Details
Mo is a distinguished data science director with over 15 years of unparalleled experience, especially recognized for his adept leadership in harnessing the power of generative AI and large language models (LLM) to foster innovative solutions. Anchored by a solid foundation in data science, Mo has adeptly pivoted towards the avant-garde domains of generative AI, prioritizing the strategic deployment and fine-tuning of LLMs to not only meet but surpass organizational ambitions. His proficiency in morphing complex datasets into actionable insights, chiefly through the application of generative AI technologies, has markedly improved decision-making processes and operational efficiencies. Mo's pioneering work in the fine-tuning and quantization of open source LLMs demonstrates an unwavering commitment to amplifying the performance and utility of generative AI across a multitude of sectors.
With an esteemed academic tenure as a former professor at the University of Maryland and adjunct faculty at George Mason University, coupled with prolific authorship, Mo's professional journey is richly augmented by his academic pursuits. This amalgamation of in-depth theoretical knowledge and hands-on practical experience in AI technologies distinguishes Mo as an innovative leader. He is relentlessly dedicated to probing and broadening the horizons of generative AI and LLMs, aiming to revolutionize business landscapes and catalyze growth. Mo's unique blend of skills and experiences solidifies his status as a vanguard in the field, poised to navigate and shape the future of generative AI and its applications.
Location: United States
Expertise
- AI, Machine Learning, Deep Learning
- Cloud Technologies
- FPGA Accelerators
- GPU Accelerators
- High Performance Computing (HPC)
- Performance Tuning & Optimization
Instructor Certification
Machine Learning Using oneAPI