Jiabin lv | Molecular Breeding | Best Researcher Award

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Dr. Jiabin lv | Molecular Breeding | Best Researcher Award

Dr. Jiabin lv, Anhui Agricultural University, China

Lv Jiabin is a dedicated researcher at Anhui Agricultural University, specializing in forest tree genetics and breeding. With extensive experience in molecular-assisted and stress-resistance breeding of both economic (e.g., Camellia oleifera, Carya illinoinensis) and timber (e.g., Eucalyptus) forest species, Lv Jiabin has significantly advanced the understanding of forest tree genomics. His work has contributed to enhancing breeding strategies and sustainable forestry practices in China and beyond. He has led and collaborated on several national and provincial research initiatives and continues to make impactful scientific contributions in the field of plant molecular biology and forestry biotechnology. 🌿🧪

Professional Profile

Scopus Profile

🏆 Strengths for the Award:

  • Focused Expertise in Forest Tree Genetics & Molecular Breeding 🌳
    Lv Jiabin has specialized in the molecular breeding and stress-resistance research of economically valuable and timber forest species such as Camellia oleifera, Carya illinoinensis, and Eucalyptus. This area is highly relevant to environmental sustainability, agroforestry productivity, and climate-resilient ecosystems.

  • Notable Publications in High-Impact Journals 📚

    • 2024 (BMC Plant Biology): Genome-wide identification and expression analysis of GRAS gene family in Eucalyptus grandis
      This article showcases cutting-edge genomic approaches and contributes to understanding gene expression under stress conditions.

    • 2020 (Industrial Crops and Products): Genetic diversity analysis of Eucalyptus cloeziana
      This paper highlights the use of microsatellite markers to inform breeding strategies, demonstrating applied research strength.

  • Active Role in Research Grants 💼
    He has led the Anhui Provincial Natural Science Foundation Youth Project and co-participated in prestigious national initiatives like the National Key R&D Program and the Forestry Public Welfare Industry Major Project, indicating leadership and teamwork in collaborative research.

  • Consistent Research Output 🔬
    With 7 research projects completed/ongoing, he maintains a strong output despite a relatively early-to-mid stage career, suggesting consistent productivity.

🎓 Education

Lv Jiabin received formal training in forest tree genetics and breeding, which laid a strong foundation for his research in molecular biology and forest biotechnology. He has continuously expanded his academic knowledge through active participation in academic research and advanced training programs. His academic journey is reflected in his thorough understanding of the genomics of economically valuable tree species and the integration of this knowledge into practical breeding programs. 🎓📚

🧑‍🔬 Experience

Over the years, Lv Jiabin has been actively engaged in both teaching and scientific research at Anhui Agricultural University. He has led numerous research projects, including those funded by the Anhui Provincial Natural Science Foundation and the Anhui Provincial University Natural Science Foundation. In addition, he has played significant roles in national-level programs such as the National Key R&D Program, National Forestry Public Welfare Industry Research Project, and the Guangxi Zhuang Autonomous Region Innovation-Driven Development Project. His professional involvement extends to managing laboratory work, mentoring students, and publishing high-quality research. Through these activities, he has developed a reputation as a meticulous, innovative, and collaborative scientist in the forestry research community. 🌱👨‍🏫

🔬 Research Focus On Molecular Breeding

Lv Jiabin’s primary research interests lie in the molecular breeding of forest trees, with an emphasis on stress resistance breeding and molecular marker-assisted selection. His research spans across the identification of key genetic traits, development of core germplasm resources, and improvement of economically and ecologically important tree species. Focusing on Eucalyptus, Camellia oleifera, and Carya illinoinensis, he has conducted in-depth studies using genomic tools such as microsatellite markers and genome-wide expression analysis, which have yielded practical tools for forest tree breeding. His research helps improve productivity, stress tolerance, and sustainability in forestry systems. 🌳🔍🧬

📘 Publication Top Note

Title: Genome-wide identification and expression analysis of GRAS gene family in Eucalyptus grandis
Authors: Lu H., Xu J., Li G., Zhong T., Chen D., Lv Jiabin* (corresponding author)
Journal: BMC Plant Biology
DOI: 10.1186/s12870-024-05288-x
Citations: 5 (as of latest available record)
Summary: This study presents a comprehensive genome-wide analysis of the GRAS gene family in Eucalyptus grandis, an important commercial timber species. GRAS genes are known for their crucial roles in plant development, signaling, and responses to abiotic and biotic stresses.

✅ Conclusion

In recognition of his exceptional contributions to molecular forest tree breeding, scholarly publications, and research leadership, Lv Jiabin is a highly deserving candidate for the Best Researcher Award. His work bridges the gap between fundamental genomics and practical forestry applications, promoting sustainable development and genetic resilience in economically significant trees. With a deep commitment to scientific excellence and innovation, Lv Jiabin stands out as a leader in forest molecular breeding and a true asset to the academic and research community. 🏅🌲🔬

Waseem Khan | Oil and Gas | Best Researcher Award

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Mr. Waseem Khan | Oil and Gas | Best Researcher Award

PhD Scholar, University of Science and Technology of China, China.

Waseem Khan is an emerging geoscientist from Pakistan with a strong background in petrography, geochemistry, sedimentology, and geochronology. Born on May 27, 1992, he has built an impressive research and professional profile across academia and industry. He holds a Master’s degree from the Institute of Tibetan Plateau Research, Chinese Academy of Sciences, where he was awarded the prestigious ANSO scholarship. Waseem has contributed to multiple high-impact publications on salt range provenance, Jurassic reservoir characterization, and paleogeographic reconstructions in journals like Gondwana Research and Carbonates and Evaporites. His cross-disciplinary expertise includes U-Pb-Hf isotopic analysis, LA-ICP-MS, reservoir modeling, and GIS-based mapping. With professional experience ranging from QA/QC engineering in Qatar to exploration geology in Pakistan, he bridges the gap between theoretical research and field practice. Waseem is recognized for his ability to combine analytical geoscience tools with hands-on industry applications, making him a valuable contributor to both academic and energy sectors.

🔹Author Profile

🔹 Education 

Waseem Khan earned his Master’s in Earth Sciences from the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), China, with a CGPA of 3.74 in 2024. His thesis focused on the provenance and paleogeography of the Salt Range Formation in Pakistan. His undergraduate studies were completed at the University of Haripur, where he earned a BS in Geology with a CGPA of 3.5. His BS thesis investigated microfacies and diagenesis in the Middle Jurassic Samana Suk Formation in the Nizampur Basin. Both degrees emphasized fieldwork, lab-based petrography, sedimentology, and tectonics. Waseem’s academic journey has been supported by competitive scholarships and enriched by international exposure and certified training from global institutions such as the University of Toronto, Macquarie University, and Duke University. This foundation has equipped him with expertise in detrital zircon geochronology, geospatial analysis, petroleum systems, and sedimentary provenance, bridging classical geology with advanced analytical techniques.

🔹Strengths for the Award

  1. Diverse and Deep Research Portfolio:

    • Waseem Khan has published seven peer-reviewed journal articles (2024–2025), including in prestigious venues like Gondwana Research, Palaeogeography, Palaeoclimatology, Palaeoecology, and Carbonates and Evaporites.

    • His research spans a wide array of geological sub-disciplines: petrography, sedimentology, reservoir characterization, detrital zircon geochronology, and paleogeography, with a regional focus on the Western Himalayas, Tethys, and Tibetan Plateau.

    • He has contributed to both applied (e.g., oil and gas reservoir studies) and fundamental research (e.g., Gondwana paleogeography reconstruction).

  2. Technical and Analytical Expertise:

    • Demonstrated strong technical proficiency with tools like LA-ICP-MS, XRF, ArcGIS, and IOLITE.

    • Conducted advanced U-Pb-Hf isotopic work, showing deep specialization in detrital zircon analysis and geochronology.

  3. Global Academic Exposure and Collaboration:

    • Completed his Master’s at the Chinese Academy of Sciences (CAS) — one of Asia’s premier research institutions — under the ANSO scholarship, indicating high academic merit.

    • Worked with globally recognized geoscientists like Eduardo Garzanti, enhancing the academic quality and international visibility of his research.

  4. Professional Experience and Applied Knowledge:

    • Extensive multidisciplinary experience across QA/QC in materials engineering, nuclear gauge operation, and mineral exploration, which enriches his research with applied industrial insights.

    • Worked on high-impact projects like Mohmand Dam Hydro Project and M-9 Motorway Construction with organizations such as FWO, NESPAK, and NHA.

  5. Training and Certifications:

    • Completed over ten international certified courses, including in GIS, petroleum engineering, environmental safety, and ISO accreditation standards, reflecting a commitment to continuous learning.

🔹 Experience 

Waseem Khan’s experience spans six diverse roles across academia, industry, and international research institutions. He most recently worked as a Research Assistant at the Chinese Academy of Sciences (2020–2025), where he conducted geochronological and geochemical analysis (U-Pb-Hf, LA-ICP-MS). He also served as a QA/QC Officer in Qatar (2021–2022), ensuring compliance with international testing standards and ISO certifications. His prior roles include Assistant Geologist at China Gezhouba Group (Mohmand Dam project), Exploration Geologist for base metals in Khyber Pakhtunkhwa, Research Associate at University of Haripur, and Material Engineer for the M-9 Motorway project with FWO. His work has included core logging, XRF sampling, seismic interpretations, reservoir assessments, and site-level geological mapping. His well-rounded field and lab experience, combined with his ability to manage geotechnical and QA/QC processes, make him uniquely suited to bridge scientific exploration with applied oil and gas geology.

🔹 Awards and Honors 

Waseem Khan has received several academic and professional accolades. Most notably, he was awarded the Alliance of International Science Organizations (ANSO) Scholarship for his Master’s studies at the prestigious Chinese Academy of Sciences, which recognizes outstanding students from developing countries in scientific research. He was also awarded a government-issued laptop for securing over 80% marks in his undergraduate program—an initiative by Pakistan’s Higher Education Commission to support merit-based excellence. In addition to formal awards, his certifications reflect a proactive approach to continuous learning. These include ISO 17025 and ISO 17020 accreditations, radiation protection training, and multiple Coursera credentials from leading universities in petroleum engineering, environmental safety, and GIS analysis. These honors underscore his commitment to excellence, scientific integrity, and professional development, positioning him as a dedicated researcher capable of contributing to global energy and environmental challenges.

🔹 Research Focus on Oil and Gas

Waseem Khan’s research centers on the petrological and geochemical evolution of sedimentary basins, with particular emphasis on reservoir potential, tectonic reconstruction, and paleogeography. He specializes in U-Pb-Hf zircon geochronology, detrital zircon provenance analysis, and basin tectonics, applying advanced tools like LA-ICP-MS, XRF, and GIS modeling. His work investigates processes within the Western Himalayas, Salt Range, and the Tibetan Plateau, unraveling Earth’s tectono-sedimentary history through integrative datasets. He bridges academic research with industrial applications, especially in the oil and gas sector, focusing on carbonate and sandstone reservoirs, diagenetic processes, and subsurface characterization. His collaborative projects span stratigraphy, seismic interpretations, and paleoclimatic reconstructions. By integrating isotopic dating with sedimentological observations, Waseem contributes to both the understanding of ancient paleoenvironments and the exploration of hydrocarbon systems, positioning him as a versatile researcher in petroleum geology and tectonics.

🔹 Publications Top Notes

1. Petrophysical characterization and reservoir potential of the lower Goru sandstone

Journal: Journal of Natural Gas Geoscience, June 2025
Contributors: Waseem Khan et al.
Summary: This study evaluates reservoir properties of Lower Goru sandstone through petrophysical logs, thin-section analysis, and core measurements. Results highlight moderate to good reservoir quality with effective porosity and permeability ranges ideal for gas production. The study provides key insights for exploration in Pakistan’s Sindh Basin.

2. Reservoir potential of middle Jurassic carbonates in the Nizampur Basin:

Journal: Physics and Chemistry of the Earth, June 2025
Contributors: Waseem Khan et al.
Summary: The paper explores Jurassic carbonates using microfacies analysis and diagenetic markers to assess reservoir viability. It finds that early marine cementation followed by dissolution-enhanced porosity created suitable reservoir zones, contributing to future petroleum exploration in Khyber Pakhtunkhwa.

3. Petrography and geochemistry of Early Cambrian phosphorites from Abbottabad:

Journal: Carbonates and Evaporites, May 2025
Contributors: Waseem Khan et al.
Summary: The authors investigate phosphorite deposits to interpret depositional environments and trace element enrichment. Their geochemical signatures suggest upwelling-driven sedimentation under anoxic to dysoxic conditions, offering a paleoceanographic perspective on Cambrian phosphorus cycles.

4. Decoding the Ediacaran Enigma: Gondwana paleogeography revisited through a provenance study of the Salt Range Formation

Journal: Gondwana Research, April 2025
Contributors: Waseem Khan et al.
Summary: This landmark paper applies detrital zircon dating to reconstruct Gondwana’s paleogeography, revealing sediment routing from northeastern Africa to the Salt Range. It reshapes tectonic models of the western Himalayas during the late Neoproterozoic.

Conclusion

Waseem Khan is a highly capable and emerging researcher in the field of geosciences with a strong academic foundation, hands-on field and lab expertise, and a growing international publication record. His combination of advanced analytical skills, cross-disciplinary work experience, and recent high-impact journal articles make him a strong contender for the Best Researcher Award, particularly in the Earth and Environmental Sciences category.

Jack Mathebula | Planning and Operations | Best Researcher Award

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Mr. Jack Mathebula | Planning and Operations | Best Researcher Award

Research Manager, Eskom, South Africa

Jack Mathebula is a veteran energy systems strategist with over two decades of experience in power system planning, operations, and renewable energy integration. Currently serving as Acting Research Manager at Eskom RT&D, Jack leads strategic grid innovation efforts aligned with global sustainability goals. His journey began with technical roles in HVDC plant operations and evolved into thought leadership in transmission planning, capital budgeting, and smart grid transformation. Jack has authored multiple Award papers and peer-reviewed articles focusing on HVDC planning and MCDA methodologies. A recipient of several international Award honors, he has also chaired global forums and mentored young engineers across Africa. He is a registered Professional Technologist (Pr Tech Eng) and a Senior Member of SAIEE, with a growing academic profile. Jack’s work directly supports energy transition efforts in South Africa and beyond, combining academic insight with real-world applications to meet the energy challenges of tomorrow.

📘Author Profile

🎓 Education

Jack Mathebula is currently pursuing a PhD in Electrical Engineering at the University of South Africa (UNISA), building on a Cum Laude MSc from the University of Pretoria (2015). His master’s thesis focused on optimizing HVDC scheme planning. He also holds a BSc Honours in Applied Sciences–Electrical from the University of Pretoria (2004), a B-Tech in Power Engineering from Technikon Pretoria (2001), and a National Diploma in Electrical Engineering from Technikon Witwatersrand (1998). His academic training blends strong theoretical knowledge with practical energy systems expertise. Jack further expanded his leadership acumen through specialized programs, including a Project Management Programme from UNISA’s School of Business Leadership and the Middle Managers Programme (MMP) via Henley Business School in collaboration with Eskom. His education reflects a lifelong dedication to combining engineering excellence with strategic project management in the energy sector.

🛠️ Experience

Jack’s career spans over 25 years at Eskom, where he has held progressively senior roles in grid planning, transmission strategy, and renewable integration. Since April 2024, he serves as Acting Research Manager (Distribution) in Eskom’s RT&D division, leading strategic energy projects and guiding national/international technical initiatives. Between 2008 and 2024, Jack was Middle Manager for Grid Planning and Operation, overseeing research portfolios and contract/resource management. Previously, he contributed to capital planning (2006–2008), network investment (2005–2006), and master planning (2000–2005). His career began in 1999 at the Apollo HVDC Converter Station, where he optimized plant performance. Jack’s career reflects deep technical competency coupled with leadership in digital transformation, grid simulation (RTDS), and policy-relevant research on EV infrastructure and hosting capacity assessments. He continues to mentor emerging engineers and drive forward-thinking energy planning initiatives.

🔬 Research Focus 

Jack Mathebula’s research concentrates on power system planning, HVDC optimization, renewable integration, and electric mobility infrastructure. He is particularly known for applying multi-criteria decision analysis (MCDA) and TOPSIS models in selecting optimal grid expansion strategies. His ongoing PhD explores advanced planning tools for dynamic energy systems under uncertainty, contributing to resilient grid development. His technical projects include hosting capacity assessments, RTDS-based simulation, and distribution-level renewable integration via DSTATCOMs. Jack is also involved in shaping EV-ready grid infrastructure and tariff structures, through cross-border collaborations with institutions like the Danish Technical University. His commitment to applied systems thinking is evident in his work linking technical feasibility, policy formulation, and national energy planning. His research is impactful not only in scholarly terms but also in operationalizing energy transition strategies for utilities and regulators.

📚 Publication Top Notes

Application of TOPSIS in Power Systems: A Review

Authors: J. Mathebula, N. Mbuli
Conference: 2024 International Conference on Electrical, Computer and Energy Engineering
Citations: 2
Summary:
This comprehensive review explores the use of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in addressing multi-criteria challenges in power systems. The paper synthesizes over a decade of applications, detailing how TOPSIS has been utilized for substation site selection, transmission route optimization, and renewable energy prioritization. It emphasizes the method’s effectiveness in quantifying trade-offs between conflicting objectives like cost, reliability, and environmental impact. The authors also discuss emerging trends such as hybrid TOPSIS models and their role in decision support systems for utilities.

Potential Factors for Multi-Criteria Evaluation of HVDC Compared to HVAC in Power Transmission

Authors: J. Mathebula, N. Mbuli
Conference: 2024 International Conference on Green Energy, Computing and Sustainable Technologies
Citations: 2
Summary:
This paper provides a structured framework for evaluating High Voltage Direct Current (HVDC) and High Voltage Alternating Current (HVAC) systems using multi-criteria analysis. Technical aspects like voltage stability, power losses, and system compatibility are considered alongside economic (CAPEX/OPEX) and environmental parameters. The study offers guidance for policymakers and transmission planners by identifying the most influential factors when choosing transmission technology for large-scale power corridors, particularly in developing countries with expanding renewable capacity.

Approach for Screening and Ranking Potential Receiving End Points in Planning New HVDC Schemes

Authors: J. Mathebula, M.N. Gitau, N. Mbuli, J.H.C. Pretorious
Conference: 2018 IEEE PES/IAS PowerAfrica
Citations: 1
Summary:
This research introduces a structured screening and ranking method to determine optimal receiving terminals for HVDC links. Using a case study in the South African grid, the authors apply decision matrix techniques based on projected load growth, geographic accessibility, system redundancy, and cost. The proposed framework supports utilities in identifying HVDC endpoints that align with long-term energy planning and enhances strategic transmission deployment in emerging economies.

Simplified Negative Load-Based Approach Versus Full HVDC Modeling in Assessing Options for the Cape Network

Authors: J. Mathebula, M.N. Gitau, N. Mbuli
Conference: 2013 13th International Conference on Environment and Electrical Engineering
Citations: 1
Summary:
This study contrasts two methodologies for evaluating HVDC implementation in the Cape region of South Africa: a simplified negative load approach and a full HVDC model. By comparing simulation results and cost-efficiency, the paper discusses the limitations and applicability of each method. The simplified model offers quicker decision support, while the full model yields greater accuracy. The work provides guidance on the trade-offs between modeling complexity and planning effectiveness in early-stage transmission projects.

Application of TOPSIS for MCDA in Power Systems: A Systematic Literature Review

Authors: J. Mathebula, N. Mbuli
Journal: Energies (2025)
Summary:
This peer-reviewed article presents a rigorous literature review of the integration of TOPSIS with multi-criteria decision analysis (MCDA) in power system engineering. Covering applications from renewable site selection to grid reinforcement prioritization, it categorizes studies by criteria sets, modeling tools, and decision contexts. The authors propose a future research agenda emphasizing the integration of real-time data, stakeholder weighting schemes, and AI-enhanced decision-making in power systems. The review positions TOPSIS as a valuable, yet underutilized, tool for navigating the complexity of modern grids.

Potential Factors for HVDC Evaluation in Selection of the Suitable Location Within HVAC System

Authors: J. Mathebula, N. Mbuli
Conference: 2024 ICECCME
Summary:
The paper investigates the suitability of integrating HVDC terminals within existing HVAC networks. Key criteria include system stability impact, proximity to generation/load centers, infrastructure compatibility, and future scalability. The study proposes a location scoring model tailored for hybrid AC-DC systems in grid modernization scenarios. Case illustrations from the South African transmission system reinforce the practical relevance of the proposed methodology, particularly for utilities preparing for high renewable penetration.

Design Options for Thermal Uprate of a Transmission Line: A Case Study in the South African Power System

Authors: J. Mathebula, N. Mbuli, S. Mushabe
Conference: 2024 International Conference on Electrical, Communication and Computer Engineering (ECCCE)
Summary:
This case study explores cost-effective design modifications to increase the thermal capacity of aging transmission lines. Options include conductor replacement, dynamic line rating, and advanced monitoring systems. Using a real-world line segment in South Africa, the paper evaluates each method based on cost, downtime, and long-term benefits. The findings aid transmission operators in choosing appropriate uprate techniques to meet increasing demand without incurring full infrastructure replacement costs.

Conclusion

Jack Mathebula is a highly suitable and deserving candidate for the Best Researcher Award, particularly in the domain of power systems, HVDC planning, and renewable energy integration. His blend of technical depth, leadership, applied research, and mentorship exemplifies the qualities of an impactful researcher driving innovation in the energy sector.

Nuttapat Jittratorn | Renewable Energy | Best Researcher Award

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Mr. Nuttapat Jittratorn | Renewable Energy | Best Researcher Award

Ph.D. Candidate in Electrical Engineering, National Cheng Kung University, Taiwan.

Nuttapat Jittratorn is a passionate Ph.D. candidate in Electrical Engineering at National Cheng Kung University, Taiwan. With a deep-rooted commitment to renewable energy innovation, he has led over 10 collaborative projects across Taiwan and Japan, applying AI to enhance energy forecasting systems. His academic and industrial experience spans solar PV, wind power, and hybrid energy systems. Nuttapat’s interdisciplinary expertise merges machine learning with real-time deployment, helping industries such as TSMC and Delta Electronics optimize energy use. Recognized with the Best Oral Presentation Award at the 2025 IEEE IAS Annual Meeting, he also contributes to academic leadership as a session chair and student mentor. A forward-thinking researcher fluent in English and Thai, he continues to bridge research with sustainable industrial solutions.

🧾Author Profile

🎓 Education

Nuttapat Jittratorn began his academic journey at Kasetsart University, Thailand, earning a Bachelor of Engineering in Electrical Engineering (2014–2018). He then pursued his Master’s degree at National Chung Cheng University in Taiwan, where he deepened his focus on renewable energy systems and intelligent computation (2018–2021). Currently, he is a Ph.D. candidate in Electrical Engineering at National Cheng Kung University, Taiwan (2021–present). His doctoral research centers on enhancing the reliability and accuracy of energy forecasting using artificial intelligence. Throughout his studies, Nuttapat has maintained a strong interdisciplinary approach, integrating engineering principles with emerging technologies like deep learning and hybrid modeling. His academic path reflects a consistent commitment to solving global energy challenges through intelligent system design and applied machine learning in energy grids.

💼 Experience 

Since 2021, Nuttapat has played pivotal roles as Team Leader, Project Advisor, and Researcher across Taiwan and Japan. He has collaborated with leading institutions and corporations such as TSMC, Delta Electronics, FarEasTone Telecom, and the National Science and Technology Council. His work involves real-time AI-powered forecasting systems for solar, wind, and multi-load applications in power and steam. Nuttapat has led the development and deployment of models in real-world industrial settings, optimizing power generation and usage. As a Thesis Advisor at Ton Duc Thang University (2022–2023), he mentored students in AI-energy research and thesis defense preparation. His projects span Changhua, Hsinchu, Tainan, Taoyuan, and Kagoshima, showcasing his ability to drive innovation in dynamic, multinational environments.

🏅 Honors & Awards 

Nuttapat Jittratorn was awarded the Best Oral Presentation Award in the Renewable and Sustainable Energy Conversion track at the 2025 IEEE IAS Annual Meeting, recognizing his research impact in intelligent PV and wind power forecasting. Additionally, he served as the Session Chair at the same Award, a testament to his leadership and recognition in the energy research community. His collaborative research and advisory roles in academia and industry have positioned him as a standout researcher in applied energy systems. These achievements underscore his ability to produce not just high-quality publications, but also real-world, industry-transforming outcomes that align with global sustainability goals.

🔬 Research Focus 

Nuttapat’s research is centered on AI-based renewable energy forecasting. He develops intelligent models for very short-term and short-term prediction of solar PV and wind power generation. His focus includes hybrid techniques that combine LSTM, Markov models, and probabilistic correction based on environmental data like wind speed. He also explores energy storage integration, such as BESS (Battery Energy Storage Systems), to enhance operational efficiency. His work bridges data science and engineering, ensuring models are not only accurate in labs but also viable for real-world deployment in industrial energy management. His interdisciplinary projects support Taiwan and Japan’s energy industries in transitioning toward smarter and more reliable grid systems. His research is forward-looking, contributing directly to the goals of a low-carbon economy and sustainable industrial operations.

Publication Top Notes

1. A Hybrid Method for Hour-Ahead PV Output Forecast with Historical Data Clustering

Authors: N. Jittratorn, G.W. Chang, G.Y. Li
Conference: 2022 IET International Conference on Engineering Technologies
Citations: 4
Summary: This paper proposes a clustering-based hybrid model for predicting hour-ahead PV output. Historical meteorological data are clustered to create more accurate baseline patterns, improving forecast accuracy. The model has industrial applications for solar plant operation scheduling.

2. Very Short-Term Wind Power Forecasting Using a Hybrid LSTM-Markov Model Based on Corrected Wind Speed

Authors: A.N. Jittratorn, B.C.M. Huang, C.H.T. Yang
Journal: Renewable Energy and Power Quality Journal, Vol. 21, pp. 433–438
Year: 2023 | Citations: 2
Summary: A hybrid forecasting framework combining LSTM and a Markov decision structure, this study corrects input wind speed for improving wind power forecasts within minutes to hours. Effective for wind turbine operational control and energy market participation.

3. A Deterministic and Probabilistic Framework Based on Corrected Wind Speed to Improve Short-Term Wind Power Forecasting Accuracy

Authors: N. Jittratorn, C.M. Huang, H.T. Yang
Journal: International Journal of Electrical Power & Energy Systems, Vol. 170, 110859
Year: 2025
Summary: This journal article presents an advanced dual-framework model integrating deterministic forecasts with probabilistic corrections, improving reliability in fluctuating wind environments. It’s particularly useful for risk-aware grid management and dispatch.

4. Short-Term Forecasting of Wind Power Plant Generation Based on Machine Learning Models

Authors: M.N. Phan, K.P. Nguyen, V. Van Huynh, C.M. Huang, H.T. Yang, N. Jittratorn, et al.
Conference: 2025 IEEE 1st International Conference on Smart and Sustainable Developments
Year: 2025
Summary: Collaborative paper exploring various machine learning models for short-term wind forecasting. Nuttapat contributed to model selection, tuning, and integration with real-time plant data.

5. PV Power Forecasting for Operation of BESS Integrated with a PV Generation Plant

Authors: N. Jittratorn, C.S. Liu, C.M. Huang, H.T. Yang
Conference: 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)
Year: 2024
Summary: Proposes a new forecasting model to manage PV+BESS operation, ensuring optimal battery use while minimizing forecast error. Critical for smart energy storage deployment in renewable infrastructure.

🏅 Conclusion

Nuttapat Jittratorn is a highly promising early-career researcher with solid technical, academic, and leadership credentials. His contributions to AI-driven energy forecasting and integration with industrial applications stand out. While still in the Ph.D. phase, his research maturity, real-world impact, and academic service position him as a strong candidate for the Best Researcher Award, particularly in the applied energy systems or smart grid technologies domain.

Anna Giuliano | Prevent Oropharyngeal Cancer | Best Researcher Award

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Prof. Anna Giuliano | Prevent Oropharyngeal Cancer | Best Researcher Award

Professor, Moffitt Cancer Center, United States

Dr. Anna R. Giuliano, Ph.D., is a distinguished professor, cancer epidemiologist, and visionary public health leader dedicated to the prevention of virus-associated cancers. She currently serves as Founding Director of the Center for Immunization and Infection Research in Cancer at the H. Lee Moffitt Cancer Center, where she pioneers research at the intersection of infectious diseases and oncology.

👩‍🔬Professional Profile

ORCID

🎓 Education

Dr. Giuliano earned her Ph.D. in Nutritional Biochemistry from Tufts University (1990), where she explored calcium transport mechanisms in intestinal cells. 🧫 Prior to that, she completed an M.S. in Nutrition and a B.A. in Psychology and Anthropology from SUNY Stony Brook, graduating summa cum laude and Phi Beta Kappa 🎓. Her postdoctoral training included an NCI Fellowship in Cancer Prevention and Etiology at the University of Arizona, further anchoring her in epidemiological research. 🧪

🧑‍🏫 Experience

With over three decades of academic and research leadership, Dr. Giuliano has held multiple tenured faculty positions at the University of Arizona and the University of South Florida. 🏛️ She served as Chair and Program Leader of Cancer Epidemiology at Moffitt Cancer Center (2004–2011) and has been a tenured professor in multiple departments since 2004.

Her international consulting roles span from Haiti 🇭🇹 to Nepal 🇳🇵 to Kenya 🇰🇪, where she collaborated with CARE, USAID, and ADRA on critical nutrition and health security assessments. She is widely respected for her culturally sensitive, data-driven, and community-based health interventions across global settings. 🌐

🔬 Research Focus On Prevent Oropharyngeal Cancer

Dr. Giuliano’s research centers on HPV-related cancer prevention, vaccine efficacy, and the natural history of HPV infection in both men and women. 💉 She has been instrumental in expanding the evidence base supporting HPV vaccination for males, leading to global policy shifts and improved cancer prevention outcomes. She also explores the role of biomarkers and epigenetics in early cancer detection, with a strong translational research approach. 🧬

🏅 Awards & Honors

Dr. Giuliano’s contributions have been celebrated with numerous accolades, including:

  • 🏆 Joseph F. Fraumeni Jr. Distinguished Achievement Award (2019)

  • 🌟 Moffitt Cancer Center Researcher of the Year (2011, 2022)

  • 📊 Top Publication Awards, JAMA Oncology (2022)

  • 👩‍⚕️ American Cancer Society Clinical Research Professor Award (2018–2023)

  • 🌸 President Elect, International Papillomavirus Society (2021–present)

  • 🎖️ Most Cited Faculty Member, Moffitt Cancer Center (2007)

📚 Publications Top Notes

1. Immunogenicity of the 9-valent human papillomavirus vaccine: Post hoc analysis from five phase 3 studies

Journal: Human Vaccines & Immunotherapeutics
Publication Date: December 31, 2025
DOI: 10.1080/21645515.2024.2425146
Contributors: Anna R. Giuliano, Joel M. Palefsky, Stephen E. Goldstone, Jacob Bornstein, Ilse De Coster, Ana María Guevara, Ole Mogensen, Andrea Schilling, Pierre Van Damme, Corinne Vandermeulen, et al.

Summary:
This comprehensive post hoc analysis combined data from five Phase 3 clinical trials to evaluate the immunogenicity of the 9-valent HPV vaccine across different demographics. Results demonstrated robust antibody responses in males and females aged 9–26 years, with especially high titers in adolescents. Differences in responses by gender and sexual orientation were also noted. The study confirms the vaccine’s strong and consistent immunogenic profile, supporting its global use for both cancer and genital wart prevention across populations.

2. Natural history of HPV-16 E6 serology among cancer-free men in a multicenter longitudinal cohort study

Journal: JNCI: Journal of the National Cancer Institute
Publication Date: May 1, 2025
DOI: 10.1093/jnci/djae326
Contributors: Jaimie Z. Shing, Anna R. Giuliano, Nicole Brenner, Birgitta Michels, Allan Hildesheim, Sudhir Srivastava, Bradley A. Sirak, John Schussler, Danping Liu, Wendy Wang, et al.

Summary:
This study tracked HPV-16 E6 antibody prevalence and persistence in nearly 4,000 cancer-free men from Brazil, Mexico, and the U.S. It found that while E6 seropositivity was rare (0.35%), it was highly persistent and more common in older men. A strong association was observed between oral HPV-16 DNA and E6 seropositivity. The findings suggest that HPV-16 E6 antibodies could serve as early biomarkers for oropharyngeal cancer risk, aiding in future screening strategies.

3. Identification of a Biomarker Panel from Genome-Wide Methylation to Detect Early HPV-Associated Oropharyngeal Cancer

Journal: Cancer Prevention Research
Publication Date: April 2, 2024
DOI: 10.1158/1940-6207.CAPR-23-0317
Contributors: Brittney L. Dickey, Ryan M. Putney, Michael J. Schell, Anders E. Berglund, Antonio L. Amelio, Jimmy J. Caudell, Christine H. Chung, Anna R. Giuliano

Summary:
In this cutting-edge translational study, researchers used genome-wide methylation data to identify a panel of biomarkers for the early detection of HPV-associated oropharyngeal cancer. The biomarker panel demonstrated high sensitivity and specificity in distinguishing early cancer cases from healthy individuals. Dr. Giuliano’s contribution to this work supports the development of non-invasive screening tools that could revolutionize early cancer detection and improve patient outcomes.

4. Data from Identification of a Biomarker Panel from Genome-Wide Methylation to Detect Early HPV-Associated Oropharyngeal Cancer

Platform: Preprint
Publication Date: April 2, 2024
DOI: 10.1158/1940-6207.c.7160199
Contributors: Same as above

Summary:
This preprint presents the raw and supplementary data associated with the biomarker panel study (see item 3). It provides detailed methodologies, validation datasets, and analytical workflows used in detecting methylation changes associated with early HPV-driven cancers. This open-access resource enables replication and further exploration by other cancer prevention researchers.

5. Data from Identification of a Biomarker Panel from Genome-Wide Methylation to Detect Early HPV-Associated Oropharyngeal Cancer (Version 1)

Platform: Preprint (Version 1)
Publication Date: April 2, 2024
DOI: 10.1158/1940-6207.c.7160199.v1
Contributors: Same as above

Summary:
This version 1 dataset provides the initial analysis and raw findings from the methylation study. The release of multiple versions reflects a commitment to transparency and continuous scientific improvement. These data have supported downstream publications and are foundational to future clinical applications in HPV cancer diagnostics.

🎯 Conclusion

Dr. Anna R. Giuliano is a pioneering figure whose lifelong commitment to cancer prevention has reshaped global health paradigms. From pioneering HPV vaccine research in men to improving cancer screening among marginalized populations, her impact spans continents, disciplines, and generations. 🌍 Her leadership, scholarship, and advocacy for equitable healthcare make her a truly deserving candidate for this award. 🏅

Xiangyu Zhang | Public Health | Best Researcher Award

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Mr. Xiangyu Zhang | Public Health | Best Researcher Award

Doctoral Researcher, CAS Institute of Automation, China

Dr. Xiangyu Zhang is a doctoral researcher at the Institute of Automation, Chinese Academy of Sciences (CASIA). With a strong foundation in mechanical engineering and robotics, Dr. Zhang transitioned into the realm of social computing and artificial intelligence. His research addresses real-world crises by developing cognitive AI agents and multi-agent systems for public health emergency response. His collaborative work with the Chinese CDC and the Beijing CDC has resulted in intelligent models that predict disease outbreaks and optimize response strategies using advanced machine learning and large language models. His forward-thinking research contributes significantly to the growing intersection between AI and epidemiology, aiming to build resilient, data-driven health systems. A rising voice in the AI and public health community, Dr. Zhang continues to lead impactful research published in leading journals and presented at top international Awards.

Author’s Profile

🎓 Education

Dr. Zhang’s educational journey exemplifies his interdisciplinary approach. He is currently pursuing his Ph.D. in Social Computing at CASIA under the University of Chinese Academy of Sciences. His research integrates AI, epidemiology, and intelligent systems at the State Key Laboratory of Multimodal Artificial Intelligence Systems. Prior to this, he earned his M.Sc. in Mechanical Engineering from the University of Electronic Science and Technology of China (UESTC), where he specialized in biomimetic actuators in robotics. His undergraduate studies in Mechanical Design (B.Eng., UESTC) laid the foundation for his engineering acumen, particularly in robotic arm systems and control mechanisms. This unique blend of mechanical design and cognitive AI enables him to craft deeply technical yet socially responsive research, blending physical systems with computational intelligence to solve contemporary global health challenges.

🏢 Experience

Dr. Zhang’s research experience spans across both engineering innovation and public health intelligence. As a Ph.D. researcher at CASIA, he is engaged in groundbreaking work on cognitive agent-based systems for pandemic response and crisis management. He has worked extensively with high-stakes research projects funded by the National Natural Science Foundation of China and the Next-Generation AI Development Plan (2015–2030). His key contributions include developing simulation models for outbreak prediction and adaptive intervention frameworks using LLMs and multi-agent systems. He has collaborated directly with the Chinese CDC and Beijing CDC, grounding his work in critical, real-world public health needs. His previous research in robotics labs focused on elastic actuators and autonomous learning systems, bringing an engineering lens to his AI-driven innovations. Despite his early career stage, his experience is already making waves in intelligent public health system design.

🔬 Research Focus

Dr. Zhang’s research centers on the development of intelligent decision-support systems for public health emergencies. His primary focus lies in the integration of multi-agent systems, epidemiological modeling, and large language models (LLMs) to create dynamic, real-time frameworks for disease surveillance, prediction, and intervention. He has proposed agent-based simulation architectures that replicate human-like decision-making in health crisis contexts, enabling systems to not only forecast disease spread but also adaptively respond with optimal strategies. His novel work in spatiotemporal forecasting for urban epidemics (IEEE ISI 2025) and cognitive frameworks for crisis modeling (Frontiers of Engineering Management) positions him as a forward-thinking researcher in AI for social good. His contributions are pioneering the use of AI to enhance epidemiological intelligence and resilience against future pandemics—an area of urgent global need.

📚 Publication Top Notes

  1. Agent-Based Modeling of Epidemics: Approaches, Applications, and Future Directions
    Technologies, 2025, 13, 272.
    This paper reviews the current methodologies and advancements in agent-based modeling (ABM) for epidemics. Dr. Zhang explores how ABMs simulate human behaviors and interactions to better understand disease spread and the effectiveness of policy interventions.

  2. Large Language Models: Technology, Intelligence, and Thought 
    Frontiers of Engineering Management
    Co-authored with Z. Cao and D. Zeng, this paper examines the philosophical and functional implications of large language models (LLMs) in understanding intelligence and cognition, particularly in the context of public service applications.

  3. LLM-Driven Spatiotemporal Forecasting of Urban Infectious Diseases
    IEEE ISI 2025 Conference
    A real-world case study on Haidian District, this work presents a cutting-edge LLM-integrated system for forecasting infection patterns, enabling early interventions based on data-driven spatial and temporal analysis.

  4. ShadowPainter: Robotic Painting via Active Learning
    Journal of Intelligent & Robotic Systems, 2022, 105(3), 61
    This research introduces an AI-powered robotic system capable of learning artistic techniques through visual replication. While outside public health, it showcases Dr. Zhang’s skills in human-like machine learning.

  5. Novel Multi-Configuration Elastic Actuator
    Advanced Intelligent Systems, 2024, 6(10): 2400079
    This paper introduces an elastic actuator capable of energy modulation, relevant for dynamic robotic systems. It highlights Dr. Zhang’s prior contributions in robotics and mechanical innovation.

Conclusion 

Dr. Xiangyu Zhang presents a strong and promising research portfolio marked by high-impact publications, innovative interdisciplinary work, and societal relevance in AI-driven public health crisis management. While still early in his career, his contributions clearly demonstrate leadership potential and research excellence, especially in emerging fields.

Prateek Chandrakar | Structural Mechanics | Best Researcher Award

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Mr. Prateek Chandrakar | Structural Mechanics | Best Researcher Award

Research Scholar, Indian Institute of Technology Kharagpur, India.

Dr. Prateek Chandrakar is an emerging expert in structural mechanics, known for his work on stochastic modeling of composite laminates. He holds a Ph.D. in Aerospace Engineering from IIT Kharagpur, with a thesis on machine learning-assisted uncertainty analysis in thermally loaded and damaged composite structures. A recipient of multiple international Award grants and reviewer for reputed journals, his innovative integration of artificial intelligence with classical mechanics has contributed to designing robust aerospace and mechanical structures. His research has led to high-impact publications in Q1 journals like Composite Structures and European Journal of Mechanics A/Solids. His dedication to computational mechanics and his skillful use of tools like ABAQUS, MATLAB, and RBF networks make him a key contributor to the next generation of structural mechanics solutions.

👤Author’s Profile

🎓 Education 

Prateek Chandrakar completed his Ph.D. in Structures from IIT Kharagpur (2020–2025), achieving a stellar CGPA of 9.36. His research focused on variable fiber spacing and curvilinear fiber composites under uncertain environments. He earned his M.Tech. in Machine Design from IIT Guwahati (2017–2019) and B.E. in Mechanical Engineering from CSVTU Bhilai (2012–2016). Prior to this, he secured 90% in his matriculation and 80.8% in his intermediate studies from the Chhattisgarh Board. He holds additional certifications in machine learning, MATLAB, ABAQUS, and HyperMesh. Throughout his academic journey, Prateek has consistently demonstrated academic excellence and an aptitude for both foundational and advanced subjects in solid mechanics and composite structures.

💼 Experience 

Prateek has served as a Senior Research Fellow and Teaching Assistant at IIT Kharagpur (2022–2025), contributing to courses such as Finite Element Methods and Engineering Mechanics. Previously, he was a Junior Research Fellow (2020–2022) in Aerospace Structures. During his M.Tech. at IIT Guwahati, he worked as a Teaching Assistant for courses like Computational Continuum Mechanics. His industrial exposure includes a short training stint at NTPC-SAIL Power Company. Beyond teaching, he has completed hands-on projects in hydraulic mechanisms, nonlinear FEM, and RVE modeling. Prateek’s blend of academic and applied experience highlights his ability to bridge theory with practical research problems.

🏅 Awards & Honors

  • Best Conference Grant – IIT Kharagpur, ECCM 2024 (France)

  • SERB ITS Grant – For ICTAM 2024 (Korea)

  • ICTAM Support Grant Award – Full fee waiver for outstanding research

  • MHRD Fellowships – Junior & Senior Research Fellow at IIT Kharagpur (2020–2025), PG Fellowship at IIT Guwahati (2017–2019)

  • GATE 2017 – 98.98 percentile in Mechanical Engineering

  • Peer Reviewer – Journals: Acta Mechanica, Awards: ISTAM, AIAA SciTech

  • Vaidik Maths Contest – Regional 2nd Position

  • Volunteer and Mentor – DISHA (Free education initiative at IIT Guwahati)

🔬 Research Focus 

Prateek’s research is centered around stochastic structural mechanics—particularly the buckling and dynamic responses of variable stiffness composite laminates under thermal loads and manufacturing defects. He has advanced the application of machine learning (RBFN, SVR, ABC) to create surrogate models for high-fidelity simulations in nonlinear environments. His studies tackle the challenges of material variability, geometric imperfection, and delamination in aerospace-grade composite materials. His Ph.D. thesis innovatively integrates third-order shear deformation theory and SPR techniques to improve stress prediction accuracy. His goal is to bridge uncertainty quantification with practical design optimizations, particularly for thermal buckling, nonlinear flexure, and damage mechanics.

📚 Publication Top Notes

1. Characterizing Flexural Randomness in Delaminated Curvilinear Fiber Composites

Journal: Composite Structures (2025) 
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
This study develops a machine learning-assisted surrogate modeling framework to predict the nonlinear flexural behavior of delaminated curvilinear fiber-reinforced composites. By combining Support Vector Regression (SVR) and Radial Basis Function Networks (RBFN), the research significantly reduces computational effort while accurately estimating the impact of tow angle randomness and damage. The paper proposes optimal tow angle layups for enhancing stiffness while accommodating uncertainties, demonstrating a reliable and fast alternative to traditional finite element simulations. The research is particularly valuable for lightweight structural applications in aerospace engineering where manufacturing defects like delamination are unavoidable.

2. Damage-Induced Buckling in Thermally Loaded VAT Laminates under Uncertainty

Journal: European Journal of Mechanics – A/Solids (2024) 
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
This paper investigates the influence of thermal loads and damage mechanisms (e.g., delamination) on the buckling performance of Variable Angle Tow (VAT) composite laminates under uncertainty. Using a probabilistic modeling approach, the authors incorporate variability in material properties and geometrical imperfections. Third-order shear deformation theory is employed along with stochastic finite element analysis to evaluate thermal buckling loads. This research provides essential insight into designing safer composite structures subjected to thermal and mechanical instability, relevant for aerospace panels and automotive body structures.

3. Stochastic Buckling of Variable Fiber Spacing Composite Plates

Journal: Journal of Composite Materials (2023) 
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
Focusing on the uncertainty quantification of buckling behavior in variable fiber spacing composites (VFSCs), this work employs Monte Carlo simulations to assess the reliability of such structures under thermal loading. The results reveal the probabilistic distribution of critical buckling loads under realistic imperfections and loading scenarios. The novelty lies in accounting for both deterministic and stochastic fiber layout variations, leading to design guidelines that improve robustness and structural integrity for high-performance composite components.

4. Buckling Variability in Damaged Composite Laminates in Thermal Fields

Journal: Journal of Thermal Stresses (2024) 
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
This study explores how delamination and temperature variations affect the buckling characteristics of composite laminates. A combination of the improved first-order shear deformation theory and a continuum damage model is applied to simulate thermomechanical behavior. Uncertainty in input parameters is tackled through a stochastic analysis, revealing critical design zones where damage-induced degradation is more severe. The findings can help in the predictive maintenance and thermal stability assessment of structural composite components in sectors such as defense and aviation.

5. Stochastic RBFN-Based Reliability Estimation in Thermal Loading

Journal: International Journal of Advances in Engineering Sciences and Applied Mathematics (2024)
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
In this work, Radial Basis Function Networks (RBFN) are used to build fast and efficient surrogate models for reliability estimation of VFSC laminates under thermal stresses. The study shows that RBFN can effectively capture nonlinearities caused by fiber angle variation, material uncertainties, and temperature-induced stresses. This method significantly reduces computational time compared to traditional Monte Carlo techniques, providing a viable tool for real-time reliability assessments in design optimization of composite structures.

6. Delamination Effects via Polynomial Neural Network on Dynamic Characteristics

Journal: Acta Mechanica (2024) 
Authors: N. Sharma, P. Chandrakar, D.K. Maiti
Summary:
This paper introduces a Polynomial Neural Network (PNN) model to quantify the influence of delamination on the uncertain dynamic response of Variable Angle Tow composites. The work includes both free and forced vibration analyses, capturing variability in material damping, boundary conditions, and geometric configurations. The model is validated against high-fidelity simulations, showing high accuracy with minimal computational resources. Applications include condition monitoring and damage detection in aerospace-grade structural panels.

7. Uncertain Buckling with Internal Defects in VFSC Laminates

Journal: Journal of Composite Materials (2024) 
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
This research evaluates the buckling behavior of variable fiber spacing composites with embedded internal flaws such as voids, microcracks, and fiber waviness. Through stochastic modeling and sensitivity analysis, the study reveals how defect parameters affect critical buckling loads. The study’s contribution is twofold: it quantifies uncertainty due to imperfections and recommends defect-tolerant configurations for high-reliability designs. This work is vital for industrial applications where manufacturing variability cannot be completely avoided.

Conclusion

Prateek Chandrakar is a deserving and promising candidate for a Best Researcher Award, especially within the domains of aerospace structures, uncertainty quantification, and composite mechanics. His combination of deep domain expertise, strong publications, machine learning integration, and international exposure demonstrates a research trajectory that is both impactful and forward-looking.

Sampath Dakshina Murthy Achanta | Machine Learning | Best Researcher Award

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Dr. Sampath Dakshina Murthy Achanta | Machine Learning | Best Researcher Award

Associate Professor, Vignan’s Institute of Information Technology, India

Dr. Achanta Sampath Dakshina Murthy is a dynamic academician and innovator currently serving as a Senior Associate Professor in the Department of ECE and Head of Vignan’s Centre for Innovations & Startups at Vignan’s Institute of Information Technology (A), Visakhapatnam 📍. With a strong foundation in electronics and communication engineering , he has carved a niche in interdisciplinary research focusing on biomedical image processing, IoT, and human motion analysis. His visionary leadership in innovation ecosystems is evident through his roles in startup incubation, curriculum development, and R&D strategy 💡. Dr. Murthy is also a prolific researcher, boasting 70+ publications, multiple patents 🧠, and editorial roles across reputed journals. His work on AI-powered gait analysis systems demonstrates his dedication to inclusive technological solutions for the physically challenged ♿. A mentor, reviewer, and award-winning educator, he exemplifies excellence in blending academic rigor with practical impact .

Professional Profile

Scopus

ORCID

Google Scholar

📘 Education 

Dr. Murthy holds a Ph.D. in Image Processing from KL University, Andhra Pradesh (2023) . He earned his M.Tech in Digital Electronics and Communication Systems in 2015 and B.Tech in Electronics and Communication Engineering in 2013, both from JNTU Kakinada . His academic journey began with an Intermediate (M.P.C) in 2009 from the Board of Intermediate, A.P. and Class X (CBSE) in 2007 🏫. His continuous pursuit of excellence in education forms the foundation of his pioneering work in research and teaching .

💼 Experience 

Dr. Murthy brings over a decade of experience, including 9 years in academia and 1 year in industry 🏭. He has served as Assistant, Associate, and now Senior Associate Professor at Vignan’s Institute of Information Technology (A), Visakhapatnam since 2016 👨‍🏫. Beyond teaching, he leads as the Head of the Vignan Centre for Innovations & Startups, and has held leadership roles like Associate Dean of R&D and IQAC Coordinator . His experience in Nirmala Industries and involvement in institutional development highlight his blend of academic, administrative, and industrial perspectives .

🔍 Research Focus

Dr. Murthy’s research centers on cutting-edge domains like Human Motion Analysis, Biomedical Image & Video Processing, and the Internet of Things (IoT) 🧬. His notable Ph.D. work developed AI-based gait analysis systems to prevent falls in physically challenged individuals, integrating wearable IoT sensors with machine learning algorithms like HMDTW, ANFIS, and PTBO . With 70+ research papers, 20+ awards, and several grants—including STPI and seed funds—he has built a robust research portfolio . His innovations, such as “Smart Shoe–Better Walking for Future,” exemplify translational research that bridges healthcare and technology . His work contributes to smart assistive technology, healthcare diagnostics, and predictive analytics, positioning him as a trailblazer in applied AI and IoT-driven solutions for human well-being .

🏅 Awards & Honors 

Dr. Murthy is the recipient of numerous national and international honors, including the “Innovative Researcher of the Year 2024” 🥇, “STEM Best Research Award 2024” , and the prestigious Dr. A.P.J. Abdul Kalam National Academician Award 🧑‍🚀. He’s been celebrated with multiple Sastra Awards and Young Scientist recognitions across years . Institutions like Vignan’s and international bodies in the UK, Germany, and the US have acknowledged his exceptional contributions to research, innovation, and teaching . With over 20 awards and 100+ certificates of appreciation, Dr. Murthy exemplifies excellence, impact, and commitment in every dimension of academic life .

Publications Top Notes

An Automated Detection of Heart Arrhythmias Using Machine Learning Technique: SVM
Authors: CU Kumari, ASD Murthy, BL Prasanna, MPP Reddy, AK Panigrahy
Journal: Materials Today: Proceedings, Volume 45, Pages 1393–1398
Year: 2021
Citations: 228
📘 Summary:
This paper presents a machine learning-based system for the automated detection of heart arrhythmias using Support Vector Machine (SVM) algorithms. The study leverages ECG datasets to train and validate the model, achieving high classification accuracy in detecting irregular heartbeat patterns. The proposed system contributes significantly to early diagnosis and risk mitigation in cardiac care through non-invasive, real-time monitoring techniques.

AI-Oriented Competency Framework for Talent Management in the Digital Economy: Models, Technologies, Applications, and Implementation
Author: A. Khang
Publisher: CRC Press
Year: 2024
Citations: 63
📘 Summary:
Although Dr. Murthy is not listed as an author in this title, it appears in the list. This book outlines how Artificial Intelligence can transform talent management strategies in organizations operating within the digital economy. It discusses competency modeling, AI tools, and frameworks for workforce development.  (Note: Please confirm Dr. Murthy’s involvement, as his name does not appear in the author list here.)

An IoT-Based Agriculture Maintenance Using Pervasive Computing with Machine Learning Technique
Authors: S Kailasam, SDM Achanta, P Rama Koteswara Rao, R Vatambeti, et al.
Journal: International Journal of Intelligent Computing and Cybernetics, Volume 15, Issue 2, Pages 184–197
Year: 2022
Citations: 61
📘 Summary:
This research introduces an IoT-driven smart agriculture model integrated with pervasive computing and machine learning algorithms. It monitors and manages agricultural parameters such as soil moisture, temperature, and irrigation efficiency. The system utilizes real-time data collection and predictive analytics to optimize farming practices, thereby enhancing productivity and sustainability in agriculture .

Implementation of Online and Offline Product Selection System Using FCNN Deep Learning: Product Analysis
Authors: MN Mohammad, CU Kumari, ASD Murthy, BOL Jagan, K Saikumar
Journal: Materials Today: Proceedings, Volume 45, Pages 2171–2178
Year: 2021
Citations: 53
📘 Summary:
This paper presents a product recommendation system using Fully Connected Neural Networks (FCNN) to support both online and offline retail environments. The model analyzes user behavior and product features to provide intelligent suggestions. The deep learning approach improves product visibility, customer satisfaction, and market analysis in e-commerce platforms .

A Wireless IoT System Towards Gait Detection Technique Using FSR Sensor and Wearable IoT Devices
Author: SDM Achanta
Journal: International Journal of Intelligent Unmanned Systems, Volume 8, Issue 1, Pages 43–54
Year: 2020
Citations: 50
📘 Summary:
This solo-authored paper by Dr. Murthy explores a wearable IoT-based solution for gait analysis using Force Sensitive Resistor (FSR) sensors. The system helps detect mobility patterns and abnormalities, primarily aimed at elderly and physically challenged individuals. It highlights the role of smart health devices in preventive diagnostics and rehabilitation .

📌 Conclusion 

Dr. Achanta Sampath Dakshina Murthy is a distinguished academic, visionary innovator, and dedicated mentor who continues to push the boundaries of research and education . His commitment to societal advancement through intelligent healthcare systems and academic excellence makes him a role model in the scientific community . Through his leadership in startup incubation, research development, and student mentorship, he shapes the future of innovation in India and beyond . With a passion for continuous learning and contribution, Dr. Murthy stands as a beacon of interdisciplinary impact and scholarly inspiration .

Kishwar Ali | Nanophotonics | Best Paper Award

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Mr. Kishwar Ali | Nanophotonics | Best Paper Award

PhD Student, University of L’Aquila, Italy.

Kishwar Ali is a doctoral researcher at the University of L’Aquila, Italy, specializing in nanophotonics and advanced electromagnetic modeling. His core expertise lies in investigating the Goos–Hänchen shift (GHS) in novel metamaterial configurations using fractional calculus and time-space modulated media. Through strong collaborations with international experts and rigorous theoretical contributions, he has developed new paradigms for controlling light reflection and propagation in hyperbolic and zero-index materials. His vision blends deep physics with real-world applications, such as hyperlensing and photonic sensors. Kishwar is an active member of the IEEE Antennas and Propagation Society and is committed to pushing the boundaries of optical theory toward practical innovation.

📌Author Profile

🎓 Education 

Kishwar Ali is currently pursuing his PhD at the University of L’Aquila in Italy. His doctoral training emphasizes advanced computational modeling and electromagnetic field theory applied to metamaterials and nanophotonics. His research bridges theoretical optics, mathematical modeling in fractional dimensions, and practical application in layered photonic structures. His academic background integrates foundational knowledge in applied physics with specialized training in electromagnetic theory and materials science. During his doctoral journey, Kishwar has been mentored by leading scientists and has contributed to multiple high-impact publications, enhancing his research rigor and interdisciplinary insights.

💼 Experience

As a PhD student, Kishwar Ali has co-authored four peer-reviewed publications, tackling complex optical phenomena such as the Goos–Hänchen shift and its manipulation in fractional and anisotropic media. He is actively engaged in a major project on spatiotemporal band engineering in photonic crystals. His research experience includes theoretical model development, analytical derivations, simulation implementation, and result validation. Kishwar collaborates with international researchers from Italy and Pakistan and contributes significantly to manuscript drafting, mathematical modeling, and peer communication. Though early in his professional journey, his impactful publications and innovative focus have already made notable impressions in the nanophotonics domain.

🔬 Research Focus 

Kishwar Ali’s research is focused on light–matter interaction in metamaterials and spatiotemporal optical media, particularly the Goos–Hänchen shift and its enhancement or suppression in engineered systems. His interests lie in understanding how electromagnetic fields behave in complex layered structures, including near-zero-index materials, fractional spaces, and hyperbolic graphene composites. He is currently working on periodic space-time modulation to explore new forms of bandgap engineering, with potential implications in light steering, hyperlensing, and optical sensing technologies. Kishwar integrates analytical modeling, numerical simulation, and physics-driven intuition to develop concepts applicable to quantum optics, nanophotonics, and optical cloaking devices.

📚 Publication Top Notes

  1. Enhanced Control of the Goos–Hänchen Shift at Graphene-Hyperbolic Boron Nitride Multilayer Hyper Crystal
    Optics & Laser Technology, 191, 113390, 2025
    Authors: K Ali, F Ferranti, F Frezza, G Antonini
    Summary: This study presents a novel way to manipulate the Goos–Hänchen shift using hybrid graphene-hBN structures, enabling improved beam control. Applications include optical sensors and super-resolution imaging.

  2. Rest-Frame Quasi-Static Analysis for a Rotating Core-Shell Structure in a Fractional Dimensional Space
    JOSA B, Vol. 42(3), pp. 611-620, 2025
    Authors: S Parveen, K Ali, A Shahzad, QA Naqvi
    Summary: Investigates light interaction in a rotating nanostructure within a fractional-dimensional framework. This work adds a new perspective to electromagnetic modeling in non-integer geometries.

  3. Magnetic and Fractional Parametric Control of Goos-Hänchen Shifts in the Anisotropic Yttrium-Iron-Garnet Film Surrounded by Isotropic Fractal Dielectric Half-Spaces
    Physics Letters A, 453, 128496, 2022
    Authors: K Ali, WI Waseer, QA Naqvi
    Summary: Explores how magnetic fields and fractional-order modeling can be used to fine-tune light shifts in complex magnetic-dielectric environments.

  4. Goos–Hanchen-Effect for Near-Zero-Index Metamaterials Excited by Fractional Dual Fields
    Optik, 243, 167501, 2021
    Authors: K Ali, AA Syed, WI Waseer, QA Naqvi
    Summary: Analyzes how fractional dual fields affect the Goos–Hänchen effect in zero-index metamaterials. This is foundational for developing cloaking and advanced light-guiding technologies.

Conclusion

Kishwar Ali demonstrates a strong theoretical foundation and thematic consistency in studying light–matter interactions through the lens of Goos–Hänchen shifts in engineered materials. His recent 2025 work on graphene-hyperbolic boron nitride multilayer crystals stands out as a highlight for its innovation and potential application.

Fazilet Gokbudak | Sustainable Tech Solutions | Best Researcher Award

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Dr. Fazilet Gokbudak | Sustainable Tech Solutions | Best Researcher Award

ML Researcher, Apple, United Kingdom.

Fazilet Gokbudak is a machine learning researcher at Apple, specializing in generative models, computational photography, and inverse rendering. She received her PhD in Computer Science from the University of Cambridge in 2024, where she worked on neural rendering and efficient image manipulation techniques. Her academic path began at Bogazici University with a high honors degree in Electrical and Electronics Engineering, followed by an MSc with distinction from the University of Edinburgh. Her industrial journey includes pivotal contributions at Amazon as an Applied Scientist Intern. Fazilet actively promotes diversity in AI as a co-chair at Women@CL and reviewer for prestigious AI Awards. Her impactful work has been recognized through awards, publications in top-tier Awards like ECCV and SIGGRAPH, and cutting-edge research contributing to sustainable visual technologies.

Author’s Profile

Education 

Fazilet Gokbudak pursued her undergraduate studies in Electrical and Electronics Engineering at Bogazici University (2014–2018), graduating with High Honors and multiple scholarships. She then completed her MSc in Signal Processing and Communications at the University of Edinburgh in 2019, earning a distinction. Between 2020 and 2024, she conducted her PhD research in Computer Science at the University of Cambridge under full funding from the Computer Laboratory Studentship. Her doctoral work focused on generative neural techniques for photorealistic appearance manipulation and inverse rendering. Fazilet’s academic training spanned signal processing, computer vision, and machine learning. Throughout her education, she demonstrated a consistent commitment to innovation, excellence, and sustainability in computational research, reflected by her active participation in research projects and her growing publication record.

Experience

Fazilet currently serves as an ML Researcher at Apple (since October 2024), where she develops advanced camera algorithms to enhance mobile photography. Previously, she interned at Amazon (July–November 2022), working on high-fidelity conditional image generation using GANs with local histogram losses for skin tone realism. She also worked as a part-time Research Assistant at the University of Cambridge (Nov 2020–Jan 2022), where she led the Cambridge team on a joint blackgrass detection project using deep convolutional networks. Her work achieved 80% classification accuracy on novel agricultural datasets, showcasing practical impacts of AI in sustainable agriculture. Her career blends foundational machine learning research with real-world, production-level deployments.

Awards and Honors 

Fazilet has earned multiple accolades recognizing her academic excellence and research impact. At the University of Cambridge, she received the prestigious Computer Laboratory Studentship (2020–2024), which fully funded her doctoral studies. She also earned a Graduate Student Travel Award from Queens’ College in 2023 to attend SIGGRAPH, a top-tier graphics Award. At Bogazici University, she graduated with High Honors and received the Dean’s High Honor Certificate in 2018. She was also a recipient of the TEKFEN Holding Private Scholarship (2009–2018) for her outstanding performance in national exams, ranking 185 out of over two million candidates. Her high school career concluded as Valedictorian, earning Summa Cum Laude. These honors reflect her long-standing dedication to academic excellence and societal impact.

Research Focus 

Fazilet’s research spans several cutting-edge areas in machine learning and computer vision, with a core focus on generative models, neural rendering, and appearance manipulation. Her PhD work centered on data-efficient methods for realistic image generation, including patch-based transformations and BRDF modeling. She explores inverse rendering techniques that enable physically consistent reconstructions of visual scenes, contributing to advancements in sustainable graphics systems. At Apple, her focus includes developing intelligent algorithms for mobile camera systems, aligning technological performance with energy efficiency and visual realism. Her research also supports sustainable tech by optimizing neural computations and minimizing training overhead, critical for eco-conscious AI applications. She brings a unique blend of academic depth and practical innovation.

Publication Top Notes

Multispectral Fine-Grained Classification of Blackgrass in Wheat and Barley Crops  (2025, Computers and Electronics in Agriculture)

Co-authored with M. Darbyshire et al. This study develops a deep learning model using multispectral image data to accurately identify blackgrass weed species in cereal crops. Fazilet was responsible for model training and evaluation, contributing to a high-accuracy, fine-grained classification system. The work supports sustainable agriculture by enabling targeted herbicide use and reducing environmental impact.

Spatial Receptor Allocation for a Multiple Access Hub in Nanonetworks (2019, IEEE Transactions on Molecular, Biological and Multi-Scale Communications)

Fazilet’s early research explored theoretical models for receptor allocation in nanoscale communication networks. The study introduces simulation-based methods to optimize signal clarity and reduce cross-interference in molecular communication systems, marking a foundational step in nano-IoT frameworks.

Hypernetworks for Generalizable BRDF Representation (2024, European Conference on Computer Vision)

This paper presents a novel hypernetwork design that enables generalization across various materials when estimating Bidirectional Reflectance Distribution Functions (BRDFs). Fazilet contributed to network architecture and experimentation, demonstrating the model’s ability to capture complex material appearances using fewer parameters, facilitating efficient rendering in graphics pipelines.

Data-efficient Neural Appearance Manipulations (2025, in preparation)

This upcoming work proposes neural models for photo editing that require less training data and computational power. Fazilet introduces modular architectures that allow intuitive and efficient image edits, especially suited for low-resource devices. The approach balances realism and sustainability by minimizing hardware dependency.

Physically Based Neural BRDF (2024, arXiv preprint)

Fazilet co-authored this paper which merges physical reflectance models with neural networks to improve accuracy in appearance modeling. The technique enhances inverse rendering applications and supports high-fidelity visual reconstruction by incorporating physical consistency into the learning process.

One-shot Detail Retouching with Patch Space Neural Transformation Blending (2023, ACM SIGGRAPH CVMP)

The paper introduces a one-shot learning-based approach for image retouching that uses patch-space neural blending. Fazilet’s contributions include model design and testing, enabling fast, high-quality transformations from a single reference image—a technique ideal for low-data environments.

Patch Space Neural Field based Transformation Blending (2022, CoRR)

A precursor to her 2023 SIGGRAPH CVMP paper, this work investigates neural field-based image transformation using patch-level information. It showcases the potential for detail-preserving edits with minimal training samples, advancing low-data generative editing.

Conclusion

Fazilet Gokbudak is an exceptionally strong candidate for a Best Researcher Award in the fields of Machine Learning, Computer Vision, and Generative AI. Her multidisciplinary background, strong publication record, and industry-academic synergy position her as a next-generation leader in AI research.