Joel Strickland, PhD, CEng

Artificial Intelligence Engineer | Problem-solving • Decision Intelligence • Practical Systems

Professional Summary

Senior AI/ML leader with 9.4 years delivering practical solutions across pharma, chemicals, materials, manufacturing, and consumer goods. Focused on problem framing, algorithm development, uncertainty-aware modelling, and end-to-end delivery.

Delivered projects to enterprise clients including Rolls-Royce, BAT, NASA, voestalpine, and FUCHS (many clients under NDA). Built AI platforms from research through production, with expertise in ML for sparse, noisy, high-dimensional industrial data and modern tooling including LLMs.

Bridge technical depth (PhD, 20+ peer-reviewed publications, chartered engineer) with commercial acumen and hands-on implementation.

Core Expertise

Problem-solving & ML ▼

Algorithm Development • Conformal Prediction • Uncertainty Quantification • Bayesian Optimization • Active Learning • Deep Learning • Federated Learning • Adaptive Experimental Design

LLM Tooling ▼

LangChain/LangGraph • RAG Systems • Multi-Agent Architectures • MCP Protocol

Technical Stack ▼

Python (NumPy, Pandas, SciKit-Learn, PyTorch, TensorFlow) • Docker • MCP (Model Context Protocol) • REST APIs • FastAPI • Backend Development • Full-Stack Product Building • SQL • GCP/Vertex AI • Git • CI/CD

Domain Expertise ▼

Materials Science • Pharmaceuticals • Chemicals • Manufacturing • Formulation Development • Process Safety • Experimental Design (DoE)

Business Impact ▼

Technical Pre-Sales • Enterprise Delivery • Stakeholder Management • C-Suite Communication • Thought Leadership • Training & Workshops

Professional Experience

Head of Agentic AI & Technical Pre-Sales

Intellegens | Cambridge, UK | May 2024 - Present

Leading agentic AI development and enterprise adoption—combining technical innovation with strategic client partnerships across pharma, chemicals, and materials.

Key Achievements:
- Architected agentic AI platform - multi-agent architectures, LangGraph, MCP integration for autonomous R&D workflows
- Led cross-functional team of ML engineers, implementing agile sprints
- Delivered technical demonstrations across pharma, chemicals, manufacturing, food, and materials sectors
- Conducted training and workshops for enterprise clients (voestalpine webinar)
- Delivered webinar "Can Agentic AI Transform Chemicals & Materials R&D?"
- Authored blog series on agentic AI for R&D
- Presented at AIChE Spring Meeting 2024 on ML applications in process safety

Technical Highlights: LangGraph • Multi-Agent Systems • RAG • MCP Protocol • Gemini/LLM Integration • Prompt Engineering • Full-Stack Development

ML Scientist → Principal ML Scientist

Intellegens | Cambridge, UK | July 2021 - May 2024

Progressed from early team member (post-spin-out from Cambridge's Cavendish Lab) to leading consultancy projects for Fortune 500 clients. Built and scaled ML solutions for sparse, noisy industrial data across pharma, chemicals, food, and materials.

Key Achievements:
- Published paper with BAT on pharmacokinetics modeling
- Delivered Yili case study on food formulations optimization
- Published OCAS case study on steel PSP modeling
- Published AMRC case study on composite manufacturing optimization
- Conducted AM optimization webinar with Lawrence Livermore National Lab
- Delivered webinar with Lucideon on materials and process development
- Published thought leadership on LLMs in materials science
- Conducted webinars on formulation development and adaptive DoE

Technical Highlights: Active Learning • Gaussian Processes • Neural Networks • Uncertainty Quantification • Feature Engineering • Embedded ML

Machine Learning Researcher

University of Leicester | Leicester, UK | September 2016 - June 2021

Doctoral research sponsored by Rolls-Royce plc, applying machine learning and statistical modeling to aerospace materials challenges.

Key Achievements:
- Published 16 peer-reviewed papers (4 as lead author) in Acta Materialia, Scientific Reports, Crystals
- Co-developed DenMap algorithm for automated microstructure recognition, adopted by international groups
- Certified AFHEA through 4 years teaching data analysis and simulation
- Led residential advisor team managing student welfare (2015-2017)

Publications highlight: "On the origin of mosaicity in directionally solidified Ni-base superalloys" (Acta Materialia, 2021)

Selected Client Engagements

Public Client Work (2021-2026) — 18 organizations ▼

- BAT - Pharmacokinetics modeling (paper)
- Zizo - Pharmacokinetics modeling (paper)
- B-Secur - Pharmacokinetics modeling (paper)
- Equivital - Core body temperature prediction (paper)
- DOW - Adaptive experimental design (book chapter)
- Photocentric - 3D printing materials ML pilot (partnership)
- Ansys - Integration partnership (partnership)
- PlantSea - Sustainable materials pre-sales (case study)
- Avery Dennison - ML pilot (LinkedIn recommendation)
- FUCHS - Lubricant formulation development (case study)
- voestalpine - Advanced materials and AM optimization (webinar)
- Yili - Food formulation optimization (case study)
- OCAS/ArcelorMittal - Steel PSP modeling (case study)
- AMRC - Composite manufacturing optimization (case study)
- Lawrence Livermore National Lab - AM process optimization (webinar)
- Lucideon - Materials development (webinar)
- CPI - Battery industrialisation (webinar)
- Rolls-Royce - Nickel-base superalloy design (PhD thesis)

Additional enterprise clients across pharma, chemicals, and manufacturing under NDA.

Publications & Thought Leadership

Recent highlights — not exhaustive.

Recent Publications (2024-2026) ▼

- "Building Trustworthy AI Agents for Science: Lessons from NOA, a schema-gated conversational research testbed" - Intellegens Technical Paper, 2026
- "Degrees of Uncertainty: Conformal Deep Learning for Core Body Temperature Prediction" - Communications Engineering, 2025
- "Rapid Residual Stress Simulation in Additive Manufacturing through Machine Learning" - Additive Manufacturing, 2025
- "Adaptive Experimental Design" - The Digital Transformation of Product Formulation, 2024
- "Quantifying Benefits of Imputation over QSAR Methods" - J. Chemical Information & Modeling, 2024

Conference Presentations (2024-2025) ▼

- AIChE Global Process Safety Conference 2025 - ML in Process Safety
- AIM 2025 Conference - Federated Learning in Materials Science
- User Group Meeting 2025 - NOA Agentic AI System Demo
- AIChE Spring Meeting 2024 - ML for Chemicals & Materials R&D

Webinars & Workshops ▼

- "Can Agentic AI Transform Chemicals & Materials R&D?" (2025)
- "Tracking LLMs in Materials Science" (2024)
- "Revolutionizing Energy Storage with AI" (2023)
- Multiple formulation development and DoE webinars (2021-2024)

Ongoing Research (2025-2026) ▼

- Targets Up Front for More Focused Adaptive Design
- Machine Learning for Oligonucleotides
- The Benefits of Accelerators for Science
- Federated Learning for Adaptive Design
- Schema-Gated Conversational Design for AI
- Calibrated Uncertainty for Rehabilitation

Total Publications: 20+ peer-reviewed papers | Citations: 290+ | Active Research Profile: Ongoing collaborations

Education & Certifications

PhD in Materials Science with Machine Learning
University of Leicester | September 2016 - June 2021
- Sponsored by Rolls-Royce plc
- Thesis: "Patterns in Directionally Solidified Alloys" (algorithmic microstructure analysis)
- Developed image feature recognition tool (DenMap) for automated microstructure analysis
- 16 publications during PhD, 4 as first author
- Advanced training: Solidification Modeling (ESI Group, Switzerland)

MEng (Mechanical) with First Class Honours
University of Leicester | September 2011 - June 2016

Professional Certifications:
- Chartered Engineer (CEng)
- Professional Member, Institute of Materials, Minerals and Mining (MIMMM)
- Associate Fellow of Higher Education Academy (AFHEA)
- Essential Management Skills Certificate
- IBM Data Science Certificate

Professional Activities