Recognition Science Institute

The Theory of Us

A parameter-free framework unifying mathematics, physical reality, consciousness, and meaning.

01
Peer-Reviewed · Axioms (MDPI)
Recognition Geometry
02
Preprint · arXiv
Coherent Comparison as Information Cost
Jonathan Washburn
Jonathan Washburn
Director

Discovered the Recognition Science framework and developed the meta-principle "Nothing cannot recognize itself" as the tautological foundation for all physical law. His insight that reality must emerge from pure logical necessity provides the first parameter-free theory of everything with zero free parameters.

Dr. Elshad Allahyarov
Dr. Elshad Allahyarov
Team Lead

Dr. Sci. Physics & Mathematics, General Physics Institute RAS & Heinrich-Heine University Düsseldorf

Leads theoretical research programs, bringing decades of expertise in many-body systems, plasma physics, and advanced materials science. Since 1988, he has served as Senior Scientific Researcher at the Joint Institute for High Temperatures of the Russian Academy of Sciences.

Dr. Sebastian Pardo Guerra
Dr. Sebastian Pardo Guerra
Research Scientist

Ph.D. Pure Mathematics, Universidad Nacional Autónoma de México (UNAM)

Leads mathematical research bridging abstract theoretical frameworks with Recognition Science principles. His expertise in Category Theory and Graph Theory provides essential foundations for understanding information flow and emergent behavior. Completed postdoctoral work at UC San Diego in applied mathematics.

Dr. Megan Simons
Dr. Megan Simons
Research Scientist

Ph.D. Theoretical and Computational Chemistry, Southern Methodist University

Applies Recognition Science principles to molecular and chemical systems, integrating quantum chemistry with data-driven modeling techniques. Her work explores how recognition-theoretic frameworks can enhance understanding of complex molecular interactions and spectroscopic phenomena.

Dr. Anil Thapa
Dr. Anil Thapa
Research Scientist

Ph.D. Theoretical Physics, Colorado State University

Investigates the frontiers of particle physics through Recognition Science frameworks, exploring connections between neutrino physics, dark matter, and beyond-Standard-Model phenomena. His research integrates effective field theory with Recognition Science principles for unified theories.

Dr. Amir Rahnamai Barghi
Dr. Amir Rahnamai Barghi
Research Scientist

Ph.D. Mathematics, TMU University; M.S. Computer Science, University of Ottawa

Work centers on algebraic combinatorics and scheme theory, with applications to theoretical frameworks supporting Recognition Science. Has 20+ years of academic experience, including contributions to the study of algebraic cycles and Hodge theory.

Dylan Funk
Dylan Funk
Research Scientist

Ph.D. Physics, Auburn University

Plasma physicist specializing in computational modeling, extended magnetohydrodynamics, and dusty plasma theory. Developed new theoretical and simulation-based methods for calculating dust charge in magnetized and strongly coupled plasmas.

Emma Tully
Emma Tully
Chief Operating Officer

Leads research operations and execution across publications, partnerships, and experimental validation—turning complex theoretical work into clear, referee-readable papers and measurable technical milestones. Her work focuses on building the infrastructure to translate Recognition Science into conventional scientific formats and real-world tests.