Rigor first
An MIT PhD designing molecules with graph neural networks, published in Nature Chemistry and ACS Central Science. I still read the paper before I buy the platform.
Director of Analytics · Caterpillar Inc.
I build the AI that runs physical industry.
Scientist, builder, operator. I trained as an MIT machine-learning researcher, ran a startup, and now lead a 90+ person AI, data science and product organization putting digital twins and agentic AI to work across Caterpillar's global manufacturing.
A 22.6M-parameter language model runs in your browser to answer. Nothing you type leaves your device.
90+people in the organization I lead
150+facilities in platform scope
15+peer-reviewed papers
Top 1%CEO Award at Caterpillar
CaterpillarMITGoogleAmgenNVIDIA GTC · CESTEDx
Hands on the keyboard
I prototype the digital twin assistant, the RAG pipeline and, yes, this site's in-browser model. Credibility with engineers is earned in code reviews.
Value at scale
Founder with a P&L, chief of staff in a $15B segment, and now an AI leader accountable for outcomes across 150+ facilities.
Trajectory
One arc: optimizing physical reality with computation.
The model that predicted failures in a peptide synthesizer at MIT is a cousin of the one predicting failures on a Caterpillar line. Each role built a reusable asset the next one used.
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- Lead a 90+ person organization of data scientists, AI/GenAI engineers, simulation developers and product managers across the US and India.
- Own the AI and product roadmap for the Modern Manufacturing digital platform: predictive maintenance, inventory, dynamic scheduling, connected assets and operational metrics.
- Architected a conversational assistant over a 3D digital twin on CUDA-accelerated NVIDIA AI Factory, Omniverse and NeMo agents; piloted across four US and UK plants.
- Two invention disclosures: hierarchical clustering for predictive maintenance in low-data regimes, and hybrid RAG for supply chain.
GTC · CESWork shown at NVIDIA GTC DC 2025 and CES 20264 → 150+Pilot plants to global rollout plan -
- Led the enterprise strategy workstream for sustainable mining, reporting into the Group President's office.
- Built the TAM/SAM framework with AI-driven adoption modelling across diesel, electric and hybrid propulsion.
- Informed M&A and venture decisions in autonomy and digital mine software; prepared board-level and Investor Day material.
$3B+Market opportunity identifiedBoardLevel strategy and capital-allocation work -
- Architected an OEM-agnostic data platform linking 10+ subsystems: battery trucks, fast chargers, energy storage, fleet and energy management.
- Negotiated interoperability and encryption protocols with OEM partners, enabling commercial data services.
- Shipped Early Learner dashboards with agentic workflows and natural-language query for the top five global miners.
10+Subsystems on one data platform60%Less analyst reporting time -
- Co-led the enterprise supply resiliency digital thread: scenario planning and risk across 1.5M parts, 7,000+ suppliers and $20B of annual spend.
- Designed ChatDocs, a GenAI platform (LLMs, hybrid RAG, fine-tuning) that extracts terms from contracts, quality certificates and risk reports.
- Led 12 Purdue Data Mine students building predictive risk analytics over 110+ datasets for 180 countries.
$1M+Annual savings from ChatDocsCEO AwardTop 1% of 120K+ employees -
- Built graph neural networks that predict reaction impurities in small-molecule drug synthesis, and an impurity propagation framework across multi-step routes.
- Adapted inverse structure elucidation from mass spec and NMR, cutting unknown-compound identification from weeks to hours.
- Presented at NeurIPS 2022 (AI for Accelerated Materials Design workshop) within four months; became my MIT Sloan thesis.
$120K+Saved per unknown identificationWeeks → hoursStructure elucidation time -
- Advised by Prof. Rafael Gómez-Bombarelli. Thesis: Designing Macromolecules Using Machine Learning and Simulations.
- Closed AI → synthesis → experiment loops with 30+ researchers across five labs, raising experimental throughput 10x.
- Open-sourced GLAMOUR and Peptimizer; helped secure $2.27M in grants; mentored nine students.
- TEDxBoston speaker on how AI can make rare diseases rarer.
15+Papers · Nature Chemistry, ACS Central Science1US patent application (AI-designed delivery peptides) -
- Built ML models for CRISPRi guide RNA design across all 20,000 human genes, beating the prior state of the art by 20%+.
- Co-developed tooling for automated model building, hyperparameter tuning, architecture search and feature attribution.
20%+Accuracy gain over prior SOTA -
- Scaled from two founders to a 30-person team across the US and India, with full P&L ownership.
- Ran the sales cycle end to end and built the customer success function.
- Flagship RPA/ML product automated customer-inquiry processing for an oil and gas pipeline client.
30Person team built from zero$3M+Annual client savings -
- Institute Gold Medal (rank 1 of 110); finished a four-year degree in three.
- SN Bose Scholar at Harvard (cancer immunotherapy) and OIST fellow (computational genomics).
- Quantitative researcher at EIS Global, Singapore: 20%+ lift in high-frequency trading models.
- Technical Executive at UIDAI (Aadhaar), delivering $3M analytics programs and privacy policy analysis.
Rank 1Of 110, IIT Roorkee
Moments
Ten years, four rooms.
Capabilities
Four disciplines, one operating system.
Selected work
Built, shipped, measured.
Each project below is in production or shaped a capital decision. Numbers are the business outcome, not the model metric.
Recent builds
Still shipping code.
What I build on my own time, in the open. Digital twins, world models and on-device AI, each with a live site or source on GitHub.
Playbook
How I think about data and AI.
A point of view across strategy, data foundations, context, the AI platform, agents, governance, economics, organization, adoption, how programs mature and how to measure them. Ten beliefs, twelve chapters, written to be argued with.
Recognition
On stage and on the record.
Writing
Three running series.
Published weekly on LinkedIn to 23,000+ followers. Browse all essays.
FAQ
Short answers.
Who is Somesh Mohapatra?
Somesh Mohapatra, PhD, MBA, is an AI, data science and product leader. He is Director of Analytics at Caterpillar Inc., where he leads a 90+ person organization building digital twins and agentic AI for global manufacturing. He holds a PhD in machine learning for molecular design from MIT and an MBA from MIT Sloan's Leaders for Global Operations program.
What does Somesh Mohapatra do at Caterpillar?
He is Director of Analytics and Head of Data Science and Product Management for Caterpillar's Modern Manufacturing digital platform, covering predictive maintenance, inventory, dynamic scheduling, connected assets and operational metrics across 150+ facilities. Earlier at Caterpillar he was chief of staff for strategy in the $15B Resource Industries segment, led minesite electrification analytics, and built ChatDocs, a GenAI platform for supplier contracts that earned the CEO Award.
Where did Somesh Mohapatra study?
He earned a PhD in machine learning for molecular design at MIT (2018 to 2022), advised by Prof. Rafael Gómez-Bombarelli, a dual-degree MBA from the MIT Sloan Leaders for Global Operations program, and a B.Tech from IIT Roorkee, where he won the Institute Gold Medal as rank 1 of 110.
What is Somesh Mohapatra's research about?
His research applies machine learning to molecules, materials and manufacturing: graph representations of macromolecules (GLAMOUR), deep learning for peptide design and fast-flow peptide synthesis, and reaction impurity prediction for drug manufacturing. He has 15+ peer-reviewed papers, including two in Nature Chemistry and several in ACS Central Science, and a US patent application on AI-designed drug delivery peptides.
How does Somesh Mohapatra approach enterprise AI?
His playbook treats data and AI as a multiplier for the business a company already wants to become: start from the business's core learning problem, fix data foundations before models, run one governed AI platform with risk-tiered governance, grant agents autonomy per workflow, and measure decisions changed and value created rather than pilots launched.
What does Somesh Mohapatra speak and write about?
He writes and speaks about enterprise AI, digital twins, agentic systems and AI in manufacturing, supply chain and operations, through his LinkedIn essay series, invited talks and his playbook on data and AI at scale. He is always glad to compare notes with practitioners, researchers and leaders working on the same problems.
Where is Somesh Mohapatra based, and how can I contact him?
He is based in the Dallas–Fort Worth area of Texas and works across the US, UK and India. The best ways to reach him are email (from the contact section of someshmohapatra.in) or LinkedIn at linkedin.com/in/pikulsomesh.
Contact
Let's talk about
Let's talk about
AI at scale.
I am always glad to compare notes with people working on AI in manufacturing, supply chain and operations, and to speak or write about enterprise AI and digital twins.