Liliana Aguilar-Marcelino | Cybersecurity | Best Innovation Award

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Dr. Liliana Aguilar-Marcelino | Cybersecurity | Best Innovation Award

Senior researcher at Instituto Nacional de Investigaciones Forestales Agricolas y Pecuarias, Mexico

Dr. Liliana Aguilar-Marcelino is a senior researcher at the Centro Nacional de Investigación Disciplinaria en Salud Animal e Inocuidad (CENID-SAI) at INIFAP, Mexico. With a focus on microbial consortia, biocontrol, biotechnology, and natural product pharmacology, she investigates the bioactivity of fungi, bacteria, insects, mites, and nematodes. Her work includes exploring sustainable pest control strategies through biocontrol agents and metabolomic studies. Dr. Aguilar-Marcelino has contributed extensively to research on the insecticidal properties of metabolites from edible mushrooms and the development of nematocidal compounds. Her innovative research aims to advance agricultural sustainability and environmental health.

 

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Education

Dr. Liliana Aguilar-Marcelino holds a distinguished academic background in the field of biological sciences. She completed her undergraduate studies in biology and later pursued advanced education, specializing in microbiology and biotechnology. Dr. Aguilar-Marcelino obtained her graduate degree (Master’s and/or Ph.D.) from a renowned institution, where she honed her expertise in microbial consortia, biocontrol, biotechnology, and the bioactivity of natural products. Her education has been instrumental in shaping her research focus, which spans a wide array of topics including natural product pharmacology, metabolomics, and the development of sustainable pest management solutions. With her strong academic foundation, Dr. Aguilar-Marcelino has become a leading researcher in her field, contributing significantly to the advancement of agricultural science and sustainable biocontrol technologies.

Experience

Dr. Liliana Aguilar-Marcelino has been a senior researcher at the Centro Nacional de Investigación Disciplinaria en Salud Animal e Inocuidad (CENID-SAI) at INIFAP, Mexico, since 2008. During her tenure, she has focused on pioneering research in microbial consortia, biocontrol, biotechnology, and metabolomics. She has led multiple studies exploring the bioactivity of fungi, bacteria, insects, mites, and nematodes, contributing to the development of sustainable solutions for pest control and agricultural health. Dr. Aguilar-Marcelino has published numerous research papers on topics such as the insecticidal effects of metabolites from edible mushrooms and the nematocidal properties of natural extracts, further establishing her expertise in the field of biocontrol and natural product pharmacology.

Research Interests

Dr. Liliana Aguilar-Marcelino’s research interests are centered on microbial consortia, biocontrol, and biotechnology, with a particular focus on the bioactivity of natural products. She specializes in metabolomics and the pharmacological potential of fungi, bacteria, insects, mites, and nematodes. Her work explores the development of natural pest control methods and sustainable agricultural practices through the study of bioactive compounds derived from edible mushrooms, fungi, and other microorganisms. Additionally, Dr. Aguilar-Marcelino investigates the potential of natural products for managing plant-parasitic nematodes and other agricultural pests, aiming to provide eco-friendly alternatives to traditional chemical controls.

 

Publication Top Noted

Title: New and future developments in microbial biotechnology and bioengineering: Trends of microbial biotechnology for sustainable agriculture and biomedicine systems: Diversity and applications

  • Authors: AA Rastegari, AN Yadav, N Yadav
  • Publisher: Elsevier
  • Cited by: 136
  • Year: 2020

Title: Micro (nano) plastics in wastewater: A critical review on toxicity risk assessment, behaviour, environmental impact, and challenges

  • Authors: S Singh, TSSK Naik, AG Anil, J Dhiman, V Kumar, DS Dhanjal, et al.
  • Journal: Chemosphere
  • Volume: 290
  • Article Number: 133169
  • Cited by: 71
  • Year: 2022

Title: Plasmodium berghei ookinetes induce nitric oxide production in Anopheles pseudopunctipennis midguts cultured in vitro

  • Authors: A Herrera-Ortíz, H Lanz-Mendoza, J Martínez-Barnetche, et al.
  • Journal: Insect Biochemistry and Molecular Biology
  • Volume: 34
  • Issue: 9
  • Pages: 893-901
  • Cited by: 65
  • Year: 2004

Title: The nematophagous fungus Duddingtonia flagrans reduces the gastrointestinal parasitic nematode larvae population in faeces of orally treated calves maintained under tropical conditions

  • Authors: P Mendoza-de-Gives, ME López-Arellano, L Aguilar-Marcelino, et al.
  • Journal: Veterinary Parasitology
  • Volume: 263
  • Pages: 66-72
  • Cited by: 52
  • Year: 2018

Title: Recent Trends in Mycological Research: Volume 1: Agricultural and Medical Perspective

  • Author: AN Yadav
  • Publisher: Springer International Publishing
  • Cited by: 49
  • Year: 2021

Title: Using molecular techniques applied to beneficial microorganisms as biotechnological tools for controlling agricultural plant pathogens and pests

  • Authors: L Aguilar-Marcelino, P Mendoza-de-Gives, LKT Al-Ani, et al.
  • Book: Molecular Aspects of Plant Beneficial Microbes in Agriculture
  • Pages: 333-349
  • Cited by: 47
  • Year: 2020

Title: Jesús Antonio Pineda-Alegría, José Ernesto Sánchez-Vázquez, Manases González-Cortazar, Alejandro Zamilpa, María Eugenia López-Arellano, Edgar Josué Cuevas-Padilla

  • Authors: P Mendoza-de-Gives, L Aguilar-Marcelino
  • Cited by: 69
  • Year: 2018

Conclusion

Dr. Liliana Aguilar-Marcelino’s innovative research in the areas of biocontrol, biotechnology, and natural product pharmacology has led to the development of several novel approaches for sustainable agricultural pest management. By focusing on natural alternatives to chemical pesticides, her work not only advances scientific understanding but also promotes environmentally friendly practices. Through her groundbreaking studies, Dr. Aguilar-Marcelino has significantly contributed to the field of sustainable agriculture, making her an outstanding candidate for the Research for Best Innovation Award.

Boquan Li | Cyber Threat | Best Researcher Award

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Dr. Boquan Li | Cyber Threat | Best Researcher Award

Assistant Professor at College of Computer Science and Technology, Harbin Engineering University, China

Dr. Boquan Li is a Tenure Track Associate Professor at the College of Computer Science and Technology, Harbin Engineering University, where he has served since January 2024. Prior to this, he was a Research Scientist at the School of Computing and Information Systems, Singapore Management University, from April 2022 to December 2023. Dr. Li holds a Ph.D. in Information Engineering from the University of Chinese Academy of Sciences and a Bachelor of Engineering from Harbin Engineering University. His research interests focus on artificial intelligence, cybersecurity, deepfake detection, and speaker recognition, with numerous publications in leading international conferences and journals. Dr. Li is also an active peer reviewer for prestigious journals like IEEE Transactions on Software Engineering.

Profile:

Education:

Dr. Boquan Li holds a Doctor of Philosophy (Ph.D.) from the University of Chinese Academy of Sciences, where he specialized in Information Engineering at the Institute of Information Engineering. He completed his Ph.D. in January 2022, building a strong foundation in artificial intelligence, cybersecurity, and data science. Prior to his doctoral studies, Dr. Li earned a Bachelor of Engineering degree from the School of Software at Harbin Engineering University in June 2016. His comprehensive academic background has equipped him with expertise in cutting-edge technologies, enabling him to contribute significantly to research in AI and cybersecurity.

Professional Experience:

Dr. Boquan Li has a diverse professional background in both academia and research. Since January 2024, he has been serving as a Tenure Track Associate Professor at the College of Computer Science and Technology, Harbin Engineering University, where he contributes to teaching and research in artificial intelligence and cybersecurity. Prior to this role, Dr. Li worked as a Research Scientist at the School of Computing and Information Systems, Singapore Management University, from April 2022 to December 2023. In this capacity, he was involved in cutting-edge research on deepfake detection, speaker recognition, and digital forensics. His professional experience highlights his expertise in developing innovative solutions to cybersecurity challenges and advancing research in AI-driven technologies.

Research Interests:

Dr. Boquan Li’s research interests focus on cutting-edge areas of artificial intelligence, cybersecurity, and multimedia forensics. He is particularly interested in deepfake detection, where he explores the vulnerabilities and robustness of detection systems across various domains. His work also covers speaker recognition, digital forensics, and adversarial attacks, aiming to develop defense mechanisms against cyber threats. Additionally, Dr. Li has a strong interest in cross-modal fusion techniques, particularly in audio-visual speech recognition, and domain adaptation methods for enhancing the accuracy of AI models across diverse datasets. His research contributes to advancing secure and reliable AI systems.

Skills:

Dr. Boquan Li possesses a diverse skill set that encompasses advanced computational techniques and a robust understanding of artificial intelligence and machine learning algorithms. He is proficient in developing and implementing deep learning models, particularly for applications in image and audio processing. His expertise extends to cybersecurity measures, with a focus on identifying vulnerabilities in AI systems and creating effective defense strategies against adversarial attacks. Additionally, Dr. Li is skilled in data analysis and statistical methods, enabling him to interpret complex datasets and derive meaningful insights. His strong programming skills in languages such as Python and proficiency with machine learning frameworks like TensorFlow and PyTorch further enhance his research capabilities in the field of computer science and technology.

Conclusion:

Dr. Boquan Li’s research addresses critical issues in AI security, deepfake detection, and adversarial defenses, areas of increasing importance in today’s technological landscape. His innovative work, combined with his academic and research experience, positions him as a strong candidate for the Best Researcher Award. His contributions have practical applications in cybersecurity and AI ethics, demonstrating both academic excellence and real-world impact.

Publication Top Noted:

  • How Generalizable are Deepfake Image Detectors? An Empirical Study
  • Two-stage Semi-supervised Speaker Recognition with Gated Label Learning
    • Authors: Xingmei Wang, Jiaxiang Meng, Kong Aik Lee, Boquan Li, Jinghan Liu
    • Year: 2024
    • Conference: International Joint Conference on Artificial Intelligence
    • Type: Conference paper
  • Assessing Backdoor Risk in Deepfake Detectors
    • Authors: Jiawen Wang, Boquan Li, Min Yu, Kam-Pui Chow, Jianguo Jiang, Fuqiang Du, Xiang Meng, Weiqing Huang
    • Year: 2024
    • Conference: IFIP WG 11.9 International Conference on Digital Forensics
    • Type: Conference paper
  • CATNet: Cross-Modal Fusion for Audio–Visual Speech Recognition
    • Authors: Xingmei Wang, Jiachen Mi, Boquan Li, Yixu Zhao, Jiaxiang Meng
    • Year: 2024
    • Journal: Pattern Recognition Letters
    • DOI: 10.1016/j.patrec.2024.01.002
  • A Residual Fingerprint-Based Defense Against Adversarial Deepfakes
  • FakeFilter: A Cross-Distribution Deepfake Detection System with Domain Adaptation
    • Authors: Jianguo Jiang, Boquan Li, Baole Wei, Gang Li, Chao Liu, Weiqing Huang, Meimei Li, Min Yu
    • Year: 2021
    • Journal: Journal of Computer Security
    • DOI: 10.3233/jcs-200124
  • Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection
    • Authors: Jianguo Jiang, Boquan Li, Min Yu, Chao Liu, Weiqing Huang, Lejun Fan, Jianfeng Xia
    • Year: 2019
    • Conference: International Conference on Artificial Neural Networks
    • DOI: 10.1007/978-3-030-30508-6_56

Hafiz Jamil | Cyber Threat | Best Researcher Award

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Dr. Hafiz Jamil | Cyber Threat | Best Researcher Award

Data Scientist at Home, United States

Dr. Hafiz Jamil is a highly accomplished researcher and engineer specializing in Electronic Engineering with a focus on Data Science, AI-driven Intelligent Systems, IoT, and Renewable Energy Solutions. With over nine years of experience, he has led numerous national and international projects, successfully developing advanced energy management systems that incorporate blockchain and AI technologies. Dr. Jamil has a proven track record in optimizing real-time data analytics, enhancing operational efficiency, and driving sustainability in energy systems. He holds a Ph.D. in Electronic Engineering, along with a Master’s in Electrical Engineering and a Bachelor’s in Electronic Engineering. Additionally, he has received multiple awards for his research excellence and is a published author in prestigious scientific journals.

Profile:

Education:

Dr. Hafiz Jamil holds a Doctor of Philosophy (Ph.D.) in Electronic Engineering, specializing in advanced energy management solutions, IoT, and intelligent systems. He also earned a Master of Science (M.Sc.) in Electrical Engineering, where he focused on integrating AI and blockchain technologies into energy systems. His academic journey began with a Bachelor of Science (B.Sc.) in Electronic Engineering. In addition to his formal degrees, Dr. Jamil has pursued specialized certifications in fields such as Advanced Machine Learning, Blockchain for Energy, Python for Data Science, MATLAB for Engineers, and IoT System Architecture.

Professional Experience:

Dr. Hafiz Jamil possesses extensive professional experience in the fields of Electronic Engineering, Data Science, and Renewable Energy Solutions. Currently serving as a Research and Development Engineer at KETEP’s Big Data Research Center in South Korea, he has spearheaded the integration of big data analytics and IoT systems, significantly enhancing operational efficiency by reducing latency and downtime. His key accomplishments include developing advanced energy management solutions that incorporate blockchain and AI technologies, resulting in a 25% improvement in operational reliability. Previously, Dr. Jamil worked as a Consultant in Project Portfolio Management at CSU Science and Technology Park in China, where he optimized human activity recognition systems and enhanced energy efficiency in electric vehicles. Earlier in his career, he served as a Data Engineer in Power Systems in Pakistan, where he led automation projects and mentored teams to improve project success rates. His work is characterized by a strong commitment to innovation, collaboration with industry leaders, and a focus on sustainability in energy management.

Research Interests:

Dr. Hafiz Jamil’s research interests lie at the intersection of Electronic Engineering, Data Science, and Renewable Energy Solutions. He is particularly focused on developing AI-driven intelligent systems and Internet of Things (IoT) applications that enhance energy management and sustainability. His work encompasses advanced data analytics and machine learning, aiming to optimize real-time data processing and improve system performance in energy systems. Dr. Jamil has a keen interest in integrating blockchain technology for enhanced transparency and security in energy transactions. His research also includes the exploration of digital twin technology for optimizing renewable energy use and reducing peak loads in energy systems. Additionally, he is dedicated to advancing federated learning and smart grid technologies to promote energy efficiency and resource management in modern energy infrastructures.

Skills:

Dr. Hafiz Jamil possesses a diverse skill set that encompasses various domains within Electronic Engineering and Data Science. He is highly proficient in developing and implementing machine learning models, data analytics, and AI-driven intelligent systems, with expertise in programming languages such as Python, MATLAB, and C++. Dr. Jamil has a strong command of advanced technologies, including blockchain integration for energy systems and IoT development for smart grids. His technical capabilities extend to real-time monitoring and predictive optimization, allowing him to enhance operational efficiency in energy management solutions. Additionally, he excels in project management and cross-functional collaboration, demonstrating leadership in guiding teams through complex technological challenges. Dr. Jamil’s skills in automation, data governance, and scalable model deployment further contribute to his ability to drive innovation and improve system performance across various projects in the field.

Conclusion:

Given Dr. Hafiz Jamil’s impressive track record in research, innovation, and practical application in fields such as AI, IoT, and renewable energy systems, he is highly suitable for the Best Researcher Award. His research contributions, technical leadership, and groundbreaking work in enhancing energy efficiency make him an outstanding candidate for this recognition.

Publication Top Noted:

An Optimized Ensemble Prediction Model Using AutoML Based on Soft Voting Classifier for Network Intrusion Detection

  • Journal: Journal of Network and Computer Applications
  • Cited By: 58
  • Year: 2023
  • Contributors: M.A. Khan, N. Iqbal, H. Jamil, D.H. Kim

PetroBlock: A Blockchain-Based Payment Mechanism for Fueling Smart Vehicles

  • Journal: Applied Sciences
  • Cited By: 52
  • Year: 2021
  • Contributors: F. Jamil, O. Cheikhrouhou, H. Jamil, A. Koubaa, A. Derhab, M.A. Ferrag

An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing

  • Journal: Sensors
  • Cited By: 48
  • Year: 2021
  • Contributors: A. Ali, M.M. Iqbal, H. Jamil, F. Qayyum, S. Jabbar, O. Cheikhrouhou, M. Baz, et al.

Optimal Scheduling of Campus Microgrid Considering the Electric Vehicle Integration in Smart Grid

  • Journal: Sensors
  • Cited By: 45
  • Year: 2021
  • Contributors: T. Nasir, S. Raza, M. Abrar, H.A. Muqeet, H. Jamil, F. Qayyum, et al.

EEG-Based Neonatal Sleep Stage Classification Using Ensemble Learning

  • Journal: Computers, Materials & Continua
  • Cited By: 37
  • Year: 2022
  • Contributors: S.F. Abbasi, H. Jamil, W. Chen