PeopleofSuperintelligence

Researcher

Allan Haldane

Allan Haldane is a Temple physicist whose research applies statistical physics and machine learning to biological sequences. He studied yeast gene regulation, used Potts models to examine protein structure and fitness, and compared protein-sequence generators. In 2025, he compared Potts models with MSA Transformer and found that phylogeny corrections improve detection of biophysical epistasis. As of October 2026, he is a Research Associate Professor in Temple's Department of Physics and holds a PhD from Rutgers University.

Works
59

Publication numbers from OpenAlex, as of 22 October 2026. Citations and h-index are hidden because this OpenAlex record may be incomplete or mix in other authors.

Timeline

  1. 2014

    Fitness landscapes for yeast binding sites

    At Rutgers in 2014, he coauthors Biophysical Fitness Landscapes for Transcription Factor Binding Sites. The study finds that epistasis is common in yeast gene regulation and relates it to interactions between transcription factors and DNA sites.

  2. 2016

    Potts models for kinase proteins

    In 2016, he and collaborators use a Potts model of residue co-variation to analyze the conformational propensities of kinase-family proteins.

  3. 2019

    Potts models and alignment depth

    In 2019, he and Ronald M. Levy examine how protein-sequence alignment depth affects Potts model predictions. They find that overfitting can inflate the estimated fitness of training sequences, while fitness and epistasis predictions decline as alignment depth falls.

  4. 2021

    Protein sequence model generation

    In 2021, he coauthors a comparison of the pairwise Potts, variational autoencoder, and site-independent models as protein-sequence generators. The Potts model has the greatest generative capacity, reproducing higher-order mutation statistics from natural sequences most closely among the three models.

  5. 2022

    HIV mutational fitness landscapes

    In 2022, he coauthors an analysis of HIV mutational fitness across protease, reverse transcriptase, and integrase. The authors find that global pairwise Potts models are necessary to predict viral proteins' mutational landscape.

  6. 2024

    Kinase conformational landscapes

    In 2024, he coauthors a study that combines a sequence-based Potts model with molecular dynamics to test how mutations affect the free-energy difference between active and inactive kinase conformations. The results support an inactive regulatory state stabilized in tyrosine kinases relative to serine/threonine kinases.

  7. 2025

    Potts models and MSA Transformer

    In 2025, with Kisan Khatri and Ronald M. Levy, he compares Potts models with MSA Transformer on higher-order protein-sequence statistics. The study finds that explicit phylogeny corrections help the Potts model detect epistatic interactions of biophysical origin.

  8. As of October 2026

    Research Associate Professor at Temple

    As of October 2026, he is a Research Associate Professor in Temple's Department of Physics and holds a PhD from Rutgers University.

Career

  1. NowTemple UniversityResearch Associate Professor

Education

  1. Rutgers UniversityPhD

From the biography.

  1. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Temple University. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  2. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Temple University. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  3. Universities keep AI researchers for a median of 6 years, compared with 2 years at frontier labsIncludes Rutgers University. Across 15,811 dated work stays (6,529 people), median role tenure runs from 6 years at universities to 2 years at frontier labs. We compare all five organization types, starting decades from the 1970s to the 2020s, and the organizations that keep people longest.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Rutgers University. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.

Sources and credits

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  1. Biophysical Fitness Landscapes for Transcription Factor Binding Sites journals.plos.org. Checked 21 October 2026.
  2. Research | Ron Levy Group | Temple University ronlevygroup.cst.temple.edu. Checked 21 October 2026.
  3. Influence of multiple-sequence-alignment depth on Potts statistical models of protein covariation | Phys. Rev. E journals.aps.org. Checked 21 October 2026.
  4. The generative capacity of probabilistic protein sequence models | Nature Communications nature.com. Checked 21 October 2026.
  5. Limits to detecting epistasis in the fitness landscape of HIV | PLOS ONE journals.plos.org. Checked 21 October 2026.
  6. Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases | Nature Communications nature.com. Checked 21 October 2026.
  7. Phylogenetic corrections and higher-order sequence statistics in protein families: Potts vs multiple sequence alignment transformer machine learning models | Phys. Rev. Research journals.aps.org. Checked 21 October 2026.
  8. College of Science and Technology | Temple University Bulletin bulletin.temple.edu. Checked 21 October 2026.