Dr. Manisha Nene | Artificial Intelligence | Best Researcher Award

Published on by AI Data Scientist

Dr. Manisha Nene | Artificial Intelligence | Best Researcher Award 

Seasoned Leader, Defence Institute of Advanced Technology, India

Dr. Manisha Nene, a seasoned leader at the intersection of research, academia, and industry, holds a Ph.D. in Computer Science and has devoted over two decades to advancing artificial intelligence and cybersecurity. Throughout her career she has held key leadership roles, including Director of the School of Mathematical Sciences and Computer Engineering and Head of the Department of Computer Science & Engineering at DIAT-DRDO. Her professional experience spans guiding doctoral and master’s scholars, leading national-level research projects, and founding MAJINE Systems Pvt. Ltd., which develops cybersecurity and AI-based solutions rooted in her patented innovations. Dr. Nene’s research interests lie in secure AI, trustworthy computing, digital transformation, and responsible infrastructure. She is proficient in advanced research skills such as machine learning, adversarial defense, threat modeling, deep neural networks, cryptographic protocols, and data analytics. Over her career she has received numerous awards, including IETE’s Smt. Triveni Devi Award for her contributions to ICT for society, the Future Crime Research Foundation’s Award of Excellence for PAN-India cyber security training, institute-level Researcher of the Year awards, and multiple Best Paper Awards at international conferences. Her Scopus profile reflects 129 documents, over 716 citations, and an h-index of 13 (Scopus ID: 35488434700).

profile: GOOGLE SCHOLAR | SCOPUS | ORCID 

Featured Publications

  • Nene, M. A secure AI framework for adversarial attack mitigation in critical infrastructures. (202, 45 citations)

  • Nene, M. Trustworthy deep learning in cyber-physical systems: techniques and challenges. (2022, 55 citations)

  • Nene, M. Privacy-preserving machine learning with homomorphic encryption in cloud environments. (2020, 38 citations)

  • Nene, M. Blockchain-enabled authentication protocols for Internet of Things security. (2019, 29 citations)

Ms. Kaiser Sun | Natural Language Processing | Young Scientist Award

Published on by AI Data Scientist

Ms. Kaiser Sun | Natural Language Processing | Young Scientist Award

Emerging Leader in AI, Johns Hopkins University, United States

Ms. Kaiser Sun is an emerging leader in artificial intelligence and computational linguistics whose work bridges fundamental research and practical impact. She is currently pursuing a Ph.D. in Computer Science at Johns Hopkins University under Professor Mark Dredze, building on her M.S. in Computer Science and Engineering from the University of Washington and dual B.S./B.A. degrees in Computer Science & Engineering and Mathematics from the same institution. Ms. Kaiser Sun has accumulated a rich portfolio of professional experience, including roles as Applied Scientist Intern at Amazon Web Services AI Labs, AI Resident at Meta AI – FAIR Labs, Software Development Engineer Intern at Amazon, Data Science Intern at Noonum, undergraduate researcher at the Washington Experimental Mathematics Lab, and intern at NOAA. Across these positions she has collaborated with leading mentors such as Peng Qi, Yuhao Zhang, Adina Williams, and Dieuwke Hupkes. Her primary research interests focus on natural language processing, large language models, interpretability, multilingual assessment of stereotypes, and the intersection of optimization and model evaluation. Ms. Kaiser Sun’s research skills span deep learning architectures, empirical foundations of machine learning, convex optimization, multilingual NLP, and large-scale model analysis; she is proficient in Python, Java, TypeScript, SQL, JavaScript, C++, R, and MATLAB, and experienced with PyTorch, AllenNLP, Spark, AWS, Microsoft Azure, and React. Her work has appeared in respected venues such as Nature Machine Intelligence, Findings of ACL, Findings of EMNLP, and NAACL, and she has contributed to influential community efforts like Queer in AI and Google Research’s CSRMP mentorship program. On Scopus, Ms. Kaiser Sun holds ID 57224529767 with 70 total citations indexed across 68 documents, 5 primary authored documents, and an h-index of 2 — impressive indicators for a researcher at her career stage.

Profile: GOOGLE SCHOLAR | SCOPUS | ORCID

Featured Publications

  • Sun, K., Marasović, A. (2021). Effective attention sheds light on interpretability. Findings of ACL. 23 citations.

  • Sun, K., Qi, P., Zhang, Y., Liu, L., Wang, W. Y., Huang, Z. (2023). Tokenization consistency matters for generative models on extractive NLP tasks. Findings of EMNLP. 17 citations.

  • Mitchell, M., Attanasio, G., Baldini, I., Clinciu, M., Clive, J., Delobelle, P., … Sun, K. (2025). SHADES: Towards a multilingual assessment of stereotypes in large language models. Proceedings of NAACL. 12 citations.

  • Sun, K., Dredze, M. (2024). Amuro & Char: Analyzing the relationship between pre-training and fine-tuning of large language models. Proceedings of the 10th Workshop on Representation Learning for NLP. 10 citations

Prof. Dr. Salem Alkhalaf | Artificial Intelligence | Best Academic Researcher Award

Published on by AI Data Scientist

Prof. Dr. Salem Alkhalaf | Artificial Intelligence | Best Academic Researcher Award

Distinguished Researcher, Qassim University, Saudi Arabia

Prof. Dr. Salem Alkhalaf is a dynamic and accomplished researcher whose work spans information and communication technology, e-learning systems, and digital transformation. He holds a Ph.D. in Information and Communication Technology from Griffith University, supported by prior degrees in ICT and Computer Education. Prof. Dr. Salem Alkhalaf currently serves in senior academic and leadership roles at Qassim University, where he has steered initiatives in enterprise architecture, digital content management, and e-learning strategy. His research interests include collaborative learning environments, information quality in learning management systems, usability evaluation, and culturally adaptive educational technologies. He excels in research skills such as mixed methods design, structural equation modeling, system evaluation, cross-cultural adaptation, and large-scale empirical studies. He maintains an outstanding scholarly footprint: Scopus ID: 41661143900, with 2,021 citations across 1,885 documents, 179 published works, and an h-index of 23. His professional engagements include membership in IEEE, ACM, ACS, contributions as a reviewer and editorial board member, and leadership in national e-government and audit teams. Recognized through institutional awards, research grants, and best paper honors, he is committed to advancing scholarship, mentoring emerging researchers, and expanding global collaborations. Prof. Dr. Salem Alkhalaf combines visionary leadership with rigorous scholarship, making him a prominent figure positioned to drive future breakthroughs in AI, educational technology, and ICT research.

Profile: GOOGLE SCHOLAR | SCOPUS  | ORCID

Mr. Yishak Beyene | Soil Science | Academic Brilliance Recognition Award

Published on by AI Data Scientist

Mr. Yishak Beyene | Soil Science | Academic Brilliance Recognition Award

Researcher, Wachemo University, Ethiopia

Mr. Yishak Beyene is a dedicated Soil Science lecturer, researcher, and advisor at Wachemo University, Ethiopia, with a distinguished academic and professional trajectory in soil chemistry, fertility, land use, and geospatial analysis. He completed his B.Sc. in Natural Resource Management and M.Sc. in Soil Science from Wolaita Sodo University, building a solid foundation for research in sustainable land and soil management. His professional experience encompasses lecturing undergraduate soil science courses, supervising student research and seminars, conducting soil laboratory and field analyses, and providing academic consultation. He has served as a reviewer for international journals including Taylor & Francis, Hindawi, and Heliyon. Mr. Beyene’s research interests focus on phosphorus sorption characteristics, land-use impacts on soil properties, soil fertility mapping, crop suitability analysis, conservation tillage, agroforestry, and hydro-climatic variability, employing advanced analytical techniques such as GIS, GPS, and statistical software including R and SAS. His key research skills include experimental soil science, field and laboratory methodologies, geospatial data analysis, project coordination, and academic mentorship. His professional achievements are highlighted by publications in reputable journals, ongoing high-impact research projects, and active participation in student mentorship, voluntary academic initiatives, and community-oriented research activities, reflecting strong leadership and collaboration capabilities. His awards and honors include recognition for academic excellence during his higher education and multiple acknowledgments for his active engagement in research and professional development programs.

Profile: ORCID

Featured Publications

  • Beyene, Y., Laekamariam, F., Alemayehu, K., Gifole, G., Lakew, G., & Alemu, A. (2022). Phosphorus sorption characteristics of acidic Luvisols and Nitisols under varying lime rates, and response validation using wheat. Communications in Soil Science and Plant Analysis. https://doi.org/10.1080/00103624.2022.2070637

  • Beyene, Y., Mulatu, C., & Tamrat, S. (2022). Effect of land use on selected soil properties in different soil types at Harar Gita Watershed, Southern Ethiopia. Uttar Pradesh Journal of Zoology, 43(13), 1–8.

  • Beyene, Y. (2022). Evaluation of adsorption kinetics and isotherm models for phosphorus in Luvisols and Vertisols under different land uses in Central Ethiopia. Advances in Environmental and Soil Science.

  • Beyene, Y., Gebre, B., Michael, M. W., & Taye, K. (Ongoing). Selected soil physicochemical characteristics under different land use and soil depth in Handosha Sub-Watershed, Hadiya Zone, Southern Ethiopia.

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.