Initializing neural pathways
Human in the loop. Code in the wild.
MSc Computer Science @ RPTU Kaiserslautern
Specializing in Intelligent Systems & Scientific Computing
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Neural pathways forged through industry and research.
We investigate inference time measurements as a proxy to approximate the associated energy costs of API-based LLMs, comparing estimations with actual measurements from locally hosted equivalents. Shows that time measurements allow us to infer GPU models for API-based LLMs, grounding energy cost estimations for end users.
An exploration of the energy and CO₂ costs of web agents (e.g. OpenAI Operator, Google Mariner) from empirical and theoretical perspectives. Shows how different agent design philosophies severely impact energy use — and that more energy consumed does not necessarily equate to better results.
We present HILL, an interactive framework allowing users to incorporate human intuition into the model training loop by reshaping latent space representations. The modifications are infused into training via a novel distillation-inspired approach, treating the user's reshaped latent space as a teacher.
The synapses that power my work.
Whether it's a research collaboration, engineering challenge, or just a conversation about AI — I'd love to hear from you.