Swathi Suddala | Information Technology | Women Researcher Award

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Mrs. Swathi Suddala | Information Technology | Women Researcher Award

Data Analyst at SP Teks, Inc, United States

Swathi Suddala is a highly analytical and results-driven Data Scientist with over 8 years of experience in the IT industry. She specializes in delivering innovative, data-driven solutions through Data Science, Machine Learning, and Data Engineering. With hands-on expertise in Python, R, SQL, and tools like Power BI and Tableau, she thrives in transforming complex datasets into strategic insights. Swathi is known for her problem-solving mindset, strong communication skills, and passion for continuous learning, making her a valuable asset across domains such as finance, supply chain, and technology. πŸŒπŸ“ŠπŸ§ 

Publication Profile

Google Scholar

Academic Background

Swathi holds a Master of Science in Information Technology Management from the University of Wisconsin, Milwaukee, USA (2023), and a Master of Technology in Environmental Geomatics from JNTU University, Hyderabad, India (2014). Her academic foundation blends technical, managerial, and environmental perspectives, equipping her with a multidisciplinary approach to problem-solving. She has also contributed significantly to academic research during her postgraduate studies and continued professional development through certifications and technical training. πŸŽ“πŸ§‘β€πŸŽ“πŸ“š

Professional Background

Swathi has served in various impactful roles across global organizations like Charles Schwab, IT Intellect Micro Solutions, Cred Avenue, and Hoch Technologies. She has designed machine learning models, performed data visualization, implemented CI/CD pipelines, and worked extensively with NLP and LLMs. Her experience spans finance, supply chain, and enterprise IT domains, where she has driven improvements in operations, analytics, and decision-making through data science. She brings strong expertise in Python, SQL, Power BI, Spark, and cloud platforms like AWS and Azure. πŸŒπŸ‘©β€πŸ’»πŸ“ˆ

Awards and Honors

Swathi is a Salesforce Data Cloud Consultant Certified professional and has earned recognition for her contributions to academic and applied research. Her work has been published in peer-reviewed journals, reflecting her dedication to advancing knowledge in Data Science and AI. She has led impactful academic and professional projects and consistently received accolades for her innovation and leadership in technology, analytics, and AI research. πŸ₯‡πŸ“‘πŸŽ–οΈ

Research Focus

Swathi’s research focuses on leveraging cutting-edge Data Science techniques, including Generative AI, LLMs, and Edge Computing. Her published works explore hybrid ARIMA-LSTM models for demand forecasting, RAG systems for enhancing LLMs, and cloud-native strategies for IoT. Her academic curiosity fuels her drive to explore the intersections of AI, big data, and real-time analytics, enabling smarter, faster, and more scalable systems for industry and research. She thrives at the forefront of innovation, making impactful contributions to the future of intelligent systems. πŸ§ͺπŸ“ˆπŸ€–

Publication Top Notes

πŸ“„ Enhancing Generative AI Capabilities Through Retrieval-Augmented Generation Systems and LLMs
πŸ“… Year: 2024 |Cited by: 3

πŸ“„ Harnessing Cloud Technology for Real-Time Machine Learning in Fraud Detection
πŸ“… Year: 2023 |Cited by: 1

πŸ“„ Ethical Challenges and Accountability in Generative AI: Managing Copyright Violations and Misinformation in Responsible AI Systems
πŸ“… Year: 2024

Conclusion

Swathi Suddala stands out as a remarkable candidate for the Research for Women Researcher Award, bringing together over 8 years of rich experience in data science with a strong academic foundation, including dual master’s degrees from the University of Wisconsin, USA, and JNTU, India. Her research contributions span high-impact areas such as Edge Computing, IoT, Hybrid ARIMA-LSTM models, and Retrieval-Augmented Generation systems, published in reputable journals and cited for their relevance and innovation. Professionally, she has delivered real-world solutions in finance, supply chain, and tech industries, showcasing expertise in machine learning, NLP, LLMs, and cloud-based model deployment. With hands-on proficiency in Python, R, Tableau, Power BI, and a consistent track record of impactful projectsβ€”from vaccine scheduling to forest fire predictionβ€”Swathi exemplifies the fusion of academic excellence, technological innovation, and applied leadership, making her a highly deserving nominee for this prestigious recognition.

 

 

Manasvi Aggarwal | Artificial Intelligence | Young Scientist Award

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Ms. Manasvi Aggarwal | Artificial Intelligence | Young Scientist Award

Senior Data Scientist, Mastercard

Name: Manasvi Aggarwal  Gender: Female Designation: Senior Data Scientist Department: AI Garage Organization: Mastercard Specialization: Artificial IntelligenceExpertise: Graph Neural Networks (GNNs), Spatio-Temporal Forecasting, Neural Algorithmic Reasoning Industry Experience: Microsoft, Myntra, Mastercard Notable Work: Developed MRP-GNN, identifying $17M in fraud and increasing fraud detection rates by 20% Research Collaborations: Oxford, Cambridge (INAR project) Publications: ICPR, IEEE BigData, ICML workshops, RecSys, Springer book Awards: Business Excellence Award (Mastercard), Google-sponsored EEML travel grant Professional Contributions: ICLR, IEEE BigData, and NeurIPS program committee member Vision: Advancing scalable AI solutions for real-world impact

profile:

Scholar

πŸŽ“ Education 

B.Tech: Computer Science, University of Delhi (Ranked 4th in cohort) Competitive Exams: GATE (99.47 percentile), JEST (All India Rank 35) M.Tech (Research): Computer Science, IISc Bengaluru Thesis: β€œEmbedding Networks: Node and Edge Representations” (Graph-based learning, NLP, Computer Vision) PhD Offers: Fully funded PhD admissions from Canada and the USA (deferred due to financial reasons) Key Research Areas: Graph Neural Networks, Spatio-Temporal Forecasting, Neural Algorithmic Reasoning Notable Academic Achievements: IISc Research Contributions in GNNs and self-supervised learning

πŸ’Ό Experience 

Microsoft: Developed multi-label categorization for Azure offers using BERT embeddings and web scraping  Myntra: Worked on pricing automation, demand forecasting, and user cohort modeling with graph-based approaches Mastercard: Leading AI-driven fraud detection initiatives, including MRP-GNN (detected $17M fraud, 20% improved detection rates) Industry Collaborations: Worked with teams in Israel, USA, and India on AI-driven solutions Mentorship: Guides junior AI researchers, participates in hiring interviews

πŸ† Awards & Honors 

Business Excellence Award – Mastercard Priceless Mentoring and Guidance Award – Mastercard Google-sponsored travel grant – EEML 2024 1st Runner-up – Myntra HackerRamp Hackathon Invited Program Committee Member – ICLR, IEEE BigData, NeurIPS

πŸ”¬ Research Focus 

Key Areas: Graph Neural Networks (GNNs), Spatio-Temporal Forecasting, Neural Algorithmic Reasoning Mastercard Research: Developed MRP-GNN, detecting high-risk merchants and preventing financial fraud  AI for Security: Fraud detection, risk prediction, secure transactions Spatio-Temporal AI: Developed GNN for terror activity forecasting in Jammu & Kashmir Academic Collaborations: Works with Oxford & Cambridge on INAR, optimizing CLRS-30 benchmarks Publications: Published in ICPR, IEEE BigData, ICML workshops, RecSys Community Contributions: Co-organized Southeast Asian Learning on Graphs (LoG) 2024 Meetup

βœ… Conclusion

Manasvi Aggarwal is a highly competitive candidate for the Best Researcher Award, with a strong blend of academic excellence, industry impact, publications, and global collaborations. If she strengthens her patent portfolio, citation metrics, and leadership in independent research projects, she would be an even stronger contender for distinguished research awards.

publication

Deep Learning – M. Aggarwal, M.N. Murty (2021) – 21 citations

 

Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs – M. Aggarwal, M.N. Murty (2021) – 18 citations

 

Self-supervised Hierarchical Graph Neural Network for Graph Representation – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2020) – 4 citations

 

Robust Hierarchical Graph Classification with Subgraph Attention – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2020) – 4 citations

 

Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2020) – 3 citations

 

A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2021) – 2 citations

 

Region and Relations Based Multi Attention Network for Graph Classification – M. Aggarwal, M.N. Murty (2021) – 2 citations

 

Using Relational Graph Convolutional Networks to Assign Fashion Communities to Users – A. Budhiraja, M. Sukhwani, M. Aggarwal, S. Shevade, G. Sathyanarayana (2022) – 1 citation

 

Node Representations – M. Aggarwal, M.N. Murty (2021) – 1 citation

 

Hierarchically Attentive Graph Pooling with Subgraph Attention – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2020) – 1 citation

 

Embedding Graphs – M. Aggarwal, M.N. Murty (2021)

 

Representations of Networks – M. Aggarwal, M.N. Murty (2021)

 

Embedding Networks: Node and Graph Level Representations – M. Aggarwal