Lisandra Díaz de la Paz | Data Science | Best Researcher Award

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Assoc. Prof. Dr. Lisandra Díaz de la Paz | Data Science | Best Researcher Award

Assoc. Prof. Dr. Lisandra Díaz de la Paz, Central University “Marta Abreu” of Las Villas, Cuba

Assoc. Prof. Dr. Lisandra Díaz de la Paz is a Cuban computer scientist and academic with a Ph.D. in Technical Sciences (2023), a Master’s (2011), and a Bachelor’s (2008) in Computer Science from the Central University “Marta Abreu” of Las Villas (UCLV). She serves as an Associate Professor and researcher specializing in databases, decision-support systems, data integration, metadata management, and artificial intelligence. With over 15 years of teaching experience, she has instructed various undergraduate and postgraduate courses in computer science and related fields. Dr. Díaz de la Paz has completed extensive postgraduate training in areas such as software engineering, machine learning, and data science. She currently leads the Information Systems discipline and serves as Vice Dean of Research and Postgraduate Studies at the MFC Faculty, UCLV. Her research focuses on data quality models, big data, Python programming, semantic web, LLMs, and generative AI. She is an active contributor to Cuba’s technological advancement.

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Academic Background

Assoc. Prof. Dr. Lisandra Díaz de la Paz is a distinguished academic in the field of Computer Science with a robust educational foundation acquired from the Central University “Marta Abreu” of Las Villas (UCLV), Cuba. She earned her Bachelor’s degree in Computer Science in July 2008, followed by a Master’s degree in the same field in December 2011. Demonstrating a continuous commitment to academic excellence and research, she completed her Doctorate in Technical Sciences in November 2023. This progression reflects her deepening expertise and scholarly dedication within computing and technical disciplines. Her academic journey at UCLV has equipped her with strong theoretical and practical knowledge, forming the basis for her professional contributions as a university professor, researcher, and academic leader. Dr. Díaz de la Paz’s qualifications underpin her role in advancing research in artificial intelligence, databases, and data systems while mentoring the next generation of computing professionals in Cuba and beyond.

Professional Role and Academic Specialization

Assoc. Prof. Dr. Lisandra Díaz de la Paz is a dedicated professor and researcher with a strong focus on the field of Computer Science. Currently holding the academic rank of Associate Professor, she plays a vital role in higher education by teaching, mentoring, and guiding students across multiple levels of university instruction. Her primary specialization lies in computing, where she has developed expertise in areas such as databases, data quality, artificial intelligence, decision-support systems, and big data technologies. As both an educator and researcher, she combines theoretical knowledge with practical applications, contributing to academic excellence and technological advancement. Her position as a faculty member enables her to engage in curriculum development, academic leadership, and innovative research initiatives. Dr. Díaz de la Paz’s dual role as a professor and researcher allows her to bridge the gap between knowledge creation and dissemination, making her an influential figure in the Cuban academic and scientific community.

Awards and Recognitions

Assoc. Prof. Dr. Lisandra Díaz de la Paz has received multiple prestigious awards in recognition of her contributions to computing and educational technologies. She was a co-author of the project “Algorithms and Tools for the Library Management System,” which earned the 2024 Provincial CITMA Award in Villa Clara. In 2021, she received the Provincial CITMA Award for her work on improving the accuracy and completeness of bibliographic records in MARC 21 format. In 2019, she received the Annual Award from the Minister of Higher Education for her research in database systems and computing. Her 2018 work on the ABCD Library Management System implementation across Cuban higher education institutions was recognized for its scientific and educational impact. She also received CITMA awards in 2016 and 2012 for her innovative contributions to active database rule maintenance and business rule implementation in relational databases, respectively—highlighting her sustained excellence in research and technical innovation.

Research Focus

Assoc. Prof. Dr. Lisandra Díaz de la Paz focuses her research primarily on data quality, metadata management, bibliographic systems, and decision support through data-driven computing. Her work encompasses key areas such as the completeness and accuracy of bibliographic records in MARC 21 format, ETL process optimization, metadata profiling, and author name disambiguation using ontologies and deep learning. She has also explored big data integration with NoSQL systems, MapReduce techniques for anomaly detection, and frameworks for metadata quality evaluation in the context of open science. Her contributions have practical applications in library science, digital repositories, and institutional decision-making, particularly within educational and academic information systems. Additionally, her interdisciplinary approach blends artificial intelligence, machine learning, semantic web technologies, and business intelligence, supporting national and international collaboration for improving data infrastructure. These efforts position her as a leading researcher in data-centric computing, database technologies, and intelligent information systems.

Publication Top Notes

  • 📘 Algorithm to correct instance-level anomalies in large data using MapReduce – Cited by 7 – 2016

  • 📗 Data quality analysis in ABCD suite sources – Cited by 7 – 2015

  • 📕 Techniques to capture changes and maintain updated data warehouse – Cited by 5 – 2015

  • 📙 Data market for decision-making on teaching/research staff at UCLV – Cited by 5 – 2013

  • 📒 Techniques to capture data changes (extended version) – Cited by 4 – 2015

  • 📓 Automation of data loading processes in HR data market at UCLV – Cited by 4 – 2014

  • 📘 Weights estimation in completeness measurement of bibliographic metadata – Cited by 3 – 2021

  • 🧠 Author name disambiguation using ontology & deep learning – Cited by 1 – 2022

  • 📊 CompMARC tool for measuring completeness in MARC 21 – Cited by 1 – 2016

  • 📚 Model for metadata quality evaluation: Proposal for open science – Published – 2024

  • 📝 Accuracy measurement of author names in MARC 21 records – Published – 2018

  • 📈 Optimal weight estimation for completeness in MARC 21 metadata – Published – 2017

  • 🔍 Metadata profiling tool in MARC 21 PMMarc v2.0 – Published – 2017

  • 💾 Method for selecting data model and NoSQL system in big data – Published – 2017

  • 🛠 Procedure to improve completeness in MARC 21 records – Published – 2017

Aditi Nag | Bioinformatics | Best Researcher Award

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Assist. Prof. Dr. Aditi Nag | Bioinformatics | Best Researcher Award

Assitant Professor at Dr. B. Lal Institute of Biotechnology, India

Dr. Aditi Nag is an Assistant Professor at Dr. B. Lal Institute of Biotechnology, Jaipur, India. She completed her B.Sc. (2007) in Industrial Microbiology, Botany, and Zoology with a 75.25% from the University of Rajasthan and her M.Sc. (2009) in Biotechnology with a 74.66% from the Department of Botany, University of Rajasthan. She obtained her PhD in 2017 from the Indian Institute of Technology, Kanpur, under the guidance of Dr. Jonaki Sen and Dr. Amitabha Bandyopadhyay, with a thesis focused on BMP signaling in adult tissue homeostasis.

Publication Profile

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Academic Qualifications 🎓

Assist. Prof. Dr. Aditi Nag holds a B.Sc. in Industrial Microbiology, Botany, and Zoology from University Maharani’s College, University of Rajasthan (2007) with 75.25%. She completed her M.Sc. in Biotechnology from the Department of Botany, University of Rajasthan, with 74.66% in 2009. Dr. Nag earned her PhD in 2017 from the Indian Institute of Technology, Kanpur, where she investigated BMP signaling in adult tissue homeostasis. Her thesis was supervised by Dr. Jonaki Sen and Dr. Amitabha Bandyopadhyay. Dr. Nag’s academic journey reflects her dedication to advancing scientific knowledge. 🎓🔬

Work Experience

Assist. Prof. Dr. Aditi Nag currently holds the position of Assistant Professor at Dr. B. Lal Institute of Biotechnology, a role she has been in since 2018. In this position, Dr. Nag contributes her expertise in biotechnology, focusing on teaching and research. She has been an integral part of the institution, helping to shape the academic environment and furthering scientific research in her field. Her dedication is evident through her work at the institute, and she currently receives a pay scale of ₹4.8 lakhs. 🌟📚

Professional Recognition & Awards

Assist. Prof. Dr. Aditi Nag has been recognized for her outstanding contributions to the field of biotechnology. She received the First Prize in Oral Presentation at the International Conference ISSUE-2022 at UPES, Dehradun (2023). Dr. Nag also secured the First Prize in Poster Presentation at the International Conference Biosangam-2020 (2020). Her academic excellence is further highlighted by prestigious awards such as GATE 2008 (IITK), JRF-UGC and SRF (UGC), and the Summer Research Fellowship-2008 (IAS-INSA-NASI). Additionally, she won the First Prize in Poster Presentation at the National Seminar on Biotechnology in Sustainable Agriculture and Second Prize at the Indian Science Congress (2008). 🌟🎓

Teaching Experience

Since June 2018, Assist. Prof. Dr. Aditi Nag has been a regular faculty member at Dr. B. Lal Institute of Biotechnology, teaching both PG and UG courses in Genetics, Developmental Biology, Genomics, Proteomics, Biostatistics, and Bioinformatics. She has also conducted tutorials for B.Tech students on Molecular Cell Biology and assisted professors in correcting answer sheets for courses like Compulsory Life Sciences, Biochemistry, and Molecular Cell Biology. Dr. Nag has extensive invigilation experience for department entrance exams and regular assessments. Additionally, she has trained over one M.Tech student in molecular biology techniques and lab practices. 🎓🔬

Research Focus

Assist. Prof. Dr. Aditi Nag’s research is primarily centered on environmental biotechnology, focusing on wastewater-based epidemiology (WBE) for tracking viruses like SARS-CoV-2. Her work investigates wastewater surveillance as an early warning system for pandemics and the role of microbial interactions in wastewater treatment. Dr. Nag’s studies also explore BMP signaling in tissue homeostasis, including skeletal, hair follicle, and intestinal health. Additionally, her research extends to nanotechnology, CRISPR-Cas systems, and the development of vaccines against emerging viruses. She has contributed to several publications on wastewater treatment, viral detection, and public health surveillance. 🌍🦠🧫

Publication Top Notes

  • Sewage surveillance for the presence of SARS-CoV-2 genome as a useful wastewater-based epidemiology (WBE) tracking tool in India – 177 citations, 2020 🌍🦠
  • Effect of earthworms in reduction and fate of antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs) during clinical laboratory wastewater treatment – 45 citations, 2021 🐛🧬
  • Design, performance evaluation and investigation of the dynamic mechanisms of earthworm-microorganisms interactions for wastewater treatment through vermifiltration technology – 37 citations, 2020 💧🌱
  • BMP signaling is required for adult skeletal homeostasis and mediates bone anabolic action of parathyroid hormone – 32 citations, 2016 🦴🔬
  • Monitoring of SARS-CoV-2 Variants by Wastewater-Based Surveillance as a Sustainable and Pragmatic Approach—A Case Study of Jaipur (India) – 22 citations, 2022 🧫🔎
  • Successful application of wastewater-based epidemiology in prediction and monitoring of the second wave of COVID-19 with fragmented sewerage systems – 18 citations, 2022 💧🦠
  • Imprints of Lockdown and Treatment Processes on the Wastewater Surveillance of SARS-CoV-2: A Curious Case of Fourteen Plants in Northern India – 17 citations, 2021 🌍🚰
  • RNA-Seq of untreated wastewater to assess COVID-19 and emerging and endemic viruses for public health surveillance – 15 citations, 2023 🧬🌍
  • Wastewater surveillance could serve as a pandemic early warning system for COVID-19 and beyond – 13 citations, 2023 💡🦠
  • Detection of SARS-CoV-2 RNA in fourteen wastewater treatment systems in Uttarakhand and Rajasthan States of North India – 11 citations, 2020 🌍🦠
  • Constructed Wetlands and vermifiltration two successful alternatives of wastewater reuse: A commentary on development of these alternate strategies of wastewater treatment – 3 citations, 2023 💧🌱
  • BMP signalling is critical for maintaining homeostasis of hair follicles and intestine in adult mice – 3 citations, 2017 🧬🔬
  • Population Infection Estimation from Wastewater Surveillance for SARS-CoV-2 in Nagpur, India During the Second Pandemic Wave – 2 citations, 2024 💧🦠
  • Nanoparticles: Characters, applications, and synthesis by endophytes – 2 citations, 2023 🌱🔬
  • COVID-19 Vaccines: An Account of Need and Efficacy vs Safety and Challenges – 2 citations, 2021 💉🦠

 

 

Dinar Ajeng Kristiyanti | Data Mining | Best Researcher Award

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Dr. Dinar Ajeng Kristiyanti | Data Mining | Best Researcher Award

Dr. Dinar Ajeng Kristiyanti, Universitas Multimedia Nusantara, Indonesia

Dr. Dinar Ajeng Kristiyanti is a passionate Lecturer and Assistant Professor with over a decade of experience in computer science. She holds a Bachelor’s and Master’s in Computer Science from Sekolah Tinggi Manajemen dan Informatika Nusa Mandiri and is pursuing her PhD at Institut Pertanian Bogor 🎓. Her research focuses on Sentiment Analysis, Machine Learning, and Data Mining 💻. Dr. Kristiyanti has published 20 national and 8 international papers 📑, earning recognition as a top 10 author in the SINTA Index (2020-2022). She is also a recipient of several awards for her academic excellence 🏅.

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Educational Background 🎓

Dr. Dinar Ajeng Kristiyanti has a strong academic foundation in computer science. She earned her Bachelor of Information Systems from Sekolah Tinggi Manajemen dan Informatika Nusa Mandiri (2011-2012) with a GPA of 3.76 📘. She continued her studies at the same institution, completing her Master’s in Computer Science (2012-2014) with an impressive GPA of 3.88 🏅. Currently, Dr. Kristiyanti is pursuing her Doctorate in Computer Science at Institut Pertanian Bogor (2020-present), further advancing her expertise in the field of data science and machine learning 💻.

 

Work Experience 🏫

Dr. Dinar Ajeng Kristiyanti has extensive teaching experience across several prestigious institutions. Since 2010, she has been a Lecturer at Universitas Bina Sarana Informatika, where she contributes to the fields of computer science and informatics. From 2015 to 2021, she also served as a Lecturer at Universitas Nusa Mandiri, imparting her knowledge to future professionals. In 2014, Dr. Kristiyanti was a Guest Lecturer at Universitas Budi Luhur, further expanding her academic reach. Her diverse teaching roles reflect her dedication to educating and mentoring students across various institutions 📚👩‍🏫.

 

Award History and Personal Achievements 🏆

Dr. Dinar Ajeng Kristiyanti has been recognized for her academic excellence and contributions to research. She ranked in the Top Ten Authors in the SINTA Science and Technology Index (2020-2022) for her performance at Universitas Bina Sarana Informatika and Universitas Nusa Mandiri 📊. She has also won awards for Best Paper and Presenter at various national and international seminars 🌍. Additionally, Dr. Kristiyanti was honored as the Best Graduate of her Master’s in Computer Science program at STMIK Nusa Mandiri 🎓. Her achievements reflect her dedication and impact in the field of computer science.

 

Publication Top Notes

  • Comparison of SVM & Naïve Bayes algorithm for sentiment analysis (2018) 📊 – Cited by 80
  • Sentiment analysis of smartphone product reviews using SVM-based PSO (2016) 📱 – Cited by 55
  • Prediction of Indonesia presidential election results using Twitter sentiment analysis (2019) 🇮🇩 – Cited by 50
  • Feature selection for cosmetic product review using GA, PSO, and PCA (2017) 💄 – Cited by 45
  • Comparison of Naïve Bayes and SVM using PSO for e-wallet review (2020) 💳 – Cited by 39
  • Sentiment analysis for Halodoc app using Naïve Bayes, SVM, and KNN (2021) 🩺 – Cited by 34
  • Sentiment analysis of cosmetic reviews using SVM and PSO (2015) 💅 – Cited by 32
  • Machine Learning for Beginners (2022) 📖 – Cited by 29
  • E-wallet sentiment analysis using Naïve Bayes and SVM (2020) 💼 – Cited by 25
  • Sentiment analysis of cosmetic product review using feature selection comparison (2015) 👗 – Cited by 25
  • Decision support system for employee bonus using AHP at Buah Hati Ciputat Hospital (2018) 🏥 – Cited by 24
  • Decision support system for employee selection with profile matching analysis (2017) 🧑‍💼 – Cited by 20
  • Web-based thesis monitoring system for Mercu Buana University (2020) 💻 – Cited by 16
  • Application of seasonal multiplicative decomposition for inventory forecasting at PT. Agrinusa (2020) 📦 – Cited by 13
  • Sentiment analysis of public acceptance of COVID-19 vaccines in Indonesia (2023) 💉 – Cited by 11
  • Feature selection using v-shaped transfer function for salp swarm algorithm in sentiment analysis (2023) 🐟 – Cited by 11

Conclusion ✅

Dr. Dinar Ajeng Kristiyanti’s strong academic credentials, prolific research output, and numerous recognitions make her highly suitable for the Best Researcher Award. Her expertise in computer science, coupled with her dedication to innovation and teaching, align well with the award’s criteria, making her a strong candidate for this prestigious recognition.