Artificial Intelligence Tools, Curricula, and Agents for Creative Learning

Children's early development of creativity contributes to their learning outcomes and personal growth. However, as children enter formal schooling systems, their creativity declines. Although Artificial Intelligence (AI)-powered tools for K-12 learning hold immense potential for reducing barriers to creative expression, access to these AI tools and AI knowledge among K-12 students and educators remains inequitable to children from groups underrepresented in STEM. In this thesis, I explore how AI, as an emerging creative medium, can be made more accessible to all young creators. I explore two mechanisms of making a mode of creation more accessible: Creative AI literacy materials for diverse classrooms and AI agentic interactions for scaffolding creative expression for diverse learners. Utilizing literacy as a mode of making Creative AI tools accessible, I outline the design and evaluation of various Creative AI curricula that I have developed for diverse groups of K-12 students and teachers. To adapt AI learning to art classrooms, I co-developed the AI and Art curriculum with creative educators, designed specifically for use in creative classrooms with creative educators and learners. I implemented the curriculum with 94 middle and high school students across six week-long sessions. I report findings from teacher co-design sessions and students’ learning experiences. Teachers designed learning objectives and AI tools for their classrooms. Students gained knowledge and skills in art concepts, AI concepts, and the application of art in AI. Students also demonstrated significant shifts in their attitudes towards using AI in the creative process, and their sense of belonging in both AI and art communities was heightened. I discuss how AI curricula can be adapted to diverse disciplines and how art can serve as a meaningful avenue for students to engage with AI concepts. Utilizing social interaction from AI agents as a mode of fostering creative expression in children with neurodevelopmental disorders, I designed and applied inclusive child-robot interactions for collaborative creativity, where 32 elementary school children and a social robot collaboratively created picture stories. The robot provided creativity scaffolding during different parts of the creative storytelling process through social interactions such as feedback, question-asking, divergent thinking, and positive reinforcement, while personalizing the scaffolding to meet the unique needs of neurodivergent children. I investigated the impact of the social robot on children’s exhibited creativity and their emergent creative collaborative interactions in storytelling over multiple sessions. Inclusive design practices eliminated creative barriers for children with neurodevelopmental disorders, and the robot's creativity scaffolding interactions positively influenced children’s creative product and creative process in storytelling. I propose Inclusive Co-creative Child-robot Interaction (ICCRI) guidelines for fostering creativity in children with neurodevelopmental disorders, and accommodating diverse creator styles in complex, open-ended creative tasks. In this thesis, I contribute curricula, learning tools, child-robot interactions, and findings from examining long-term child-AI co-creative interactions. I discuss design implications for integrating AI tools, curricula and agents in creative learning environments. This thesis is a step towards empowering all children with powerful modes of creation, while helping them be responsible creators, thinkers and citizens in an AI-driven future.Ph.D

Using AI to Improve Price Transparency in Real Estate Valuation

This thesis explores the integration of artificial intelligence (AI) into real estate valuation, focusing on visual property attributes to enhance traditional Hedonic models. By incorporating Vision Language Models (VLMs) and generative AI, the research evaluates the potential of these technologies to assess non-standard variables like aesthetic appeal, condition and cohesiveness of interior and exterior property photos. The study contrasts traditional hedonic regression models, which rely on quantifiable factors such as square footage and location, with a new approach that includes AI-generated scores derived from property photos. The study employs three distinct models: the No_Rubric Model, the Composite Model, and the Verbose Model with the Hedonic model serving as the baseline for evaluating their performance. The results demonstrate that incorporating visual data significantly improves model accuracy, aligning valuations more closely with buyer preferences and sold prices. This shift addresses the industry's need for price transparency and highlights how developers can design properties that better meet market demands.S.M

Information-centric Algorithms for Feature Extraction in High-Dimensional Sequential Data

Hidden Markov Models (HMMs) are a cornerstone of sequential data analysis, offering a robust framework for modeling observable events influenced by hidden internal states. With applications spanning speech recognition, video analysis, bioinformatics, and financial time series, HMMs enable the prediction and classification of raw data by leveraging their dual-layer stochastic structure: hidden Markov states and observable outputs. However, as real-world data grows increasingly high-dimensional, extracting meaningful features from observations becomes critical to reduce computational complexity while retaining relevant information. This thesis addresses key challenges in feature extraction for high-dimensional HMMs. Current methods, such as neural networks (NNs), are widely used for nonlinear feature learning but lack mechanisms to prioritize useful features or incorporate known structural constraints. To bridge this gap, this work proposes novel algorithms to decouple representation learning from task-specific objectives and extract features aligned with predefined constraints. The theoretical foundation, including local information geometry and Hirschfeld-Gebelein-Rényi (HGR) maximal correlation, is introduced in Chapter 2. Chapter 3 details three innovative feature extraction algorithms and their corresponding neural network architectures, highlighting their strengths and limitations. Convergence analyses and tail bounds for these methods are presented in Chapter 4. Numerical simulations validating the efficacy of the proposed approaches are provided in Chapter 5, while Chapter 6 concludes with a summary of contributions and potential future research directions. This thesis advances the field by offering structured, constraint-aware feature extraction techniques tailored for high-dimensional sequential data, setting the stage for more effective and interpretable inference in HMMs.Ph.D

A Power Efficient Analog Front End for Continuous Ultrasound Imaging of the Bladder

Continuous bladder monitoring is important for the monitoring of bedridden patients. One method to continuously monitor the bladder is to capture ultrasound images and use machine learning processing to measure the bladder volume from these images. Circuits for implementing these functions can be integrated onto a wearable device, and each of these functions can be integrated onto a single chip. In this thesis, we analyze ultrasound imaging in the context of the bladder to come up with algorithms and hardware to perform continuous bladder monitoring. We first assemble a discrete setup which can form ultrasound images. Using this setup, we describe a new algorithm for generating an ultrasound image by to power gate the hardware during the imaging process to save additional power when capturing the image. We combine these concepts into a single Analog Front End (AFE) chip that can capture images in a power efficient manner.S.M

Co-Living in Seoul: Addressing Housing Needs and Redefining Rental Market Trends

Co-living emerged as a novel asset class in the mid-2010s, addressing the housing needs of urban residents affected by rising housing costs, increasing urban migration, and the growing prevalence of single-person households. In South Korea, co-living has gained attention as a viable alternative to traditional housing, driven by unique local dynamics, including the decline of the dominant Jeonse system and a significant shortage of housing tailored to single-person households. With a growing preference for monthly rental systems over the Jeonse systems, both local conglomerates and start-ups have capitalized on the opportunity to offer company-operated co-living spaces. As the market grows, major international investors and global co-living providers have also entered, reflecting a unique market environment where institutionalized housing options are expanding alongside a notable shift in rental transaction systems. In this new era of urban housing, co-living is rapidly expanding and gaining popularity. This thesis seeks to answer the following question: What factors have driven the emergence and growth of the co-living market in Seoul, and what is its growth potential? To address this, it starts with an analysis of market drivers, provider strategies, and regulatory developments, followed by projections of market potential and an assessment of potential threats and mitigation strategies for long-term viability of co-living in Seoul. The goal is to offer insights for co-living providers to optimize their spaces and services. The findings suggest that while co-living addresses unmet housing demand, its long-term success depends on balancing operational efficiency with tenant satisfaction. While these strategies are applicable in other cities, they are particularly critical in Seoul, where the Jeonse system remains a strong and historically preferred alternative. In Seoul, co-living serves a dual mission: introducing an innovative housing model and reshaping the paradigm of the Wolse rental housing system. To succeed, co-living operators must clearly articulate their unique value proposition, addressing both the housing needs of urban residents and the broader evolution of the rental market.S.M

Decoupling Economic Growth and Carbon Emissions

All economic activity requires energy; to the extent this energy comes from fossil fuels, the energy use results in emissions of carbon dioxide, CO2. The nature of this link between the growth in economic activity and carbon emissions is a critical question for climate change.1 Linkage implies that deep emission reductions will constrain economic growth; decoupling implies that deep emission reductions are possible with little or no effect on growth. An answer to this question is important for the United States, but more crucial for rapidly growing emerging economies such as China and India that seek to improve their citizens' access to low-cost energy while respecting the need to protect the global environment

Computer Science Behind Bars: Lessons Learned from Teaching Incarcerated Students in Prisons and Jails

SIGCSE TS 2025, February 26-March 1, 2025, Pittsburgh, PA, USAEducational programs for incarcerated individuals, often called "behind bars" initiatives, have been shown to improve participants' social and economic outcomes upon release. Since its founding in 2018, MIT's Education Justice Institute (TEJI) has offered accredited classes for incarcerated students, with an increasing focus on computer education. Our courses have been delivered both in person and remotely (e.g., via Zoom). In this poster, we share insights into the challenges present in the incarcerated education environment, and highlight how remote learning offers unique advantages to incarcerated students. We also present preliminary findings from two years of data collected across four recurring computer science courses. This poster aims to foster a dialogue with the broader computer science education community, focusing on: (i) qualitative insights gained from extensive interactions with incarcerated education systems, (ii) preliminary empirical results obtained through IRB-approved surveys, (iii) common challenges faced during data collection, and (iv) an opportunity to seek feedback and pose questions to computer science education experts

The Robust Malware Detection Challenge and Greedy Random Accelerated Multi-Bit Search

AISec’20, November 13, 2020, Virtual Event, USATraining classifiers that are robust against adversarially modified examples is becoming increasingly important in practice. In the field of malware detection, adversaries modify malicious binary files to seem benign while preserving their malicious behavior. We report on the results of a recently held robust malware detection challenge. There were two tracks in which teams could participate: the attack track asked for adversarially modified malware samples and the defend track asked for trained neural network classifiers that are robust to such modifications. The teams were unaware of the attacks/defenses they had to detect/evade. Although only 9 teams participated, this unique setting allowed us to make several interesting observations. We also present the challenge winner: GRAMS, a family of novel techniques to train adversarially robust networks that preserve the intended (malicious) functionality and yield high-quality adversarial samples. These samples are used to iteratively train a robust classifier. We show that our techniques, based on discrete optimization techniques, beat purely gradient-based methods. GRAMS obtained first place in both the attack and defend tracks of the competition

On Modular Invariance of Quantum Affine W-Algebras

Abstract We find modular transformations of normalized characters for the following W-algebras: (a) W k min ( g ) , where g = D n ( n ≥ 4 ) , or E 6 , E 7 , E 8 , and k is a negative integer ≥ - 2 , or ≥ - h ∨ 6 - 1 , respectively; (b) quantum Hamiltonian reduction of the g ^ -module L ( k Λ 0 ) , where g is a simple Lie algebra, f is its non-zero nilpotent element, and k is a principal admissible level with the denominator u > θ ( x ) , where 2x is the Dynkin characteristic of f, and θ is the highest root of g . We prove that these vertex algebras are modular invariant. A conformal vertex algebra V is called modular invariant if its character t r V q L 0 - c / 24 converges to a holomorphic modular function in the complex upper half-plane on a congruence subgroup. We find explicit formulas for their characters. Modular invariance of V is important since, in particular, conjecturally it implies that V is simple, and that V is rational, provided that it is lisse

Search for long-lived heavy neutral leptons in proton-proton collision events with a lepton-jet pair associated with a secondary vertex at √s = 13 TeV

A search for long-lived heavy neutral leptons (HNLs) using proton-proton collision data corresponding to an integrated luminosity of 138 fb−1 collected at s = 13 TeV with the CMS detector at the CERN LHC is presented. Events are selected with a charged lepton originating from the primary vertex associated with the proton-proton interaction, as well as a second charged lepton and a hadronic jet associated with a secondary vertex that corresponds to the semileptonic decay of a long-lived HNL. No excess of events above the standard model expectation is observed. Exclusion limits at 95% confidence level are evaluated for HNLs that mix with electron and/or muon neutrinos. Limits are presented in the mass range of 1–16.5 GeV, with excluded square mixing parameter values reaching as low as 2 × 10−7. For masses above 11 GeV, the presented limits exceed all previous results in the semileptonic decay channel, and for some of the considered scenarios are the strongest to date