WAI USA Research | Women in AI (WAI)

WAIRESEARCH USA

WaiResearch USA is the research arm of Women in AI USA (WAI USA), created to open doors for aspiring and established researchers to explore, collaborate, and contribute meaningfully to the field of Artificial Intelligence. Rooted in community, inclusion, and real-world impact, WaiResearch USA empowers women and allies to advance AI research that matters.

Through WAI USA Research Labs, we support research across a wide range of AI topics that align with the expertise, interests, and lived experiences of our community. Our work spans exploratory research, applied studies, and policy-relevant investigations, always with a focus on producing tangible, practical outcomes that benefit society.

WaiResearch is not just about publishing papers—it’s about building pathways. These pathways help transform curiosity and expertise into funded research, conference participation, and real-world implementation.

Our research team plays a central role in this mission. Team members collaborate on submitting research grants on behalf of WAI USA, contribute to conferences and workshops, and represent the community across global AI research forums. Together, they form a multidisciplinary, globally-minded group committed to ethical, inclusive, and impactful AI.

WaiResearch is where research, community, and impact meet, supporting women in AI to not only study the future, but actively shape it.

Meet the Team

Dr. Aditi Singh

Research Team Lead

Dr. Aditi is an AI researcher focused on Large Language Models, Generative AI, and Agentic AI, with a strong emphasis on inclusive and responsible AI. She serves on conference program committees, contributes as peer reviewer, and holds leadership roles within ACM-W. A WAI Awards North America 2025 (Research) finalist, she has received honors including IEEE Best Paper, Best Presenter, and a Gold Medal in Computer Science. At WAI, she supports major grant proposals, contributes to developing a shared research knowledge repository, and advances women’s leadership in inclusive AI across the US.

Akshata Kishore Moharir

Research Team Member

Akshata is a Lead Data Scientist with 12+ years of experience across Support, Gaming, Retail, and Aviation, specializing in Generative AI, Responsible AI, Explainable AI, NLP, and predictive systems. She has hands-on expertise in fine-tuning Large Language Models and holds 7 patents, a trade secret, and publications in ICML, NeurIPS, IJCAI, Springer, ICDM, and IEEE. A Responsible AI (RAI) 2025 North America award winner, she is known for leading cross-functional teams and developing human-in-the-loop, interpretable ML/AI models, combining technical depth with strategic vision to build scalable solutions that deliver measurable business value.

Dr. Srimonti Dutta

Project Manager

Dr. Srimonti leads global, cross-functional initiatives in responsible AI and multi-stakeholder collaboration. Her work spans applied AI systems across interdisciplinary domains such as engineering, energy, sustainability, policy, and climate, including an award-winning AI-driven environmental early-warning system at InnovateFPGA Americas. A former IEEE Women in Engineering Chairperson, she has advanced women’s leadership in STEM while partnering with global organizations to deliver innovative, ethical, and high-impact AI solutions at scale across diverse industrial sectors.

Dr. Ratna Nirupama

Research Team Member

Dr. Ratna’s research applies Natural Language Processing to study how everyday language reflects and shapes emotions, behavior, and well-being. By integrating computational methods with psychology and linguistics, her work aims to advance understanding of the connections between human language and experience. Her work has been published at NeurIPS, ICML, and ICDM. She is the Lingathon 2024 winner. At WAI, she collaborates on research in mental health and cultural AI.

Shreeya Dhakal

Research Team Member

Shreeya is an Applied Scientist specializing in Machine Learning and NLP, with a focus on multilingual and low-resource languages. She develops models that extend modern AI systems to underrepresented languages and shares research insights on multilingual NLP research, including the architecture and foundations of LLMs, the complexities of cross-lingual transfer and grammar, through her blog, icodeformybhasa.com(#icodeformyभाषा!). Her work covers LLM architectures and cross-lingual transfer.

Naga Harini Kodey

Research Team Member

Harini is a Principal QA Engineer with expertise in Quality Engineering and Machine Learning testing, specializing in validating predictive and data-driven AI systems. Her work focuses on testing and strengthening predictive analysis technologies. She explores challenges in ML testing and actively shares insights through talks, publications, and mentorship, where she focuses on the future of ML testing, scalable QA automation, and bridging the gap between quality engineering and intelligent systems.

Swati Tyagi

Research Team Member

Swati's work focuses on AI/ML, data science, responsible AI, LLM evaluation, and MLOps. She has developed open-source tools for generative model evaluation, contributed to ethical AI research, and regularly mentors at global hackathons. Her expertise lies at the intersection of scalable AI systems and fairness in machine learning. Swati is an IEEE Senior Member, Forbes Tech Council Member, published author, hackathon judge, and founder of a nonprofit AI mentorship platform.

WAI USA Research Labs

1. Community-Driven AI Research

Supports applied and foundational AI research aligned with the technical expertise and interests of the WAI community, spanning machine learning, generative AI, AI systems, and responsible AI.

2. Interdisciplinary Lab Model

Operates through topic-focused research labs that bring together researchers, engineers, domain experts, and external partners to collaboratively design, experiment, and validate AI solutions.

3. Rigorous Technical Methodology

Emphasizes reproducible research practices, empirical evaluation, benchmarking, and real-world testing, including model performance, robustness, fairness, and scalability.

4. Responsible & Impact-Focused AI

Integrates ethics, transparency, privacy, and governance into the research lifecycle, ensuring AI systems are trustworthy, compliant, and socially beneficial.

5. Pathways from Research to Deployment

Enables translation of research into tangible outcomes such as open-source tools, technical reports, prototypes, policy insights, and deployable AI systems with real-world impact.

Join WAI USA Research Team

We actively enable community members to gather around shared AI-related topics, form research cohorts, and study the societal impact of AI in structured, collaborative ways via our Research Interest Form.

Research Interest Form