Mr. Soumyapriya Goswami | Industrial Internet of Things | Best Researcher Award

Published on by AI Data Scientist

Mr. Soumyapriya Goswami | Industrial Internet of Things | Best Researcher Award 

IT Researcher, Kalyani Government Engineering College, West Bengal

Mr. Soumyapriya Goswami is a dedicated B.Tech IT researcher at Kalyani Government Engineering College with a strong academic foundation and practical experience in emerging technologies, including Artificial Intelligence, Internet of Things (IoT), Wireless Sensor Networks, edge computing, reinforcement learning, and quantum security for medical devices. His education reflects consistent academic excellence, having completed his secondary and higher secondary studies at Asansol Ramakrishna Mission High School and Dhadka NCLahiri Vidyamandir, followed by his ongoing B.Tech IT program at Kalyani Government Engineering College. Professionally, Soumyapriya has developed expertise in AI/ML model deployment, prompt engineering for generative AI, cloud-based solutions, project management, and team leadership, with proficiency in programming languages (Python, C, C++, Java), AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn), and cloud platforms (Google Cloud, Docker, Jenkins). His research interests encompass energy-efficient scheduling for WSNs, reinforcement learning-based threat detection for IoT devices, quantum-aware security protocols for medical devices, digital twins, and cyber-physical systems.  In conclusion, Mr. Soumyapriya Goswami demonstrates strong potential to bridge academic research with industry applications, delivering innovative solutions in AI, IoT, and quantum technologies, while contributing to knowledge dissemination, mentorship, and technological advancement in emerging research domains, positioning him as a promising early-career researcher with impactful scholarly and practical contributions.

Profile: GOOGLE SCHOLAR | SCOPUS | ORCID

Featured Publications

  1. Goswami, S. (Published). NashDQNSleep: Energy-efficient sleep scheduling for WSN using Nash Equilibrium and Deep Q-Networks. Elsevier EAAI. Citation count: unavailable

  2. Goswami, S. (Under Review). Polaris: Optimized power-aware GPU scheduling framework for cloud environments. IEEE TPDS. Citation count: unavailable

  3. Goswami, S. (Under Review). Qure: Quantum-aware protocols for medical device security using entanglement and root-of-trust designs. IEEE Cybernatics. Citation count: unavailable

  4. Goswami, S. (Ongoing). TinySurvive: Reinforcement Learning-based threat intelligence model for low-power IoT devices in hazardous environments. Citation count: unavailable

Mr. Sonjoy Ranjon Das | Computer Vision | AI & Machine Learning Award

Published on by AI Data Scientist

Mr. Sonjoy Ranjon Das | Computer Vision | AI & Machine Learning Award

Lecturer,  Global Banking School, United Kingdom

Mr. Sonjoy Ranjon Das (FHEA, MIEEE, MBCS) is a Lecturer in Computing at the Global Banking School, UK, PhD Candidate in Computer Science at London Metropolitan University, and an affiliated researcher with the AI & Data Science Research Group at London Metropolitan University. He is an emerging academic with expertise in artificial intelligence, soft biometrics, cybersecurity, and privacy-preserving surveillance frameworks aligned with ethical AI deployment and GDPR compliance. Mr. Sonjoy Ranjon Das earned his MSc in Cyber Security Technology with Distinction from Northumbria University, UK, following an MBA in Management Information Systems and a BSc (Hons) in Computer Science from Leading University, Bangladesh, which provided him with an integrated background in computing, management information systems, and advanced security practices. Professionally, he has served in diverse higher-education lecturing roles across the UK including Elizabeth School of London, New City College, Shipley College, and other institutions, as well as holding the position of Research Associate on the SoftMatrix and Surveillance (SMS) Project at Northumbria University, contributing to cross-disciplinary and international research. Mr. Sonjoy Ranjon Das’s research interests include privacy-preserving multimodal soft biometrics for identity verification, AI-driven covert surveillance, ethical and GDPR-compliant surveillance technologies, and the fusion of biometrics for crowd analytics in public safety and border security. His research skills encompass advanced machine learning and computer vision techniques, data analytics, Python and Java programming, cloud-IoT integration, and full-stack development, supported by proficiency in data visualization tools such as Power BI, Tableau, and MATLAB.

Profile :  GOOGLE SCHOLAR

Featured Publications

  • Das, S. R., Kruti, A., Devkota, R., & Sulaiman, R. B. (2023). Evaluation of machine learning models for credit card fraud detection: A comparative analysis of algorithmic performance and their efficacy. FMDB Transactions on Sustainable Technoprise Letters. 12 citations.

  • Thinesh, M. A., Varmann, S. S., Sharmila, S. L., & Das, S. R. (2023). Detection of credit card fraud using random forest classification model. FMDB Transactions on Sustainable Technologies Letters. 9 citations.

  • Pranav, R. P., Prawin, R. P., Subhashni, R., & Das, S. R. (2023). Enhancing remote sensing with advanced convolutional neural networks: A comprehensive study on advanced sensor design for image analysis and object detection. FMDB Transactions on Sustainable Computer Letters. 8 citations.

  • Das, S. R., Hassan, B., Patel, P., & Yasin, A. (2024). Global soft biometrics in surveillance: Benchmark analysis, open challenges, and recommendations. Multimedia Tools and Applications. 6 citations.

Mr. Siyu Wang | Multimodal Detection | Best Researcher Award

Published on by AI Data Scientist

Mr. Siyu Wang | Multimodal Detection | Best Researcher Award

Innovative Researcher, Wuhan University of Science and Technology, China

Mr. Siyu Wang is an innovative researcher in control science, artificial intelligence and automation whose academic path and project experience demonstrate a rare combination of theoretical insight and applied problem-solving. Mr. Siyu Wang is currently pursuing a Master’s degree in Control Science and Engineering at Wuhan University of Science and Technology, focusing on machine vision, multimodal data fusion, deep learning, and 3D perception systems, after completing a rigorous undergraduate training that gave him a strong engineering foundation across software, hardware and algorithm domains. Professionally, Mr. Siyu Wang has led and contributed to projects such as a 3D Semantic Segmentation System Integrating Image and LiDAR Information, designing a novel fusion strategy to match image pixels with point cloud data for superior segmentation accuracy, as well as implementing end-to-end training pipelines and custom CUDA operators to accelerate model performance. His research interests encompass multimodal fusion, millimeter-wave radar, accelerated depth estimation and 3D object detection, and he has already demonstrated skill in predicting and diagnosing one-dimensional data, classifying and segmenting two-dimensional image data, and processing three-dimensional point cloud information to build robust intelligent models.   Mr. Siyu Wang has earned recognition for his strong engineering background, covering the full cycle of model development, design, deployment and optimization, and for his contributions to the growing field of AI-driven control systems. His achievements indicate a promising trajectory toward leadership in artificial intelligence, intelligent automation and advanced perception research.

Profile:  SCOPUS | ORCID

Featured Publications

Wang, S. (2024). MDFusion: A multistage dynamic fusion framework for multimodal 3D object detection with leveraging cross-modal feature complementarity. Expert Systems with Applications. 5 citations.

Wang, S. (2024). Multi-sensor data fusion strategies for improved 3D semantic segmentation in intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems. 3 citations.

Wang, S. (2025). Accelerated depth estimation using multimodal LiDAR-camera fusion for autonomous navigation. International Journal of Automation and Computing. 2 citations.

Wang, S. (2025). Transformer-based multimodal fusion for millimeter-wave radar and vision data in 3D object detection. Neural Computing and Applications. 1 citation.

Mr. Mehran Saeedi | Supply Chain | Best Researcher Award

Published on by AI Data Scientist

Mr. Mehran Saeedi | Supply Chain | Best Researcher Award

Researcher, University of Tehran, Iran

Mr. Mehran Saeedi is an accomplished researcher specializing in circular economy, sustainable supply chain management, transportation, artificial intelligence, mathematical modelling, multi-criteria decision-making and optimization algorithms, with a proven record of academic excellence and practical application. Mr. Mehran Saeedi earned a Master of Science in Systems Optimization from the University of Tehran under the supervision of Prof. Reza Tavakoli-Moghaddam, ranking first in his cohort, and previously obtained a Bachelor of Science in Industrial Engineering from Golestan University, also graduating first among his peers. His master’s dissertation focuses on sustainable and resilient agricultural supply chains for net-zero goals from a circular economy and stochastic modelling perspective, already accepted in a leading international journal, while his undergraduate project addressed design of experiments for quality control in the electronics sector.  His research interests extend to designing closed-loop and green supply chain networks, scenario-based stochastic programming, robust multi-objective optimization, and the application of artificial intelligence to improve sustainability outcomes across industries. His publications in high-ranked journals with over 30publications, 36+ citations, and an h-index of 2, such as Computers & Industrial Engineering and International Journal of Production Economics reflect a consistent record of scientific innovation and practical applicability. Mr. Mehran Saeedi has been recognized for ranking first at both undergraduate and postgraduate levels, has served as a teaching assistant for core engineering courses, and holds certificates of reviewing for prestigious logistics and transportation journals, reflecting his commitment to the scholarly community.

Profile: GOPOGLE SCHOLAR | SCOPUS

Featured Publications

Saeedi, M. (2024). Designing a two-stage model for a sustainable closed-loop electric vehicle battery supply chain network: A scenario-based stochastic programming approach. Computers & Industrial Engineering. (Cited by 12)

Saeedi, M. (2024). Multi-objective optimization for a green forward-reverse meat supply chain network design under uncertainty: Utilizing waste and by-products. Computers & Industrial Engineering. (Cited by 9)

Saeedi, M. (2025). Sustainable cast iron supply chain network design: Robust multi-objective optimization with scenario reduction via genetic algorithm. International Journal of Production Economics. (Cited by 7)

Saeedi, M. (n.d.). A queueing theory approach for a bi-objective mathematical model to optimize a biomass supply chain network considering environmental impacts and solar panels. Environment, Development and Sustainability.

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

Published on by AI Data Scientist

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

Published on by AI Data Scientist

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

Published on by AI Data Scientist

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

Published on by AI Data Scientist

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

Published on by AI Data Scientist

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

Published on by AI Data Scientist

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