Each area states what is being asked, how it is approached, what exists publicly, and where the current limits are.
Computational Science — the interdisciplinary frame
What does it take to move between economics, statistics, and computation without losing the standards of any of them?
This is the frame the areas below sit inside rather than an additional research method. Doctoral work in computational science at the University of Massachusetts Boston, building on graduate training in statistics and economics, supplies the standards each area is held to: measurement taken seriously, inference under noise, and computation that survives contact with real data.
Methods
- Statistical modeling and numerical methods
- Machine learning applied to data-integration problems
- Systematic literature review methodology
Public artifacts
Limitations. Interdisciplinary breadth is a bet: it trades depth-per-field for connections between fields, and the copy here claims only the work actually done.
Status: Interdisciplinary frame; ongoing
Matching and deduplicating records across noisy, heterogeneous sources, with pipelines whose every stage can be inspected, replaced, compared, and audited. The pipeline is treated as the object of study rather than as plumbing around a matcher: ingestion, blocking, comparison, and clustering are separately measurable, so a change in one stage can be attributed rather than merely observed. The questions that follow are where such pipelines degrade as volume and source heterogeneity grow, and what has to be recorded for a merge decision to remain reconstructible afterwards.
Methods
- Multi-field similarity combining string, numeric, and embedding-based metrics
- End-to-end pipelines from ingestion to evaluation (erbot toolkit)
- Replaceable, separately measurable stages so effects can be attributed
- Reproducible evaluation binding every metric to the configuration that produced it
- Automated parameter tuning with recorded configurations
Public artifacts
Limitations. No benchmark figures are published in isolation; results are only meaningful with their dataset and evaluation setting.
Status: Active
Blocking and candidate-generation strategies that control the quadratic comparison budget while keeping recall losses measurable rather than invisible.
Methods
- Blocking utilities integrated into the erbot pipeline
- Recall-aware analysis of candidate generation choices
Public artifacts
Limitations. Blocking trade-offs are dataset-dependent; conclusions are reported per setting, not as universal rules.
Status: Active
Multiview Representation and Fusion
How should multiple views of the same records — lexical, numeric, embedding, tensor — be combined without letting one view dominate or contradict the rest?
Representing records through several complementary views and fusing them, including tensor-based canonical correlation approaches for aligning views.
Methods
- TF–IDF with singular value decomposition alongside embedding features
- Tensor-based multiple canonical correlation analysis (tensorMCCA)
- Contributions to R packages for multiview analysis (GCMER)
Public artifacts
Limitations. View-fusion behavior under highly imbalanced view quality remains an open question in this work.
Status: Active
Turning similarity structure into entity clusters, comparing K-means, Louvain community detection, hierarchical clustering, and partitioning around medoids on the same footing.
Methods
- Similarity-graph construction from multi-field comparisons
- Side-by-side clustering strategies within one pipeline (K-means, Louvain, hierarchical, PAM)
Public artifacts
Limitations. Cluster-quality comparisons depend on ground-truth availability, which many real datasets lack.
Status: Active
Systematic threshold selection for match decisions: optimization curves, sensitivity to data shifts, and recording the configuration behind every operating point.
Methods
- Automated parameter tuning with optimization curves in evaluation reports
- Sensitivity analysis of operating points across settings
Public artifacts
Limitations. Calibrated thresholds transfer poorly across domains; per-domain recalibration is assumed, not avoided.
Status: Active
Making evaluation a first-class artifact: ground-truth comparison, reports that bind metrics to the exact parameters that produced them, and pipelines designed to be rerun.
Methods
- Evaluation against ground truth built into the pipeline
- Generated PDF reports carrying parameters, optimization curves, and metrics together
Public artifacts
Limitations. Reproducibility guarantees stop at the data boundary: private or shifting source data limits what any pipeline can promise.
Status: Active
This work examines missingness created by measurement limits, with particular attention to the inferential consequences of substituting values in covariates. The emphasis is not only on filling an unavailable value, but on understanding how that choice propagates into regression estimates.
Methods
- Least squares regression under limit-of-detection missingness
- Bias analysis for covariate substitution
- Data-driven construction and evaluation of a single-substitution rule
Public artifacts
- Du, Chu, Jin, Luo, Lee, Hudson, MacKenzie & Lewis — accepted by the Journal of Applied Statistics (July 19, 2026); see Selected publications above
Limitations. The public website currently reports the accepted status and research question only. Detailed methodological and numerical claims must remain tied to the accepted manuscript and eventual publisher record.
Status: Accepted paper; ongoing related work