Cenk M. Yetis, PhD

Staff AI/ML Engineer · Edge AI & Efficient Models

I build compact neural models — video and audio codecs, speech models, and small language models — that run on-device within real memory, latency and power budgets.

Download CV · LinkedIn · GitHub 

30 peer-reviewed papers, 1400+ citations · Google Scholar

My career has one thread: getting the most out of constrained systems. For 15+ years I designed wireless and signal-processing algorithms in industry and research labs, where bandwidth, latency and hardware limits were the whole problem. Today I apply that discipline to deep learning: knowledge distillation, quantization and pruning for edge devices, neural codecs, and hybrid edge–cloud systems that route between small and large language models. I'm at my best where model design meets deployment constraints.

• 20% → 3% image degradation under channel errors with an end-to-end learned transmission model. Related ideas appeared in a Huawei satellite image-sharing feature.

• 1/8 the compute of brute-force search, at equal performance, for deep-learning-based beam selection (Lund University / WASP).

• Transformer-based neural audio codecs for low-bitrate transmission over unreliable links, plus edge–cloud SLM/LLM orchestration (Huawei).

• Compact neural video codec and speech models for edge AI using distillation, quantization and pruning (Analog Devices).

What I do

Edge AI & model efficiency: knowledge distillation, quantization, pruning, tinyML and small language models, low-latency C++ deployment, hardware–software co-design  

Audio, speech & video: neural audio and video codecs, speech-to-speech, ASR/TTS, keyword spotting, acoustic event detection  

LLM & agentic systems: edge–cloud SLM/LLM orchestration, multimodal RAG, LangChain/LangGraph agents  

Signal processing & wireless: 15+ years of PHY-layer algorithms (beamforming, channel estimation, semantic communication), PhD in Communication Systems