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Sami Nas
Senior Partnership Manager
Professional Background
Sami Nas is a dynamic Exponential Change Leader who possesses a profound commitment to life-long learning and continuous improvement. With an impressive professional background steeped in data science, information technology, automation, and artificial intelligence (AI) techniques, Sami has built a remarkable career primarily focused on transforming businesses through innovative solutions. Known for his adeptness at leading through influence in complex corporate organizations, he has successfully built, developed, and executed corporate-wide strategies designed to drive digital transformation and operational excellence.
Sami's journey spans a diverse array of roles and organizations, showcasing his versatility and depth of knowledge across multiple sectors. His experience as Head of Information Services and Senior Manager in the healthcare industry is particularly noteworthy, as he has propelled numerous initiatives aimed at enhancing operational efficiency and improving patient outcomes through advanced technological applications.
As a Digital Transformation Leader, Sami has been at the forefront of integrating applied AI and machine learning techniques within the healthcare domain, facilitating the development of innovative solutions tailored for clinical and biomedical datasets. He has demonstrated an exceptional ability to manage high-performance teams while focusing on customer success, an essential aspect of ensuring that the transformative strategies he champions align with clients' needs and expectations.
Education and Achievements
Sami's educational background is foundational to his success as a digital transformation expert. He holds a Bachelor of Science degree in Management Information Systems from Boğaziçi University, a prestigious institution known for its rigorous academic programs and emphasis on critical thinking skills. This educational experience equipped Sami with the technical acumen and strategic mindset essential for navigating the fast-paced world of information technology.
Prior to his university education, Sami attended St. Georg Avusturya Lisesi ve Ticaret Okulu (Österreichisches St. Georgs-Kolleg), where he cultivated his analytical skills and passion for problem-solving. These formative years played an instrumental role in shaping his approach to business and technology, allowing him to understand the intersection of management, technology, and innovative solutions.
Achievements
Throughout his extensive career, Sami has amassed a wealth of achievements that underscore his expertise and leadership in the field. His notable contributions include:
- AI Applications in Healthcare: Sami has been pivotal in implementing applied AI and natural language processing techniques in healthcare contexts, resulting in optimized workflows and enhanced patient care methodologies.
- GxP Validation: His knowledge in good practice (GxP) validation significantly contributes to ensuring that technology implementations comply with industry regulations, fostering trust and reliability in the systems put in place.
- High Performance Team Management: Sami's leadership style emphasizes fostering high-performing teams that excel in delivering digital transformation initiatives. His ability to motivate and guide teams has consistently resulted in successful project outcomes.
- Engagement in Start-up Ecosystem: Sami actively engages in the start-up ecosystem, further illustrating his commitment to innovation and collaboration in technology.
- Strategic Managerial Roles: Having held various roles across leading organizations, including John Snow Labs, Amgen, and Philip Morris International, Sami has demonstrated his capability to influence corporate direction and integrate information technology profoundly.
His professional journey is a testament to his dedication, foresight, and strategic vision, making him a formidable leader in the spheres of technology and healthcare.
As he looks to the future, Sami Nas continues to inspire those around him with his passion for creating meaningful change through technology and his belief in the power of knowledge and collaboration to enhance productivity and transform lives.
Highlights
🔶 A Continued Pretrained LLM Approach for Automatic Medical Note Generation →HEAL is introduced, the first continuously trained 13B LLaMA2-based LLM that is purpose-built for medical conversations and measured on automated scribing and surpasses GPT-4 and Med-PaLM 2 and exceeds the performance of human scribes and other comparable models in correctness and completeness → LLMs are revolutionizing NLP tasks. However, the use of the most advanced LLMs, such as GPT-4, is often prohibitively expensive for most specialized fields. We introduce HEAL, the first continuously trained 13B LLaMA2-based LLM that is purpose-built for medical conversations and measured on automated scribing. Our results demonstrate that HEAL outperforms GPT-4 and PMC-LLaMA in PubMedQA, with an accuracy of 78.4%. It also achieves parity with GPT-4 in generating medical notes. Remarkably, HEAL surpasses GPT-4 and Med-PaLM 2 in identifying more correct medical concepts and exceeds the performance of human scribes and other comparable models in correctness and completeness.
⬛ Open Source Language Models Can Provide Feedback: Evaluating LLMs' Ability to Help Students Using GPT-4-As-A-Judge
→ Large language models (LLMs) have shown great potential for the automatic generation of feedback in a wide range of computing contexts. However, concerns have been voiced around the privacy and ethical implications of sending student work to proprietary models. This has sparked considerable interest in the use of open source LLMs in education, but the quality of the feedback that such open models can produce remains understudied. This is a concern as providing flawed or misleading generated feedback could be detrimental to student learning. Inspired by recent work that has utilised very powerful LLMs, such as GPT-4, to evaluate the outputs produced by less powerful models, we conduct an automated analysis of the quality of the feedback produced by several open source models using a dataset from an introductory programming course. First, we investigate the viability of employing GPT-4 as an automated evaluator by comparing its evaluations with those of a human expert. We observe that GPT-4 demonstrates a bias toward positively rating feedback while exhibiting moderate agreement with human raters, showcasing its potential as a feedback evaluator. Second, we explore the quality of feedback generated by several leading open-source LLMs by using GPT-4 to evaluate the feedback. We find that some models offer competitive performance with popular proprietary LLMs, such as ChatGPT, indicating opportunities for their responsible use in educational settings.
