🌍 Zürich, Switzerland | 🎓 MSc in AI @ University of Zürich | 💼 LLM Engineer @ UZH Institute for Computational Linguistics
Highly driven ML engineer with a passion for pushing the boundaries of AI. Currently diving deep into LLMs, NLP, and multi-agent systems. Always eager to learn and contribute to cutting-edge research and development.
🎯 Are you looking for a versatile AI engineer who can:
- Fine-tune and optimize large language models?
- Develop innovative NLP solutions?
- Bridge the gap between research and practical applications?
Then I'm the right person for your role! With experience in academic research and industry projects, I bring a unique blend of theoretical knowledge and hands-on skills to tackle complex AI challenges.
- Fine-tuning large-scale LLMs on Cerebras chips
- Exploring multi-agent simulations with LLMs
- Developing novel approaches in adversarial NLP
Python | PyTorch | TensorFlow | SQL | Git | Hugging Face Transformers | NLP | Time Series Forecasting
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BeamAttack: SOTA adversarial attacks against NLP classifiers (CLEF2024 Competition Submission)
- Published in CEUR-WS (click here)
- Developed a novel algorithm that significantly outperforms traditional greedy search methods in generating adversarial text
- Achieved up to 2.8x and 3.3x improvement in success rates against BiLSTM and BERT models, respectively
- Implemented adaptive beam search techniques to balance attack effectiveness and computational efficiency
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Distilling Chain-of-Thought Reasoning: Enhancing task-specific workflows
- Created an innovative graph-to-text pipeline by breaking down complex tasks into manageable stages
- Implemented knowledge distillation to incorporate chain-of-thought reasoning into smaller, more efficient models
- Leveraged synthetic data from LLMs to improve performance in low-resource settings
- Achieved a 9.22% increase in BLEU score and 12.31% improvement in TER
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Metaphors Unveiled: Exploring LLMs for figurative text interpretation
- Evaluated various LLMs (FLANT5 large, Mistral, Phi1.5B) on their ability to interpret and simplify figurative language
- Combined sequence-to-sequence fine-tuning with advanced prompt engineering techniques
- Boosted accuracy from 61.5% to 71.4% through optimized prompt design
- Conducted human evaluations to provide insights beyond automated metrics
Open to full-time opportunities in ML/AI research and development roles! If you're seeking a dedicated AI engineer who can drive innovation and deliver results, let's connect!