activity
20212026
most citedOn The Reliability Of Machine Learning Applications In Manufacturing Environments

9 citations · 15 across the 6 of their papers we have counts for

collaborators

7 papers

cs.SE2026

Engineering Sustainable Agents: A Systematic Comparison of Agentic LLMs for Developer Workflows

Merve Astekin, Yan Naing Tun, Arda Goknil +5

Large language models (LLMs) are increasingly used in software engineering, including agentic systems that coordinate multiple agents, but impose higher computational and environme…

cs.CY2026

Wasted large language models: A life cycle thinking approach

Erik Johannes Husom, Maria Emine Nylund, Ophelia Prillard

Large Language Models (LLMs) are machine learning (ML) models that have an increasingly large carbon footprint through their development and use. Efforts to increase the energy eff…

cs.LG2026

Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequences

Arash Nikzad, Sasan Sarbishegi, Ali Dasmeh +8

How can an agent build a structured map of its world from nothing but an ongoing sequence of raw sensory input and its own movements, especially when natural variation means exact…

cs.CY2026

Overview over the first decade of LIMITS

Maria Emine Nylund, Erik Johannes Husom, Ophelia Prillard

Computing within limits is a promising field, that follows principles of a) questioning endless growth narrative, b) considering and preparing for models of scarcity and c) reducin…

cs.CY2025★ 2 cited

Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency

Erik Johannes Husom, Arda Goknil, Merve Astekin +5

Deploying Large Language Models (LLMs) on edge devices presents significant challenges due to computational constraints, memory limitations, inference speed, and energy consumption…

cs.CY2024★ 4 cited

The Price of Prompting: Profiling Energy Use in Large Language Models Inference

Erik Johannes Husom, Arda Goknil, Lwin Khin Shar +1

In the rapidly evolving realm of artificial intelligence, deploying large language models (LLMs) poses increasingly pressing computational and environmental challenges. This paper…

cs.LG2021★ 9 cited

On The Reliability Of Machine Learning Applications In Manufacturing Environments

Nicolas Jourdan, Sagar Sen, Erik Johannes Husom +3

The increasing deployment of advanced digital technologies such as Internet of Things (IoT) devices and Cyber-Physical Systems (CPS) in industrial environments is enabling the prod…