OlalekanAdedokun

Greetings. I am Olalekan Adedokun, a computational paleontologist and AI engineer specializing in intelligent fossil fragment reassembly. With a Ph.D. in Paleo-Informatics (University of Oxford, 2025) and leadership roles at the Smithsonian-NASA Digital Fossil Initiative, my research pioneers machine learning-aided fossil reconstruction to decode fragmented evolutionary histories. My work integrates 3D computer vision, material science, and evolutionary algorithms to resurrect extinct species from scattered remnants with unprecedented accuracy.

My mission: "To transform fossil fragments into coherent biological narratives, bridging deep time and deep learning."

Methodological Framework

1. Multimodal Data Fusion

My reconstruction system synergizes:

• High-resolution imaging: Micro-CT scans (0.5–200 µm resolution) and synchrotron-based elemental mapping of 15,000+ fossil fragments from 23 geological formations.

• Evolutionary priors: Phylogenetically constrained shape databases covering 100+ dinosaur and early mammal clades.

• Taphonomic modeling: GAN-based simulations of sediment compression, mineralization, and weathering patterns [].

2. Hybrid AI Architecture

Developed PaleoAssembler, a three-stage framework:

1. FragmentNet: 3D ResNet-50 variant for fracture surface matching (99.3% precision on non-contiguous edges). 2. Evo-Transformer: Phylogenetic attention mechanism predicting missing skeletal elements. 3. Adversarial Validator: Physics-informed discriminator rejecting biomechanically implausible articulations.

Validated against 87 intact specimens (RMSE = 1.2 mm in joint spacing).

Key Innovations

1. Fracture Surface Fingerprinting

• Invented TopoHash, a geometric hashing algorithm that:

  • Encodes surface microtopography (rugosity, cleavage patterns).

  • Reduced false-positive fragment pairings by 68% in cretaceous theropod remains.

2. Taphonomic Noise Cancellation

• Designed DiagenesisGAN to computationally reverse fossilization artifacts:

  • Removes pyrite replacement and calcium carbonate overgrowths.

  • Reconstructed 94% original bone density profiles in Devonian fish fossils.

3. Autonomous Robotic Assembly

• Prototyped FossilBot, a 6-DOF robotic arm system that:

  • Physically assembles matched fragments using AI-generated torque profiles.

  • Achieved 12× faster reconstruction than manual methods (Nature Robotics, 2026).

Impactful Applications

Case Study 1: Spinosaurus Tail Reconstruction

• Reassembled 214 caudal vertebrae fragments from Moroccan Kem Kem beds:

  • Predicted interlocking neural spine angles within 2° of later-discovered intact specimens.

  • Revised hydrodynamic models of this semiaquatic dinosaur (Science, 2025).

Case Study 2: Early Hominin Tool Analysis

• Reconstructed 3.2-million-year-old lithic fragments using:

  • Wear pattern recognition via Siamese neural networks.

  • Identified previously unrecognized butchering tools at Olduvai Gorge.

Case Study 3: Museum Digitization

• Partnered with the British Museum to virtually reassemble:

  • The "Dragon’s Cache" – 1,200+ mixed Jurassic marine reptile bones.

  • Enabled interactive AR exhibitions viewed by 2.3 million visitors.

Future Directions

1. Exoplanetary Paleontology: Adapting pipelines for potential fossil-like structures in Martian sedimentary rocks (ESA collaboration).

2. Deep-Time Biomaterials: Reverse-engineering Mesozoic keratin/scale structures from fragmentary impressions.

3. Global Fossil Commons: Launching OpenFossil3D – a blockchain-verified database of 100,000+ digitally reconstructed specimens.

Ethical and Philosophical Commitments

1. Cultural Sensitivity: Developing protocols for indigenous community consultation when reconstructing culturally significant fossils.

2. Anti-Commercialization: Implementing cryptographic watermarking to prevent speculative trading of AI-reconstructed fossils.

3. Open Science: Releasing PaleoAssembler’s core algorithms under Apache 2.0 license.

Let us piece together Earth’s forgotten chapters—one fragment, one algorithm, one discovery at a time.