Liangliang Sun | Optimization | Best Researcher Award

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Prof. Liangliang Sun | Optimization | Best Researcher Award

Dean at Northeastern University, China

Prof. Liangliang Sun 🌟 is a distinguished scholar in control theory and intelligent scheduling systems, currently serving at Northeastern University, China 🏫. With a robust academic background including a Ph.D. from both Northeastern University and the University of Connecticut 🎓, he has cultivated deep expertise in steelmaking-continuous casting optimization, energy systems, and industrial automation ⚙️. His prolific research spans over 18 high-impact publications 📚, multiple national-level projects 🎯, and several patented innovations 🔬. Prof. Sun has earned accolades such as the Xingliao Talent Plan 🌟 and has been a two-time champion 🏆 in China’s prestigious Challenge Cup competitions. His contributions extend into teaching, editorial responsibilities 🖋️, and international collaborations 🌐. Known for integrating theory with real-world industrial applications 🔄, he bridges gaps between research and practice seamlessly. As a dynamic thought leader 🚀 in control engineering and smart manufacturing, Prof. Sun continues to inspire innovation and academic excellence across global platforms 🌍.

Professional Profile 

Scopus Profile

🎓 Education

Prof. Liangliang Sun’s academic journey is marked by scholarly excellence and international immersion 🌍. He earned his Bachelor’s and Master’s degrees in Automation and Control Engineering from Northeastern University, China 🇨🇳, laying the groundwork for a solid technical foundation. He later pursued a prestigious dual Ph.D. from Northeastern University and the University of Connecticut 🇺🇸, enriching his expertise through cross-cultural academic synergy. His doctoral research focused on intelligent optimization and industrial system control, setting the stage for impactful innovation. This global academic trajectory reflects a deep commitment to advancing modern engineering frontiers 🔬. With early exposure to cutting-edge research environments, Prof. Sun developed a unique ability to integrate Eastern precision with Western analytical frameworks, a blend that defines his distinctive academic persona 📘.

🏢 Professional Experience

Prof. Sun has cultivated an impressive professional track record in academic and applied engineering settings 🔧. Currently a full professor at Northeastern University’s School of Information Science and Engineering 🏫, he teaches and mentors in systems engineering and automation. His career includes a postdoctoral tenure at the University of Connecticut 🇺🇸, where he deepened his research in smart manufacturing. He has led numerous national research projects as Principal Investigator, collaborating with top-tier steel and energy enterprises 🏭. Beyond academia, he offers expert consultations on intelligent scheduling, production line efficiency, and energy optimization 💡. Prof. Sun also holds leadership roles in editorial boards and technical committees, actively shaping global discourse in control systems and industrial AI 📊. His trajectory exemplifies research-driven engineering leadership ⚙️.

🔍 Research Interest

Prof. Sun’s research orbits around intelligent optimization, industrial control, and smart scheduling algorithms 🚀. He explores dynamic production scheduling in steelmaking, aiming to enhance process integration, reduce emissions, and boost system efficiency 🔄. His work leverages artificial intelligence, deep learning, and energy modeling to address complex industrial challenges. With a passion for merging theory with real-world applicability 🛠️, his investigations span multi-objective optimization, cyber-physical systems, and data-driven control frameworks. He has also ventured into hydrogen-rich energy systems and low-carbon manufacturing pathways 🌱. His scientific vision aligns seamlessly with the evolving demands of Industry 4.0 and sustainable engineering 🌐. Through over 18 peer-reviewed papers, Prof. Sun has contributed significantly to reshaping production intelligence and process automation across diverse industrial landscapes 📚.

🏅 Awards and Honors

Prof. Liangliang Sun has garnered prestigious awards that reflect his academic leadership and innovation excellence 🏆. He’s a dual Champion of the National Challenge Cup, a rare feat highlighting his creativity and technical mastery early on 🧠. He is a selected recipient of the Xingliao Talent Plan, the “Hundred and Ten Thousand Talents Project”, and the Young Top Talent in Liaoning Province 🌟. Recognized nationally and provincially, he exemplifies the profile of a high-impact scholar pushing disciplinary boundaries 🚧. His awarded projects from the National Natural Science Foundation of China (NSFC) underline the national trust in his research potential 🧪. These honors demonstrate his sustained excellence in advancing automation, energy intelligence, and industrial digitization. Each accolade fortifies his status as a distinguished researcher 💼.

🧩 Conclusion

Prof. Liangliang Sun represents a remarkable blend of intellectual rigor, innovation, and practical relevance 🎯. With interdisciplinary strength across automation, AI, and energy systems, he bridges theoretical research with industrial transformation 🌐. His impactful publications, pioneering projects, and patented inventions underscore a career built on thoughtful innovation and global relevance 💡. He leads with purpose, engages in collaborative discovery, and mentors the next generation of scientific leaders 👨‍🏫. Prof. Sun’s contributions enhance the technological landscape and redefine the future of smart manufacturing and sustainable engineering 🔋. As a thought leader, he continues to shape high-impact solutions for complex industrial ecosystems, leaving a lasting imprint on global engineering science 🌟. His career is a testament to vision, resilience, and relentless pursuit of excellence 🔝.

Publications Top Notes

📘 Title: A learning-enhanced ant colony optimization algorithm for integrated planning and scheduling in hot rolling production lines under uncertainty
Authors: S. Jiang, L. He, L. Cao, L. Sun, G. Peng
Year: 2025
Citations: 1
Source: Swarm and Evolutionary Computation

📘 Title: A Self-adaptive two stage iterative greedy algorithm based job scales for energy-efficient distributed permutation flowshop scheduling problem
Authors: Y. Yu, Q. Zhong, L. Sun, X. Jing, Z. Wang
Year: 2025
Source: Swarm and Evolutionary Computation

📘 Title: Research on steelmaking-continuous casting cast batch planning based on an improved surrogate absolute-value Lagrangian relaxation framework
Authors: C. Li, L. Sun
Year: 2025
Source: International Journal of Automation and Control

📘 Title: An Online Learning-Based mACO Approach for Hot Rolling Scheduling Problems Involving Dynamic Order Arrivals
Authors: S. Jiang, Q. Liu, L. Cao, L. Sun
Year: 2025
Source: IEEE Transactions on Automation Science and Engineering

📘 Title: A robust optimization approach for steeling-continuous casting charge batch planning with uncertain slab weight
Authors: C. Li, L. Sun
Year: 2024
Citations: 1
Source: Journal of Process Control

📘 Title: Optimal control of three-dimensional unsteady partial differential equations with convection term in continuous casting
Authors: Y. Yu, Y. Wang, X. Pang, L. Sun
Year: 2024
Citations: 1
Source: Computers and Mathematics with Applications

📘 Title: Optimal Scheduling Works for Two Employees with Ordered Criteria
Authors: N.M. Matsveichuk, Y.N. Sotskov, L. Sun
Year: 2024
Source: WSEAS Transactions on Business and Economics

Farshid Dehghan | Optimization | Best Researcher Award

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Dr. Farshid Dehghan | Optimization | Best Researcher Award

Doctoral Researcher at Universidad Politécnica de Madrid, Iran

Farshid Dehghan is a dedicated Building Energy Performance Analyst with expertise in simulation-based optimization, energy efficiency, and machine learning applications. He is affiliated with Escuela Técnica Superior de Edificación, Universidad Politécnica de Madrid, Spain, where he focuses on sustainable building solutions. His research includes optimizing building retrofits in Iran to improve energy consumption, emissions reduction, comfort, and indoor air quality in the face of climate change. He is currently working on predicting energy consumption and emissions using machine learning approaches, reflecting his innovative mindset in data-driven sustainability. His scholarly contributions include a publication in the Sustainability journal, showcasing his ability to address real-world energy challenges. While his research impact is growing, expanding his indexed publications, securing patents, and increasing industry collaborations could further enhance his profile. With his commitment to sustainable energy solutions, Farshid Dehghan is a promising researcher in the field of building energy performance and smart optimization techniques.

Professional Profile 

Google Scholar

Education

Farshid Dehghan is affiliated with Escuela Técnica Superior de Edificación, Universidad Politécnica de Madrid, Spain, where he has built a strong academic foundation in building energy performance, sustainable design, and simulation-based optimization. His educational background is deeply rooted in engineering and environmental sustainability, equipping him with the necessary skills to tackle challenges related to energy efficiency, emissions control, and indoor air quality. His studies have provided him with expertise in machine learning applications for energy prediction and optimization, making him a forward-thinking researcher in the field. Throughout his academic journey, he has developed a strong analytical approach and a problem-solving mindset, allowing him to apply innovative methodologies to complex building energy problems. His educational background has played a crucial role in shaping his research focus, emphasizing the intersection of technology, energy efficiency, and sustainability, which forms the core of his work in simulation-based multi-objective optimization.

Professional Experience

Farshid Dehghan is a Building Energy Performance Analyst with expertise in sustainable building solutions, energy efficiency modeling, and simulation-based optimization techniques. His professional experience includes research on building retrofits in Iran, where he focuses on optimizing energy consumption, minimizing emissions, and improving occupant comfort while considering climate change impacts. His work integrates machine learning and data-driven approaches to predict energy consumption and emissions, demonstrating his strong analytical and computational skills. Through his research, he has gained experience in working with building simulation software, optimization tools, and statistical modeling techniques. His role requires him to analyze real-world building performance, propose effective retrofit solutions, and contribute to the advancement of energy-efficient building designs. Additionally, his work in academic publishing and industry-related consultancy projects has enabled him to apply his research to practical applications, making him a valuable asset in the field of sustainable building energy performance.

Research Interest

Farshid Dehghan’s research primarily focuses on building energy performance, simulation-based optimization, and machine learning applications in sustainability. He is particularly interested in multi-objective optimization for energy-efficient building retrofits, aiming to reduce energy consumption, minimize emissions, and enhance indoor air quality while ensuring occupant comfort. His work extends to predictive modeling using machine learning techniques, where he applies advanced algorithms to forecast energy usage patterns and environmental impacts. Additionally, he is exploring the integration of smart building technologies to develop data-driven strategies for optimizing building operations. His research aligns with global efforts to combat climate change by promoting energy-efficient and low-carbon building solutions. He is also interested in developing policy-driven strategies for sustainable urban environments, collaborating with experts across disciplines to create innovative frameworks for energy management and optimization. His research contributions reflect his commitment to sustainability and technological innovation in the built environment.

Awards and Honors

Farshid Dehghan’s contributions to building energy performance research have positioned him as a promising researcher in his field. While he is in the early stages of his career, his publication in the Sustainability journal and ongoing research projects demonstrate his growing impact. His work in simulation-based optimization for building retrofits has gained recognition, and as he continues to expand his research, he is likely to attract more academic and industry accolades. By securing indexed journal publications, patents, and industry collaborations, he has the potential to achieve prestigious honors in sustainable building research. His dedication to improving energy efficiency and indoor air quality aligns with global sustainability goals, making him a strong candidate for future research awards. As he continues to contribute to innovative energy solutions, his work is expected to receive further recognition in academic, industry, and policy-making circles.

Conclusion

Farshid Dehghan is a dedicated researcher and analyst specializing in building energy performance, sustainable design, and machine learning-driven energy optimization. His work addresses critical challenges in energy efficiency, emissions reduction, and occupant comfort, making significant contributions to the field of sustainable built environments. While his research is gaining traction, further expansion in indexed journal publications, patents, and industry partnerships will strengthen his profile. His expertise in simulation-based optimization and predictive modeling demonstrates his forward-thinking approach to sustainability. As he continues his research, his contributions will play a vital role in shaping the future of energy-efficient building solutions. His strong technical background, research-driven mindset, and commitment to innovation make him a valuable asset in the pursuit of sustainable and climate-resilient building technologies.

Publications Top Noted