Laxmi Kantham Durgam | Deep Learning | Best Scholar Award

Mr. Laxmi Kantham Durgam | Deep Learning | Best Scholar Award

National Institute of Technology Warangal | India

Publication Profile

Google Scholar

🧑‍🎓 Education

Mr. Laxmi Kantham Durgam is currently pursuing his PhD in Electrical Engineering at National Institute of Technology Warangal, Telangana, India (2021–Present). His research focuses on Speech Recognition, Age and Gender Identification from Speech, and the application of Machine Learning and Deep Learning techniques. He is also a Visiting Researcher at the Technical University of Kosice, Slovakia, as part of the NSP SAIA Fellowship (2024). Mr. Durgam holds an M.Tech in Digital Systems and Computer Electronics from Jawaharlal Nehru Technological University, Hyderabad (2017–2020) and a B.Tech in Electronics and Communication Engineering from the same institution (2011–2015).

📚 Publications

Mr. Durgam has contributed to various publications in the fields of speech processing, deep learning, and edge computing. His notable works include papers on speaker age and gender classification, real-time age estimation from speech, and dress code detection using MobileNetV2 on NVIDIA Jetson Nano. He has been published in journals like Computing and Informatics and Journal of Signal Processing Systems, as well as conferences like ICIT-2023 in Kyoto, Japan.

🔬 Research Experience

Since 2021, Mr. Durgam has been a PhD Research Scholar at NIT Warangal, where his research includes projects on real-time age identification from speech using Edge devices like Jetson Nano and Arduino Nano BLE. He has also worked on real-time projects like vehicle logo classification using Edge Impulse and dress code detection using MobileNetV2. His work is focused on implementing Machine Learning and Deep Learning algorithms for practical, real-time applications.

🏆 Fellowships and Awards

Mr. Durgam was awarded the NSP SAIA Fellowship (2024), an international mobility program funded by the European Union, allowing him to collaborate with the KEMT, Faculty of Electrical Engineering and Informatics at the Technical University of Kosice, Slovakia. He also received the MHRD Fellowship for his PhD from the Government of India. He has passed the NITW PhD Entrance and the GATE exam (2016), and he received financial support as an undergraduate student from the Andhra Pradesh State Government.

📖 Academic Achievements

Mr. Durgam has significantly contributed to the academic environment through his role in conducting hands-on programming sessions in Machine Learning, Deep Learning, and AI applications. He has mentored students in Faculty Development Programs (FDPs), summer and winter internships, and workshops at various institutions. His workshops have reached institutions such as BRIT Vijayanagaram and VBIT Hyderabad, where he focused on advanced deep learning applications on Edge devices.

💻 Technical Skills

Mr. Durgam is highly skilled in programming with languages like Python, C, Embedded C, Matlab, and frameworks such as Keras, TensorFlow, and OpenCV. He has experience working with edge devices like Jetson Nano, Jetson Xavier, and Arduino Nano BLE 33 Sense. His expertise also extends to Machine Learning, Deep Learning, Computer Vision, and Digital Signal Processing.

🧑‍🏫 Teaching and Mentoring

Mr. Durgam has been an active Teaching Assistant and mentor at NIT Warangal, where he has assisted in courses like Artificial Intelligence, Machine Learning, and Digital Signal Processing. He has guided B.Tech students in lab sessions, and his expertise in AI/ML has also been shared through guest lectures and faculty development programs.

🚀 Key Projects

Some of Mr. Durgam’s key projects include age and gender estimation from speech using MFCC features, dress code detection with MobileNetV2 on Edge devices, and real-time object detection using deep learning algorithms. He has successfully developed these applications on devices like Jetson Nano and Arduino Nano BLE for real-time deployment.

🌍 Professional Engagement

Mr. Durgam is an IEEE Student Member and an active participant in IEEE Young Professionals. He has been involved in organizing and coordinating internships, workshops, and training programs related to AI/ML and Deep Learning at NIT Warangal and other institutes. He is passionate about bridging the gap between academia and real-world applications, particularly through Edge Computing and AI deployment.

📚 Top Notes Publications 

Real-Time Dress Code Detection using MobileNetV2 Transfer Learning on NVIDIA Jetson Nano
    • Authors: LK Durgam, RK Jatoth

    • Journal: Proceedings of the 2023 11th International Conference on Information

    • Year: 2023

Real-Time Classification of Vehicle Logos on Arduino Nano BLE using Edge Impulse
    • Authors: D Abhinay, SV Vighnesh, LK Durgam, RK Jatoth

    • Journal: 2023 4th International Conference on Signal Processing and Communication

    • Year: 2023

Age Estimation based on MFCC Speech Features and Machine Learning Algorithms
    • Authors: LK Durgam, RK Jatoth

    • Journal: IEEE International Symposium on Smart Electronic Systems (iSES)

    • Year: 2022

Age Estimation from Speech Using Tuned CNN Model on Edge Devices
    • Authors: LK Durgam, RK Jatoth

    • Journal: Journal of Signal Processing Systems

    • Year: 2024

Age and Gender Estimation from Speech using various Deep Learning and Dimensionality Reduction Techniques
    • Authors: MPJJ Laxmi Kantham Durgam*, Ravi Kumar Jatoth, Daniel Hladek, Stanislav Ondas

    • Journal: Acoustics and Speech Processing at Speech analysis synthesis, Institute of

    • Year: 2024

 

Yuhuan Fei | Object Detection | Best Researcher Award

Assist Prof Dr. Yuhuan Fei | Object Detection | Best Researcher Award

Qufu Normal University | China

PUBLICATION PROFILE

Scopus

👤 Summary

Dr. Yuhuan Fei is an Associate Professor at Qufu Normal University, China, with a strong background in Machine Learning, Intelligent Manufacturing, and Ceramic Cutting Tools. He holds a Ph.D. in Mechanical Manufacturing and Automation and focuses on the development and performance of advanced ceramic cutting tools, material microstructure, and mechanical properties. Dr. Fei has made substantial contributions to scientific research, including numerous journal publications, patents, and participation in national research projects.

🎓 Education

Dr. Fei completed his Ph.D. in Mechanical Manufacturing and Automation at Shandong University in 2012, with a thesis on fabrication and cutting performance of Al2O3-TiC-TiN ceramic cutting tools. Prior to that, he earned a Master’s degree in Materials Processing Engineering from Northeastern University in 2007, focusing on the phase analysis of Ti-700 alloy. He also holds a Bachelor’s degree in Material Forming and Controlling Engineering, completed in 2004 at Northeastern University.

🧑‍🏫 Professional Experience

Dr. Fei has held various academic and professional positions. He served as a Visiting Scholar at McGill University from 2017 to 2018. Since 2013, he has been an Associate Professor at Qufu Normal University in Shandong Province. Before that, he worked as a Patent Examiner at the Patent Examination Cooperation Center (SIPO) in Jiangsu Province, focusing on intellectual property evaluations related to material sciences.

📚 Academic Citations

Dr. Fei has authored numerous papers in high-impact journals. Some of his most cited works include those published in the Journal of Real-Time Image Processing (2025), Journal of Ceramic Science and Technology (2021, 2019), and Ceramics International (2014). His research also appears in Materials Science and Engineering A, Rare Metal Materials and Engineering, and several other prestigious journals, significantly contributing to the fields of material science and engineering.

🛠️ Technical Skills

Dr. Fei’s technical expertise includes:

  • Advanced ceramic material development and cutting tool fabrication

  • Phase composition analysis and microstructure characterization of materials

  • Machine learning applications in intelligent manufacturing systems

  • Patent analysis and intellectual property research

👨‍🏫 Teaching Experience

Dr. Fei has an active teaching role at Qufu Normal University, where he imparts knowledge on material science and engineering, particularly focusing on ceramic cutting tools and intelligent manufacturing. He supervises graduate students, guiding them through research projects and theses, while also leading research seminars and educational workshops.

🔬 Research Interests

Dr. Fei’s primary research interests include:

  • Development of ceramic cutting tools for advanced manufacturing

  • Study of material processing techniques and phase transitions in cutting materials

  • Application of machine learning to optimize manufacturing processes

  • Investigation of mechanical properties and microstructural characteristics of ceramics

📚 Top Notes Publications 

The Influence of Ni Addition on the Microstructures and Mechanical Properties of Al2O3–TiN–TiC Ceramic Materials
    • Authors: Yuhuan Fei, Chuanzhen Huang, Hanlian Liu, Bin Zou

    • Journal: Not mentioned in the provided text

    • Year: 2018

Study on the Cutting Performance of Al2O3-TiC-TiN Ceramic Tool Materials
    • Authors: Yuhuan Fei, Chuan Zhen Huang, Han Lian Liu, Bin Zou

    • Journal: Not mentioned in the provided text

    • Year: 2016

On the Formation Mechanisms of Fine Granular Area (FGA) on the Fracture Surface for High Strength Steels in the VHCF Regime
    • Authors: Yong-De Li, Limin Zhang, Yuhuan Fei, Mei-Xia Li

    • Journal: Not mentioned in the provided text

    • Year: 2015

Mechanical Properties of Al2O3–TiC–TiN Ceramic Tool Materials
    • Authors: Yuhuan Fei, C.Z. Huang, H.L. Liu, Bin Zou

    • Journal: Not mentioned in the provided text

    • Year: 2014

Establishment of the Low Defect Ceramic Cutting Tool Database
    • Authors: Chang Bin Zou, Chuan Zhen Huang, Bin Zou, Jun Wang

    • Journal: Not mentioned in the provided text

    • Year: 2013

The Effects of Sintering Process on Microstructure and Mechanical Properties of TiB2-Ti(C0.5N0.5)-WC Composite Tool Materials
    • Authors: Yan Zhao, Chuan Zhen Huang, Bin Zou, Jun Wang

    • Journal: Not mentioned in the provided text

    • Year: 2012

 

Fatemeh Shah-Mohammadi | Clinical Data Science | Best Researcher Award

Dr. Fatemeh Shah-Mohammadi | Clinical Data Science | Best Researcher Award

Dr. Fatemeh Shah-Mohammadi at University of Utah, United States

 Professional Profiles👨‍🎓

 Early Academic Pursuits 🎓

Dr. Fatemeh Shah-Mohammadi completed her Master of Science in Electrical and Communication Engineering at Birjand University of Technology, where she laid the groundwork for her expertise in engineering and technology. She then pursued her PhD at Rochester Institute of Technology, specializing in Machine Learning. Her dissertation focused on “Machine Learning-enabled Resource Allocation in Underlay Cognitive Radio Networks,” demonstrating her commitment to applying advanced technologies in innovative ways.

Professional Endeavors 💼

Dr. Shah-Mohammadi currently serves as a Research Assistant Professor of Biomedical Informatics at the University of Utah, where she applies her skills in health informatics and clinical data science. Her professional journey includes significant roles as a Biostatistician II/NLP Engineer at Mount Sinai Health System and a postdoctoral fellow at the Icahn School of Medicine. These experiences have equipped her with a robust foundation in data analysis and machine learning applications across diverse domains.

Contributions and Research Focus 🔍

Dr. Shah-Mohammadi’s research primarily centers on the intersection of health informatics and machine learning. She has developed machine learning models that leverage cross-domain datasets to improve clinical outcomes. Notable projects include NLP-assisted pipelines for clinical note analysis and differential diagnosis, highlighting her dedication to enhancing healthcare delivery through technology.

Impact and Influence 🌍

Dr. Shah-Mohammadi’s work has significant implications for health informatics, particularly in the use of natural language processing (NLP) for clinical applications. Her publications in respected journals, such as the IEEE Open Journal of the Communications Society and Studies in Health Technology and Informatics, showcase her contributions to advancing knowledge in this field. Her research efforts aim to reduce diagnostic uncertainty and improve patient care outcomes, underscoring her influence in biomedical informatics.

Academic Cites 📚

Dr. Shah-Mohammadi’s scholarly output includes multiple peer-reviewed articles that have garnered citations and recognition within the academic community. Her ORCID and Google Scholar profiles further highlight her impact, showcasing a growing body of work that contributes to the fields of health informatics and machine learning.

Technical Skills 🛠️

Dr. Shah-Mohammadi is proficient in various technical areas, including:

  • Machine Learning and Data Science
  • Natural Language Processing
  • Statistical Analysis
  • Software Development for Biomedical Applications Her expertise enables her to build and implement sophisticated models that address real-world clinical challenges.

Teaching Experience 👩‍🏫

As an instructor at the University of Utah, Dr. Shah-Mohammadi teaches courses like “Generative AI in Healthcare” and “Intro to Programming.” Her teaching philosophy emphasizes the integration of cutting-edge technology with foundational principles, preparing students for future challenges in biomedical informatics.

Grants and Funding 💰

Dr. Shah-Mohammadi has actively sought and secured funding for her research initiatives. As the Principal Investigator for a project on automated live meta-analysis of clinical outcomes using generative AI, she demonstrates leadership in advancing research in health informatics. Her involvement as a co-investigator in various projects further highlights her collaborative spirit and dedication to impactful research.

Legacy and Future Contributions 🌟

Looking ahead, Dr. Shah-Mohammadi aims to continue her work at the forefront of health informatics, leveraging machine learning and NLP to drive innovations in clinical practice. Her commitment to research, teaching, and community engagement positions her as a leading figure in the field, poised to make lasting contributions to both academia and healthcare.

📖 Top Noted Publications 

 Addressing Semantic Variability in Clinical Outcome Reporting Using Large Language Models

Authors: Fatemeh Shah-Mohammadi, Joseph Finkelstein
Journal: BioMedInformatics
Year: 2024

 Extraction of Substance Use Information From Clinical Notes: Generative Pretrained Transformer–Based Investigation

Authors: Fatemeh Shah-Mohammadi, Joseph Finkelstein
Journal: JMIR Medical Informatics
Year: 2024

Accuracy Evaluation of GPT-Assisted Differential Diagnosis in Emergency Department

Authors: Fatemeh Shah-Mohammadi, Joseph Finkelstein
Journal: Diagnostics
Year: 2024

Neural Network Cognitive Engine for Autonomous and Distributed Underlay Dynamic Spectrum Access

Authors: Fatemeh Shah-Mohammadi, Hatem Hussein Enaami, Andres Kwasinski
Journal: IEEE Open Journal of the Communications Society
Year: 2021

 NLP-Assisted Differential Diagnosis of Chronic Obstructive Pulmonary Disease Exacerbation

Authors: Fatemeh Shah-Mohammadi, Joseph Finkelstein
Journal: Studies in Health Technology and Informatics
Year: 2024

Using Electronic Medical Records and Clinical Notes to Predict the Outcome of Opioid Treatment Program

Authors: W Cui, Fatemeh Shah-Mohammadi, Joseph Finkelstein
Journal: Healthcare Transformation with Informatics and Artificial Intelligence
Year: 2023