Library · Machine learning & AI
What Dimensionality Reduction & PCA got wrong, from 63 theses
Summary written by a model from the records below: The records document various failure cases where principal component analysis and dimensionality reduction techniques degrade downstream modeling performance. Across diverse domains, these failures frequently arise from the loss of class-relevant signals, inability to handle non-linear structures, loss of semantic interpretability, or underperforming unreduced inputs. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 8 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Unsupervised variance maximization discards critical class and target signals
Principal component analysis ranks dimensions purely by total variance without considering target labels, causing predictive low-variance components to be dropped. This leads to collapsed minority class representations, loss of subtle discriminative features, and degraded downstream classification and regression accuracy.
Tried and failed
PCA dimensionality reduction before one-class SVM applied to anomaly detection from sensor data. Outcome: worse than baseline. Reason: Nominal-only PCA bases discarded variance directions critical for distinguishing faulty signals
Applications of Probabilistic Machine Learning Models to Semiconductor Fabrication · MIT · PhD
Tried and failed
PCA preprocessing before independent component analysis applied to sparse low-signal genomic data. Outcome: no signal. Reason: signal was discarded during the PCA dimension reduction step prior to ICA
Any-way and Sparse Analyses for Multimodal Fusion and Imaging Genomics · Georgia Tech · PhD
Tried and failed
PCA dimensionality reduction without class resampling applied to imbalanced multi-feature classification. Outcome: worse than baseline. Reason: Unsupervised projection collapsed minority class variance, causing classifier recall to drop near zero
Exploration of computer vision and machine learning integration strategies for cotton identification in complex agricultural environments · Iowa State · Master's
Tried and failed
PCA dimensionality reduction for feature selection applied to brain connectivity network classification. Outcome: worse than baseline. Reason: Unsupervised variance maximization discarded domain-specific discriminative connectivity features captured by hypothesis-driven network-based statistics
Brain microstructural and functional MRI: developments and application to a rat model of Alzheimer's disease · EPFL · PhD
Tried and failed
principal component regression applied to phenotype prediction from transcriptomic data. Outcome: worse than baseline. Reason: unsupervised variance maximization discarded components predictive of the target compared to partial least squares
Tried and failed
PCA dimensionality reduction before linear SVM classification applied to spectroscopic image classification. Outcome: worse than baseline. Reason: Discarded components contained variance relevant to class separation
Advancing Biological BCARS Imaging: Simulation-Based Optimization, and Machine Learning Analysis · Georgia Tech · PhD
Tried and failed
PCA dimensionality reduction before classification applied to high-dimensional tabular genomic data. Outcome: worse than baseline. Reason: Reduced classification performance compared to raw features and obstructed feature-level SHAP interpretability
Applications of Machine Learning in Source Attribution and Gene Function Prediction · Virginia Tech · Master's
Tried and failed
PCA dimensionality reduction before linear discriminant analysis applied to mass spectrometry profiles. Outcome: did not generalise. Reason: PCA discarded low-intensity features that contained critical discriminative signal
Validating the ability of REIMS to differentiate lamb flavor performance based on consumer preference · Texas Tech · Master's
Considered and rejected
Considered and rejected: Rejected using Principal Component Analysis prior to logistic regression (PC-LR) because PCA ignored class labels and destroyed group structure
Statistical Analysis of Mid-Infrared Spectroscopy Data to support Dairy Cow Management · University College Dublin · PhD
Tried and failed
PCA dimensionality reduction before supervised classification applied to spectroscopy classification of mixed polymers. Outcome: worse than baseline. Reason: High variance within heterogeneous classes caused loss of discriminative spectral variance during unsupervised dimensionality reduction
Enhancing the Mechanical Recycling of Plastics · MIT · PhD
Considered and rejected
Considered and rejected: Rejected pixel-level PCA dimensionality reduction preprocessing for hyperspectral detection due to loss of subtle discriminative spectral information in faint signals.
Intelligent decision making on-board satellites · Oxford · PhD
Tried and failed
PCA dimensionality reduction before SVM classification applied to molecular descriptor property prediction. Outcome: worse than baseline. Reason: Unsupervised variance maximization discarded non-linear features predictive of class boundaries compared to supervised selection
Tried and failed
Principal component analysis dimensionality reduction before regression applied to functional data regression. Outcome: worse than baseline. Reason: input-space variance does not correlate with target predictive relevance
Automatic relevance determination for Gaussian process regression with functional inputs · Iowa State · PhD
Considered and rejected
Considered and rejected: Rejected using PCA dimensionality reduction (83 components) because it discards low-variance class-discriminative features and degraded F1 score by 0.11.
Tried and failed
PCA variance-based dimensionality reduction applied to surface EMG signal classification. Outcome: worse than baseline. Reason: Discarded low-variance components that contained critical discriminative motor unit action potential features
Non-invasive neural interfacing for wearable electromyographic systems · Imperial · PhD
Linear projections fail to capture complex non-linear structures and manifold geometry
Linear dimensionality reduction algorithms cannot represent non-linear manifolds, polynomial relationships, or complex physical diffraction effects. Consequently, linear projections capture insufficient variance, fail to resolve distinct phenotypic clusters, and require excessively large latent dimensions compared to non-linear methods.
Tried and failed
PCA dimensionality reduction applied to single-cell expression of disease states. Outcome: no signal. Reason: linear principal components failed to separate healthy from diseased phenotypes
Computational Modeling of Biological Function · MIT · PhD
Tried and failed
principal component analysis for dimensionality reduction applied to patient survival subgroup stratification. Outcome: worse than baseline. Reason: linear projection failed to capture complex non-linear patterns compared to autoencoders
Integrated systems approach for mechanistic understanding of human cancers · UT Austin · PhD
Considered and rejected
Considered and rejected: Standard PCA for dimensionality reduction of multi-omics data, rejected due to failure to cluster survival-distinguishable glioma subtypes compared to deep autoencoders.
Integrated systems approach for mechanistic understanding of human cancers · UT Austin · PhD
Considered and rejected
Considered and rejected: Rejected Principal Component Analysis (PCA) for dimensionality reduction of power flow observations because it performs linear mapping, cannot represent complex polynomial relationships, and fails to preserve local cluster structure unlike T-SNE
Operational moving target defences for improved power system cyber-physical security · Imperial · PhD
Tried and failed
principal component analysis for dimensionality reduction applied to high-dimensional molecular descriptor space. Outcome: no signal. Reason: linear projection failed to capture complex nonlinear variance, yielding poor separation on early components
Predictive modeling of optical enantiomeric excess determination assays for high-throughput asymmetric reaction screening · UT Austin · PhD
Tried and failed
principal component analysis on raw diffraction patterns applied to polycrystalline grain segmentation. Outcome: no signal. Reason: dynamical diffraction effects introduced strong nonlinearities that PCA could not separate
Transforming 4D-STEM Data: From Diffraction to Cepstra & Aberrations to Brightness · Cornell · PhD
Tried and failed
principal component analysis for 2D clustering applied to high-dimensional audio feature vectors. Reason: Non-linear relationships across features prevented distinct cluster separation in linear projection
Real-Time Sound Visualization · Texas Tech · Master's
Tried and failed
principal component analysis for trajectory clustering applied to intrinsically disordered protein conformational ensembles. Outcome: no signal. Reason: linear components failed to resolve distinct clusters and captured under half of trajectory variance
Unravelling the molecular dynamics of c-MYC’s TAD domain: a journey from simulation optimisation to drug discovery · Imperial · Doctorate
Considered and rejected
Considered and rejected: Rejected PCA (Principal Component Analysis) for feature selection due to limitations with nonlinear correlation structures
Artificial intelligence driven anomaly detection for big data systems · Imperial · PhD
Considered and rejected
Considered and rejected: Linear dimensionality reduction (SVD/PCA) for mobility time-series due to failure to capture intrinsic nonlinear manifold geometry
Applications of Optimization and Machine Learning to Healthcare · University of Washington · PhD
Considered and rejected
Considered and rejected: Rejected linear POD/SVD dimensionality reduction for full workflow due to inability to capture nonlinear spatial features across varying parameter sets.
Enabling computer-aided food engineering: mechanistic models and their deep learning surrogates · Cornell · PhD
Lost to a baseline
Linear PCA dimensionality reduction on agent-based stochastic data found a latent space size of n - 1 (25x larger than non-linear DR), resulting in poor reducibility compared to non-linear methods.
Reduced Order Non-INtrusive (RONIN) Modeling for Strategic Defense Planning · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Discarded Cartesian and dihedral PCA as dimensionality reduction techniques because of poor explained variance and unclusterable landscapes.
Unravelling the molecular dynamics of c-MYC’s TAD domain: a journey from simulation optimisation to drug discovery · Imperial · Doctorate
Considered and rejected
Considered and rejected: Rejected Proper Orthogonal Decomposition (POD) / Principal Component Analysis (PCA) for reduced-order modeling of cislunar tracking uncertainty due to poor handling of non-linear behavior and scalability limitations.
Uncertainty-Based Methodology for the Development of Space Domain Awareness Architectures in Three-Body Regimes · Georgia Tech · PhD
Reduced representations underperform raw inputs and simple random baselines
Applying dimensionality reduction often yields no predictive benefit and performs worse than retaining raw unreduced feature sets or domain-engineered metrics. In several comparisons, principal component transformations overcomplicated representations and lost to direct regression or simple random feature subsets.
Tried and failed
Principal component analysis for feature extraction applied to microstructural image representations. Outcome: worse than baseline. Reason: overcomplicated data representation, degrading downstream surrogate model accuracy compared to simple summary metrics
Exploration of the Additive Manufacturing Process Development Space using High-throughput Mechanical Property Assays · Georgia Tech · PhD
Lost to a baseline
PCA dimensionality reduction performed comparably to random sets and worse than curated supersets for average indication accuracy.
Comprehensive Elucidation of Small Molecule Therapeutic Behavior Using Multitarget Theory · University at Buffalo · PhD
Tried and failed
PCA dimensionality reduction before regression applied to tabular physical test data. Outcome: worse than baseline. Reason: PCA feature extraction did not systematically improve predictive performance compared to direct regression
Compacted Snow Testing Methodology and Instrumentation · Virginia Tech · Doctorate
Lost to a baseline
PCA-based feature selection (Jaccard 0.9624) was beaten by random feature selection (Jaccard 0.985) in K-Means segmentation dimensionality reduction
MORPHOLOGICAL CLASSIFICATION OF SUBTYPES OF VOLUMETRIC PROJECTION NEURONS FROM MOUSE BRAIN SCANS · Johns Hopkins · Master's
Lost to a baseline
Two-dimensional Principal Component Analysis (PCA) score projections failed to separate distinct ink groups that were clearly resolved by visual spectral inspection.
DEVELOPMENT AND VALIDATION OF A TECHNIQUE FOR ANALYSIS OF TONER PRINTED DOCUMENTS USING MAGNETIC FLUX MEASUREMENTS · Sam Houston State University · PhD
Considered and rejected
Considered and rejected: Rejected feature selection dimensionality reduction (PCA, ANOVA, Fisher Score) for EEG ErrPs because unreduced covariance and correlation features performed better.
Tried and failed
PCA dimensionality reduction on raw time-series applied to degradation trajectory remaining useful life prediction. Outcome: worse than baseline. Reason: linear projection discarded critical degradation dynamics captured better by domain-engineered features
Machine learning-based aging models for estimating battery state of health and predicting future degradation · Iowa State · PhD
Tried and failed
PCA feature reduction for tabular signal features applied to time-series signal classification. Outcome: worse than baseline. Reason: linear dimensionality reduction on mixed statistical and domain features produced inconsistent and degraded classification performance
AI-Driven Approaches for Real-Time Beat-by-Beat ECG Signal Classification and Cardiac Health Monitoring in Smart Healthcare Systems · Texas Tech · PhD
Tried and failed
PCA projection for cross-correlation alignment applied to multimodal time-series synchronization. Outcome: worse than baseline. Reason: dimensionality reduction degraded cross-correlation accuracy without downstream confidence weighting mechanisms
Analyzing Health-related Behaviors Using First-person Vision · Georgia Tech · PhD
Tried and failed
PCA preprocessing before regression applied to open circuit potential sensor signals. Reason: dimensionality reduction did not improve multivariate calibration accuracy compared to raw inputs
Hardware and Software Interfaces Design for Multi-Panel Electrochemical Sensors · EPFL · PhD
Lost to a baseline
PCA dimensionality reduction to 100 components (18.70% accuracy) lost drastically to the 20,000-channel baseline (93.15%).
Electromagnetic Side-Channel Analysis Methods for Digital Forensics on Internet of Things · University College Dublin · PhD
Considered and rejected
Considered and rejected: PCA-based dimensionality reduction for filtered K-means feature selection was rejected in favor of 700 random features due to small sample size vs high dimensionality
MORPHOLOGICAL CLASSIFICATION OF SUBTYPES OF VOLUMETRIC PROJECTION NEURONS FROM MOUSE BRAIN SCANS · Johns Hopkins · Master's
Tried and failed
PCA dimensionality reduction after dynamic time warping applied to time series neural network classification. Outcome: did not generalise. Reason: PCA subspace projection did not consistently improve accuracy and hurt cross-domain generalization compared to raw aligned inputs
Physical Side-Channel Vulnerability Assessment of Implementations of Cryptographic Algorithms · Georgia Tech · PhD
Tried and failed
feature selection and dimensionality reduction applied to error-related potential classification from EEG. Outcome: worse than baseline. Reason: yielded no significant performance increase over using the full feature set
Dominant variance modes reflect noise, scaling disparities, and confounding artifacts
Extracted principal components frequently align with unstandardized feature scales, sensor noise, or individual baseline variations rather than the true biological or physical phenomena of interest. Additionally, retaining higher-order components tends to incorporate sample-specific noise that corrupts cluster quality and out-of-sample generalization.
Tried and failed
Simultaneous spectroscopy acquisition using CCD detectors applied to hyperspectral data processing with PCA. Reason: Detector introduced correlated channel noise that disrupted downstream dimensionality reduction algorithms.
Tried and failed
PCA using unstandardized covariance matrix applied to multivariate time series with varying units. Reason: features with the largest raw variance completely dominated the extracted principal components
Macroeconomic Forecasting: Statistically Adequate, Temporal Principal Components · Virginia Tech · Doctorate
Tried and failed
PCA for coordinate axis alignment applied to non-uniformly distributed point clouds. Outcome: did not generalise. Reason: Principal components biased toward high-density spatial clusters rather than true structural axes
Behavior-Coupled Neural Circuit Analysis of Chemosensory Responses in C. elegans · Georgia Tech · PhD
Tried and failed
principal component analysis on untargeted lipidomics applied to phenotypic group separation in metabolomics. Outcome: no signal. Reason: unsupervised variance was dominated by factors other than the disease phenotype
Mapping the metabolome in the developing gut · Imperial · PhD
Tried and failed
retaining high-order principal components for weak signals applied to linear factor asset pricing models. Outcome: did not generalise. Reason: high-order components captured primarily sample-specific noise rather than true weak latent factors, causing severe out-of-sample degradation
Asset pricing with unsystematic risk · Imperial · PhD
Tried and failed
PCA dimensionality reduction on fiducial covariance applied to summary statistics for simulation-based inference. Outcome: worse than baseline. Reason: compresses along dominant noise variance modes rather than parameter-sensitive variations
Probabilistic Methods in Cosmology and Inference · Penn · PhD
Considered and rejected
Considered and rejected: Rejected dimensionality reduction via PCA because it introduces unwanted noise, diminishes important attributes, and adds computational overhead.
Efficient Multi-GPU K-means Clustering · Texas State University · Master's
Considered and rejected
Considered and rejected: Rejected Principal Component Analysis (PCA) for joint word-document space reduction due to disproportionate vector magnitude variance between words and documents and edge skew from high-frequency terms.
Temporal Topic Embeddings with a Compass · Virginia Tech · Master's
Tried and failed
principal component analysis applied to multi-subject neural time series. Outcome: no signal. Reason: inter-individual baseline activation differences masked the shared underlying dynamical structure
COMPUTATION THROUGH NOISY DYNAMICS · Penn · PhD
Tried and failed
k-means clustering on higher-order principal components applied to functional connectivity feature representations. Outcome: worse than baseline. Reason: higher-order principal components introduced noise that progressively degraded cluster separation quality
Sustained attention heterogeneity in post-9/11 Veterans: Functional connectivity profiles across large-scale cortical networks and subcortical regions during task-based fMRI · Harvard · Master's
Transforming data into composite orthogonal components destroys domain interpretability
Projecting original measurements into composite linear combinations obscures the underlying physical, ecological, and biological semantics of individual variables. This transformation prevents practitioners from attributing model decisions back to specific genes, physical bands, or neuroanatomical connectivity features.
Considered and rejected
Considered and rejected: Rejected using PCA dimensionality reduction for the final SHAP interpretation pipeline because mapping principal components back to distinct genes introduces loss of biological specificity
Applications of Machine Learning in Source Attribution and Gene Function Prediction · Virginia Tech · Master's
Considered and rejected
Considered and rejected: Rejected using Principal Component Analysis (PCA) for dimensionality reduction across connectivity models because transforming data out of pairwise connectivity space hindered the interpretability of neuroanatomical feature weights.
Multimodal Connectome Mapping of Sex Differences and Cognitive Abilities: a Machine Learning Approach · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected using Principal Component Analysis (PCA) on all derived variables for dimensionality reduction because composite PCA axes obscure specific ecological and physical interpretations of individual terrain drivers.
Considered and rejected
Considered and rejected: Rejected dimensionality reduction via PCA for experimental subset selection because PCA projections do not preserve original feature semantics.
Metabolic phenotypes of marine heterotrophic bacteria · Boston University · PhD
Considered and rejected
Considered and rejected: Rejected standard Principal Component Analysis (PCA) for sensor design because it creates linear combinations of all bands rather than selecting discrete physical spectral bands.
Towards Transferable Pollution Detection Methods in Aquatic Environments Using Hyperspectral Technology · University of Las Palmas de Gran Canaria · PhD
Considered and rejected
Considered and rejected: Rejected Principal Component Analysis (PCA) for clustering GM biomarkers because PCA components are linear signed combinations causing difficult ROI interpretation across multiple clusters.
In vivo MRI perfusion and structural correlates of pathology in frontotemporal lobar degeneration · Penn · PhD
Considered and rejected
Considered and rejected: Rejected using Principal Component Analysis (PCA) or ordination-based data reduction axes as continuous morphospaces for threshold models because the axes lack mechanistic generative meaning and fail under trait correlation.
A Morphological and Phylogenetic Comparative approach to investigating Branching Architecture Evolution in Monocots A Dissertation · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected PCA dimensionality reduction prior to k-means because it requires loading the full dataset and projecting centroids back to original space yields inaccurate centroids and lower cluster quality.
Approaches to extract, characterize, and interpret dynamic functional network connectivity in fMRI data · Georgia Tech · PhD
Component reduction collapses temporal dynamics and structured spatial dependencies
Dimensionality reduction algorithms discard dynamic sequential correlations, spatial frequency information, and temporal structures present in time-series and curve data. Compressing features across temporal waves or unaligned observations impairs trajectory estimation, state prediction, and calibration accuracy.
Tried and failed
traditional dimensionality reduction applied to single-cell RNA sequencing data visualization. Outcome: no signal. Reason: failed to preserve local density information and clustering structure in high-dimensional biological data
Similarity Metrics for Biological Data: Algorithmic developments for high-dimensional datasets · MIT · PhD
Tried and failed
PCA dimensionality reduction on temporal sequence features applied to hidden Markov model state prediction. Outcome: worse than baseline. Reason: Component reduction discarded informative variance and dynamic correlations present in raw sequential features
Development of A Trajectory Population Data and its Application in CAV Research · Virginia Tech · Doctorate
Tried and failed
adjusted covariate principal component analysis applied to multimodal neuroimaging pattern extraction. Outcome: no signal. Reason: severe loss of primary phenotype information leading to low correlations and poor prediction
Statistical Methods for Extracting and Comparing Patterns in Multimodal Neuroimaging Studies · Penn · PhD
Considered and rejected
Considered and rejected: Rejected using Principal Component Analysis (PCA) before cluster analysis because pooling panel waves obscures period-specific resource allocation dynamics and trades accuracy for simplicity.
Essays on the transformation of the rural economy in rubber producing areas in Southwest China · Leibniz Universität Hannover · PhD
Considered and rejected
Considered and rejected: Rejected using Principal Component Analysis (PCA) for ROI directional analysis because frequency domain 2D Fourier transformation preserves spatial frequency components.
Tried and failed
functional principal component analysis without phase alignment applied to unaligned curve data. Reason: fails to capture peak heights and creates nonlinear dependence across principal component scores
Heteroscedastic Functional Data Models · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected non-parametric Principal Component Analysis (PCA) for latent factor extraction due to lack of time-series structure and inability to propagate parameter uncertainty
Parameter Uncertainty, Cashflow Betas, and Earnings Announcement Premia · University of Oregon · Doctorate
Tried and failed
PCA dimensionality reduction for covariance estimation applied to covariance and precision matrix estimators. Outcome: worse than baseline. Reason: increased estimation bias and Frobenius norm error across multiple simulated return distributions
Portfolio optimization with stochastic volatility models · Imperial · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Apply principal component analysis to the consensus matrix to analyze latent subspaces and branching structures in single-cell developmental trajectories. Blocker: None
Ensemble Methods for Latent Structure Detection from Heterogeneous Genomic and Phenotypic Data · Harvard · PhD
Left open
Quantify cell-type differences using multiple dimensionality reduction runs and XGBoost models trained on gene subsets to reduce multicollinearity. Blocker: None
Developing Graph-based Computational Algorithms for Single-cell Data Science · Georgia Tech · PhD
Left open
Apply Principal Component Analysis and tensor decomposition to the combination dose-response L1000 gene expression dataset to identify emergent transcriptional changes. Blocker: None
A Framework for The Study of Compound Interactions in L1000 · Harvard · Master's
Left open
Implement and evaluate feature exchange augmentation using higher-order feature statistics like principal components instead of mean and standard deviation. Blocker: None
Learning with and beyond visual knowledge · Cornell · PhD
Left open
Apply hierarchical dimensionality reduction to public molecular omics datasets to test if principal component scaling trends persist across biological layers. Blocker: None
MODELLING CAUSAL MECHANISMS IN ORGANISMAL AGING · Dalhousie · PhD
Left open
Implement dimensionality reduction (e.g., PCA) for shape latent code projections in the differentiable corrector to avoid linear memory scaling with object count. Blocker: None
Resilient Object Perception for Robotics · MIT · PhD
Left open
Implement PCA-based dimensionality reduction before Heterogeneous Compound Symmetry modeling in the MAGMa proteomics pipeline for large sample size datasets. Blocker: None
Mass spectrometry based proteomics and its applications in understanding disease progression and designing therapeutic strategies. · Cornell · PhD
Left open
Explore spatial EEG features and hybridize dimensionality reduction methods with high-performing classifiers for cognitive state detection. Blocker: Lacks specific implementation details and relies on proprietary flight crew psychophysiological dataset
Flight crew’s cognitive states detection using psychophysiological measurements and machine learning techniques · Cranfield · PhD
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Rafid Bendimerad · Cornell · PhD · 2025
Advancing Space Electric Propulsion through Optimization of On-Orbit Refueling, Propellant Discovery, and Molecular Modeling of Electrospray Thruster Plumes - Sandeep Chinnareddy · Virginia Tech · Master's · 2024
Applications of Machine Learning in Source Attribution and Gene Function Prediction - Joseph DelPreto · MIT · PhD · 2021
Robots as Minions, Sidekicks, and Apprentices: Using Wearable Muscle, Brain, and Motion Sensors for Plug-and-Play Human-Robot Interaction - Sandra Sullivan · Imperial · Doctorate · 2021
Unravelling the molecular dynamics of c-MYC’s TAD domain: a journey from simulation optimisation to drug discovery - Sanika Phatak · Johns Hopkins · Master's · 2021
MORPHOLOGICAL CLASSIFICATION OF SUBTYPES OF VOLUMETRIC PROJECTION NEURONS FROM MOUSE BRAIN SCANS - FATMA ALSUBHI · Harvard · Master's · 2026
Sustained attention heterogeneity in post-9/11 Veterans: Functional connectivity profiles across large-scale cortical networks and subcortical regions during task-based fMRI - Alexis Hocken · MIT · PhD · 2026
Enhancing the Mechanical Recycling of Plastics - Massimo Dello Preite · Imperial · PhD · 2026
Asset pricing with unsystematic risk - Debmalya Ghosh · Cornell · PhD · 2025
Enabling computer-aided food engineering: mechanistic models and their deep learning surrogates - Rebecca Danning · Harvard · PhD · 2025
Ensemble Methods for Latent Structure Detection from Heterogeneous Genomic and Phenotypic Data - Evan L. Kramer · MIT · PhD · 2025
Visibility in synthetic aperture radar satellite data: metric formulation, observation scheduling, and orbit design - Jingnan Shi · MIT · PhD · 2025
Resilient Object Perception for Robotics
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