Sebastian Pardo Guerra | Applied Mathematics | Best Researcher Award

Dr. Sebastian Pardo Guerra | Applied Mathematics | Best Researcher Award

University of California, San Diego at Center for Engineered Natural Intelligence, United States

Dr. Sebastián Pardo Guerra 🎓 is a distinguished mathematician and researcher at the Center for Engineered Natural Intelligence, University of California San Diego 🧠. With a Ph.D. from UNAM, his expertise spans pure mathematics—particularly module theory, lattice theory, and category theory—and their innovative applications in neuroscience, information theory, and quantum systems 🔬. He has published extensively in high-impact journals 📚, with several feature papers and editor’s picks in 2024–2025. A seasoned lecturer and active conference presenter 🎤, Dr. Pardo Guerra bridges theoretical depth with interdisciplinary innovation. His work reflects a unique synthesis of mathematical abstraction and real-world relevance, earning him recognition in both academic and applied domains 🌐. His scholarly excellence, international engagement, and commitment to advancing mathematical frontiers make him a strong candidate for top research honors 🏆.

Professional Profile 

🎓 Education

Dr. Sebastián Pardo Guerra holds a Ph.D. in Mathematics from the National Autonomous University of Mexico (UNAM) 🎓, where he also earned his M.S. and B.S. degrees in Mathematics. His academic journey reflects a deep commitment to foundational studies, with theses exploring Galois Theory, Abelian groups, and lattice preradicals 📘. His doctoral dissertation, On the Big Lattice of Lattice Preradicals, showcases early engagement with complex algebraic structures. Through prestigious CONACYT fellowships for both his master’s and doctoral studies 🏅, he demonstrated academic excellence and promise. His education laid a rigorous foundation for blending theoretical elegance with applied insight, positioning him to make substantial contributions at the intersection of algebra, logic, and emerging scientific domains 🔍.

💼 Professional Experience

Dr. Pardo Guerra is a researcher at the Center for Engineered Natural Intelligence, University of California San Diego (UCSD) 🧠, with a trajectory that includes appointments as a lecturer and postdoctoral researcher at UCSD and UNAM. From 2012 to 2019, he served as a mathematics lecturer at UNAM 🇲🇽, followed by postdoctoral roles in bioengineering and applied mathematics at UCSD 🌐. He currently contributes to cutting-edge projects integrating category theory with neuroscience and network theory. His dual focus on research and teaching spans over a decade of academic engagement, including recent instruction in Linear Algebra, Precalculus, and Vector Calculus 🧮. This combination of pedagogical strength and high-level research underpins his influential presence in both academic and applied mathematics spheres 🔧.

📚 Research Interests

Dr. Pardo Guerra’s research encompasses a compelling blend of pure and applied mathematics. In the pure domain, he focuses on module theory, lattice theory, and category theory 🧩—exploring structural properties and their implications in algebraic systems. In the applied space, he delves into neuroscience, information theory, and quantum information, applying advanced categorical frameworks to model complex, dynamic systems 🌐. His innovative use of preradicals to redefine information entropy and analyze network topologies exemplifies his unique methodological approach 🧠. By bridging abstract mathematics with emerging scientific challenges, his research offers novel insights into both foundational theory and interdisciplinary applications, contributing to the evolution of intelligent systems and computational structures ⚙️📊.

🏅 Awards and Honors

Dr. Pardo Guerra has received notable honors throughout his academic career, including the prestigious CONACYT Fellowships for both his M.S. and Ph.D. programs in Mexico 🇲🇽. These competitive awards recognize academic excellence and research potential among top Mexican scholars. His recent papers have earned “Feature Paper” and “Editor’s Choice” selections in international journals like Mathematics 📰—a testament to the originality and relevance of his work. His consistent presence at respected conferences such as BLAST, Ohio State–Denison Math Conference, and UCSD Colloquia further affirms his reputation within the global mathematics community 🌍. These accolades highlight his innovative thinking and valuable contributions to contemporary mathematical discourse 🏆.

🛠️ Research Skills

Dr. Pardo Guerra brings a powerful set of research skills combining deep theoretical insight with interdisciplinary modeling capabilities 🧠. He is adept at constructing and analyzing abstract structures within lattice theory, module theory, and category theory—fields requiring a high degree of mathematical precision 🔬. His work applies these constructs to neural networks, graph theory, and information systems, utilizing tools like preradicals, entropy models, and Markov categories. He is also proficient in academic writing, publishing, and presenting in both Spanish and English, and skilled in engaging audiences through lectures, seminars, and collaborative forums 🧾🎤. As a reviewer for Mathematical Reviews, he contributes to the peer-review process, underscoring his commitment to scholarly rigor and intellectual advancement ⚖️.

Publications Top Note 📝

Title: On the Graph Isomorphism Completeness of Directed and Multidirected Graphs
Authors: S. Pardo-Guerra, V.K. George, G.A. Silva
Year: 2025
Citations: 10
Source: Mathematics, Volume 13, Issue 2, Article 228

Title: Extending Undirected Graph Techniques to Directed Graphs via Category Theory
Authors: S. Pardo-Guerra, V.K. George, V. Morar, J. Roldan, G.A. Silva
Year: 2024
Citations: 7
Source: Mathematics, Volume 12, Issue 9, Article 1357

Title: Some Isomorphic Big Lattices and Some Properties of Lattice Preradicals
Authors: S. Pardo-Guerra, H.A. Rincón-Mejía, M.G. Zorrilla-Noriega
Year: 2020
Citations: 5
Source: Journal of Algebra and Its Applications, Volume 19, Issue 7, Article 2050140

Title: Big Lattices of Hereditary and Natural Classes of Linear Modular Lattices
Authors: S. Pardo-Guerra, H.A. Rincón-Mejía, M.G. Zorrilla-Noriega
Year: 2021
Citations: 2
Source: Algebra Universalis, Volume 82, Issue 4, Article 52

Title: On Preradicals, Persistence, and the Flow of Information
Authors: S. Pardo-Guerra, G.A. Silva
Year: 2024
Source: International Journal of General Systems, Volume 53, Issues 7–8, Pages 1121–1145

Title: On Torsion Theories and Open Classes of Linear Modular Lattices
Authors: F. González-Bayona, S. Pardo-Guerra, H.A. Rincón-Mejía, et al.
Year: 2024
Source: Communications in Algebra, Volume 52, Issue 1, Pages 371–391

Title: On the Lattice of Conatural Classes of Linear Modular Lattices
Authors: S. Pardo-Guerra, H.A. Rincón-Mejía, M.G. Zorrilla-Noriega, et al.
Year: 2023
Source: Algebra Universalis, Volume 84, Issue 4, Article 29

Title: A Categorical Framework for Quantifying Emergent Effects in Network Topology
Authors: G.A.S. Johnny Jingze Li, S. Pardo-Guerra, Kalyan Basu
Year: 2025
Source: Neural Computation (in press)

Title: On Semi-Projective Modular Lattices
Authors: F.G. Bayona, S.P. Guerra, M.G.Z. Noriega, H.A.R. Mejía
Source: International Electronic Journal of Algebra, Pages 1–35

🧾 Conclusion

Dr. Sebastián Pardo Guerra is a dynamic and forward-thinking researcher whose contributions span theoretical depth and applied innovation 🌟. With solid academic credentials, international teaching and research experience, and a growing portfolio of impactful publications, he exemplifies excellence in mathematical sciences 📈. His work not only advances abstract algebraic theory but also pioneers new applications in intelligent systems and complex networks 🧠💡. Recognized through awards, invited talks, and feature publications, Dr. Pardo Guerra is well-positioned as a leading voice in contemporary mathematical research. His diverse expertise, professional integrity, and global academic engagement make him an outstanding candidate for high-level honors such as the Best Researcher Award 🏅.

Misha Urooj Khan | Applied Mathematics | Best Researcher Award

Prof. Misha Urooj Khan | Applied Mathematics | Best Researcher Award

AM (Tech) at CERN, Pakistan

Prof. Misha Urooj Khan is an accomplished electronics engineer and researcher whose multifaceted expertise spans embedded systems, quantum computing, AI/ML, and cybersecurity. 🎓 With a master’s degree focused on FPGA-based real-time SLAM and extensive experience at CERN, NCP, COMSATS, and UET, she has authored 10 journal papers, 17 conference articles, and earned 658 citations. 💡 Her work includes groundbreaking innovations like drone-resistant cryptography, AI-driven healthcare devices (USteth, ThalaScreen), and predictive analytics for disaster management. 🛰️ As an inventor on a patented drone-detection system and mentor to numerous interns and students across global institutions, she demonstrates strong leadership and social impact. 🌍 Recognized with awards and competitive startup funding, Prof. Khan’s strategic vision and interdisciplinary contributions make her a standout candidate for the Best Researcher Award. 🏆

Professional Profile

📚 Education

Professor Misha Urooj Khan holds a Master’s degree in Electronics Engineering from the University of Engineering & Technology Taxila (2019–2022), specializing in real-time FPGA-based Simultaneous Localization and Mapping (SLAM). She earned her B.Sc. in Electronics Engineering (2015–2019) from the same institution, focusing on embedded systems, FPGA design, and neural networks, and implemented an automatic wheezing detection system for her thesis. With a solid grounding in both hardware and software design, she developed strong analytical and technical skills in digital design, signal processing, and machine learning. Her rigorous academic training laid the foundation for her multidisciplinary research career, enabling seamless integration of theory and application across quantum computing, AI-enhanced embedded systems, cyber‑physical systems, and robotics. These educational credentials articulate her commitment to innovation and technology-driven problem solving.

💼 Professional Experience

Professor Khan’s career spans internationally recognized institutions such as CERN, NCP, COMSATS, UET, and King Fahd University. As a Software Developer for CERN’s CMS experiment (2024–2025), she developed database schemas, business logic, and automated migrations, contributing to high-performance scientific computing environments. At Open Quantum Initiative and NCP (2023–2026), she implemented quantum machine learning, error mitigation techniques, sensor-fusion robotics, and AI-driven predictive systems. Her research at COMSATS (2022) focused on intelligent UAV detection using edge devices. Earlier roles included designing biomedical signal-processing systems and embedded real-time detection boards (UET Taxila, 2018–2022). Recently, at King Fahd University, she’s spearheading lightweight, quantum-resistant cybersecurity protocols for drones. Across each role, she has demonstrated exceptional technical proficiency, leadership in mentoring interns, and impactful contributions to system deployment, publication, and product innovation.

🔬 Research Interests

Professor Khan’s research spans quantum computing, artificial intelligence, embedded systems, and cybersecurity—integrating these domains to solve complex real-world problems. Within quantum computing, she investigates noise modeling, error mitigation, quantum machine learning (QSVM, QNN, VQC), and oracle‑based functions on IBM quantum processors. Her AI/ML projects include domain-generalized image translation frameworks like R2TGenNet and T2RGenNet, predictive fault‑diagnosis for rotary equipment, YOLO-based object detection, and AI‑enhanced decision support. In embedded systems, she specializes in FPGA‑based SLAM, real‑time sensor fusion (LiDAR, RGB/depth cameras, IMU), and custom hardware for biomedical signal acquisition. Her current interest lies in quantum‑resistant cryptographic protocols tailored for UAV communication systems. She is passionate about bridging quantum‑AI with cybersecurity to enable secure, intelligent, and autonomous applications across healthcare, robotics, disaster response, and aerospace.

🏅 Awards and Honors

Professor Khan has earned recognition across academia, innovation, and professional excellence. She holds 658 citations (2025) and was awarded 2nd place for her presentation on “Noise Modeling and Error Mitigation on Quantum Computers” at ICTP Trieste, March 2024. Other distinctions include runner-up in the PMNIA startup pitching (June 2023), Best Presenter shields at IBCAST’23 and IEEC’21, and funding awards for USteth and ThalaScreen prototypes (2022). Her startup PAK‑AeroSafe qualified at regional and national levels and achieved runner-up status at Hackathon’23 (February 2023). Academic engagement includes first positions in university fairs (2019), community science awards since 2012, and multiple national scholastic honors. These accolades highlight her consistent excellence in research, presentation, innovation, and community engagement.

🛠️ Research Skills

Professor Khan possesses a versatile and comprehensive set of skills across computing, hardware design, and data science. She is adept in FPGA/embedded system design (Verilog/VHDL), real‑time algorithm development, and robotics navigation with ROS and Jetson hardware. Her ML proficiency spans classic and deep learning (SVM, KNN, RF, YOLOv5-v11, VGG16/19, GANs, Autoencoder), and she designs bespoke frameworks (R2TGenNet, T2RGenNet). In quantum research, she handles noise modeling, quantum gate design, error mitigation, oracle functions, and algorithm implementation on IBM quantum simulators and hardware. She also excels in sensor fusion (LiDAR/IMU/RGB/Depth), GUI creation, digital signal processing, and AI-based healthcare tools. Her programming languages include Python, Qiskit, MATLAB, and Linux-based deployment, complemented by strong skills in mentoring, proposal writing, and cross-disciplinary collaboration.

Publications Top Notes 📝

  • Title: A comparative survey of lidar-slam and lidar based sensor technologies
    Authors: MU Khan, SAA Zaidi, A Ishtiaq, SUR Bukhari, S Samer, A Farman
    Year: 2021
    Citations: 156
    Source: Mohammad Ali Jinnah University International Conference on Computing

  • Title: Artificial neural network-based cardiovascular disease prediction using spectral features
    Authors: MU Khan, S Samer, MD Alshehri, NK Baloch, H Khan, F Hussain, SW Kim, et al.
    Year: 2022
    Citations: 39
    Source: Computers and Electrical Engineering 101, Article 108094

  • Title: Classification of eye diseases and detection of cataract using digital fundus imaging (DFI) and inception-V4 deep learning model
    Authors: A Raza, MU Khan, Z Saeed, S Samer, A Mobeen, A Samer
    Year: 2021
    Citations: 34
    Source: 2021 International Conference on Frontiers of Information Technology (FIT)

  • Title: Safespace mfnet: Precise and efficient multifeature drone detection network
    Authors: MU Khan, M Dil, MZ Alam, FA Orakazi, AM Almasoud, Z Kaleem, C Yuen
    Year: 2023
    Citations: 33
    Source: IEEE Transactions on Vehicular Technology 73(3), 3106-3118

  • Title: Spectral analysis of lung sounds for classification of asthma and pneumonia wheezing
    Authors: SZH Naqvi, M Arooj, S Aziz, MU Khan, MA Choudhary
    Year: 2020
    Citations: 31
    Source: 2020 International Conference on Electrical, Communication, and Computer

  • Title: Supervised machine learning based fast hand gesture recognition and classification using electromyography (EMG) signals
    Authors: MU Khan, H Khan, M Muneeb, Z Abbasi, UB Abbasi, NK Baloch
    Year: 2021
    Citations: 29
    Source: 2021 International Conference on Applied and Engineering Mathematics (ICAEM)

  • Title: A review of system on chip (SoC) applications in Internet of Things (IoT) and medical
    Authors: A Ishtiaq, MU Khan, SZ Ali, K Habib, S Samer, E Hafeez
    Year: 2021
    Citations: 28
    Source: ICAME21, International Conference on Advances in Mechanical Engineering

  • Title: Identification of leaf diseases in potato crop using Deep Convolutional Neural Networks (DCNNs)
    Authors: Z Saeed, MU Khan, A Raza, N Sajjad, S Naz, A Salal
    Year: 2021
    Citations: 23
    Source: 16th International Conference on Emerging Technologies (ICET)

  • Title: Classification of Multi-Class Cardiovascular Disorders using Ensemble Classifier and Impulsive Domain Analysis
    Authors: MU Khan, SZZ Ali, A Ishtiaq, K Habib, T Gul, A Samer
    Year: 2021
    Citations: 22
    Source: Mohammad Ali Jinnah University International Conference on Computing

  • Title: Automated system design for classification of chronic lung viruses using non-linear dynamic system features and k-nearest neighbour
    Authors: MU Khan, A Farman, AU Rehman, N Israr, MZH Ali, ZA Gulshan
    Year: 2021
    Citations: 22
    Source: Mohammad Ali Jinnah University International Conference on Computing

  • Title: Embedded system design for real-time detection of asthmatic diseases using lung sounds in cepstral domain
    Authors: MU Khan, A Mobeen, S Samer, A Samer
    Year: 2021
    Citations: 22
    Source: 6th International Electrical Engineering Conference (IEEC)

  • Title: Stability enhancement of commercial Boeing aircraft with integration of PID controller
    Authors: AU Rehman, MU Khan, MZH Ali, MS Shah, MF Ullah, M Ayub
    Year: 2021
    Citations: 21
    Source: 2021 International Conference on Applied and Engineering Mathematics (ICAEM)

  • Title: Classification of pulmonary viruses X-ray and detection of COVID-19 based on invariant of inception-V3 deep learning model
    Authors: Z Saeed, MU Khan, A Raza, H Khan, J Javed, A Arshad
    Year: 2021
    Citations: 19
    Source: 2021 International Conference on Computing, Electronic and Electrical

  • Title: Classification of phonocardiography based heart auscultations while listening to Tilawat-e-Quran and music using vibrational mode decomposition
    Authors: MU Khan, S Samer, A Samer, A Mobeen, A Arshad, H Khan
    Year: 2021
    Citations: 18
    Source: 2021 International Conference on Applied and Engineering Mathematics (ICAEM)

  • Title: MSF-GhostNet: Computationally-Efficient YOLO for Detecting Drones in Low-Light Conditions
    Authors: M Misbah, MU Khan, Z Kaleem, A Muqaibel, MZ Alam, R Liu, C Yuen
    Year: 2024
    Citations: 5
    Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

  • Title: Multi-Sensor Fusion for Remote Sensing of Metallic and Non-metallic Object Classification in Complex Soil Environments and at Different Depths
    Authors: MU Khan, MA Kamran, WR Khan, MM Ibrahim, MU Ali, SW Lee
    Year: 2024
    Citations: 5
    Source: IEEE Transactions on Geoscience and Remote Sensing

  • Title: Mathematical Modelling and Implementation of 2DOF Standard, Parallel & Series PID Controllers
    Authors: AU Rehman, MU Khan, MT Rehman, W Shehzad, S Zaman, MW Khan
    Year: 2021
    Citations: 5
    Source: 6th International Multi-Topic ICT Conference (IMTIC)

  • Title: Diabetes Prediction Using an Optimized Variational Quantum Classifier
    Authors: WR Khan, MA Kamran, MU Khan, MM Ibrahim, KS Kim, MU Ali
    Year: 2025
    Citations: 4
    Source: International Journal of Intelligent Systems 2025 (1), Article 1351522

  • Title: Deep Learning Empowered Fast and Accurate Multiclass UAV Detection in Challenging Weather Conditions
    Authors: MU Khan, M Dil, M Misbah, FA Orakazi, MZ Alam, Z Kaleem
    Year: 2022
    Citations: 4
    Source: Conference Publication

  • Title: Brain Tumor Detection Based on Magnetic Resonance Imaging Analysis Using Segmentation, Thresholding and Morphological Operations
    Authors: MU Khan, H Khan, A Arshad, NK Baloch, A Shaheen, F Tariq
    Year: 2021
    Citations: 3
    Source: 6th International Multi-Topic ICT Conference (IMTIC)

  • Title: SMSAT: A Multimodal Acoustic Dataset and Deep Contrastive Learning Framework for Affective and Physiological Modeling of Spiritual Meditation
    Authors: A Suleman, Y Alkhrijah, MU Khan, H Khan, MAHA Faiz, MA Alawad, et al.
    Year: 2025
    Source: arXiv preprint arXiv:2505.00839

  • Title: Migration of CADI to Fence
    Authors: M Imran, MU Khan, RMA Shad, A Samantas, A Pfeiffer, J Closier
    Year: 2025

✅ Conclusion

Professor Misha Urooj Khan exemplifies a visionary researcher whose interdisciplinary breadth and leadership set her apart. With robust academic credentials, global professional experience at centers like CERN and NCP, and impactful publications and patents, she drives innovation in quantum-AI, embedded systems, robotics, and cybersecurity. Her product-oriented mindset—evident in startups like USteth and PAK‑AeroSafe—coupled with her mentoring of junior researchers, underscores both strategic vision and social impact. Her consistent accolades and scholarly presence (658 citations) affirm her research quality and influence. Combining groundbreaking technical achievements, real-world applications, and academic excellence, Professor Khan stands as a compelling candidate for top-tier research distinctions and awards.

Saif Ur Rehman | Applied Mathematics | Best Researcher Award

Dr. Saif Ur Rehman | Applied Mathematics | Best Researcher Award

Research Assistant at Consiglio Nazionale delle Ricerche (CNR), Genoa, Italy, Pakistan.

Saif Ur Rehman is a dedicated researcher in Computational Fluid Dynamics (CFD), Numerical Analysis, and Partial Differential Equations, with a strong academic and research background. He has worked as a Research Assistant at the National Research Council, Italy, and previously at the University of Calabria, Italy, and the University of Management and Technology, Pakistan. His research focuses on nanofluid dynamics, MHD flows, bioconvection, and heat transfer, leading to multiple Q1 and Q2 publications in high-impact journals such as Nanomaterials, Mathematics, and Waves in Random and Complex Media. He has received merit-based scholarships and a Research Publication Award, demonstrating his academic excellence. Additionally, he possesses strong technical skills in Python, MATLAB, C++, and LaTeX, aiding in his numerical modeling research. With international research exposure and a growing publication record, Saif Ur Rehman is an emerging scholar in applied mathematics, aiming to expand his contributions to mathematical modeling and computational sciences.

Professional Profile 

Google Scholar

Education

Saif Ur Rehman holds a Master of Science in Mathematics from the University of Management and Technology, Lahore, Pakistan (2019–2021), where he specialized in Advanced Numerical Analysis, Fluid Dynamics, and Differential Equations. His master’s thesis focused on the MHD Williamson Nanofluid Flow over a Slender Elastic Sheet in the Presence of Bioconvection. Before this, he earned a Bachelor of Science in Mathematics from Government College University, Faisalabad (2015–2019), securing a 3.55/4.00 GPA. His undergraduate studies included Fluid Mechanics, Numerical Analysis, Real and Complex Analysis, and C++ Programming, laying a strong foundation for his research in computational mathematics. Throughout his academic journey, he received multiple merit-based scholarships, including the Punjab Education Endowment Fund Scholarship (PEEF) and a fully funded master’s scholarship, reflecting his dedication and academic excellence. His education has equipped him with expertise in mathematical modeling, numerical simulations, and applied mathematics, which he continues to explore in his research.

Professional Experience

Saif Ur Rehman has gained extensive research experience through multiple roles at renowned international institutions. He is currently a Research Assistant at the National Research Council, Genoa, Italy (2024–Present), working on Optimal Robust Shape Control for Distributed Parameter Systems. Previously, he was a University Research Assistant at the University of Calabria, Italy (2023–2024), focusing on applications of heat and mass transfer. His earlier role as a Research Assistant at the University of Management and Technology, Pakistan (2021–2023) involved a major research project on Numerical Methods for Partial Differential Equations, where he contributed to multiple high-impact publications. Alongside research, he worked as a Visiting Lecturer in Mathematics (2021–2022), teaching Calculus, Linear Algebra, Fluid Dynamics, and Numerical Analysis at the undergraduate level. His professional experience demonstrates his ability to conduct applied mathematics research, develop numerical solutions, and contribute to theoretical and computational fluid dynamics.

Research Interest

Saif Ur Rehman’s research is deeply rooted in Computational Fluid Dynamics (CFD), Numerical Analysis, and Partial Differential Equations (PDEs), with a strong focus on heat and mass transfer, MHD flows, and bioconvection. He has extensively studied the dynamics of nanofluids, micropolar fluids, and dusty fluids under various physical constraints, contributing significantly to theoretical and computational modeling in applied mathematics. His work integrates Artificial Neural Networks (ANNs) and Machine Learning techniques to enhance numerical simulations and solve complex mathematical physics problems. His research contributions, published in Q1 and Q2 impact factor journals, cover topics such as the effects of Lorentz and Coriolis forces, Darcy–Forchheimer flow models, and stability analysis of fluid flows. With expertise in Python, MATLAB, C++, and LaTeX, he continues to explore innovative numerical methods for solving real-world mathematical problems, aiming to bridge the gap between theory and industrial applications.

Awards and Honors

Saif Ur Rehman has received multiple scholarships and research excellence awards in recognition of his academic achievements. He was honored with the Research Publication Award (2022) at the University of Management and Technology, Pakistan, for his outstanding contributions to applied mathematics research. His academic journey has been supported by fully funded merit-based scholarships, including the Punjab Education Endowment Fund Scholarship (PEEF) during his bachelor’s studies and a fully funded master’s scholarship for his postgraduate studies. Additionally, he was awarded a Prime Minister’s Laptop under the Government of Pakistan’s Higher Education Initiative, recognizing his academic excellence. Beyond research, he has demonstrated leadership and management skills, serving as a class representative throughout his bachelor’s studies and actively participating in academic societies and awards. His awards reflect his dedication to mathematical research, academic excellence, and contributions to the global scientific community.

Conclusion

Saif Ur Rehman is an emerging researcher in Computational Fluid Dynamics, Numerical Analysis, and Partial Differential Equations, with a strong academic background and international research exposure. His work in nanofluid dynamics, MHD flows, and heat transfer has resulted in high-impact publications and significant contributions to applied mathematics. His expertise in Python, MATLAB, and numerical modeling techniques has strengthened his research capabilities. Having worked at renowned institutions in Italy and Pakistan, he has gained experience in both theoretical and applied research, positioning himself as a promising scholar in mathematical modeling and computational sciences. His awards, scholarships, and research achievements demonstrate his dedication to scientific innovation. Moving forward, he aims to further his research in numerical simulations, machine learning applications in CFD, and advanced mathematical modeling, contributing to both academic advancements and real-world engineering applications.

Publications Top Noted

  • Title: Insight into significance of bioconvection on MHD tangent hyperbolic nanofluid flow of irregular thickness across a slender elastic surface

    • Authors: MZ Ashraf, SU Rehman, S Farid, AK Hussein, B Ali, NA Shah, W Weera
    • Year: 2022
    • Citations: 92
    • Source: Mathematics, 10(15), 2592
  • Title: Numerical computation of buoyancy and radiation effects on MHD micropolar nanofluid flow over a stretching/shrinking sheet with heat source

    • Authors: SU Rehman, A Mariam, A Ullah, MI Asjad, MY Bajuri, BA Pansera, et al.
    • Year: 2021
    • Citations: 86
    • Source: Case Studies in Thermal Engineering, 25, 100867
  • Title: Micropolar dusty fluid: Coriolis force effects on dynamics of MHD rotating fluid when Lorentz force is significant

    • Authors: Q Lou, B Ali, SU Rehman, D Habib, S Abdal, NA Shah, JD Chung
    • Year: 2022
    • Citations: 84
    • Source: Mathematics, 10(15), 2630
  • Title: The Casson dusty nanofluid: Significance of Darcy–Forchheimer law, magnetic field, and non-Fourier heat flux model subject to stretch surface

    • Authors: SU Rehman, N Fatima, B Ali, M Imran, L Ali, NA Shah, JD Chung
    • Year: 2022
    • Citations: 72
    • Source: Mathematics, 10(16), 2877
  • Title: MHD Williamson nanofluid flow over a slender elastic sheet of irregular thickness in the presence of bioconvection

    • Authors: F Wang, MI Asjad, SU Rehman, B Ali, S Hussain, TN Gia, T Muhammad
    • Year: 2021
    • Citations: 63
    • Source: Nanomaterials, 11(9), 2297
  • Title: Significance of dust particles, nanoparticles radius, Coriolis and Lorentz forces: The case of Maxwell dusty fluid

    • Authors: Y Wei, SU Rehman, N Fatima, B Ali, L Ali, JD Chung, NA Shah
    • Year: 2022
    • Citations: 36
    • Source: Nanomaterials, 12(9), 1512
  • Title: Computational analysis for bioconvection of microorganisms in Prandtl nanofluid Darcy–Forchheimer flow across an inclined sheet

    • Authors: J Wang, Z Mustafa, I Siddique, M Ajmal, MMM Jaradat, SU Rehman, B Ali, et al.
    • Year: 2022
    • Citations: 23
    • Source: Nanomaterials, 12(11), 1791
  • Title: First solution of fractional bioconvection with power law kernel for a vertical surface

    • Authors: MI Asjad, S Ur Rehman, A Ahmadian, S Salahshour, M Salimi
    • Year: 2021
    • Citations: 18
    • Source: Mathematics, 9(12), 1366
  • Title: Dynamics of Eyring–Powell nanofluids when bioconvection and Lorentz forces are significant: The case of a slender elastic sheet of variable thickness with porous medium

    • Authors: A Manan, SU Rehman, N Fatima, M Imran, B Ali, NA Shah, JD Chung
    • Year: 2022
    • Citations: 13
    • Source: Mathematics, 10(17), 3039
  • Title: Hydrodynamical study of couple stress fluid flow in a linearly permeable rectangular channel subject to Darcy porous medium and no-slip boundary conditions

    • Authors: M Ishaq, SU Rehman, MB Riaz, M Zahid
    • Year: 2024
    • Citations: 10
    • Source: Alexandria Engineering Journal, 91, 50-69