Anindita Nath

As artificial intelligence continues to transform research, healthcare, and society, the need for inclusive leadership has never been more urgent. This talk examines the evolving role of women in AI through the lenses of leadership, visibility, and purpose. Drawing on a background in data science, health AI research, and STEM mentorship, it offers a broader reflection on what it means to build an impactful career in AI while navigating questions of representation, credibility, and belonging.

In technical spaces where women are still underrepresented, visibility is closely tied to influence, opportunity, and leadership. This session explores how meaningful impact in AI is shaped not only by technical excellence but also by communication, mentorship, advocacy, and a commitment to opening pathways for others. It highlights the importance of building careers that extend beyond individual achievement to include community, responsibility, and service, particularly in fields where technology increasingly shapes human health and well-being.

By connecting professional growth with inclusion and intentional leadership, the talk positions AI as both a field of innovation and a space for cultural and social influence. Intended for students, researchers, and professionals, this session invites reflection on how women in AI can move beyond mere participation to help shape the field's future with purpose, integrity, and lasting impact.

Biography:

Anindita Nath is an accomplished Data Scientist with over 10 years of research and industry experience building AI/ML solutions that transform complex data into actionable insights. Her expertise spans generative AI, large language models (LLMs), NLP, deep learning, and scalable data platforms, with over two years focused on biomedical and public health informatics. She specializes in developing reliable, measurable systems that reduce manual effort and support decision-making in high-stakes environments.

A key achievement in her recent work is co-leading an agentic deep-research evaluation initiative in the public health space, where she engineered prompts and designed evaluation frameworks across multiple domains, contributing to early efforts in responsible adoption of agentic AI.

She has led AI-driven data modernization efforts and built LLM-enabled tools for metadata retrieval, analysis, and visualization at scale, including solutions that reduced manual analysis time by over 50%. Previously, she led GENEVIC, an Azure OpenAI-enabled copilot for genomic data exploration, and contributed to research in proteomics, multi-omics integration, and single-cell analysis.

Her work was recently honored with an Executive Director’s Citation from the American Public Health Association (APHA) and was previously named a Generation Google Scholar for her notable contributions. She publishes and presents on topics such as health AI, bioinformatics, and speech/multimodal interaction, with first-author papers in ACM, Bioinformatics, and AMIA.

Beyond her technical contributions, she is deeply committed to mentorship, STEM advocacy, and digital literacy through work with Microsoft TEALS, Microsoft TechSpark, Girls Who Code, MINT, CodePath, AnitaB.org, and IEEE, as well as related initiatives that expand access to computing for women and underrepresented communities and foster inclusivity in the STEM field.