Carlos Alberto Vasquez Jalpa | Robotics | Best Researcher Award

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Mr. Carlos Alberto Vasquez Jalpa | Robotics | Best Researcher Award

Student at National Polytechnic Institute, Mexico

A multidisciplinary mechanical engineer with a robust foundation in artificial intelligence, robotics, and microelectronics, this researcher has consistently merged hands-on engineering with deep theoretical understanding. From soft robotics to hybrid neural networks, the researcher has demonstrated the ability to innovate across domains, contributing to both academia and industry through projects, leadership, and international collaboration.

Profile

ORCID

Best Researcher Award

This researcher is highly suitable for the “Best Researcher Award” due to a strong combination of practical engineering accomplishments and innovative AI-driven research. Their multidisciplinary expertise, publications, leadership in rocketry and robotics, and contributions to international research efforts highlight a strong profile. They also demonstrate a consistent commitment to global problem-solving through co-creative education and technical application.

Education

The academic journey began with a technical diploma in Industrial Maintenance (2012–2015), followed by a degree in Mechanical Engineering (2015–2020), focusing on hydrogen peroxide separation for soft energy in robotics. The researcher then pursued a Master of Science in Engineering in Microelectronics (2020–2022), with a thesis on neuroevolution of hybrid neural networks in robotic agents, showcasing a progression from mechanical systems toward intelligent automation and AI applications.

Experience

With experience spanning design, AI development, and international academic collaboration, the researcher held several significant roles. As an AI Developer at INNOVA 3D Mexico, they led neural network deployments across robotic platforms. They designed hybrid neural networks at ESIME Culhuacan, contributing to global institutions like the Tokyo University of Electro-communications and the University of Science and Technology of China. As a mechanical designer at Ticsa Grupo EPM, they led water treatment design projects, and at Antares Space, they led advanced rocketry designs and CAD optimization efforts.

Research Interest

Their research interests center on artificial intelligence, robotics, neural network design, and hybrid neuroevolution models. They are particularly focused on applying deep reinforcement learning to robotic systems and using soft robotics as a sustainable alternative in automation. Their interests also extend into IoT, electronics, and precision engineering tools, enabling cross-domain innovations.

Publication

A significant contribution to academic literature includes the publication titled “A deep reinforcement learning algorithm based on modified Twin delay DDPG method for robotic applications,” which was presented at the 21st International Conference on Control, Automation and Systems in 2021. This research explored enhanced reinforcement learning strategies tailored to robotic systems, highlighting the author’s advancement of practical AI applications in control systems.

Conclusion

This researcher exemplifies technical excellence, global collaboration, and innovative research. Their ability to transition between mechanical systems and advanced AI frameworks, paired with leadership roles in research and engineering, positions them as a compelling candidate for the “Best Researcher Award.” With a consistent trajectory toward impactful, interdisciplinary problem-solving, their contributions have set a strong foundation for future breakthroughs in intelligent systems and robotics.

Oscar Alejandro Ángeles-Ramírez | Robotics and Control Systems Engineering | Best Researcher Award

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Mr. Oscar Alejandro Ángeles-Ramírez | Robotics and Control Systems Engineering | Best Researcher Award

Bachelor’s Degree in Physical Engineering | Universidad Iberoamericana, Ciudad de México

Oscar Alejandro Ángeles-Ramírez is a highly skilled physical engineer with a strong foundation in control systems, data analysis, and financial risk management. Graduating with distinction from Universidad Iberoamericana with a GPA of 9.9/10, he has established a robust academic and professional trajectory. Oscar combines expertise in theoretical research, especially in robotics and control systems, with practical applications in financial risk consulting. He holds a key role at Management Solutions, providing risk consulting to financial entities, while also contributing to academia as an adjunct professor. Oscar’s contributions have earned him prestigious awards, including the CENEVAL Award for Outstanding Performance and several academic scholarships.

Profile

Scopus

Education

Oscar pursued his Bachelor’s degree in Physical Engineering at Universidad Iberoamericana in Mexico City, where he achieved exceptional academic results, graduating with a GPA of 9.9/10 in 2023. He is currently advancing his education by pursuing a Business and Consulting Diploma at the Instituto Tecnológico de Estudios Superiores de Monterrey. His academic background has provided a solid base in both engineering principles and financial consulting, reinforcing his ability to bridge the gap between theoretical research and industry applications.

Experience

Oscar’s professional experience is multifaceted, blending academic research with industry consulting. He has been employed as a Financial and Commodities Risk Consultant at Management Solutions in Mexico City since 2022, where he works on validating and replicating credit risk models for financial entities, as well as ensuring regulatory compliance in liquidity risk metrics like LCR and NSFR. His consulting experience extends to working with ESG criteria for financial institutions. In parallel, he serves as a Junior Academic Researcher and Data Analyst at Universidad Iberoamericana, contributing to innovative research in control systems and robotics, and designing automation tools for academic data collection. Additionally, Oscar has gained valuable teaching experience as an adjunct professor, where he integrates digital tools to enhance students’ understanding of complex mathematical concepts.

Research Interests

Oscar’s research interests lie primarily in control systems engineering and robotics, with a particular focus on iterative learning control and decentralized systems. His work has significantly advanced the study of multi-agent systems, especially in terms of formation control and collision avoidance, which is critical for robotic applications. He has developed a deep understanding of fractional-order systems, exploring their applications in the context of robotics and control systems. Oscar also has a keen interest in integrating innovative data analysis techniques to improve process optimization and decision-making in financial systems, particularly in risk management and regulatory compliance.

Award

Oscar has received multiple awards and recognition for his academic and professional achievements. Notably, he was awarded the Si Quieres, ¡Puedes! Scholarship in 2019 for demonstrating exceptional academic potential despite limited financial resources. He earned an Academic Research Scholarship from the Physics and Mathematics Department at UIA in 2020 and was honored with an Honorable Mention upon completing his Bachelor’s degree in 2023. In 2024, he received the CENEVAL Award for Outstanding Performance on the General Examination for Bachelor’s Degree Graduation. Additionally, his research paper on decentralized formation of multi-agent systems has been provisionally selected for the Best Researcher Award at the International AI Data Scientist Awards.

Publications

Oscar has published a select number of impactful research articles in the prestigious Asian Journal of Control, where he is the first author in all of his publications:

Iterative Learning Control Applied to Distributed-Order LTI MIMO Systems to Achieve Learnability (2023), Asian Journal of Control, Vol. 25, pp. 2508-2520. DOI: 10.1002/asjc.2973

Decentralized Formation of Multi-Agent Conformable Fractional Nonlinear Robot Systems (2024), Asian Journal of Control, Vol. 26, pp. 831-844. DOI: 10.1002/asjc.3242

Decentralized Formation with Collision Avoidance of Multi-Agent Conformable Fractional Nonlinear Robot Systems (2025), Asian Journal of Control. DOI: 10.1002/asjc.3580 – This paper has been provisionally selected for the Best Researcher Award at the International AI Data Scientist Awards by ScienceFather.

Conclusion

Oscar Alejandro Ángeles-Ramírez exemplifies the intersection of academia, engineering, and industry practice. His innovative contributions to control systems and robotics, alongside his pivotal role in financial risk consulting, demonstrate his versatility and commitment to applying knowledge in real-world contexts. With a strong academic foundation, notable research publications, and recognition in both professional and academic circles, Oscar continues to drive forward-thinking solutions in engineering and finance. His pursuit of excellence in both teaching and research solidifies his place as a rising leader in the fields of control systems and data analysis.