jianzhao Wu | Computational Methods | Best Researcher Award

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Assoc. Prof. Dr. jianzhao Wu | Computational Methods | Best Researcher Award

Huazhong University of Science and Technology | China

Jianzhao Wu  is a renowned mechanical engineer specializing in laser manufacturing technologies and sustainability-focused research. His academic and professional journey has spanned several prestigious institutions, including the National University of Singapore (NUS) and Huazhong University of Science and Technology (HUST), where he obtained his PhD in Mechanical Engineering. Wu has made significant contributions to the fields of laser-arc hybrid welding, laser additive manufacturing, and optimization algorithms for manufacturing processes. His works have been widely recognized and published in high-impact journals.

👨‍🎓Profile

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Early Academic Pursuits 🎓

Wu’s academic career began with a Master’s degree in Mechanical Engineering at Ningbo University, where he explored cutting performance and chip control in Polycrystalline Diamond (PCD) tools. His research interests were initially shaped around tool performance and tribology, paving the way for his later work in laser processing and sustainability. His excellence in research was quickly recognized, with awards such as the National Scholarship and the “Self-strengthening Star” Nomination Award for university students.

Professional Endeavors 💼

Wu’s professional development saw a significant leap when he joined Huazhong University of Science & Technology (HUST), where he worked on cutting-edge research in digital manufacturing and environmentally sustainable technologies. As a Joint Ph.D. student at NUS, Wu collaborated on international projects with Manchester University and Loughborough University to promote low-carbon laser processing technologies. His research involves carbon emission modeling, multi-objective optimization using machine learning algorithms, and laser surface treatment.

Contributions and Research Focus 🔬

Wu’s research focuses on several key areas, including:

  • Low-carbon Laser Manufacturing: He is particularly interested in laser-arc hybrid welding, laser cleaning, and laser additive manufacturing, seeking to optimize these processes for environmental sustainability while maintaining high mechanical properties.
  • Optimization Algorithms: Wu uses machine learning, deep learning models, and convolutional neural networks (CNN) to develop advanced algorithms that optimize the efficiency of manufacturing processes and reduce energy consumption.
  • Tribology and Chip Control: He has conducted pioneering studies in chip breaking mechanisms for PCD tools, particularly in turning operations, focusing on tribological properties and surface textures for improved tool performance.

Research Skills 🔧

Wu has developed expertise in the following key areas:

  • Laser Processing Technologies: Mastery in laser-arc hybrid welding and additive manufacturing techniques for sustainability.
  • Optimization Algorithms: Skilled in data-driven models, ensemble learning, and meta-modeling to optimize manufacturing systems.
  • Carbon Emission Modeling: Advanced techniques to measure and reduce carbon emissions in laser-based processes.
  • Tribology and Surface Engineering: In-depth understanding of tribological properties and laser-textured surfaces for enhanced tool life and performance.

Teaching Experience 📚

Wu has mentored and supervised several undergraduate and postgraduate students in their research projects. His teaching experience at both HUST and NUS has allowed him to guide students in areas related to laser technologies, tribology, and sustainable manufacturing. His involvement in both teaching and research enables him to integrate theoretical knowledge with practical applications, preparing students for the evolving demands of the manufacturing industry.

Legacy and Future Contributions 🔮

Wu is poised to make substantial contributions to sustainable manufacturing and green technologies in the coming years. His work in laser-based technologies has already influenced the global manufacturing landscape, and he continues to explore innovative solutions for low-carbon processes.

Publications Top Notes

Multi-Objective Parameter Optimization of Fiber Laser Welding Considering Energy Consumption and Bead Geometry

  • Authors: Jianzhao Wu, Ping Jiang, Chaoyong Zhang, et al.
    Journal: IEEE Transactions on Automation Science and Engineering
    Year: 2021

Data-driven Multi-objective Optimization of Laser Welding Parameters of 6061-T6 Aluminum Alloy

  • Authors: Jianzhao Wu
    Journal: Journal of Physics: Conference Series
    Year: 2021

Tribological Properties of Bronze Surface with Dimple Textures Fabricated by the Indentation Method

  • Authors: Jianzhao Wu, Aibing Yu, Qiujie Chen, et al.
    Journal: Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology
    Year: 2020

Study on Position of Laser Cladded Chip Breaking Dot on Rake Face of HSS Turning Tool

  • Authors: Jianzhao Wu, Chenchun Shi, Aibing Yu, et al.
    Journal: International Journal of Machine Tools and Manufacture
    Year: 2017

Comparisons of Tribological Properties Between Laser and Drilled Dimple Textured Surfaces of Medium Carbon Steel

  • Authors: Jianzhao Wu, Aibing Yu, Chenchun Shi, et al.
    Journal: Industrial Lubrication and Tribology
    Year: 2017

 

 

Minseok Ryu | Power Systems Computation | Best Researcher Award

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Assist Prof Dr. Minseok Ryu | Power Systems Computation | Best Researcher Award

PHD at the University of Michigan, United States

Minseok Ryu is an Assistant Professor at Arizona State University’s School of Computing and Augmented Intelligence. He earned his Ph.D. in Industrial and Operations Engineering from the University of Michigan in 2020. Prior to his current role, he held a postdoctoral position at Argonne National Laboratory and conducted research at Los Alamos National Laboratory. Ryu’s expertise spans privacy-preserving federated learning, optimization, and power system resilience. He has secured significant funding, including from DOE-ASCR and NSF, and developed the open-source APPFL package for federated learning. Ryu is actively involved in professional societies like INFORMS and IEEE, contributing to conferences and serving as a panelist for NSF reviews.

Professional Profiles

Education

Ph.D. Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI; May 2020 M.S. Aerospace Engineering, KAIST, Daejeon, Korea; Feb 2014 B.S. Aerospace Engineering, KAIST, Daejeon, Korea; Feb 2012

Work Experience

Assistant Professor, School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ Aug 2023–present Postdoctoral Appointee, Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL Aug 2020–Jul 2023 Research Assistant, Applied Mathematics and Plasma Physics Group, Los Alamos National Laboratory, Los Alamos, NM May 2019–Aug 2019 Post Baccalaureate Research Fellow, Kellog School of Management, Northwestern University, Evanston, IL Nov 2014–Apr 201

Honors & Awards

2024 Alliance Fellow, Mayo Clinic and ASU Alliance for Health Care 2023 Highlighted Research, Department of Energy, Advanced Scientific Computing Research (DOE-ASCR) 2022 Highlighted Research, DOE-ASCR

Professional Activities

Membership in Professional Societies: INFORMS, IEEE, IISE, SIAM Proposal Review: Panelist for National Science Foundation (NSF) Journal/Conference Review: Numerous journals and conferences including IEEE Transactions on Power Systems, Management Science, and others.

Research Focus

Minseok Ryu’s research primarily focuses on advanced optimization techniques and privacy-preserving federated learning systems. His work spans several key areas including privacy-preserving distributed control in power systems, data-driven distributionally robust optimization for scheduling, and mitigating uncertain disturbances in electric grids. Ryu has also contributed significantly to the development of algorithms for differentially private federated learning, enhancing security and robustness in biomedical research and heterogeneous computing environments. His expertise extends to heuristic algorithms for geomagnetically induced current blocking devices, showcasing a deep commitment to advancing resilient infrastructure and secure data handling in complex operational environments.

Publications

  1. A GPU-based Distributed Algorithm for Linearized Optimal Power Flow in Distribution Systems, Publication date: 2023.
  2. Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx (preprint), Publication date: 2023.
  3. Efficient Heuristic Approaches to Binary Optimization: a Sensor Placement Application, Publication date: 2023.
  4. APPFLX: Providing privacy-preserving cross-silo federated learning as a service, Publication date: 2023.
  5. Heuristic Algorithms for Placing Geomagnetically Induced Current Blocking Devices, Publication date: 2023.
  6. Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx, Publication date: 2022.
  7. APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning, Publication date: 2022.
  8. A Privacy-Preserving Distributed Control of Optimal Power Flow, Publication date: 2022.
  9. Differentially private federated learning via inexact ADMM with multiple local updates, Publication date: 2022.
  10. Mitigating the Impacts of Uncertain Geomagnetic Disturbances on Electric Grids: A Distributionally Robust Optimization Approach,  Publication date: 2022.
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Sajjad Ali | Computational Methods | Best Researcher Award

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Dr. Sajjad Ali | Computational Methods | Best Researcher Award

PHD at Abdul Wali Khan University Mardan, Pakistan

Dr. Sajjad Ali is a Lecturer in Mathematics at Shaheed Benazir Bhutto University Sheringal, Dir (Upper), Pakistan. He received his Ph.D. in Mathematics from Abdul Wali Khan University Mardan in 2019. With over a decade of teaching experience, Dr. Ali specializes in Bio-Mathematics, Fractional Differential Equations, and Advanced Homotopy Methods. He supervises M.Phil and Ph.D. students and is involved in departmental administration. His research interests include mathematical modeling and computational methods. Dr. Ali is proficient in MATLAB, MS Office, Excel, and LaTeX, and he is fluent in Urdu, English, Pashto, and Khohar.

Professional Profiles

Education

Ph.D. in Mathematics (2015 – 2019) Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, Pakistan M.Phil in Mathematics (2009 – 2012) Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, Pakistan Bachelor of Education (B.Ed) (2007 – 2008) Allama Iqbal Open University, Islamabad, Pakistan Master of Science in Mathematics (2005 – 2007) University of Malakand Chakdara, Khyber Pakhtunkhwa, Pakistan Bachelor of Science in Mathematics (2002 – 2004) Government Degree College Tangi, Charsadda, Khyber Pakhtunkhwa, Pakistan F.Sc. Pre-Engineering (2000 – 2002) Government Degree College Tangi, Charsadda, Khyber Pakhtunkhwa, Pakistan Metric in Science (1999 – 2000) Government School No.2 Tangi, Charsadda, Khyber Pakhtunkhwa, Pakistan.

Skills

Research in Mathematics Teaching and Administrative Skills Software MATLAB MS Office Excel LaTeX

Work History

Shaheed Benazir Bhutto University Sheringal, Dir (Upper), Pakistan Lecturer in Mathematics (2011 – Present) Teaching Ph.D./M.Phil courses in Bio-Mathematics, Fractional Differential Equations, and Advanced Homotopy Methods Teaching BS courses in Partial Differential Equations and Ordinary Differential Equations Supervising M.Phil and Ph.D. research students M.Phil/Ph.D. Coordinator in the Department of Mathematics Member of the Departmental Admission Committee Warden of Ihsan Boys Hostel Tameer e Seerat Degree College, Mardan Campus, Pakistan Lecturer in Mathematics (2010 – 2011) Taught graduate-level Mathematics Hostel Warden Farabi Degree College, Peshawar, Pakistan Lecturer in Mathematics (2009 – 2010) Taught graduate-level Mathematics Government Degree College, Tangi, Charsadda, Pakistan Lecturer in Mathematics (2007 – 2009) Taught graduate-level Mathematics

Interests

Mathematical modeling, Computational methods

Awards

Top Position Holder in Master of Mathematics at University of Malakand (2007) Awardee as Lecturer in Mathematics through the Higher Education Commission Pakistan (2008)

Research Focuse

Dr. Sajjad Ali’s research focuses on the numerical treatment and computational solutions of fractional order differential equations, with applications in reaction-diffusion systems, biological population models, and ion-acoustic waves. His work includes developing iterative and stable methods for solving boundary value problems of nonlinear fractional differential equations. Dr. Ali has collaborated extensively with international researchers, contributing to journals such as Chaos, Solitons & Fractals and the Journal of Advanced Research. His studies also explore the stability analysis and exact solutions of complex mathematical models, emphasizing fractional calculus and its applications in various scientific and engineering problems.

Publications

  1. Nonlinear coupling of upper-hybrid waves with lower-hybrid waves in a degenerate dense plasma, Publication date: 2021.
  2. Unstable mode of ion-acoustic waves with two temperature q-nonextensive distributed electrons, Publication date: 2021.
  3. Computation of solution to fractional order partial reaction diffusion equations, Publication date: 2020.
  4. On stable iterative solutions for a class of boundary value problem of nonlinear fractional order differential equations, Publication date: 2019.
  5. Computation of iterative solutions along with stability analysis to a coupled system of fractional order differential equations, Publication date: 2019.
  6. Approximate solutions to nonlinear fractional order partial differential equations arising in ion-acoustic waves, Publication date: 2019.
  7. Stable monotone iterative solutions to a class of bound-ary value problems of nonlinear fractional order differential equations, Publication date: 2019.
  8. Monotone iterative technique and Ulam-Hyers stability analysis for nonlinear fractional order differential equations with integral boundary value conditions, Publication date: 2019.
  9. Optimum solutions of space fractional order diffusion equation, Publication date: 2018.
  10. On Approximate solutions of fractional Order partial differential equations, Publication date: 2018.
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