Second-year AI student at Inland Norway University of Applied Sciences, currently on exchange at UC Berkeley. I build ML systems that work under real-world constraints — crash-prone hardware, tight budgets, noisy quantum backends — and I publish the results honestly, including when things don't work.

Papers on arXiv:

• Hidden Device Heterogeneity in Constrained ML Deployment — PyTorch's INT8 quantization silently switches from GPU to CPU, creating 39% feasibility flip rates. (submitted April 2026)
• Feasible-First Exploration for Constrained ML Deployment Optimization — Crash-aware TBA→TPE hybrid optimizer. (on hold, April 2026)

• Expanding multi-GPU benchmark results for the TBA deployment optimizer (H100, A100, RTX 5080, L4, T4)
• Waiting on D-Wave LaunchPad QPU access to complete the quantum annealing benchmark
• Coursework at UC Berkeley (CS 61C, concurrent with research)

Enrolled:

• CS 61C — Great Ideas in Computer Architecture
• EECS 127 — Optimization Models in Engineering
• ENGIN 183 — Technology Innovation and Entrepreneurship
• ASTRON C12 — The Planets (astrophysics breadth)

Auditing:

• CS 152/252A — Computer Architecture and Engineering
• CS 170 — Efficient Algorithms and Intractable Problems
• CS 185/285 — Deep Reinforcement Learning, Decision Making, and Control
• CS 61B — Data Structures

Focus areas: systems architecture, optimization theory, and deep RL — chosen to complement my ML deployment research with hardware-level understanding and formal optimization foundations.

ML systems and deployment optimization — How do you find the best inference configuration (backend, quantization, batch size) when most of the search space crashes or violates constraints? I built a two-phase optimizer (Thermal Budget Annealing → constrained TPE) that treats crashes as data and maps feasible regions before exploiting them.

Quantum-classical benchmarking — Fair comparisons between classical simulated annealing, D-Wave quantum annealing, and QAOA on IBM hardware. I design standardized solver interfaces and report negative results when the hypothesis doesn't hold.

Agentic AI and applied CV — LLM-powered tool-calling agents for domain-specific automation, and competition-grade object detection pipelines with ONNX inference and ensemble methods.

Constrained-ML-Deployment Two research papers sharing the DeployBench infrastructure. (1) TBA: crash-aware two-phase optimizer for constrained ML deployment. (2) Hidden Device Heterogeneity: empirical study showing INT8 dynamic quantization silently moves inference to CPU, creating stochastic feasibility boundaries. 2,150 measurement trials, 5 GPU types, full reproducibility.

SLO-Guard SLO-Guard: Crash-aware autotuner for LLM serving. Optimizes vLLM configs (batching, memory, quantization) under hard latency/memory SLOs. Treats crashes as data — learns feasibility boundaries to avoid wasted trials. Two-phase TBA→TPE hybrid, curl-based benchmarking, 5 optimizer baselines. Built on top of Constrained-ML-Deployment research. First results: 8/15 configs feasible on A100, best config 443ms avg latency.

deploy-agent Productized version of TBA. CLI + FastAPI dashboard + MCP server for automated ML deployment optimization. Give it a model and hardware constraints, it searches backends/quantization/batch sizes and returns the best feasible config with full evidence. Crash handling, structured JSON logs, live WebSocket charts.

dwave-benchmark Classical SA vs D-Wave quantum annealing on Max-Cut and spin glass problems. Phase 1 complete: all classical solvers converge to identical solutions up to n=500 with zero quality gap. Phase 2 (QPU) pending D-Wave access. Common solver interface, reproducible seeds, timing analysis showing Neal SA ~400x faster than pure Python SA.

qaoa-benchmark Negative-result study: budget-aware classical optimizers vs COBYLA/SPSA for QAOA parameter tuning under noisy simulation. Finding: shallow QAOA landscapes are too smooth — COBYLA with fixed defaults matched or beat the learning optimizer. 375 total runs, 3 graphs, 5 budget levels, 5 seeds.

Norwegian-AI-Championship NM i AI 2026 competition entry (Team INNBerkeley). Three tasks: YOLOv8x object detection with ONNX inference, multi-scale TTA, and WBF ensembling; a FastAPI accounting agent using Gemini 2.5 Flash + Tripletex API; and an A* pathfinding agent for Norse world prediction.

ML2-Exam Machine Learning 2 exam work.

Python, PyTorch, vLLM, ONNX Runtime, Optuna, Qiskit, D-Wave Ocean SDK, FastAPI, Docker, LaTeX, SciPy, NetworkX, Matplotlib, NumPy, scikit-learn, Qiskit Aer, Google Colab

UC Berkeley (exchange 2025–2026) · INN Norway (home institution)