安若鵬 (An Ruopeng), 博士

ニューヨーク大学

Constance and Martin Silver Endowed

Professor in Data Science and Prevention &

Director of the Constance and Martin Silver

Center on Data Science and Social Equity

Transition Economy Program(2006年修了)

Please tell us about your career path so far. What is your area of specialization and how did you come to work in this area?

I've been on a long and winding yet always exhilarating road in public policy, public health, and data science. My academic journey began at Peking University in China, where I studied Political Science and Public Administration. This grounding in social sciences piqued my interest in how policy decisions and societal contexts shape everyday life. Soon after, an IMF Scholarship gave me the opportunity to study at GRIPS in Japan from 2005 to 2006. That year in Tokyo broadened my horizons in ways I never anticipated--it was a bustling environment where I came to appreciate the integration of empirical evidence and policy-making, in particular, under Prof. Wade Pfau's supervision.

Eventually, I pursued my PhD in Policy Analysis at the Pardee RAND Graduate School in the United States under Prof. Roland Sturm's supervision, diving deep into the economics of healthy eating, obesity prevention, and beyond. Over time, my research broadened from public health nutrition and obesity prevention to more extensive population health issues, all with a firm policy lens. In recent years, I've been fascinated by the potential of artificial intelligence and data science to tackle the same problems from fresh angles. As I see it, these fields aren't just about algorithms or numbers--they're about shaping interventions and policies that can improve people's lives.

This blend of policy, public health, and AI has come to define my specialization. I'm particularly passionate about social-ecological perspectives on obesity and chronic disease prevention, bringing forward data- and evidence-driven solutions. By applying advanced analytics, I hope to provide decision-makers with robust evidence that can lead to more equitable and sustainable health outcomes for communities worldwide.

Have you always wanted to pursue a career in research? What attracts you most about it, and what keeps you inspired?

Interestingly, my aspiration to become a researcher wasn't always set in stone--initially, I was drawn to the public sector and considered working directly for government agencies. But once I experienced firsthand the thrill of uncovering insights through data and analysis (particularly at RAND, where I engaged in real-world policy cases), I found myself hooked on the process of discovery.

Research challenges me to remain curious, to peel back layers of complexity, and to test assumptions about how the world works. Whether I'm running a cost-effectiveness simulation for a school nutrition policy or training a large language model to analyze social media data, I'm constantly in awe of how data can transform abstract ideas into concrete evidence for change.

But what truly keeps me going is the human element behind every dataset. When I see how a seemingly small policy tweak--like adding water dispensers in schools--can encourage kids to develop healthier habits for life, that's the kind of feedback loop that fuels my next question, my next study, my next push to innovate. The merging of social impact with empirical rigor makes research not only intellectually fulfilling but also personally meaningful.

You have recently been appointed as Professor in Data Science and Prevention and the Director of the Constance and Martin Silver Center on Data Science and Social Equity at New York University (NYU). What are your immediate research priorities, and how do you see this role advancing your leadership in public health and social equity?

Stepping into this new role feels both exhilarating and humbling. As Director of the Constance and Martin Silver Center on Data Science and Social Equity, my first priority is to establish a coherent research agenda at the intersection of data science, public health, and social justice. One immediate goal is to strengthen our capacity for large-scale data collection and analysis that highlights health inequities--whether it's disparities in access to nutritious foods, or the uneven impact of environmental pollution on marginalized populations.

On a more methodological level, I'm eager to expand our center's expertise in applied machine learning and generative AI. We want to make these advanced tools accessible to policymakers, nonprofit partners, and grassroots organizations so they can quickly translate data-driven insights into on-the-ground interventions. Essentially, I see the Center as both an innovation hub--where cutting-edge AI meets community priorities--and a convening space for collaboration across disciplines.

In terms of leadership, I hope this role will allow me to mentor young researchers and graduate students who share our vision of equitable health outcomes. By co-creating projects and courses, we can train a new generation of data-savvy public health professionals who approach technology with a conscience. The ultimate aim is for our collective work to reduce health disparities and advance social equity on a global scale.

How do you see artificial intelligence helping to reduce health inequities and tackle broader social issues?

I think AI, when developed responsibly, holds tremendous promise in broadening access to health resources and tailoring interventions to people's unique contexts. For instance, natural language models can create health chatbots that offer immediate, reliable guidance to underserved communities--especially for those who struggle to find culturally competent care. Similarly, advanced machine learning algorithms can comb through public health datasets and reveal patterns that might otherwise go unnoticed--like how subpopulations are disproportionately burdened by certain chronic diseases.

Beyond healthcare, AI can help us detect and address systemic inequities in areas like education, housing, or financial services. Imagine a scenario where an algorithm can predict whether a child is at high risk of dropping out due to certain stressors, prompting early interventions to keep that child in school. Of course, I always underscore the importance of ethical guardrails--transparency, fairness, data privacy protections. When AI is aligned with community input and meaningful human oversight, it can become a powerful amplifier for social good.

In short, the potential is there to provide personalized, targeted supports and to optimize how resources are allocated. I see it as a chance to break the cycle of inequality, provided we remain vigilant against the pitfalls and biases that can creep into automated systems.

What are the biggest challenges in using AI and data science to address public health and social equity issues, and how can they be overcome?

The two most immediate challenges that come to mind are data bias and the lack of interpretability. Often, the datasets we rely on are incomplete or skewed by historical inequities--like underrepresentation of certain racial or socioeconomic groups. If we feed these biased datasets into our models, we risk perpetuating the very disparities we're trying to mitigate. Overcoming that means actively seeking diverse, high-quality data sources and adopting fairness checks throughout the modeling process. We also need to work closely with community partners, so we know we're asking the right questions and contextualizing the data correctly.

A second major challenge is bridging the gap between complex AI systems and the policymakers or community organizations that might implement them. Many advanced algorithms--think deep neural networks--can seem like "black boxes." Their lack of transparency can breed mistrust, especially when policy or funding decisions hinge on the model's output. I believe the answer lies in clearer communication and collaboration. Researchers must simplify the technical complexities, while policymakers must engage early to shape how algorithms are built and validated.

Additionally, we can't ignore resource constraints--small local health departments, for example, might not have the infrastructure to run advanced AI. Public-private partnerships, open-source platforms, and capacity-building efforts all have vital roles to play in democratizing AI tools.

What have been the most interesting or rewarding aspects of your career thus far?

I've worn many hats--researcher, educator, mentor, policy consultant--and each of these roles has offered its own rewards. One of the absolute highlights has been seeing how a careful piece of analysis can directly influence policy. Early in my career, I contributed to a study evaluating a national rebate program in South Africa that incentivized healthy food purchases. Not only did we see measurable improvements in people's diets, but the findings also informed broader conversations on how private-public partnerships might tackle obesity. That felt like real, tangible impact--data turned into policy insight that eventually reached people's dinner plates.

Another deeply fulfilling experience has been mentoring students. Nothing compares to watching them grow from novices in data analysis or policy evaluation into confident researchers who can craft their own questions and design their own studies. And, of course, building collaborations across disciplines--public health, AI, social work, epidemiology--has enriched my perspective. These partnerships often spark that "aha" moment where you see that combining different lenses leads to better, more inclusive solutions.

In all honesty, I also find great joy in bridging cultural contexts. My experiences in China, Japan, and now the U.S. have shaped how I approach research questions and community-based interventions. Every new insight reaffirms why I set out on this path in the first place.

Looking ahead, what are your professional goals, and where do you envision your work and contributions in the next five to ten years?

My vision for the coming decade centers on forging truly interdisciplinary collaborations that push the boundaries of AI for health and society. I see myself leading initiatives where computer scientists, epidemiologists, social workers, and policymakers come together around a single table to craft bold new strategies for reducing health inequities. I'd like the Constance and Martin Silver Center on Data Science and Social Equity at NYU to become a global beacon in this domain--where we lead large-scale research projects, publish in top-tier journals, and offer robust training programs for the next generation of data scientists in public health and social work.

I also want to explore innovative methods to make AI ethically grounded and community-informed. There's a lot of emerging research on how to incorporate interpretability techniques or participatory design frameworks directly into the development cycle. My hope is that in five to ten years, we'll have large-scale demonstration projects--ranging from obesity prevention to mental health screenings to social services--that show how technology can truly narrow, rather than widen, the equity gap.

At a personal level, I'm eager to continue building mentorship networks that extend globally. If I can support more students--especially those from underrepresented backgrounds--to flourish in data science and public health, I'll feel I've done my part in shaping a more inclusive and forward-thinking field.

What led you to GRIPS? What is the most important thing you gained from it, and how has your experience at GRIPS prepared you for future endeavors?

I came to GRIPS in 2005 with the help of an IMF Scholarship. I was captivated by GRIPS's mission to mold future leaders through rigorous policy analysis and vibrant international dialogue. The program's focus on empirical, evidence-based approaches to policymaking resonated strongly with me. It wasn't just about theoretical frameworks--professors and classmates brought real policy challenges to the classroom, which taught me how to see beyond textbooks and into real solutions.

One crucial lesson I absorbed was the value of collaboration in tackling complex problems. My peers came from countries across Asia, Africa, and beyond. Through countless group projects, late-night study sessions, and seminars, I learned how different perspectives can mesh to craft more thoughtful policies. That synergy now shapes my current work in interdisciplinary data science teams.

Moreover, my time at GRIPS instilled a sense of global civic duty in me. Observing Japan's own public health landscape--health screenings, robust community programs, advanced technology--showed me practical models that I continue to reference in my projects today. Overall, I credit GRIPS for giving me a rock-solid foundation in policy analysis and for planting seeds of curiosity that spurred me onward to a PhD and beyond.

Have you had any involvement, professional or otherwise, with Japan since your graduation?

From 2006 to 2008, right after completing my study at GRIPS, I was living in Beijing and had the pleasure of hosting several GRIPS professors and alumni when they visited China. During that time, I also stayed in close contact with many fellow GRIPS alumni who were working in various sectors across the country. It was wonderful to keep the GRIPS spirit alive through meetups, collaborative discussions, and joint projects.

However, once I moved to the U.S. for my doctoral studies and subsequent career opportunities, I found it more challenging to stay as actively engaged with GRIPS professors and alumni. The geographical distance and my professional commitments have limited my ability to participate in GRIPS-related events. That said, I'm always eager to reconnect and collaborate whenever the opportunity presents itself, and I hope to rekindle some of those bonds in the near future.

How do you balance your demanding professional responsibilities with personal life, and what do you enjoy doing outside of work?

Balancing academic and administrative duties with a personal life is an ongoing experiment in time management. I've learned to set boundaries around weekends and certain evenings, especially when it comes to my family or personal creative pursuits. I actually find that carving out time away from my laptop makes me sharper and more energized when I return to my work.

As for hobbies, I love exploring big cities on foot--Tokyo is obviously a favorite from my GRIPS days, but New York certainly offers no shortage of places to roam. There's something about the rhythm of walking in a bustling metropolis that helps me decompress and recharge. I'm also an avid reader of science and technology books, which often inspires me to think about how future technologies can intersect with public health and ethics.

What are some of your fondest memories of your time spent at GRIPS?

There are so many--where to begin? One memory that stands out is the late-night study marathons in the student lounge. People often brought snacks from their home countries--Japanese senbei, Chinese herbal candy, Korean kimchi--and we shared everything in that small space, discussing theoretical frameworks while sampling each other's treats. That sense of camaraderie was unmatched.

I also remember the incredible field trips organized by GRIPS. We visited Japanese government agencies, historical sites, and rural communities. These trips highlighted the diversity of Japan's regions, the intricacies of local policymaking, and the historical context behind various reforms. I still recall how kindly local residents welcomed us, sharing personal stories that brought policy debates to life.

More than anything, it was the sense of belonging to a global community of learners. We'd celebrate each other's cultural festivals, exchange words in different languages, and bond over a shared passion for improving the world through policy. It really felt like we were building something meaningful together.

If you could give one piece of advice to anyone considering studying at GRIPS, what would it be?

Go in with an open mind and a willingness to embrace truly international collaboration. It's easy to stay within your comfort zone--maybe stick to people who speak your language or share a similar cultural background--but at GRIPS, you have a rare chance to connect with classmates from every corner of the planet. So my advice would be: step outside that comfort zone and really dive into the diversity of ideas around you.

Also, leverage Tokyo as your living laboratory. Beyond the classroom, you're in one of the most dynamic cities in the world. Take advantage of it--whether it's visiting government offices, exploring different wards and neighborhoods, or volunteering in community health initiatives. The synergy between GRIPS's academic rigor and Tokyo's unique urban environment is what makes the experience so transformative.

How would you like to stay connected with GRIPS as an alumnus? Do you have any suggestions for strengthening the GRIPS alumni network or making it more impactful?

I'd love to reconnect with GRIPS through a virtual talk or perhaps an in-person visit to share my ongoing research at the intersection of AI and social equity. Whether it's walking through my latest findings on data science applications in health equity or discussing potential collaborations, I think these kinds of direct engagements can spark new ideas and meaningful partnerships.

In terms of strengthening the alumni network, career development resources would be incredibly valuable. GRIPS' close ties to the Japanese government and the IMF could provide additional grant-funding opportunities that encourage collaborative research among alumni. For instance, hosting regular conferences, meetups, or symposiums would be a great way to foster dialogue, facilitate mentorship, and inspire collective problem-solving. Through these avenues, alumni not only maintain their sense of community but also benefit from continuous professional growth and collaboration opportunities.