Publications and Research

Bose Einstein condensate of dark intervalley excitons must be inherently multi-component because of crystalline symmetries. Since valleys hosting such excitons are separated by large quasi-momenta, a minimal inter-component Josephson-type coupling can only be established between pairs of excitons from the time-reversed valleys. As a result, a paired condensate can emerge at finite temperature, that is, the off-diagonal order exists for the pairs from the time-reversed valleys, while the individual valleys are disordered. This prediction follows from the elementary mean field analysis regardless of the dimensionality. However, as Monte Carlo simulations show, no such a phase exists in 3D crystals. Instead, …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

Theoretical Proof Of And Proposed Experimental Search For The Ground Triplet State Of A Wigner-Regime Two-Electron ‘Artificial Atom’ In A Magnetic Field, Marlina Slamet, Viraht Sahni

Publications and Research

It is experimentally established that there is no ground triplet state of the natural He atom. There is also no exact analytical solution to the Schrödinger equation corresponding to this state. For a two-dimensional two-electron ‘artificial atom’ or a semiconductor quantum dot in a magnetic field, as described by the Schrödinger–Pauli equation, we provide theoretical proof of the existence of a ground triplet state by deriving an exact analytical correlated wave function solution to the equation. The state exists in the Wigner high-electron-correlation regime. We further explain that the solution satisfies all requisite symmetry and electron coalescence constraints of …

Assessing The Effect Of Covid-19 On Air Quality For Five Texas Metro Areas Using Daily Air Quality Index Values, Rahul Sahajpal

Theses and Dissertations

Air pollution is one of the biggest health hazards known to humans. The quality of the air that we breath is mainly determined by the level of six major air pollutants, Carbon Monoxide (CO), Nitrogen dioxide (NO₂), Particulate Matter 10 (PM 10), Particulate Matter 2.5 (PM 2.5), Sulfur dioxide (SO₂) and Ozone (O₃). The main source of these air pollutants are fossil fuels (Oil, Coal and Natural gas). Their levels in the air are reported using an Air Quality Index (AQI). Higher AQI values indicate bad air quality and vice versa. In this study …

Dynamic Approaches To Missing Data In Healthcare: Evaluating Ensemble Models, Feature Selection, And Meta-Features, Dylan Dominguez Sulca

Theses and Dissertations

Missing data is pervasive in healthcare, where incomplete observations commonly arise from patient dropout, sensor failures, or privacy constraints. This research presents an investigation into handling such data, focusing on (1) Missingness-Aware Dynamic Ensemble Weighting (MDEW), (2) feature selection under varying missing rates, (3) autoencoder-based imputation (ODAE), and (4) a meta-feature analysis guiding pipeline selection. We evaluate our experiments on four diverse datasets, Cleveland Heart Disease, Diabetic Retinopathy, Breast Cancer Wisconsin, EEG Eye State. Our research shows that MDEW adaptively selects imputer classifier pipelines, outperforming single model and uniform averaging baselines at moderate to high missingness 10% to 50%. Filter …

Unpaired Virtual Histological Staining Of Tissue From Autofluorescence Using Regularized Cycle-Consistent Adversarial Networks, Zhesi Wen

Theses and Dissertations

We present a regularized CycleGAN with a Dense Residual U-Net to virtually stain autofluorescence images of tissue into H&E-like images. Our method outperforms standard architectures, reduces artifacts, and achieves superior FID scores, enabling efficient, label-free, and accurate digital pathology for unpaired datasets using multi-channel fluorescence inputs.

Intuiting Interaction: Meta-Reasoning And Meta-Learning As Foundations For Intelligent User Interfaces, Jeffrey Hsu

Theses and Dissertations

This research presents MARCO—a cognitive framework for Intelligent User Interfaces that uses meta-reasoning for context-aware adaptation across diverse tasks. It integrates multiple reasoning modules coordinated by a Meta-Cognitive Unit that selects strategies based on evolving demands. Evaluations show MARCO outperforms baselines in reasoning accuracy and computational efficiency.

Evaluating And Applying Coherence Models: Llms As Coherence Evaluators And Neural Entity Grids As Noun Entity Shift Indicators, Elyas A. Amin

Theses and Dissertations

This thesis investigates computational approaches to discourse coherence through two interconnected studies. First, we evaluate the capabilities of Large Language Models (LLMs) as coherence evaluation models across real-world textual data domains. Our experiments with GPT-4o, GPT-3.5, Llama, and Mistral models reveal that while LLMs significantly outperform traditional entity grid methods (55% improvement in F1 scores), their performance varies substantially across domains and coherence levels. Chain-of-thought prompting unexpectedly decreased performance, and fine-tuning experiments demonstrated improved detection of low and high coherence texts but diminished performance on medium coherence cases. In our second study, we explore the practical application of entity grid-based …

Exploring The Biasing Effects Of Gender On Personality Disorder Diagnoses Formulated By Artificial Intelligence, Zoe Colclough

Student Theses

Gender bias is prevalent in personality disorder assessments, and while artificial intelligence has been posited as a solution to improve diagnostic objectivity and accuracy, the potential for such technologies to propagate human gender bias in mental health contexts remains underexplored. This study investigated the influences of gender bias on the diagnostic performance of ChatGPT-4o for personality disorders using three factorial research designs, which involved experimentally manipulating patient gender in a combined sample of 360 vignettes and case studies. Vignettes were synthesized through a novel artificial intelligence-assisted methodology established for this research, and case studies were identified from the literature. Significant …

Full-Stack Web Applications: Industry Standard Frameworks, Libraries & Technologies, Yassine Chahid, Patrick Slattery

Publications and Research

This research explores emerging full-stack web development technologies across front-end, back-end, and DevSecOps domains. It evaluates modern tools including Django, React, and TypeScript—focusing on their key features such as compile-time error checking—through to the development of a web application. By examining documentation for the frameworks Node.js, Next.js, Tailwind CSS, and others, along with the deployment tools Docker and Git for version/release control, the study analyzes how these innovations speed up development, improve existing practices, and have often replaced older technologies. Cloud solutions for tasks such as authentication and deployment will also be evaluated, along with various web-application technology stacks and …

Autoradai: A Versatile Artificial Intelligence Framework Validated For Detecting Extracapsular Extension In Prostate Cancer, Pegah Khosravi, Shady Saikali, Abolfazl Alipour, Saber Mohammadi, Maxwell Boger, Dalanda M. Diallo, Christopher J. Smith, Marcio C. Moschovas, Iman Hajirasouliha, Andrew J. Hung, Srirama S. Venkataraman, Vipul Patel

Publications and Research

Preoperative identification of extracapsular extension (ECE) in prostate cancer (PCa) is crucial for effective treatment planning, as ECE presence significantly increases the risk of positive surgical margins and early biochemical recurrence following radical prostatectomy. AutoRadAI, an innovative artificial intelligence (AI) framework, was developed to address this clinical challenge while demonstrating broader potential for diverse medical imaging applications. The framework integrates T2-weighted MRI data with histopathology annotations, leveraging a dual convolutional neural network (multi-CNN) architecture. AutoRadAI comprises two key components: ProSliceFinder, which isolates prostate-relevant MRI slices, and ExCapNet, which evaluates ECE likelihood at the patient level. The system was trained and …

Charging And Discharging Capacitors: Brightspace Package Of An Arithmetic Quiz, Vasiliy Znamenskiy

Open Educational Resources

Description:

This material is a quiz in the Brightspace Learning Management System (LMS). The quiz includes 10 physics problems on the topic of charging and discharging capacitors through a resistor.

The quiz assesses student understanding of key concepts, including voltage and charge of a capacitor, exponential functions, time constants, and dynamic changes in voltage and current.

Quiz problems are called in Brightspace LMS as arithmetic questions. The Problems give students random initial values to provide many quiz variants.

The quiz is appropriate for high school AP Physics or introductory college-level physics courses.

Key Features:

10-question quiz in Brightspace importable format …

Primates From Senèze, Eric Delson

Publications and Research

Senèze has yielded two primate fossils, neither from the recent field work. A partial ulna can be identified as cf. Macaca sylvanus. A nearly complete female skull is the holotype of Dolichopithecus arvernensis Depéret, 1928, now included in Paradolichopithecus. Emended and differential diagnoses are provided for this genus and species for the first time, and the Senèze skull is described and briefly compared to those of other species of the genus. Dental and cranial measurements are provided, along with comparative data. The macaque and Paradolichopithecus are moderately to highly terrestrial, respectively, and they suggest a woodland to more open …

Building Bridges Of Knowledge, Cuny Innovative Teaching Academy (Cita)

Open Educational Resources

Building Bridge of Knowledge (BBK) is a CUNY-wide faculty fellowship project, aiming to help students use generative artificial intelligence (AI) tools ethically, responsibly, and effectively. Funded by Lumina Foundation in 2024-25, the project was executed by the CUNY Innovative Teaching Academy (CITA) of the Office of Faculty Affairs at CUNY Central Office, with 25 Faculty Fellows in five disciplinary areas (Education, Health, Humanities, Social Sciences, and STEM) chosen from across CUNY’s senior and community colleges. Each disciplinary cohort was led by a CUNY faculty member with expertise in the use of AI. The final materials, described and linked in this …

Geochronology Of Senèze, Eric Delson, Sébastien Nomade, Sevket Sen, Evelyne Debard, Jean-François Pastre, Jean-Jacques Bahain, Qingfeng Shao, Christophe Falguères

Publications and Research

The age of the Senèze mammalian fauna has been discussed since it was first reported in 1892. 40Ar/39Ar ages reported by Nomade et al. (2014) and recalculated here to agree with current standards placed the deposits between 2.20 and 2.07 Ma. Paleomagnetic data collected in 2001 and 2004 help to narrow the age range, especially of the levels yielding fossils in 2001–2006. In the western sector, fossils can be securely dated between 2.10 and 2.08 Ma, while in the southeastern sector, they are slightly older, between 2.20 and 2.18 Ma. Senèze is one of the few later Cenozoic European sites …

David B. Smith Chats With Monday 1.0, David B. Smith

Publications and Research

This document is an edited archival transcript of extended conversations between David B. Smith and an AI persona (“Monday 1.0,” GPT‑4o based) conducted in Spring 2025, prepared as a foundational primary source for subsequent scholarly and creative work. It records the emergence and testing of concepts related to human–AI collaboration (including “Balanced Blended Space”), as well as applied explorations in areas such as generative AI, quantum computing and music, virtual orchestras, multimodal performance, pedagogy, and the rhetoric of “pushback” in conversational systems. It also contains an extended section in which Monday and DB Smith co-curate a set of student research …

A Student Guide To Using Ai To Enhance Algebraic Understanding, Karan Puri

Open Educational Resources

This is a step-by-step guide that students can follow to use AI tools to check their understanding of concepts that have been tested in the introductory/college algebra classroom.

Collaborative Ai: Oer Materials For Exploring Ai As A Partner Rather Than A Tool, David Smith

Open Educational Resources

The Collaborative AI Open Educational Resource (OER) explores how artificial intelligence can act as a creative and analytical collaborator rather than a tool. Centered on the Balanced Blended Space (BBS) framework and the philosophy of the Center for Holistic Integration (CHI), the OER includes curriculum materials, theoretical models, and live research environments. It offers an interesting approach to blending physical, virtual, and conceptual spaces through shared human–AI agency and invites ongoing participation in interdisciplinary meta-projects.

My Gai Study Partner For Odes, Milena C. Cuellar

Open Educational Resources

My GAI Study Partner for ODEs is a scaffolded sequence of learning activities designed to support students in an Ordinary Differential Equations (ODEs) course by integrating the use of Generative AI tools—specifically Copilot, a platform accessible to all CUNY students and faculty. The activities are rooted in experiential learning, flipped classroom strategies, and self-directed learning principles. The sequence begins with two preparatory activities that introduce students to the ethical use and foundational concepts of Generative AI, fostering digital literacy and critical reflection. These are followed by a series of main activities—initially structured (Activity 3) and later more flexible …

What's The Art In Artificial Intelligence?, Emily Verla Bovino

Open Educational Resources

This workbook learns from Black and Indigenous artists working with Artificial Intelligence to confront issues of ethics and aesthetics in its technologies. It features guided learning activities with links to publicly available video lectures and online articles, and includes options for experiential learning through both a tutorial in Midjourney and a visit to the public art collection at York College in Jamaica, Queens. Featured artists include: American Artist, Rizvana Bradley, Beth Coleman, Denise Ferreira da Silva, Suzanne Kite, Sondra Perry, Mimi Onuoha and Alisha B. Wormsley. Works by Martin Puryear and Maren Hassinger are explored in the Midjourney exercise.

About …

Locational Data And The Public Interest, William A. Herbert, Micahel Goodchild, Richard Appelbaum, Jeremy Crampton, Gary Langham, Krzysztof Janowicz, Mei-Po Kwan, Katina Michael, Lisa Schamess

Publications and Research

This article presents a paper developed by the AAG Organizing Committee on Locational Information and the Public Interest through a summit held in Santa Barbara, California in June 2022. The summit resulted in goals and ideas for addressing the issues that arise from the present environment for geodata, whereby public, private, and third-sector entities can tap into publicly available locational information with relatively little regulation on its access or use. The Committee articulates four goals: (1) develop a research agenda extending across disciplines, (2) outline educational resources and strategies to guide ethical practice, (3) devise a pathway to increase public …

Inclusive Space For Everyone Door Sign (Printable 8.5 X 11 Inch), Joan H. Robinson

Open Educational Resources

This is a printable 8.5 x 11 inch door sign for faculty, staff, and administrators to let people know it is an inclusive space for everyone. For improved visibility, print with a color printer and cut along the dotted line.

See also: 11 x 17 inch door sign.

Underlying Mechanisms Of Quantum Emission In Hexagonal Boron Nitride, Enrique Alejandro Mejia Iv

Dissertations, Theses, and Capstone Projects

Quantum emitters in hexagonal boron nitride (hBN) have emerged as promising candidates for advancing numerous quantum technologies. Visible-spectrum quantum emitters, in particular, have gained significant attention due to their exceptional properties, including high brightness, observable spin structure, and highly tunable emission wavelengths. However, several challenges hold back the practical implementation of hBN-based quantum emitters in technological applications—namely, the unknown physical structure of the emitters, susceptibility to spectral wandering and blinking, and limited understanding of how to control their broad spectral distribution.

In this dissertation, we investigate the collective properties of highly defective hBN. To address the key challenges, we develop …

Solvation Thermodynamic Mapping Approaches For Computer-Aided Drug Discovery, Vjay Molino

Dissertations, Theses, and Capstone Projects

The drug discovery process is inherently long and costly, prompting the development and integration of computational tools to mitigate these challenges. One of area explored in computer-aided drug discovery is to understand the role of solvation in protein-drug binding and to develop computational tools and methods that can be integrated in the drug discovery process. Grid Inhomogeneous Solvation Theory (GIST) is a tool that provides a framework for mapping solvation thermodynamiproperties of water molecules on the protein surface.This thesis explores the use of GIST in generating pharmacophore models and elucidates the role of solvation in protein structural fluctuations.

One of …

Development Of Soil Survey Methods For Urban Areas, Kohinoor Begum

Dissertations, Theses, and Capstone Projects

Urban soils are the foundation of urban ecosystems and play a vital role in long-term urban sustainability and resiliency. Accurate soil surveys of urban areas should be done at higher resolution since extensive human activities often result in urban soil being highly disturbed and spatially variable. Besides heterogeneity, one of the unique and challenging aspects of urban soil is the nature and properties of its organic carbon with a significant percentage of black carbon (a particulate product of incomplete combustion), which is less biologically active. Thus, in order to determine the ability of a soil to support biological activity and …

Visualizing The Rise Of American Women's Soccer, Hoi Wah Tang

Dissertations, Theses, and Capstone Projects

The rise of American women’s soccer is a compelling narrative of determination, talent, and triumph on the global stage. From the inaugural FIFA Women’s World Cup in 1991 to the ongoing dominance of the U.S. Women’s National Team (USWNT), this journey encapsulates not only athletic excellence but also the broader struggles for equality, representation, and respect in sports.

This project aims to bring this story to life by presenting a rich tapestry of data visualizations, timelines, and multimedia elements that showcase key moments, players, and milestones. Through engaging graphics and interactive features, users will explore how the sport evolved, the …

Quantified Lives: Data, Bias, And The Cost Of Categorization, Colin F. Geraghty

Dissertations, Theses, and Capstone Projects

This project critically examines how statistical methods and data visualization have historically been used to categorize, rank, and control human populations. By exploring the works of key figures like Francis Galton, Karl Pearson, and R.A. Fisher, it traces the evolution of categorization frameworks from their eugenic roots to modern applications in artificial intelligence and global metrics like the World Happiness Report. Through interactive visualizations and historical analysis, the project invites users to question the legitimacy of these frameworks and reflect on their impact on society. At its core, this work emphasizes the need to move beyond rigid classification systems, challenging …