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Pagadala Usha
National Institute of Technology
TADEPALLIGUDEM, India
 

Pagadala Usha is an experienced academic and researcher with more than ten years of teaching experience in the field of electronics and communication engineering. A passionate educator, she has successfully taught a diverse range of subjects to both undergraduate and postgraduate students, as well as conducted coding sessions on Python programming. She strives to explain complex topics in a clear and simple way, in order to encourage student understanding.

Currently pursuing a PhD at the National Institute of Technology (NIT), Andhra Pradesh, Pagadala specializes in machine learning, neural networks, and automatic speech recognition (ASR). Her groundbreaking work in creating a Telugu speech dataset has significantly contributed to advancing ASR research for low-resource languages, underscoring her commitment to impactful and inclusive innovation. She has presented her research at prestigious forums, including IEEE international conferences, and authored several impactful publications.

Beyond her research, Pagadala has made notable administrative contributions. In her role as department head, she has been instrumental in steering accreditation processes for National Assessment and Accreditation Council (NAAC) and National Board of Accreditation (NBA), ensuring academic excellence within her institution. Additionally, she has organized numerous workshops and faculty development programs in collaboration with industry leaders such as Texas Instruments and Maven Silicon, enhancing the technical proficiency of both educators and students.

Pagadala’s efforts have been recognized through various awards, such as the DrishTI Online Contest Appreciation from Texas Instruments and honors from the Institution of Electronics and Telecommunication Engineers (IETE) for promoting artificial intelligence (AI) and machine learning for societal benefit. Her contributions to AI were also recognized with a Silver Claro Business Award. Furthermore, she showcased her expertise by participating as a judge for the Claro Awards in the AI, technology, and data analytics categories, underscoring her active role in the global technology community.

As a senior member of IEEE and an active member of ACM, she is deeply engaged with the global research community. She also contributes as a reviewer for esteemed journals and conferences, furthering advancements in her field.


     

Meta-learning approaches for few-shot learning: a survey of recent advances
Gharoun H., Momenifar F., Chen F., Gandomi A. ACM Computing Surveys 56(12): 1-41, 2024.  Type: Article

Gharoun et al. provide a comprehensive overview of meta-learning techniques, with a focus on how these methods facilitate learning from limited data (few-shot learning, FSL). They categorize recent advancements into metric-based, memory-based, and...

 

Exploding the myths: an introduction to artificial neural networks for prediction and forecasting
Maier H., Galelli S., Razavi S., Castelletti A., Rizzoli A., Athanasiadis I. Environmental Modelling & Software 1672023.  Type: Article

Maier et al. examine artificial neural networks (ANNs) in this paper, aiming to clarify misconceptions and outline best practices for their application in prediction and forecasting....

 
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