Lakshmi Devi P | Generative AI and LLM | AI Breakthrough Award

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Mrs. Lakshmi Devi P | Generative AI and LLM | AI Breakthrough Award

Senior Associate – Data Scientist at JP Morgan& Chase, India

Lakshmi Devi P is a seasoned data science professional currently serving as a Senior Associate – Data Scientist at JPMorgan Chase, with additional academic contributions as an Adjunct Faculty member at the Manipal Academy of Higher Education (MAHE). With more than a decade of experience in artificial intelligence, machine learning, and data-driven innovation, she brings an expert lens to the domain of Generative AI and NLP. A published author, active mentor, and patent contributor, her work is grounded in ethical, scalable applications of AI that span enterprise systems and educational initiatives. Her leadership on GenAI solutions exemplifies innovation that drives measurable impact across sectors.

Profile

ORCID

Education

Lakshmi is currently pursuing her Ph.D. in Artificial Intelligence, where her research focuses on designing scalable and ethical AI systems. This doctoral journey builds upon her robust academic and professional background, including foundational degrees in computer science and information technology. Her academic rigor complements her industry-focused innovations, bridging the gap between theoretical advancements and real-world applications. As an Adjunct Faculty member at MAHE, she has also contributed to curriculum development and has trained over 900 learners in a single session, reinforcing her commitment to AI education and knowledge dissemination.

Experience

Over the course of her career, Lakshmi Devi P has built a dynamic portfolio combining technical expertise, leadership, and community engagement. At JPMorgan Chase, she leads multiple enterprise-grade AI initiatives such as Zoom Transcribe GenAI, real-time anomaly detection systems, and semantic search engines. Her prior engagements with Capgemini, RetailOn, and Honeywell involved diverse projects including sentiment analysis, ROI forecasting, and OCR-driven automation. Beyond her corporate role, her teaching position at MAHE and collaborations with academic bodies like CIT and SSIT have enabled her to mentor aspiring data scientists and contribute meaningfully to AI literacy.

Research Interest

Lakshmi’s primary research interests lie at the intersection of Generative AI, Natural Language Processing, and ethical AI frameworks. She is particularly focused on the integration of Large Language Models (LLMs) into software engineering and system architecture. Her patented method for using LLMs to generate updated software architectures is a hallmark of her contribution to AI-driven automation. Additional interests include real-time anomaly detection, AI infrastructure design, vector embeddings, and retrieval-augmented generation systems. Her emphasis on ethical and inclusive AI underlines her belief that technological advancement must align with social responsibility and fairness.

Award

Lakshmi has been nominated for the AI Breakthrough Award in recognition of her innovative work in deploying GenAI solutions within the financial sector, publishing educational content, and mentoring underrepresented groups in AI. Her achievements exemplify groundbreaking contributions across research, enterprise application, and community upliftment. Her involvement in the Force for Good initiative reflects her dedication to leveraging AI for meaningful societal impact.

Publication

Lakshmi Devi P has authored a book titled “Transformers and Beyond: Building the Next Generation of Generative AI Systems” (ISBN: 979-8281458283), offering deep insights into foundation models and multimodal AI. She has also published the following journal articles:

  1. Real Valued Outputs of Cab Bookings using Regression and Ensemble Techniques Comparison Analysis, IJ for Research & Development in Technology, Vol. 13(2), Feb 2020, IF: 6.88.

  2. IOT Based Illegal Trees Cutting Prevention and Monitoring with Web App Using Raspberry Pi, IJ of Innovative Research in Science, Engineering and Technology, Vol. 8(7), Jul 2019, IF: 7.089.

  3. IOT based Waste Management System for Smart City, IAETSD Journal for Advanced Research in Applied Sciences, Vol. 4(7), Dec 2017, IF: 5.2.

  4. Helmet using GSM and GPS Technology for Accident Detection and Reporting System, IJRITCC, Vol. 4(5), May 2016, IF: 5.837.

  5. Real Time Tele Health Monitoring System, IJRITCC, Vol. 4(3), Mar 2016, IF: 5.837.

  6. Matlab Code For Identification Of Graphics Objects In Aircraft Displays, IJRITCC, Vol. 4(3), Mar 2016, IF: 5.837.

  7. SMS based Home Automation using CAN Protocol, IJRITCC, Vol. 4(3), Mar 2016, IF: 5.837.

Each of these publications demonstrates Lakshmi’s commitment to blending practical solutions with academic rigor, often cited for their interdisciplinary applications in IoT, automation, and AI.

Conclusion

Lakshmi Devi P represents the archetype of a modern AI leader—technically adept, ethically grounded, and socially conscious. Her body of work spans patented innovations, impactful AI deployments in high-stakes industries, academic contributions, and grassroots mentorship. By aligning enterprise performance with societal benefits, she embodies the transformative promise of AI. Whether through cutting-edge research, large-scale training, or community initiatives, Lakshmi continues to push boundaries, making her a deserving candidate for the AI Breakthrough Award and a role model in the data science ecosystem.

Hemad Zareiforoush | Machine Learning | Best Academic Researcher Award

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Dr. Hemad Zareiforoush | Machine Learning | Best Academic Researcher Award

Associate Professor at University of Guilan, Rasht, Iran

Dr. Hemad Zareiforoush is an Assistant Professor at the Department of Biosystems Engineering, University of Guilan, Rasht, Iran, where he has been contributing to both academic and practical advancements in biosystems engineering since 2015. With a focus on agricultural machinery, automation, and quality inspection systems, his work bridges engineering and food science, particularly in areas like computer vision, image processing, and renewable energy applications. His research is highly interdisciplinary, combining mechanical engineering principles with computational intelligence for improving the agricultural industry’s efficiency.

Profile

Google Scholar

Education

Dr. Zareiforoush’s educational background is robust, with a PhD in Mechanical and Biosystems Engineering from Tarbiat Modares University in Tehran, Iran, completed in 2014. His academic excellence is evident in his GPA of 17.84 out of 20. He earned his MSc in Mechanical Engineering of Agricultural Machinery at Urmia University in 2010, where he graduated with a remarkable GPA of 19.29 out of 20. Earlier, Dr. Zareiforoush obtained his BSc in the same field from Urmia University in 2007, graduating with a GPA of 15.75 out of 20. He also attended a specialized governmental high school for excellent pupils, where he focused on mathematics and physics, graduating with a GPA of 18.71 out of 20.

Experience

Since joining the University of Guilan in 2015, Dr. Zareiforoush has been teaching various courses, including Engineering Properties of Food and Agricultural Products, Renewable Energy, and Measurement and Instrumentation Principles. His practical experience spans various engineering disciplines, with a particular emphasis on instrumentation, automation in agriculture, and food quality monitoring. Notably, his research has led to the development of innovative systems for rice quality inspection using computer vision and fuzzy logic. Additionally, he has been involved in numerous projects related to agricultural machinery, renewable energy, and automation for optimizing food production processes.

Research Interests

Dr. Zareiforoush’s research interests lie at the intersection of biosystems engineering, computational intelligence, and food science. He is particularly interested in computer vision applications for food quality inspection, using advanced image processing techniques to enhance product quality and safety. His work also explores hyperspectral imaging and spectroscopy for monitoring the quality of food materials. Another key area of his research is the application of machine learning algorithms for modeling and classifying food products based on their quality attributes. Additionally, he is involved in renewable energy applications in agriculture, focusing on solar-assisted drying systems and energy-efficient food processing methods.

Awards

Dr. Zareiforoush has received several prestigious awards throughout his academic career. He was honored with the Iran Ministry of Science, Research, and Technology Scholarship in 2012 and the National Elite Scholarship by the Iran National Foundation for Elites (INFE) in 2011. His exceptional academic performance earned him the title of “Best Student” at Urmia University in 2009. Additionally, he has been recognized as a “Talented Student” at Tarbiat Modares University and ranked 1st among MSc students in his department.

Publications

Dr. Zareiforoush has published several influential papers in high-impact journals. Some of his notable publications include:

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2020). Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features. Journal of Food Measurement and Characterization, 14: 1402–1416, Cited by: 43.

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2020). Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features. Plant Methods, 16:153, Cited by: 25.

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2021). Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared-assisted continuous hybrid solar dryer. Food Science & Nutrition (JCR), 9(1), 532-543, Cited by: 12.

Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A. (2016). Design, Development, and Performance Evaluation of an Automatic Control System for Rice Whitening Machine Based on Computer Vision and Fuzzy Logic. Computers and Electronics in Agriculture, 124: 14-22, Cited by: 67.

Soodmand-Moghaddam, S., Sharifi, M., Zareiforoush, H. (2020). Mathematical modeling of lemon verbena leaves drying in a continuous flow dryer equipped with a solar pre-heating system. Quality Assurance and Safety of Crops & Foods, 12(1): 57-66, Cited by: 30.

Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A. (2015). Qualitative Classification of Milled Rice Grains Using Computer Vision and Metaheuristic Techniques. Journal of Food Science and Technology (Springer), 53(1): 118-131, Cited by: 45.

Zareiforoush, H., Komarizadeh, M.H., Alizadeh, M.R. (2010). Effects of crop-screw parameters on rough rice grain damage in handling with a horizontal screw auger. Journal of Food, Agriculture and Environment, 8(3): 132-138, Cited by: 19.

Conclusion

Dr. Hemad Zareiforoush’s academic and professional contributions significantly impact the fields of biosystems engineering, food science, and agricultural machinery. His work in developing intelligent systems for quality inspection and automation has improved agricultural productivity and food safety. His expertise in computational techniques, including fuzzy logic and machine learning, continues to shape the future of smart farming and food processing. With numerous awards, highly cited publications, and a track record of excellence, Dr. Zareiforoush is a leading figure in his field.