WAI RESEARCH USA
Applied AI research and systems development for real-world impact.
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 practical 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.
WAI USA Research Labs
Advancing AI Through High-Impact Research and Execution
WAI Research Labs is a focused environment for building, testing, and deploying advanced AI systems that address real-world challenges. Our work sits at the intersection of rigorous research and practical implementation, with an emphasis on systems and approaches that translate effectively into industry contexts.
We welcome motivated individuals; progression into research tracks is based on demonstrated consistent commitment, capability, clarity of thinking, and execution. Participation in active research is selective and aligned with project goals.
1. Problem-First, Impact-Driven Research
We prioritize work that addresses meaningful global problems, often informed by challenges encountered in practical and industry settings. Research efforts span machine learning, generative AI, and AI systems, guided by clearly defined objectives, measurable outcomes, and practical relevance. Each project is expected to move beyond exploration into tangible results.
2. Structured Research Tracks
Research at WAI is organized into distinct pathways designed to reflect professional research and engineering environments:
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Grants & Sponsored Research — execution of funded projects with defined scope, timelines, and deliverables
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Conference & Publication Track — development of research aimed at top-tier venues and technical contributions
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Bring Your Own Project (BYOP) — independent ideas developed through structured review, mentorship, and iteration
Each pathway emphasizes accountability, depth of execution, and high-quality output.
3. Lab-Based Execution
Our labs operate as small, focused teams that bring together researchers, engineers, and domain experts across disciplines. This structure enables the integration of research depth with practical domain insight, supporting the development of systems that are both technically rigorous and relevant to industry and applied use cases.
Work is iterative, review-driven, and aligned with practical settings.
4. Technical Standards
All work is held to a high bar of technical rigor. Projects are expected to demonstrate reproducibility, strong empirical validation, benchmarking against relevant baselines, and careful consideration of robustness and scalability. Outputs are expected to meet the standards of both serious research and real-world deployment, with performance, reliability, and scalability in applied environments as core considerations.
Research to Deployment
Projects are developed to produce systems and outputs that extend beyond experimentation, including deployable systems, open-source contributions, and applied research. Findings contribute to the broader research community through publications, with opportunities to develop work suitable for leading research venues.
Systems Shipped
Translating research into deployable AI systems designed for meaningful impact in applied contexts.
Open Source
Contributing tools and frameworks for the broader community.
Real-World Impact
Applications deployed across industry, research, and public sectors.
What Defines WAI Researchers
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Motivated, committed, consistent
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Ownership and accountability in execution
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Depth of thinking and technical rigor
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Ability to translate ideas into working systems with practical applicability
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Contributions that extend into real-world impact
Research Pathways
Work on real-world AI problems. Build systems that matter.
WAI Research Labs is designed for motivated individuals who want to contribute to meaningful research and operate at a high level of execution. Participation is merit-based and aligned with our standards for commitment, rigor, ownership, and impact.
Pathways to Join
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Apply to Existing Research Tracks: Join ongoing projects aligned with active lab work.
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Bring Your Own Project (BYOP): Submit an original idea to be developed within the lab.
Submit a Proposal
Pitch an original idea to be developed within the WAI Research Lab.
Collaborative Initiatives
Engage with lab-driven projects and share research goals.
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, developing a shared research knowledge repository, and advances women’s leadership in inclusive AI across the US.
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 like engineering, energy, sustainability, policy, climate, including an award-winning AI-driven environmental solution at InnovateFPGA, best paper at IEEE, and publications in ACL, NeurIPS, EMNLP. 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.
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 LLMs and holds 7 patents, and publications in ICML, NeurIPS, IJCAI, Springer, ICDM, and IEEE. A Responsible AI 2025 North America award winner, she has led cross-functional teams and developed human-in-the-loop, interpretable ML/AI models, combining technical depth with strategic vision to build scalable solutions that deliver measurable business value.
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. 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.
Latha Ramamoorthy
Research Grant Coordinator
Latha's work at WAI focuses on identifying and developing research opportunities that advance the organization’s mission, with an emphasis on structured, high-impact initiatives. She has authored and submitted grant proposals to build a strong foundation for WAI’s research efforts while fostering cross-team collaboration and strategic alignment. She is strengthening the research ecosystem through thoughtful planning and actively contributing in her first year of the Ambassador pairing program.
Apply to WAI USA Research Team
Work on real-world AI systems. Build, ship, and contribute.
WAI Research Labs is designed for individuals who want to engage in rigorous, execution-driven research and contribute to systems with global relevance. Participation is structured, collaborative, and focused on producing high-quality outputs.
Apply to a Research Track
Join ongoing projects aligned with the lab's active research directions. Work within a structured team environment focused on delivering high-quality outputs.
Bring Your Own Project (BYOP)
Submit an original idea to be developed within the lab. Proposals should demonstrate clear problem definition, technical feasibility, and a path to execution.