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Michael C. Burkhart

University of Chicago

19 papers hereh-index 10464 citations39 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author8
  • first author5
  • middle author4

Across the 17 of 19 papers where every author was matched, so the position is known.

fields
  • cs.LG8
  • stat.ML5
  • math.GR4
  • math.CO1
  • math.PR1
affiliations
  • University of Chicago
Homepage
same name
  • Michael C. Burkhart — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20122026
most citedDeep Low-Density Separation for Semi-Supervised Classification

6 citations · 16 across the 18 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

math.GR2026

Local conjugacy in prosolvable groups

Michael C. Burkhart

For prosolvable groups, we provide conditions under which two locally conjugate supplements of a normal pronilpotent subgroup are conjugate, analogous to those given by Losey and S…

cs.LG2026

Federated generative event models for tokenized electronic health records

Michael C. Burkhart, Luke Solo, Inhyeok Lee +8

Electronic health record foundation models are limited by institutionally siloed data and substantial performance degradation under cross-site transfer. We evaluated federated trai…

cs.LG2026

Representation Before Training: A Practical Benchmark for Generative Medical Event Model Tokenization

Inhyeok Lee, Luke Solo, Michael C. Burkhart +5

Generative medical event models use tokenized sequences of patient timelines as input, but practical guidance on the many decisions around tokenization is limited. We benchmark qua…

stat.ML2026

Efficient Generative Prediction for EHR Foundation Models: The SCOPE and REACH Estimators

Luke Solo, Matthew B. A. McDermott, William F. Parker +3

Generative foundation models trained on tokenized electronic health record (EHR) timelines show promise for clinical outcome prediction via Monte Carlo sampling of simulated future…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.