Ali Beikmohammadi
I build AI systems that have to work twice: rigorously enough to publish, reliably enough to run in production.
Stockholm, Sweden
Google Scholar · 30 Oct 2026
About
I am an AI Tech Lead at Sandvik / Seco Tools and a PhD candidate in Computer and Systems Sciences at Stockholm University, specialising in reinforcement learning, deep learning and federated learning. I work at the point where research and production meet — and I find that point more interesting than either side on its own.
My thesis, Toward Sample-Efficient Reinforcement Learning, asks how agents can learn efficiently when prior knowledge is available, and how federated and distributed methods converge when the convenient assumptions fail — without data similarity, under compressed or clipped gradients, and under model mismatch between clients.
In industry I take that work to production: computer-vision systems for industrial defect detection, LLM pipelines generating content for 35,000+ products, and agentic assistants serving customer, sales, HR and shop-floor users, along with the MLOps practice that keeps them running.
As AI Tech Lead I now own how those systems are designed, built and operated — including the governance and Responsible AI practice around them — and the role formally includes guiding academic collaboration and Master's thesis work, which keeps both halves of my work connected rather than parallel.
I ranked first in my cohort in both my Bachelor's and Master's studies in Electrical Engineering, at Bu-Ali Sina University and Amirkabir University of Technology respectively. I spent time as a visiting PhD student with the Artificial Intelligence and Machine Learning group at Universitat Pompeu Fabra in Barcelona, and I have collaborated with SCANIA CV AB, Hitachi Energy, SINTEF AS and KTH.
Teaching matters to me: I have taught or assisted on 14 courses and supervised more than 30 Master's theses. What I care about most, though, is the part in between research and delivery — turning an ambiguous problem into something a team can actually build, deploy and maintain. I am always open to research collaboration, teaching, and applied AI work.
Research
Reinforcement learning and distributed optimisation, with an emphasis on efficiency and guarantees.
- Sample-Efficient Reinforcement Learning
- Federated & Distributed Optimization
- Reinforcement Learning for Control & Cyber-Physical Systems
- Agentic & LLM-Based Systems
Engineering
Production AI systems — computer vision, LLM pipelines, agents and the MLOps around them.
- Automated Tool Wear Inspection
- AI-Generated Product Descriptions at Scale
- Domain-Specific AI Agents
- MLOps Platform Standards
Selected publications
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GICA: The Gap-Index Compositional Arm Framework for Sample-Efficient Test-Time Scaling
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A Cost-Sensitive Transformer Model for Prognostics Under Highly Imbalanced Industrial Data
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Human-inspired framework to accelerate reinforcement learning
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Compressed Federated Reinforcement Learning with a Generative Model
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Accelerating actor-critic-based algorithms via pseudo-labels derived from prior knowledge
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On the Convergence of Federated Learning Algorithms without Data Similarity
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TA-Explore: Teacher-assisted exploration for facilitating fast reinforcement learning
News
- Appointed AI Tech Lead in the AI & Automation Center for Enablement at Sandvik / Seco Tools, taking technical ownership for how AI and automation solutions are designed, built and operated.
- GICA: The Gap-Index Compositional Arm Framework for Sample-Efficient Test-Time Scaling published in TMLR.
- Doctoral thesis Toward Scalable, Robust, and Efficient Reinforcement Learning: Theoretical Foundations and Algorithms for Sample-Efficient, Communication-Aware, and Heterogeneity-Robust RL published in DiVA. Defence 16 October 2026.
- GICA: The Gap-Index Compositional Arm Framework for Sample-Efficient Test-Time Scaling accepted at TMLR.
- Active Inference for Adaptive Traffic Signal Control in Noisy Nonstationary IoT Environments accepted at IEEE WF-IoT 2026.
- A cost-sensitive transformer model for prognostics under highly imbalanced industrial data published in Cluster Computing.