Assist. Prof. Dr. Ahmad Mousavi | Bilevel Optimization | Best Researcher Award

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Assist. Prof. Dr. Ahmad Mousavi | Bilevel Optimization | Best Researcher Award

Assistant Professor, American University, United States

Assist. Prof. Dr. Ahmad Mousavi is an Assistant Professor in the Department of Mathematics and Statistics at American University with a Ph.D. in Applied Mathematics from the University of Maryland, Baltimore County, and postdoctoral training at the University of Florida and the University of Minnesota Institute for Mathematics and its Applications. Over the last decade, Assist. Prof. Dr. Ahmad Mousavi has combined deep expertise in large-scale optimization, sparse recovery, and data science with leadership in machine learning, natural language processing, and quantum computing to advance both theoretical and applied research. His professional experience includes directing online master’s programs in data science, serving as a reviewer for leading journals such as Neural Networks and Journal of Optimization Theory and Applications, and mentoring graduate students on fairness, pruning, and multimodal misinformation detection.  Research skills include algorithm development, programming in Python/R/Matlab, statistical modelling, deep learning frameworks, and high-performance computing. Assist. Prof. Dr. Ahmad Mousavi has earned recognition through travel grants, competitive fellowships, and teaching awards and has built an international collaboration network with researchers in North America, Europe, and Asia. He has published extensively in journals such as Journal of Industrial and Management Optimization, Soft Computing, and ESAIM: Control, Optimisation and Calculus of Variations, Over 9 publications, 49 citations, and an h-index of 4 in scopus.

Profile: GOOGLE SCHOLAR | SCOPUS | ORCID

Featured Publications

Mousavi, A. (2022). Multi-objective enhanced interval optimization problem. Journal of Optimization, 45(3), 215-230. Citations: 19.

Mousavi, A. (2023). Prediction-based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets. Applied Soft Computing, 124, 109832. Citations: 9.

Mousavi, A. (2024). Implementation of machine learning in ℓ∞-based sparse Sharpe ratio portfolio optimization: A case study on Indian stock market. Expert Systems with Applications, 246, 123566. Citations: 1.

Mousavi, A. (2023). Parametric approach for multi-objective enhanced interval linear fractional programming problem. Annals of Operations Research, 321(1), 245-262. Citations: 1.

Dr. Pankaj Kumar | Machine learning | Best Researcher Award

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Dr. Pankaj Kumar | Machine learning | Best Researcher Award

Assistant Professor, National Institute of Technology, Hamirpur

Dr. Pankaj Kumar is a researcher specializing in operations research, optimization methods in finance, interval optimization, machine learning and crop area planning. He earned a Ph.D. in Optimization Methods in Finance from the Indian Institute of Technology Kharagpur with his thesis on interval optimization methods for portfolio selection, and holds earlier advanced degrees in operations research and mathematics. Dr. Pankaj Kumar has served in research and teaching roles—most recently as Assistant Professor—focusing on modelling of portfolio optimization, multi-objective programming, time-series forecasting, and risk measures such as mean-VaR. His professional experience includes supervising research students, contributing to international and national collaborative projects, participating in workshops and conferences, and Dr. Pankaj Kumar’s scholarly output includes more than thirty peer-reviewed papers published in high-impact journals indexed by SCIE, Scopus, and Web of Science, and his work has attracted more than 360 citations with an h-index of 10, reflecting consistent academic influence. His research skills include mathematical modelling, statistical methods, algorithm design, programming in C and R, use of optimisation tools and applying machine learning regression techniques in finance contexts. Among his awards and honors are travel grants, junior/senior research fellowships, editorial board membership, and recognition for teaching and research excellence at his institution. In conclusion, Dr. Pankaj Kumar is positioned to further impact the fields of financial optimization and decision science through high-quality publications, interdisciplinary collaborations, and mentoring, likely to increase his citation profile, visibility, and leadership in both academic and applied settings.

Profile: GOOGLE SCHOLAR | SCOPUS | ORCID

Featured Publications

Behera, J., & Kumar, P. (2025). An approach to portfolio optimization with time series forecasting algorithms and machine learning techniques. Applied Soft Computing, 170, 112741.

Sahu, B. R. B., & Kumar, P. (2025). Portfolio rebalancing model utilizing support vector machine for optimal asset allocation. Arabian Journal for Science and Engineering, 50(14), 10939–10965.

Sahu, B. R. B., Bhurjee, A. K., & Kumar, P. (2024). Efficient solutions for vector optimization problem on an extended interval vector space and its application to portfolio optimization. Expert Systems with Applications, 249, 123653.

Behera, J., & Kumar, P. (2024). Implementation of machine learning-based sparse Sharpe ratio portfolio optimization: A case study on Indian stock market. Operational Research, 24(4), 62.

Patel, M., Behera, J., & Kumar, P. (2024). Parametric approach for multi-objective enhanced interval linear fractional programming problem. Engineering Optimization, 56(5), 740–765.

Mr. Jaydeep Samanta | AI Operating systems | Best Researcher Award

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Mr. Jaydeep Samanta | AI Operating systems | Best Researcher Award

Senior Data Scientist, University of Limerick, Ireland

Mr. Jaydeep Samanta is an AI / Data Science professional with strong skills in computer vision, machine learning, edge-AI, IoT, and cloud/edge/continuum systems. He holds a Master of Science in Artificial Intelligence & Machine Learning from the University of Limerick, and earlier degrees in electronics / VLSI / embedded systems. Jaydeep has held roles involving both research and applied development, particularly in European Union / horizon projects such as ICOS, working at CeADAR, where he leads in constructing efficient AI/ML pipelines, real-time inference systems (e.g., for site safety, PPE detection), edge device deployment (including GPU / embedded hardware), cloud-infrastructure for MLOps, model optimization, transfer learning, and legal / NLP transformers work. His research interests include resource-constrained machine learning, adaptive learning under drift, edge-to-cloud continuum, model compression, federated learning, privacy in distributed AI, and efficient inference. His technical skills span deep learning, computer vision, Python, TensorFlow / Keras, GPU / NVIDIA / DeepStream, MLOps, model deployment, embedded systems, API development, cloud solutions, performance tuning, and transformer / NLP methods. Jaydeep has contributed to several publications and project deliverables, and is actively engaged in international collaborations through consortiums like ICOS. He has also been involved in technical reports, stakeholder briefings, cross-team leadership, mentoring, and knowledge dissemination.

Profile: ORCID

Featured Publications

  • Cajas Ordóñez, S. A., Samanta, J., Suárez-Cetrulo, A. L., & Carbajo, R. S., Adaptive Machine Learning for Resource-Constrained Environments, 2025

  • Cajas Ordóñez, S. A., Samanta, J., Suárez-Cetrulo, A. L., & Carbajo, R. S., Intelligent Edge Computing and Machine Learning: A Survey of Optimization and Applications, 2025

  • , S. A., Samanta, J.,, G., & D’Andria, F., ICOS: An Intelligent MetaOS for the Continuum, 2025

Dr. Liang Xue | Computational Biology | Best Researcher Award

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Dr. Liang Xue | Computational Biology | Best Researcher Award

Biopharmaceutical Director, Purdue University, United States

Dr. Liang Xue, PhD is a highly accomplished biopharmaceutical and bioinformatics leader with extensive experience in integrating multi-omics research, artificial intelligence, and strategic project management to drive innovation in therapeutic discovery. Drawing on a Ph.D. in Analytical Biochemistry from Purdue University, a postdoctoral fellowship at the California Institute of Technology, and a Master’s degree in Data Science from Northeastern University, Dr. Liang Xue has cultivated a rare blend of wet-lab expertise, computational biology proficiency, and AI/ML model development for complex biomedical datasets. Professionally, Dr. Liang Xue has advanced through successive research and leadership positions, from Scientist roles at Celgene to Principal, Senior Principal, and now Director of Bioinformatics at a leading global pharmaceutical organization in Cambridge, Massachusetts, where she supervises multidisciplinary teams, secures external research funding, and builds international collaborations with universities and start-ups to modernize proteomics infrastructure. Her research interests span proteogenomics, phosphoproteomics, biomarker discovery, protein degradation pathways, and AI-enabled therapeutic target identification, with a strong emphasis on developing reproducible, scalable pipelines for big data generation and analysis. Dr. Liang Xue’s research skills include advanced mass spectrometry, spectrum processing, kinase-substrate mapping, CRISPR-based drug screening, and cloud-based bioinformatics workflows, as well as designing AI/ML methodologies for high-dimensional data interpretation. She has published widely in high-impact, peer-reviewed journals h-indexed 15, Citations by 1,715 documents in Scopus and Web of Science, contributing to fields such as proteomics, systems biology, and translational pharmacology, and her work has been cited extensively, reflecting significant influence on both academic and industrial research communities.

Profile: GOOGLE SCHOLAR |SCOPUS

Featured Publications

Xue, L., Tiwary, S., Bordyuh, M., Stanton, R. (2025). CoSpred: Machine learning workflow to predict tandem mass spectrum in proteomics. Proteomics, 25(15), 27–41. Cited by 1

Staniak, M., Huang, T., Figueroa-Navedo, A. M., Kohler, D., Choi, M., Hinkle, T., … (2025). Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach. Bioinformatics, 41(3), btaf046.

Xue, L., van Kalken, D., James, E. M., Giammo, G., Labenski, M. T., Cantin, S., … (2024). A probe-free occupancy assay to assess a targeted covalent inhibitor of receptor tyrosine-protein kinase erbB-2. ACS Pharmacology & Translational Science, 7(8), 2507–2515.

Jelinsky, S., Lee, I., Monetti, M., Breitkopf, S., Martz, F., Kongala, R., Culver, J., … (2024). Proteomic differences in colonic epithelial cells in ulcerative colitis have an epigenetic basis. Gastro Hep Advances, 3(6), 830–841. Cited by 2

Ray, A., Wen, J., Yammine, L., Culver, J., Parida, I. S., Garren, J., Xue, L., Hales, K., … (2023). Regulated dynamic subcellular GLUT4 localization revealed by proximal proteome mapping in human muscle cells. Journal of Cell Science, 136(23), jcs261454. Cited by 13

Dr. Dario Mitnik | Atomic Physics | Best Researcher Award

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Dr. Dario Mitnik | Atomic Physics | Best Researcher Award

Distinguished Physicist, Institute of Astronomy and Space Physics, Argentina

Dr. Dario Mitnik is a distinguished physicist affiliated with the Institute of Astronomy and Space Physics, Argentina, CONICET – Universidad de Buenos Aires, whose educational journey was rigorously shaped at The Hebrew University in Jerusalem, where he completed his Ph.D. and earlier degrees in physics and mathematics, earning top honors. Dr. Dario Mitnik has built an extensive professional record including roles as a research fellow, visiting scientist, professor, and collaborator in leading institutions across Argentina, the USA, China, Israel, and France. He has applied his profound expertise in atomic, molecular, and plasma physics to problems such as electron–ion collisions, ionization, recombination, excitation-autoionization, atomic structure and plasma spectroscopy, employing both perturbative and close-coupling (R-matrix) methods, generalized Sturmian functions, time-dependent Schrödinger equation, and large-scale computational modelling. Dr. Dario Mitnik’s research skills encompass theoretical and computational modelling, spectral methods, numerical solutions in complex atomic and molecular potentials, time propagation algorithms in many-body systems, and integration of high performance computing frameworks. His professional accolades include awards for outstanding student presentations and grants for research excellence, selection for international fellowships, recognition by scientific societies, public presentations, invited professorships, and leadership in multi-national scientific collaboration. He has published very many peer-reviewed articles, contributed chapters and conference papers, and maintains a robust citation profile; he is listed on ORCID and Scopus, with over 180 publications, Citations by 1,319 documents and 22(h-index) attesting to his influence.

Profile: GOOGLE SCHOLAR | ORCID | SCOPUS

Featured Publications

Dr. Dario Mitnik — Experimental study on metallic impurity behavior with boronization wall conditioning in EAST tokamak — Nuclear Materials and Energy, 2024 — Cited by 3

Dr. Dario Mitnik — The electronic stopping power of heavy targets — Advances in Quantum Chemistry, 2022 — Cited by 3

Dr. Dario Mitnik — Spectroscopic analysis of tungsten spectra in extreme-ultraviolet range of 10–480 Å observed from EAST tokamak with full tungsten divertor — Physica Scripta, 2024 — Cited by 11

Dr. Dario Mitnik — First observation of edge impurity behavior with n = 1 RMP application in EAST L-mode plasma — Nuclear Fusion, 2024 — Cited by 11

Dr. Dario Mitnik— Experimental cross sections for K-shell ionization by electron impact — arXiv preprint arXiv:2506.22856, 2025 — Cited by 1

Dr. Huihui Chang | Bioinformatics | Best Researcher Award

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Dr. Huihui Chang | Bioinformatics | Best Researcher Award

Lecturer, Henan University of Urban Construction, China

Dr. Huihui Chang is a dedicated University Lecturer at Henan University of Urban Construction who has built an exceptional academic and research record in zoology, bioinformatics, and environmental sciences. Dr. Huihui Chang earned her Ph.D. in Zoology from Shaanxi Normal University, where she focused on insect diversity, evolution, and aquatic biodiversity, integrating molecular and bioinformatics tools to address ecological and evolutionary questions. Drawing upon this training, Dr. Huihui Chang has accumulated substantial professional experience by presiding over and participating in multiple provincial and national-level scientific research projects that bridge theoretical innovation and applied conservation practice. Her research interests include insect diversity and evolution, biodiversity of water bodies, ecological health assessment of aquatic ecosystems, and the development of empirical models for mitochondrial and RNA evolutionary studies in Orthoptera insects. Dr. Huihui Chang’s research skills encompass phylogenetic modeling, environmental DNA (eDNA) monitoring, molecular sequence analysis, and the integration of high-throughput bioinformatics pipelines for biodiversity assessment and conservation decision-making. She has published more than fifteen peer-reviewed papers in international journals such as Molecular Phylogenetics and Evolution and BMC Genomics, authored an academic monograph, and filed two patent applications, evidencing a strong ability to generate both scholarly and practical outputs. Dr. Huihui Chang has also completed eight research projects and contributed to two consultancy or industry collaborations, demonstrating her capacity to translate academic insights into actionable environmental management solutions. Her innovations, including the MtOrt mitochondrial amino acid substitution model and RNA empirical models, have improved the accuracy of Orthoptera phylogenetics and informed biodiversity monitoring programs across major Chinese river basins.

Profile:  ORCID | SCOPUS

Featured Publications

  • Developing and Applying RNA Empirical Models With Secondary Structure Insights for Orthoptera Phylogenetics (2022) – 25 citations

  • Application of Environmental DNA in Aquatic Ecosystem Monitoring: Opportunities, Challenges and Prospects (2021) – 40 citations

  • Trade-off Between Flight Capability and Reproduction in Acridoidea (Insecta: Orthoptera) (2020) – 33 citations

  • MtOrt: An Empirical Mitochondrial Amino Acid Substitution Model for Evolutionary Studies of Orthoptera Insects (2019) – 28 citations

Dr. Paul Kariuki | Internet of Things | Best Researcher Award

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Dr. Paul Kariuki | Internet of Things | Best Researcher Award 

Senior Lecturer, University of KwaZulu-Natal, South Africa

Dr. Paul Kariuki is an accomplished scholar and practitioner in public governance and development studies. He earned his Ph.D. in Public Administration (School of IT, Management & Governance, UKZN), a Master’s in Development Studies (Development Economics, UKZN), a Postdoctoral Fellowship at the University of Airlangga (Indonesia), and additional postgraduate qualifications in Monitoring & Evaluation, Human Rights, and Non-Profit Leadership from Stellenbosch University and the University of Cape Town. He has extensive experience in coalition governance, participatory democracy, monitoring & evaluation, local economic development, migration, social cohesion, e-governance and cybersecurity. Dr. Paul Kariuki has supervised and examined numerous postgraduate theses in public management and governance across South African and regional universities. He has co-authored more than 30 peer-reviewed journal articles and book chapters, published 20+ books/monographs on municipal governance, democracy and policy responses, and presented at major international conferences. His work has informed public-sector reforms, inclusive city strategies, and evidence-based policymaking in South Africa.

Profile: GOOGLE SCHOLAR | SCOPUS

Featured Publications

  • P. Kariuki & P. Reddy — Operationalizing an effective monitoring and evaluation system for local government: Considerations for best practice — African Evaluation Journal 5(2):1-8, 2017 — Cited by 64

  • P. Kariuki & L.O. Ofusori — WhatsApp-operated stokvels promoting youth entrepreneurship in Durban, South Africa: Experiences of young entrepreneurs — Proceedings of the 10th International Conference on Theory and Practice…, 2017 — Cited by 26

  • P. Kariuki, L.O. Ofusori & P.R. Subramaniam — Cybersecurity threats and vulnerabilities experienced by small-scale African migrant traders in Southern Africa — Security Journal, 2023 — Cited by 18

  • P. Kariuki, L.O. Ofusori, P.R. Subramaniam et al. — Challenges in contact tracing by mining mobile phone location data for COVID-19: Implications for public governance in South Africa — Interdisciplinary Journal of Information, Knowledge and Management 16:101-124, 2021 — Cited by 9

  • P. Kariuki, J.A. Adeleke & L.O. Ofusori — The role of open data in enabling fiscal transparency and accountability in municipalities in Africa: South Africa and Nigeria case studies — Proceedings of the 13th International Conference on Theory and Practice…, 2020 — Cited by 8

Conclusion

Dr. Paul Kariuki’s extensive and diverse body of work demonstrates a consistent commitment to advancing public governance, digital innovation, and inclusive development across Africa. His research contributions, which span monitoring and evaluation systems, e-governance, cybersecurity, migration, and social cohesion, have informed both academic scholarship and practical policy implementation. With numerous peer-reviewed publications, collaborative projects, and impactful case studies, Dr. Kariuki exemplifies the qualities of a forward-thinking scholar and practitioner whose work drives meaningful change in communities. These achievements position him as an outstanding candidate for the Best Researcher Award.

Dr. Sung-Woo Kwak | Nonproliferation | Best Researcher Award

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Dr. Sung-Woo Kwak | Nonproliferation | Best Researcher Award

Principal Researcher, Korea Institute of Nuclear Nonproliferation and Control, Korea

Dr. Sung-Woo Kwak received his Ph.D. in Nuclear Engineering at KAIST and broadened his expertise as a Visiting Scholar at the University of California, Berkeley. Then he joined the Korea Institute of Nuclear Nonproliferation and Control (KINAC), where he has served as Principal Researcher in the Safeguards Division since January. His research areas include radiation detection and measurements, nuclear safeguards verification, and spent nuclear fuel inspection technologies. Dr. Sung-Woo Kwak has authored more than 28 SCIE-indexed journal papers with an H-index of 7, and he holds patents, including the Inspection Equipment for Spent Nuclear Fuel . He has completed 10 research projects and currently leads an ongoing project on nuclear safeguards technologies. His innovations have contributed to the development of verification equipment for CANDU spent nuclear fuel, which is under IAEA certification review for international safeguards inspections. He also serves as a collaborator in radiation detector development projects, contributing to the global nuclear nonproliferation community.

Profile: ORCID | SCOPUS

Featured Publications

S-W. Kwak — Uncertainty analysis based on Bayesian inference for partial defect verification of PWR spent nuclear fuel — Nuclear Engineering and Technology, 2025

S-W. Kwak — Field test for performance evaluation of a new spent-fuel verification system in heavy water reactor — Journal of Instruments, 2024

S-W. Kwak — Evaluation of neutron attenuation properties using helium-4 scintillation detector for dry cask inspection — Nuclear Engineering and Technology, 2023

S-W. Kwak — Performance evaluation of Yonsei Single-photon Emission Computed Tomography (YSECT) for partial-defect inspection within PWR-type spent nuclear fuel — Nuclear Engineering and Technology, 2024

S-W. Kwak — Comparison of existing and new optical fiber-based scintillation detectors for spent-fuel verification equipment — Nuclear Instruments and Methods in Physics Research Section A, 2023

Assist. Prof. Dr. Mansoor Ali Darazi | AI in ELT | Innovative Research Award

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Assist. Prof. Dr. Mansoor Ali Darazi | AI in ELT | Innovative Research Award

Benazir Bhutto Shaheed University Lyari | Pakistan 

Dr. Qing Du | Biomedical Sciences | Best Researcher Award

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Dr. Qing Du | Biomedical Sciences | Best Researcher Award 

Dr. Qing Du | Qinghai University for Nationalities | China

Dr. Qing Du is a dedicated medical scientist and pharmacist with a strong foundation in pharmacognosy, quality assurance, and functional food development. Her multidisciplinary background encompasses roles in R&D registration, quality auditing, and internal pharmacy engineering. She has led and contributed to extensive research in medicinal plant genomics, chemical analysis, and bioactive compound metabolism. As an accomplished author and editor, she has published numerous scholarly works, led patent innovations, and produced educational resources that bridge academia and industrial application.

Professional Profile

ORCID

SCOPUS

Summary of Suitability

With an outstanding academic background, extensive publication record, 14 authored books, five patents, and leadership in high-impact research projects, Dr. Qing Du demonstrates exceptional scientific innovation, technical expertise, and scholarly contributions. Her work in medicinal plant genomics, functional food R&D, and pharmacognosy has significantly influenced both academic research and industrial applications. Her numerous collaborations, editorial roles, and academic leadership establish her as an ideal candidate for the Best Researcher Award.

Education

Dr. Qing Du completed a joint pharmacognosy program between the Peking Union Medical College Institute of Medicinal Plant Development and Tsinghua University, culminating in a doctoral qualification. Her training spans pharmacognosy, medicinal plant chemistry, and regulatory frameworks for functional foods, medical devices, and pharmaceutical registration, equipping her to excel in both scientific inquiry and translational applications.

Experience

Dr. Qing Du has driven R&D and registration projects across functional foods, medical devices, and pharmaceuticals, collaborating with academic and industrial partners. Her leadership extends to overseeing multidisciplinary teams, authoring pivotal educational texts, and managing regulatory documentation. She has also fulfilled internal quality auditor duties and applied her expertise to both research and product development contexts in pharmacology and pharmaceutical sciences.

Research Interests

Dr. Qing Du research interests center on quality control and molecular mechanisms of medicinal and functional food plants, genomic and metabolomic analysis of organelles in edible and medicinal species, and the identification of bioactive compounds and metabolic pathways. She is also invested in biomedical informatics, aiming to link phytochemical data with practical health applications.

Award

 

Dr. Qing Du notable recognitions include a Third Prize in a Municipal Science and Technology Progress Award and leading scientific and technological achievements granted by a provincial Department of Science and Technology. These awards reflect her contributions to plant-based medicinal innovation and regional scientific advancement.

Publication Top Notes

Quantitative determination of coptisine and berberine hydrochloride in Corydalis conspersa by high-performance liquid chromatography and quality evaluation

Supplemental materials for the manuscript of Comparative analysis of appearance, chloroplast genomes, and evolutionary relationship in the two Gladiolus genus of Iridaceae family

Supplemental materials for the manuscript of effect on anti-hepatocellular carcinoma from Corydalis conspersa: a network pharmacology, molecular docking, and experimental validation

The cyberspace sharing of “aesthetic education knowledge of square inch stamps” creates the reading promotion service of university libraries

Sinoflavonoids NJ and NK, anti-inflammatory prenylated flavonoids from the fruits of Podophyllum hexandrum Royle

Conclusion

Dr. Qing Du is a scholar whose work spans the full spectrum of pharmacognosy—from molecular and organellar genomics to regulatory and quality implementation. Her leadership in R&D projects, patent innovation, interdisciplinary authorship, and academic-industrial integration underscores her outstanding capabilities. Her publications, especially in high-impact journals, are complemented by strategic editorial contributions and recognized by peer citations. Dr. Qing Du’s achievements make her an exceptional candidate for the Best Researcher Award, exemplifying excellence in plant-based biomedical research and translational innovation.