MCiM Mentor Spotlight

New York, NY

Citizenship

United States

Education

BA (Economics), Wellesley College 

MS (Policy & Management), Harvard University

DPhil/PhD, University of Oxford

Current Role

Generative AI Strategy and Acceleration Lead, Amazon Web Services (AWS)

You have an impressive background in AI, data science, and technology innovation across various industries. Could you share what initially drew you to this work and how your journey led you to your current role?

 I’ve always had a deep curiosity about emergent technologies and how technology could drive transformation across various fields. Through both rigorous academic training and an extensive interdisciplinary background in technology, innovation, finance, and healthcare in the past, I was always intrigued by the intersection of tech, strategy, and human needs. Coupling curiosity with problem solving, technology offers various tools to develop solutions that make an impact. Early on, I worked in industries where I could see firsthand how innovation was reshaping traditional practices, and I was particularly drawn to how AI had the potential to create new efficiencies and insights that were previously unimaginable.

My transition into generative AI specifically came as this technology began to gain traction. The transformational potential is exponential. The ability of generative AI to create, analyze, and generate insights seemed like a natural evolution in my path—bridging the gap between my interest in cutting-edge tech and my drive to solve complex, real-world problems. It’s been a journey of constant learning, adaptation, and excitement as generative AI is transforming industries from medicine, education, finance, and beyond. I’m thrilled to be contributing to this shift and ultimately helping shape the future across industries.

As a mentor in the MCiM program, how do you approach guiding students who are just starting to navigate the complex applications of AI and technology in healthcare?

Given my background in both technology and finance, I guide students by aligning technology with healthcare economics as they consider the problem for which they’re solving. It’s critical to understand how AI and technology can impact the patient or end-user as well as healthcare costs, efficiency, and revenue. Break down how predictive modeling, workflow automation, and data analytics can create value by optimizing resource allocation, reducing readmissions, or predicting patient outcomes. This framing helps students see the economic rationale behind tech investments in healthcare. I also encourage students to build technical fluency and financial literacy by recommending they build foundational knowledge in AI methods like machine learning, NLP, and data visualization as well as economic concepts around cost-benefit analysis, ROI calculations, cost control, and scalability, which are vital when evaluating potential AI projects. Understanding how these two areas overlap helps students approach problems with both a technical and economic lens.

As healthcare increasingly shifts to value-based models, I try to explain how AI-driven technologies can support this transition. For instance, predictive analytics can help identify high-risk patients for preventive care, which can align with value-based incentives. Discussing these models gives students insight into how AI can advance both patient outcomes and financial goals. It’s also crucial for students to understand the legal and ethical implications of AI in healthcare, especially with regard to privacy laws and the protection of patient data. I integrate into discussion the importance of transparent, accountable AI practices, and how technology can inadvertently lead to issues like algorithmic bias or discrimination. A helpful tool to learn from is the exploration of pragmatic case studies reviewing successes and failures of real-world cases. I suggest focusing on their technical, financial, and ethical implications as well as how these initiatives were funded, scaled, and how financial models were used to justify their costs.

Lastly, I strongly encourage entrepreneurial thinking. Given the transformative nature of AI in healthcare, I guide students to think like entrepreneurs. How can they create value through AI solutions in a healthcare environment? Help them explore venture capital, start-up opportunities, and business models in the healthcare tech space. Aligned with this, I recommend cross-functional networking so that students connect with medical, tech, and finance professionals to hear firsthand about the complexities of launching AI solutions in healthcare. Industry exposure can help students bridge their theoretical learning with practical, economically sound strategies for real-world implementation.

You have been a mentor with MCiM since before it was launched as a program at Stanford. How have you seen the landscape change, and has that impacted your role as a mentor to our students?

Over the past 3.5 years, medical informatics has evolved rapidly with advancements in AI and Generative AI, creating new opportunities and challenges in medicine. Generative AI Models are transforming data analysis given the availability of Large Language Models (LLMs) for text analysis and synthesized clinical data for research. Generative AI has significantly improved diagnostic capabilities, particularly in imaging, where models also allow for creating new imaging data, which can help train algorithms in detecting rare conditions. AI models are increasingly used to analyze patient history, genetic data, and lifestyle factors to predict health risks and recommend preventive care. This shift has transformed the focus from reactive to proactive healthcare, where informatics tools provide real-time insights for individual patient care. There are now more enhanced patient - provide interactions with tools such as conversational AI for patient support along with increased language and cultural accessibility. As AI’s role in clinical decision-making grows, so does scrutiny from regulatory bodies. For example, many institutions have implemented AI governance frameworks to ensure ethical and safe use of generative AI in healthcare. There is a heightened focus on preventing algorithmic bias and ensuring that AI tools do not disproportionately harm certain demographic groups. This has led to more research and investment in making models explainable, fair, and transparent. To protect patient privacy, generative AI techniques have advanced de-identification methods, allowing researchers to share and analyze data while reducing the risk of re-identification. Generative AI models have also helped improve clinical workflows by predicting patient inflows, optimizing staffing levels, and managing supply chains, helping healthcare facilities reduce costs and improve efficiency. There has been significant integration with wearable and IoT data for patient monitoring and data synthesis. With the growth of wearable devices, generative AI has helped synthesize data from these sources, allowing healthcare providers to get a more holistic view of a patient’s health outside clinical settings. These AI systems analyze trends in vital signs, activity levels, and other data to predict and prevent potential health issues. The past 3.5 years have seen medical informatics evolve from being a data-oriented discipline to one where AI, especially generative AI, is a core part of the healthcare ecosystem. This evolution is pushing the boundaries of patient care, diagnostics, and healthcare management, and we need to evaluate new challenges around ethics, equity, and data security.

What is most rewarding about working with our students in the mentorship program? Can you share any memorable experiences or insights gained from these interactions?

One of the most exciting and rewarding aspects of working with students in the program is witnessing the conversion of their idea and strategy into an executable plan that is deliverable. Each cohort has a highly impressive, incredible group of students, who are leaders and problem solvers. Seeing how they grow in their understanding and skills over the course of the program is fulfilling to watch. Helping students cultivate the connections and bridges between their medical and/or healthcare contextual backgrounds with technology and financial / economic implications is a big part of how I see my role. They are proactively applying tech concepts to solve real-world medical challenges and persist with results-oriented solutions, and observing their developmental process toward making a substantive impact is a special process.

A particularly memorable experience was working with a student on their practicum, which was developing a prototype and business plan for a start-up business. They were passionate about using data science and machine learning to improve patient outcomes, but struggled to translate that vision into actionable steps that were marketable to potential investors. As they progressed, their ability to communicate complex ideas clearly improved, and they developed a strong, clear plan for a tool to support clinical decision-making with a robust business plan to support execution. By the end, they felt ready to present it to healthcare professionals and potential investors.

Mentorship is as much about the human element as the technical. Empathy, listening, and understanding each student’s unique perspectives and challenges make a major difference. Every student has a distinct journey, and the combination of support, constructive feedback, and encouragement can lead to transformative outcomes for them. Watching the real-world impact these amazing students will have over time is the best result of all. It has been an honor to serve as a mentor to this incredible program for several years.

Given your extensive experience in the AI and technology sectors, what advice would you give to students who aspire to work in these fields? How can they prepare for the future of this rapidly evolving industry?

Remember that technology is a tool, which could provide solutions to a myriad of problems. Be clear about your problem statements. What are you solving for? That’s number one. Then, what tools or solutions are optimal to solve that problem? Be prepared to have alternative strategies and adapt as needed throughout your learning and development process to execute on your vision. Also start by building a strong technical foundation. Develop proficiency in data science, machine learning, deep learning, and natural language processing, with a focus on medical and healthcare-specific applications. Gain data engineering knowledge, since medical informatics requires managing large, complex datasets. Understand how to preprocess, manage, and scale data. It’s also important to understand the context in which you’re operating. For healthcare, get familiar with clinical and operational challenges. The rapid pace of change in the space demands that you stay informed on healthcare regulations. Privacy and data security regulations including HIPAA in the U.S. and GDPR in Europe are fundamental. Understanding these frameworks is crucial for developing compliant and ethical AI systems in healthcare. Consider responsible AI, especially when working with patient data, which demands a commitment to ethical practices. Familiarize yourself with key concepts in responsible AI, such as transparency, fairness, and bias mitigation. Many healthcare organizations prioritize these principles, so being well-versed in ethical AI can give you a strong advantage. Aim to understand AI explainability. Clinicians often need to trust and understand AI outputs. Learn techniques for model interpretability and explainability; aiming to balance model performance with transparency.

In order to cultivate interdisciplinary collaboration skills, work with clinicians and engineers. Be adept at communicating technical insights to non-technical stakeholders. To stay on top of industry trends, research AI and medical journals, industry content, and conferences. Commit to ongoing education through platforms that offer courses on advanced AI, responsible AI, and other topics that can keep skills sharp. Apply those learnings to hands-on pragmatic opportunities through real projects with real data. Seek projects involving real healthcare data, as these provide invaluable learning opportunities and allow you to apply knowledge, learn about deployment challenges, and gain exposure to healthcare workflows. Overall, remain agile with emerging tech. With the rise of generative AI and image generation models, there will be more applications in diagnostics, patient engagement, and data analysis. Embracing these new technologies will give you an edge as healthcare increasingly adopts them.