Prof. Dr. Ke Xiong | Optimization Award | Best Researcher Award

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Prof. Dr. Ke Xiong | Optimization Award | Best Researcher Award

Prof. Dr. Ke Xiong, School of Computer Science and Technology, Beijing Jiaotong University, China

Ke Xiong is a Full Professor and Vice Dean at the School of Computer and Information Technology, Beijing Jiaotong University (BJTU), where he earned both his B.S. and Ph.D. degrees. After completing his postdoctoral fellowship at Tsinghua University, he has held various prominent roles in academic and industry organizations. His research focuses on wireless cooperative networks, wireless powered networks, and network information theory, with over 200 academic publications. He has been recognized as a Highly Cited Chinese Researcher by Elsevier in 2023 and ranked among the top 2% of scientists globally by Stanford University in 2022. His numerous accolades include best paper awards and prestigious prizes in natural science and technology.

Professional Profile:

Google Scholar

Ke Xiong’s Suitability for the Research for Best Researcher Awards:

Ke Xiong exemplifies the qualities of a leading researcher deserving of the Best Researcher Award. His extensive publication record, influential leadership roles, and recognition within the academic and professional communities underscore his contributions to wireless networks and smart technologies. His ongoing efforts to enhance education and research collaboration further reinforce his suitability for this prestigious award. As he continues to advance his research and impact the field, he stands out as an inspiring figure in engineering and technology

🎓Education:

Ke Xiong earned his Bachelor of Science (B.S.) degree from Beijing Jiaotong University (BJTU) in 2004, followed by a Doctor of Philosophy (Ph.D.) degree from the same institution in 2010. His educational journey at BJTU laid a strong foundation for his subsequent research and academic career in computer and information technology.

🏢Work Experience:

After completing his Ph.D. in 2010, Ke Xiong served as a Postdoctoral Research Fellow in the Department of Electronics Engineering at Tsinghua University in Beijing, China, from April 2010 to February 2013. In March 2013, he joined Beijing Jiaotong University (BJTU) as a Lecturer, and over the years, he progressed to the roles of Associate Professor and eventually Full Professor and Vice Dean of the School of Computer and Information Technology. Additionally, he was a Visiting Scholar at the University of Maryland, College Park, MD, USA, from September 2015 to September 2016, further enhancing his research and academic expertise.

🏅Awards and Recognition:

Ke Xiong has received numerous accolades throughout his academic career, including recognition as a Highly Cited Chinese Researcher by Elsevier in 2023 and being ranked among the top 2% of scientists globally by Stanford University in 2022. He was awarded the Second Prize in Natural Science from the China Institute of Communications (CIC) and the Second Prize in Science and Technology from the China Railway Society. Additionally, he has won best student paper awards at several prestigious conferences, including HWMC 2014, IEEE ICC 2020, IEEE ICSTSN 2023, and IEEE ICCCS 2023 and 2024.

Publication Top Notes:

  • Title: AoI-minimal trajectory planning and data collection in UAV-assisted wireless powered IoT networks

    Cited by: 273

  • Title: UAV-assisted wireless powered cooperative mobile edge computing: Joint offloading, CPU control, and trajectory optimization

    Cited by: 245

  • Title: Rate-energy region of SWIPT for MIMO broadcasting under nonlinear energy harvesting model

    Cited by: 225

  • Title: Wireless information and energy transfer for two-hop non-regenerative MIMO-OFDM relay networks

    Cited by: 206

  • Title: Energy efficiency in secure IRS-aided SWIPT

    Cited by: 134

 

 

 

 

 

 

Dr. Maryam Sharifi | Optimization Award | Best Researcher Award

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Dr. Maryam Sharifi | Optimization Award | Best Researcher Award

Dr. Maryam Sharifi, ABB Corporate Research, Sweden

Dr. Maryam Sharifi is a Senior Research Scientist and Control Systems Engineer at ABB Corporate Research in VasterĂĄs, Sweden, where she focuses on developing optimized planning solutions for autonomous systems and conducting stability studies for renewable-based microgrids. With a Ph.D. in Electrical Engineering from the University of Tehran, Dr. Sharifi has extensive research experience in artificial intelligence, optimization-based solutions, multi-agent systems, and robotic systems. She has held various academic positions, including postdoctoral researcher at KTH Royal Institute of Technology, and has contributed significantly to peer-reviewed journals. Additionally, she has received several honors, including ranking second in her Ph.D. entrance examination among 1,500 participants. Dr. Sharifi is proficient in multiple programming languages and has delivered scientific talks at prestigious institutions.

Professional Profile:

Google Scholar

Orcid

Suitability for Researcher Awards: Dr. Maryam Sharifi

Summary of Suitability for the Award:

Dr. Maryam Sharifi is a highly accomplished Senior Research Scientist and Control Systems Engineer with a rich academic and professional background that positions her as a leading candidate for a Best Researcher Award. Her expertise spans various critical areas, including Artificial Intelligence, Optimization, Multi-Agent Systems, Robotic Systems, and Formal Methods, making her contributions vital to advancing control systems and autonomous technologies.

🎓Education:

Dr. Maryam Sharifi earned her Ph.D. in Electrical Engineering with a focus on Control Systems from the University of Tehran, Iran, achieving a GPA of 18.73/20 from September 2015 to April 2020. During her doctoral studies, she was a visiting researcher at the Automatic Control Laboratory at ETH Zurich, Switzerland, from January to June 2019. Prior to her Ph.D., she completed her M.S. in Electrical Engineering (Control Systems) at Amirkabir University of Technology, Tehran, with a GPA of 18.29/20 from September 2013 to September 2015. She also holds two bachelor’s degrees in Electrical Engineering: the first in Control Systems with a GPA of 18.02/20 (September 2009 – September 2013) and the second in Power Systems with a GPA of 17.9/20 (September 2011 – September 2014), both from Amirkabir University of Technology

🏢Work Experience:

Dr. Maryam Sharifi currently serves as a Senior Research Scientist at ABB Corporate Research in VasterĂĄs, Sweden, a position she has held since January 2024. Prior to this role, she was a Research Scientist at the same institution from February 2022 to December 2023. Before joining ABB, Dr. Sharifi worked as a Postdoctoral Researcher at KTH Royal Institute of Technology in Stockholm, Sweden, from September 2020 to February 2022, where she contributed to various advanced research projects in control systems and autonomous technologies.

🏅Awards:

Dr. Maryam Sharifi has received several accolades throughout her academic career. She was ranked 2nd in the Ph.D. entrance examination among 1,500 participants in 2015 and achieved 3rd place in her M.S. admission, which exempted her from the entrance examination in 2013. These accomplishments reflect her strong academic performance among a competitive group of 100 electrical engineering students.

Publication Top Notes:

  • “Control barrier function based visual servoing for Mobile Manipulator Systems under functional limitations”
  • “Safe Force/Position Tracking Control via Control Barrier Functions for Floating Base Mobile Manipulator Systems”
  • “Control barrier function based visual servoing for underwater vehicle manipulator systems under operational constraints”
  • “Platoons Coordination Based on Decentralized Higher Order Barrier Certificates”

 

 

 

Assoc Prof Dr. Yang Wang | Optimization Awards | Best Researcher Award

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Assoc Prof Dr. Yang Wang | Optimization Awards | Best Researcher Award

Assoc Prof Dr. Yang Wang, Shanghai DianJi university, China

Yang Wang is a Professor at Shanghai Dianji University and serves as the Deputy Director of the Planning and Development Office. Her expertise lies in fault diagnosis of complex industrial processes and energy system optimization. As a Master’s Supervisor and a reviewer for the Journal of ISA Transactions, she contributes significantly to her field. Yang Wang has earned recognition for her innovative work, including a second prize in the “Shanghai Teaching Innovation Competition” and the “Design Star” in the “5th National College Hybrid Teaching Design Innovation Competition.” Her research includes designing resilient multi-energy systems and employing advanced techniques to manage forecast errors and uncertainties.

Professional Profile:

Scopus

Evaluation for Prof. Yang Wang’s Suitability for the Best Researcher Award

Prof. Yang Wang’s impactful research, innovative achievements, and dedication to education and professional contributions position her as a highly suitable candidate for the Best Researcher Award. Her expertise and recognition in energy system optimization and fault diagnosis underscore her significant contributions to her field, making her a deserving recipient of this honor.

🏢Current Roles:

Yang Wang is a Professor at Shanghai Dianji University, where she also serves as the Deputy Director of the Planning and Development Office. In her academic role, she supervises Master’s students and contributes as a reviewer for the Journal of ISA Transactions. Her responsibilities encompass both administrative leadership and academic mentorship, reflecting her significant role in advancing research and development at the university.

🏅Awards and Recognitions:

Yang Wang has been honored with the Second Prize in the Shanghai Teaching Innovation Competition and has received the “Design Star” award in the 5th National College Hybrid Teaching Design Innovation Competition. These accolades recognize her exceptional contributions to educational innovation and her commitment to advancing teaching methodologies.

Publication Top Notes:

  • Distributed Monitoring of Nonlinear Plant-Wide Processes Based on GA-Regularized Kernel Canonical Correlation Analysis
  • Optimizing Multi-Energy Systems with Enhanced Robust Planning for Cost-Effective and Reliable Operation
  • Interpretable Chiller Fault Diagnosis Based on Physics-Guided Neural Networks
  • Short-Term Power Load Forecasting Based on IWOA-GRU
  • Flexible Adaptive Marine Predator Algorithm for High-Dimension Optimization and Application in Wind Turbine Fault Diagnosis