Research Projects

Kinetic Parameter Inference in Metabolic Networks via Latent Space Exploration

1 minute read

Published: April 05, 2025

We present a novel framework to interpret and control the latent spaces of generative neural network models for kinetic metabolic modeling. By perturbing structured latent spaces learned via REKINDLE or RENAISSANCE, our method generates new dynamic models with targeted properties such as specific response times, regulatory bottlenecks, or alternative physiologies, unlocking deeper insight and reusability across metabolic contexts.

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Evaluating LLM Context Compression: Benchmarks, Agents, and Noise

3 minute read

Published: January 10, 2026

How do you tell whether compressing an LLM’s context actually cost you anything? At Compresr I built the evaluation programme that answers that: 12 long-context suites, a sandboxed harness for running coding agents under compression, a 200-task assistant-agent benchmark, and — the part that changed how we read all of it — a measurement of our own judging noise.

Structured Representations for Fine-Grained Text-to-Image Retrieval in Remote Sensing

2 minute read

This thesis introduces a multimodal framework for fine-grained text-to-image retrieval in remote sensing, combining global and structured representations through scene graphs and semantic region embeddings. It addresses the limitations of CLIP-style global embeddings by capturing spatial, semantic, and relational details critical for fine-grained reasoning.

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GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning

1 minute read

Published: November 05, 2025

Conducted at EPFL dlab with Prof. Robert West; first author (equal contribution), published in Findings of EMNLP 2025. We propose GRAD, a dynamic demonstration-based approach where an LLM model is trained to generate input-specific concise demonstrations. By tailoring demonstrations to each input, our method offers better contextual support than traditional RAG approaches.

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GemmaEdu: Enhancing Scientific Learning via Fine-Tuned Language Models and RAG

1 minute read

We developed an educational chatbot built on the quantized Gemma 2 7B model, optimized with Direct Preference Optimization (DPO) and enhanced with Retrieval-Augmented Generation (RAG). By leveraging fine-tuning on student-generated preference data and incorporating relevant external documents, our system significantly improves accuracy in answering STEM multiple-choice questions, outperforming baseline models like Mistral and Llama2.

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From Novice to Expert: Dimensionality Reduction and Policy Distillation in Reinforcement Learning for Motor Control

2 minute read

This project investigates how to accelerate motor skill acquisition in reinforcement learning using curriculum-based learning, dimensionality reduction, and policy distillation. Using the Myosuite Baoding balls task, we explore how expert policies can be transferred to novice agents via PCA-reduced feature and action spaces, offering an efficient alternative to prolonged training times.

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Learning-Based Multi-Robot Lane Navigation: Scalable Trajectory Prediction using Neural Networks

1 minute read

This project was conducted at DISAL, EPFL. We explore trajectory generation for multi-robot navigation using neural networks. We propose a scalable alternative to Webots simulation by training models using graph neural network, reinforcement and imitation learning. The final approach produces accurate trajectories in a lane-based environment, balancing precision and efficiency in robotic control.

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