Nisha Agrawal | Cell Structure Analysis | Best Researcher Award

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Mrs. Nisha Agrawal | Cell Structure Analysis | Best Researcher Award

Mrs. Nisha Agrawal, Centre for Development of Advanced Computing (C-DAC), India

Dr. Nisha Agrawal is a seasoned HPC (High-Performance Computing) specialist and currently serves as Scientist E at the Centre for Development of Advanced Computing (C-DAC), Pune. With over 20 years of expertise, she specializes in GPGPU computing, parallel programming, and the optimization of scientific applications on heterogeneous supercomputers. A NVIDIA-Certified Mentor, she is a recognized leader in GPU-accelerated simulation and a frequent mentor at OpenHackathons worldwide. She is an accomplished speaker with 30+ invited talks at IITs, IISERs, and major scientific forums. Her portfolio includes numerous peer-reviewed publications and the Best Paper Award at IEEE TEMSMET 2021. Dr. Agrawal’s deep commitment to scientific research, HPC education, and her passion for innovation in parallel computing make her a standout candidate for the Best Researcher Award.

Publication Profile: 

Scopus

✅ Strengths for the Award:

  1. Extensive Domain Expertise (20+ Years)
    Dr. Agrawal has over two decades of hands-on experience in High-Performance Computing (HPC), which showcases both her technical maturity and consistency in research.

  2. Specialized Skill Set in GPGPU & Scientific Computing
    She is highly proficient in CUDA, OpenACC, MPI, OpenMP, and has successfully ported and optimized complex applications like DFT, NAMD, ANUGA, and WRF on heterogeneous (CPU+GPU) systems.

  3. Mentorship and Knowledge Dissemination
    As an NVIDIA-Certified Mentor and participant in OpenHackathons, she contributes not only as a researcher but also as a mentor and educator, spreading HPC knowledge to broader communities.

  4. Recognized Publications & Awards
    With 12+ peer-reviewed papers, including a Best Paper Award at IEEE TEMSMET-2021, she has established a credible presence in the international research community.

  5. Invited Speaker & Thought Leader
    Having delivered 30+ invited talks at premier institutions (IITs, IISERs), her influence in promoting HPC education and innovation is notable.

  6. Cross-Disciplinary Impact
    Her research spans multiple domains such as molecular dynamics, weather modeling, and flood simulation, indicating interdisciplinary relevance and applicability.

⚠️ Areas for Improvement:

  1. Broader International Collaboration
    While her national contributions are significant, increased participation in international research collaborations or joint projects could amplify her global academic impact.

  2. Patents and Industry Innovation
    Though she has notable research publications, contributions in the form of patents, industry tech transfer, or commercialization could further strengthen her applied research portfolio.

  3. Exploration of AI-HPC Synergies
    Given the growing overlap between AI/ML and HPC, engaging in hybrid research areas could help future-proof her work and widen its application.

🎓 Education:

Dr. Nisha Agrawal holds a Master of Technology (M.Tech) in Computer and Information Technology from S.P. Pune University (2016–2018), where she graduated with an Outstanding grade. Her foundational technical background stems from a Bachelor of Engineering (B.E.) in Information Technology from the University of Rajasthan, completed in 2005. Her academic journey is marked by both excellence and consistency, having also cleared the GATE 2005 with an impressive All India Rank of 695 in IT. Throughout her education, she has built a robust foundation in programming, system architecture, and computational modeling, which became pivotal in shaping her research interests in HPC, GPU programming, and scientific computing. Her transition from academia to research has been marked by a clear focus on applying theory to practical, high-impact computational problems.

💼 Professional Experience:

Dr. Agrawal has been with C-DAC Pune since May 2005, rising through the ranks to her current role as Scientist E. Over the years, she has led and contributed to critical national-level HPC projects, focusing on the GPU acceleration of computational simulations like DFT, Molecular Dynamics (MD), ANUGA, and WRF. She actively mentors participants at NVIDIA/OpenACC Hackathons, fostering GPU adoption across disciplines. Her projects typically involve complex parallel and distributed programming tasks, using CUDA, OpenACC, OpenMP, and MPI. She also plays a strategic role in tuning applications for GPU+CPU clusters, especially on Ampere GPU architecture. Her professional growth has been characterized by her ability to bridge cutting-edge theory with real-world computational applications in science and engineering domains.

🏆 Awards & Honors:

Dr. Nisha Agrawal’s research and mentoring excellence have been acknowledged through numerous accolades. She won the Best Paper Award at IEEE TEMSMET 2021, recognizing her pioneering work in GPU-based parallel computing. As a NVIDIA/OpenACC Certified HPC Mentor (2023), she contributes significantly to India’s HPC knowledge ecosystem. In 2017, she received the ACM Scholarship to attend the prestigious Grace Hopper Celebration India (GHCI), a testament to her commitment to women in technology. Earlier, she was conferred the GARGI Puraskar by the Government of Rajasthan in 1999 for academic excellence. Her qualifying performance in GATE 2005 (AIR 695) further highlighted her strong academic and analytical foundation. These honors collectively reflect a career marked by consistency, innovation, and community impact in the field of high-performance and scientific computing.

🔬 Research Focus:

Dr. Agrawal’s research revolves around the acceleration and optimization of scientific applications using GPU-centric architectures. Her core domains include molecular dynamics, weather forecasting, and flood modeling, where she leverages tools such as CUDA, OpenACC, OpenCL, and MPI to port and tune code for parallel execution on HPC clusters. A key area of her focus lies in the performance analysis of simulation software across different hardware platforms like NVIDIA GPUs and Intel Xeon processors. Through her work on multi-GPU scaling, memory bandwidth analysis, and hardware counters, she has contributed to making scientific simulations more efficient and scalable. Her interdisciplinary collaborations enable domain scientists to harness the power of heterogeneous computing architectures for complex simulations. As an educator and mentor, she also drives HPC awareness, especially in academic and R&D circles, reinforcing her dual mission of innovation and education.

📚 Publication Top Notes:

  1. 🧪 Experience with Adapting to a Software Framework for a Use-Case in Computational Science – Journal of Parallel and Distributed Computing, May 2025

  2. 🚀 GPU Implementation and Performance Evaluation of AMDKIIT for DFT – SupercomputingAsia (SCA), Mar 2025

  3. 📊 Hardware Counter Based Performance Analysis of ANUGA – IC3 (ACM), Sep 2023

  4. 🌩 Scalability Analysis of WRF on NVIDIA Ampere – IC3SIS, Dec 2022

  5. 🔬 NAMD Simulation on Dense GPU Supercomputer – ICTCS (Springer), Oct 2022

  6. 🖥 Highly Portable C++ Simulator with Dual Parallelism – IEEE, Aug 2022

  7. 🏆 Single Instruction Stream for Massively Parallel Ops – Best Paper, IEEE TEMSMET, May 2022

  8. 💡 Multi-GPU MD Simulation on Ampere Infrastructure – Springer LNNS, Aug 2021

  9. 📈 Performance Analysis of ANUGA on Modern Architectures – IC3 (ACM), Aug 2021

  10. ⚙️ Memory Bandwidth Analysis: Xeon Phi vs Xeon – Women in HPC at ISC, Jun 2017

🧾 Conclusion:

Dr. Nisha Agrawal is an exceptionally qualified and highly deserving candidate for the Best Researcher Award. Her robust combination of long-term experience, technical depth in GPU-based parallel computing, impactful publications, active mentoring, and commitment to HPC education position her as a leader in the field. While expanding global collaborations and venturing into AI-HPC convergence could further enhance her profile, her existing contributions already make a strong and compelling case for recognition.

M. A. El-Shorbagy | Cell Structure Analysis | Best Researcher Award

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Prof. M. A. El-Shorbagy | Cell Structure Analysis | Best Researcher Award

Prof. M. A. El-Shorbagy, Prince Sattam Bin Abdulaziz University, Saudi Arabia

Prof. Mohammed Abd El-Rahman El-Shorbagy Hassan is a distinguished academic specializing in engineering mathematics and optimization. Born in Egypt on March 4, 1982, he currently serves as a Professor in the Department of Mathematics at the College of Science and Humanities Studies, Prince Sattam Bin Abdulaziz University, Saudi Arabia. With a career spanning over a decade in higher education and applied research, Prof. El-Shorbagy has contributed significantly to the fields of numerical optimization, artificial intelligence, and multiobjective programming. He is widely published and recognized for his scholarly books and high-impact journal articles. His work integrates mathematical rigor with real-world engineering applications. In addition to teaching and mentoring, he actively participates in international conferences and collaborative research projects. Known for his hybrid approaches combining swarm intelligence with classical methods, Prof. El-Shorbagy stands as a prominent figure in optimization and computational engineering.

Publication Profile: 

Google Scholar

✅ Strengths for the Award:

  1. Extensive Research Output:
    Prof. El-Shorbagy has a substantial publication record that includes books, high-impact journal articles, and international conference proceedings. His research appears in prestigious outlets such as Scientific Reports, Renewable Energy, and Materials, with multiple papers exceeding 100 citations.

  2. Strong Interdisciplinary Focus:
    His work bridges mathematics, artificial intelligence, and engineering, showcasing versatility. He effectively combines theoretical frameworks (e.g., optimization theory) with practical applications in power systems, materials science, and fluid dynamics.

  3. Innovative Methodologies:
    Known for hybridizing Particle Swarm Optimization, Genetic Algorithms, and Trust Region methods, he has demonstrated an innovative approach to solving complex, real-world optimization problems.

  4. Global Academic Impact:
    His presence in international conferences across Egypt, France, and Saudi Arabia highlights his global engagement. His work on swarm intelligence and nanofluid modeling reflects both theoretical significance and technological relevance.

  5. Authorship of Academic Books:
    Author of three technical books with Lambert Academic Publishing, which extend his research to a broader academic audience, enhancing educational and professional value.

⚠️ Areas for Improvement:

  1. Greater Industry Collaboration:
    While his academic output is exceptional, engaging in more industry-linked projects (e.g., smart grids, AI in civil infrastructure) could boost the applied relevance of his work.

  2. Editorial and Review Roles:
    Taking on editorial positions in leading journals or acting as a regular peer reviewer could enhance his visibility and influence in the academic publishing landscape.

  3. Funding and Grants:
    Participation in or leadership of internationally funded research programs (such as Horizon Europe or NSF collaborations) would strengthen his research infrastructure and international profile.

🎓 Education:

Prof. El-Shorbagy earned his Ph.D. in Engineering Mathematics from the Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Egypt, in July 2013. His doctoral thesis, “A Study of Some Numerical Optimization Methods to Solve Multiobjective Mathematical Programming Problems”, focused on advanced optimization algorithms. Prior to this, he completed his M.Sc. in February 2010 at the same institution, where he presented a thesis titled “Hybrid Particle Swarm Optimization Algorithm for Multiobjective Optimization”. Both degrees reflect his specialization and depth in engineering mathematics with emphasis on numerical methods and artificial intelligence. Throughout his academic formation, he was mentored by prominent researchers in the fields of computational mathematics and optimization. His educational background has laid a solid foundation for his subsequent contributions to academia, allowing him to merge theoretical principles with cutting-edge applications in engineering and data-driven modeling.

🧑‍🏫 Experience:

Prof. El-Shorbagy has amassed over 15 years of academic and research experience. He currently holds the position of Professor in the Department of Mathematics, College of Science and Humanities Studies, at Prince Sattam Bin Abdulaziz University in Saudi Arabia. His career began in Egypt, where he contributed to both teaching and research at Menoufia University. Over the years, he has developed and delivered undergraduate and postgraduate courses in mathematics, optimization techniques, numerical methods, and computational intelligence. In parallel, he has maintained an active research agenda, collaborating with global institutions and publishing extensively in top-tier journals. He is well-versed in supervising theses and projects related to AI-based optimization methods and engineering simulations. Prof. El-Shorbagy is also a frequent speaker at international conferences and has been part of organizing committees, reviewer boards, and technical panels—proving his leadership in academic and scientific communities.

🔬 Research Focus:

Prof. El-Shorbagy’s research revolves around optimization theory and its practical application to engineering and mathematical models. His primary focus is on developing and enhancing numerical methods for solving multiobjective optimization problems using artificial intelligence. Key techniques in his portfolio include Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Trust-Region Methods, and hybrid models that integrate local search strategies. Recently, he has extended his expertise to smart materials, nanofluids, energy optimization, and AI-driven modeling in civil and mechanical engineering systems. His interdisciplinary approach bridges theory and practice, using computational models to solve complex real-world problems such as wind turbine placement, reactive power compensation, and fluid dynamics. Prof. El-Shorbagy’s research is not only high in theoretical impact but also has broad industrial and environmental relevance, making him a valuable contributor to applied sciences and engineering optimization domains.

📰 Publications Top Notes:

  • 🧬 Integrating PSO with GA for Solving Nonlinear Optimization Problems, JCAM, 2011

  • 🐝 Local Search-Based Hybrid PSO for Multiobjective Optimization, Swarm & Evolutionary Computation, 2012

  • 💧 Darcy Ternary-Hybrid Nanofluid Flow with Induction Effects, Micromachines, 2022

  • ⚗️ Green Synthesis of ZnO–CuO Nanoparticles and Cytotoxicity, Scientific Reports, 2021

  • 🧱 Glass Fiber-Reinforced Concrete: Mechanical and Microstructure Analysis, Materials, 2022

  • 🌬️ Optimization of Wind Turbines Siting Using GA-Based Local Search, Renewable Energy, 2018

  • 🔥 Hybrid Nanofluid Flow with Hall Current and Chemical Reaction, Alexandria Eng. Journal, 2022

  • 🌴 Coconut Fiber Reinforced Concrete: State-of-the-Art Review, Materials, 2022

  • ⚡ MHD Stagnation Point Flow with Joule Heating and Convective Effects, Case Studies in Thermal Eng., 2021

  • ♻️ Concrete with Waste Glass and Recycled Aggregate Substitution, Materials, 2022

🧾 Conclusion:

Prof. Mohammed Abd El-Rahman El-Shorbagy Hassan is a highly suitable candidate for the Best Researcher Award. His consistent and high-quality contributions to engineering mathematics and optimization, impactful publication record, and innovative research approaches mark him as a leading scholar in his domain. By expanding his engagement with industry and international grant opportunities, he could further elevate his already outstanding academic profile. He exemplifies the blend of research excellence, innovation, and practical relevance that such an award seeks to recognize.

Irena Roterman | Protein structure | Best Researcher Award

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Irena Roterman | Protein structure | Best Researcher Award

Prof. Irena Roterman , Jagiellonian University – Medical College , Poland

Irena Roterman-Konieczna is a distinguished biochemist specializing in bioinformatics and protein structure. With a PhD in biochemistry from the Nicolaus Copernicus Medical Academy Krakow, she has held significant academic positions, including Professor of Medical Sciences at Jagiellonian University. Irena is recognized for her innovative contributions, particularly the fuzzy oil drop model, which emphasizes environmental influence on protein folding. She has published extensively, contributing to the understanding of protein dynamics and interactions. As a committed educator, she has guided numerous PhD students and served as the Chief Editor for the journal Bio-Algorithms and Med-Systems. Her work continues to impact the fields of protein folding, membrane proteins, and systems biology.

Publication Profile

Scopus

Strengths for the Award

Irena Roterman-Konieczna’s extensive academic background and innovative contributions to the field of bioinformatics and protein structure make her an exceptional candidate for the Best Researcher Award. Her pioneering work on the fuzzy oil drop model has provided critical insights into the environmental influences on protein folding. With a prolific publication record of 149 articles, she has consistently advanced the understanding of protein dynamics, particularly in membrane proteins and chaperonins. Additionally, her role as Chief Editor of the journal Bio-Algorithms and Med-Systems demonstrates her leadership in the scientific community. Her commitment to mentoring future researchers is evident through her advisory work with 15 PhD students, ensuring the continued growth of the field.

Areas for Improvement

While Irena’s contributions to theoretical models are significant, there may be opportunities to further integrate experimental validation into her research. Collaborating with experimentalists could enhance the practical applications of her models, particularly in understanding real-world protein behavior. Additionally, increasing outreach to interdisciplinary fields could broaden the impact of her research on medicine and biotechnology.

Education

Irena Roterman-Konieczna completed her basic education in theoretical chemistry at Jagiellonian University in 1974. She earned her PhD in biochemistry in 1984, focusing on the structure of the recombinant IgG hinge region at the Nicolaus Copernicus Medical Academy in Krakow. Following her doctoral studies, Irena undertook postdoctoral research at Cornell University from 1987 to 1989 in Harold A. Scheraga’s group, where she analyzed force fields in molecular modeling programs like Amber and Charmm. In 1994, she achieved habilitation in biochemistry at Jagiellonian University’s Faculty of Biotechnology and later attained the title of Professor of Medical Sciences in 2004. This strong educational foundation laid the groundwork for her extensive research and contributions to the field of biochemistry and bioinformatics.

Experience

Irena Roterman-Konieczna has a robust academic and research background spanning several decades. She has held key academic positions at Jagiellonian University, where she is currently a Professor of Medical Sciences. Irena’s postdoctoral research at Cornell University deepened her expertise in molecular modeling and protein interactions. Throughout her career, she has authored numerous publications and books, significantly advancing the understanding of protein folding and structure. As Chief Editor of the journal Bio-Algorithms and Med-Systems from 2005 to 2020, she played a vital role in disseminating research in the field. Additionally, she has supervised 15 PhD students, fostering the next generation of researchers. Irena’s collaborative efforts and advisory roles in various projects highlight her commitment to scientific advancement and education in biochemistry and bioinformatics.

Research Focus

Irena Roterman-Konieczna’s research centers on bioinformatics, particularly in understanding protein structure and dynamics. Her innovative fuzzy oil drop model explores the role of environmental factors in protein folding, proposing that external force fields influence hydrophobic core formation and overall structure. Irena investigates the effects of membrane environments on protein behavior, examining how hydrophobic factors can alter folding dynamics. Her work also delves into chaperonins and their role in facilitating proper protein folding under varying conditions. Additionally, she explores domain-swapping structures and their implications for complex formation in proteins. Irena’s research emphasizes the necessity of simulating external force fields in computational protein folding, integrating both internal and external interactions. Her contributions to systems biology and the development of quantitative models for protein behavior continue to advance the field, making significant impacts in both theoretical and practical applications.

Publications Top Notes

  • Chameleon Sequences─Structural Effects in Proteins Characterized by Hydrophobicity Disorder 🌊
  • Transmembrane proteins—Different anchoring systems ⚓
  • External Force Field for Protein Folding in Chaperonins─Potential Application in In Silico Protein Folding 💻
  • Structural features of Prussian Blue-related iron complex FeT of activity to peroxidate unsaturated fatty acids 🔬
  • Domain swapping: a mathematical model for quantitative assessment of structural effects 📊
  • Editorial: Structure and function of trans-membrane proteins 🧬
  • Model of the external force field for the protein folding process—the role of prefoldin 🌐
  • Role of environmental specificity in CASP results 📈
  • Ab initio protein structure prediction: the necessary presence of external force field as it is delivered by Hsp40 chaperone 🔍
  • Secondary structure in polymorphic forms of alpha-synuclein amyloids 🧪

Conclusion

Irena Roterman-Konieczna’s innovative research, leadership in academia, and dedication to mentorship position her as a strong contender for the Best Researcher Award. Her groundbreaking work in bioinformatics not only advances scientific understanding but also lays the groundwork for future discoveries in protein dynamics and interactions. Recognizing her contributions would not only honor her achievements but also inspire ongoing research in the field.