Rishabh Anand | Computer Science | Best Researcher Award

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Dr. Rishabh Anand | Computer Science | Best Researcher Award

Associate Vice President at India

Dr. Rishabh Anand is a distinguished professional with over 19 years of experience spanning technology, business management, and academia. His expertise lies in program and delivery management, strategic leadership, and digital transformation, with a strong foundation in IT and education. As a thought leader, he has successfully integrated academic theories with real-world business applications, fostering innovation and excellence. His global experience across the USA, UK, India, Denmark, France, the Middle East, and ASEAN has given him a unique perspective on technology and business evolution. Dr. Anand is known for his mentorship and coaching abilities, shaping the next generation of professionals and students through his academic and industry engagements. His ability to drive strategic initiatives, coupled with his passion for education and research, has positioned him as a leader in the fields of artificial intelligence, machine learning, and digital transformation.

Professional Profile

Education

Dr. Rishabh Anand has an impressive academic background with multiple degrees spanning technology, management, and psychology. He earned his B.E. in Electronics and Communication Engineering from Dronacharya College of Engineering, MDU, in 2006. His passion for advanced technical research led him to pursue an M.Tech in Electronics and Communication Engineering from the Indian Institute of Technology (IIT), Delhi, in 2010. Expanding his expertise into business and finance, he completed an MBA in Finance from New York University (NYU) in 2014. Understanding the significance of human behavior in technology and business, he pursued an MS in Psychology from the University of Texas at Dallas in 2016. His dedication to research culminated in a Ph.D. in Computer Science from the University of Bristol, UK, in 2020. Further solidifying his expertise, he completed a dual postdoctoral degree in Artificial Intelligence and Machine Learning from São Paulo State University, Brazil, in 2024.

Professional Experience

Dr. Anand has an extensive professional career, demonstrating expertise in global technology, business strategy, and academic leadership. He has been a key figure at Google India Private Limited since 2006, leading strategic initiatives, managing multi-million-dollar IT projects, and driving digital transformation across various industries. As a Program and Delivery Manager, he has played a pivotal role in managing large-scale engineering teams, ensuring efficiency, innovation, and profitability. His work spans industries such as airlines, pharmaceuticals, financial services, FMCG, tourism, logistics, and technology. He has successfully transitioned over 350-400 roles globally, demonstrating his expertise in workforce transformation and leadership. In academia, he has mentored students and professionals, bridging the gap between theoretical learning and industry expectations. His extensive experience working with C-suite executives and leading digital initiatives has established him as a global thought leader in technology-driven business solutions.

Research Interest

Dr. Rishabh Anand’s research interests primarily focus on artificial intelligence, machine learning, digital transformation, and strategic IT management. His work revolves around integrating cutting-edge AI and ML technologies into business strategies to enhance efficiency, automation, and customer experience. He is deeply invested in enterprise IT strategies, cybersecurity, cloud computing, and predictive analytics, ensuring that businesses stay ahead in the digital era. His interest in digital transformation includes process automation, technology adoption in organizations, and data-driven decision-making frameworks. With his background in psychology, he also explores human-computer interaction, cognitive computing, and behavioral AI. Through his published case studies and academic collaborations, Dr. Anand continues to contribute valuable insights into how AI and digital solutions can drive innovation and economic growth. His research aims to bridge the gap between academia and industry, ensuring that emerging technologies align with real-world business challenges.

Awards and Honors

Dr. Rishabh Anand has received multiple awards and recognitions for his contributions to technology, research, and academia. He was recognized for his excellence in digital transformation and IT strategy at Google India, where he led high-impact projects, driving profitability and innovation. His “Thinking Breakthrough” workshops have received industry recognition for aligning client visions with cutting-edge business and IT strategies. As a dedicated mentor, he has been honored for his contributions to student career development and academic excellence. His research publications on AI, digital transformation, and strategic IT management have been acknowledged in international conferences and journals. Dr. Anand’s work in mentorship and workforce transformation has also earned him leadership awards from various professional organizations. With a stellar career spanning technology, business, and academia, he continues to be an influential figure in shaping the future of AI, machine learning, and enterprise IT solutions.

Conclusion

Dr. Rishabh Anand is a strong contender for the Best Researcher Award, given his significant contributions to research, industry-academia collaboration, and leadership in digital transformation. Strengthening his publication record and patents could further solidify his case as an outstanding researcher.

Publications Top Noted

Industry 4.0 Technologies

Author: Dr. Rishabh Anand (2025)
Publisher: S Chand and Company Ltd

Smart Factories for Industry 5.0 Transformation (Industry 5.0 Transformation Applications)

Authors: Dr. Rishabh Anand, R. Nidhya, Manish Kumar, S. Karthik, S. Balamurugan (2025)
Publisher: Wiley-Scrivener

Foundation Course in Universal Human Values and Professional Ethics

Author: Dr. Rishabh Anand (2025)
Publisher: CBS Publishers and Distributors Pvt. Ltd.

Blockchain Technology

Author: Dr. Rishabh Anand (2023)
Publisher: Khanna Publishers

Computer Organization and Architecture (Designing for Performance)

Authors: Dr. Rishabh Anand, R.S. Salaria (2023)
Publisher: Khanna Publishers

Digital Signal Processing: An Introduction

Author: Dr. Rishabh Anand (2022)
Publisher: Mercury Learning & Information

Wireless Communication

Author: Dr. Rishabh Anand (2022)
Publisher: S Chand And Company Ltd

An Integrated Approach to Software Engineering

Author: Dr. Rishabh Anand (2021)
Publisher: Khanna Publishers

Digital Signal Processing

Author: Dr. Rishabh Anand (2021)
Publisher: Khanna Publishers

Object-Oriented Programming using C++

Author: Dr. Rishabh Anand (2021)
Publisher: Khanna Publishers

Optical Fiber Communications

Author: Dr. Rishabh Anand (2021)
Publisher: Khanna Publishers

Satellite Communications

Author: Dr. Rishabh Anand (2021)
Publisher: Khanna Publishers

Nanotechnology

Author: Dr. Rishabh Anand (2020)
Publisher: Khanna Publishers

Digital Electronics

Author: Dr. Rishabh Anand (2019)
Publisher: Khanna Book Publishing Company

Signals and Systems

Author: Dr. Rishabh Anand (2018)
Publisher: Khanna Book Publishing Company

Mobile Computing

Author: Dr. Rishabh Anand (2017)
Publisher: Khanna Publishers

Computer Networks

Author: Dr. Rishabh Anand (2016)
Publisher: Satya Prakashan

Linear Integrated Circuits

Author: Dr. Rishabh Anand (2014)
Publisher: Khanna Book Publishing Company

Electromagnetic Field Theory

Author: Dr. Rishabh Anand (2013)
Publisher: Khanna Book Publishing Company

Computer Graphics

Author: Dr. Rishabh Anand (2013)
Publisher: Khanna Book Publishing Company

Digital System Design Using VHDL

Author: Dr. Rishabh Anand (2013)
Publisher: Khanna Book Publishing Company

Intelligent Instrumentation for Engineers

Author: Dr. Rishabh Anand (2013)
Publisher: Khanna Book Publishing Company

Software Project Management

Author: Dr. Rishabh Anand (2013)
Publisher: S K Kataria and Sons

Wireless and Mobile Computing

Author: Dr. Rishabh Anand (2013)
Publisher: S K Kataria and Sons

Network Management

Author: Dr. Rishabh Anand (2012)
Publisher: Not Specified

Neural Networks

Author: Dr. Rishabh Anand (2012)
Publisher: Satya Prakashan

Communication Systems: Analog and Digital

Author: Dr. Rishabh Anand (2011)
Publisher: Khanna Book Publishing Company

 

Arturo Benayas Ayuso | Computer Science | Best Researcher Award

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Prof. Arturo Benayas Ayuso | Computer Science | Best Researcher Award

PhD Candidate, Universidad Politécnica de Madrid, Spain

Arturo Benayas Ayuso is a highly skilled Naval Architect with a distinguished career in naval shipbuilding and digital transformation. He currently leads the integration of the “El Cano” platform at NAVANTIA, spearheading Industry 4.0 innovations in ship design, construction, and management. His expertise in integrating PLM systems and IoT into shipbuilding projects has positioned him as a leader in naval digitization. Fluent in multiple languages, Arturo also serves as a lecturer, sharing his knowledge of statistics at Universidad Complutense de Madrid. 🚢💡

Publication Profile

ORCID

Education

Arturo holds a Master’s in Naval Architecture from Universidad Politécnica de Madrid and is currently pursuing a PhD, focusing on IoT applications in ship design, shipbuilding, and management. His academic background, combined with his professional experience, allows him to seamlessly bridge the gap between theory and practice in the maritime industry. 🎓📚

Experience

As the Integration Lead of NAVANTIA’s “El Cano” platform, Arturo manages the digitization and PLM integration of naval shipbuilding processes. His past roles include overseeing the FORAN-PLM integration for Spain’s S80 submarine and collaborating on several high-profile naval projects, including the Royal Navy’s CVF program. His work has consistently focused on improving digital workflows in naval engineering using systems like Windchill and Teamcenter PLM. 🛠️⚙️

Research Focus

Arturo’s research revolves around applying IoT technology to ship design and manufacturing. His work aims to enhance the efficiency of shipbuilding processes by integrating advanced digital tools and IoT into ship management systems. This focus on Industry 4.0 in naval architecture ensures future-ready solutions in naval engineering. 🔍🌐

Awards and Honors

Arturo has contributed significantly to both industry and academia, sharing his insights at conferences like RINA and publishing in prestigious industry magazines. His thought leadership in naval shipbuilding and PLM system integration has earned him recognition within the maritime and technology sectors. 🏅📜

Publications

Integrated Development Environment in Shipbuilding Computer Systems – ICAS 2011, cited in studies related to shipbuilding digitization

Automated/Controlled Storage for an Efficient MBOM Process in Shipbuilding Managing IoT Technology – RINA, 2018, discussed in articles on smart ship management

Data Management for Smart Ship: Reducing Machine Learning Cost in IoS Applications – RINA, 2018, frequently referenced in works on IoT and machine learning integration

María Inmaculada Mohino-Herranz | Artificial Intelligence| Best Research Article Award

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Dr. María Inmaculada Mohino-Herranz | Artificial Intelligence| Best Research Article Award

Investigador, INTA, Spain

Inmaculada Mohíno Herranz is a distinguished researcher in the fields of signal processing, pattern recognition, and emotion detection. She currently works at the National Institute of Aerospace Technology (INTA), bringing her extensive expertise in physiological signal analysis to the forefront of innovative research. 🌟 Her career reflects a commitment to advancing technology and science, contributing to both academia and industry.

Publication profile

Scopus

Education

Inmaculada holds an impressive academic background, beginning with her M.Eng. in Telecommunication Engineering (2010), followed by a second degree in Electronics Engineering (2012), and a Master’s degree in Information and Communication Technologies (2015). 📚 She culminated her academic journey with a Ph.D. in Information and Communication Technologies (2017, with honors) from the University of Alcalá, Madrid, Spain. 🎓

Experience

She has built a solid career in academia and research, having worked at the Signal Theory and Communications Department of the University of Alcalá, where she was part of the Applied Signal Processing research group until 2021. 📡 Currently, she continues her research at INTA, contributing to projects related to aerospace technology. She has also been actively involved in supervising final degree and master’s projects, shaping future innovators. 👩‍🏫

Research Focus

Inmaculada’s research revolves around physiological signal processing, pattern recognition, emotion recognition, and stress detection. 💡 Her work is especially significant in understanding how physiological data can be used to monitor emotional states, which has applications ranging from healthcare to technology-enhanced well-being. 💻

Awards and Honors

Inmaculada has received recognition for her outstanding contributions to the field of Information and Communication Technologies, including supervising several successful degree projects and participating in numerous public and private-funded research initiatives. 🏆 Her efforts in academic and industrial projects further solidify her reputation as a leading researcher.

Publication Top Notes

Inmaculada Mohíno Herranz has authored various impactful papers. She has published nine journal papers, six of which are indexed in the Journal Citation Report. 📄 She has also written a book chapter and around 20 conference papers, showcasing her active engagement in research dissemination.

Metrological analysis on measuring techniques used to determine solubility of solids in supercritical carbon dioxide – Published in Measurement: Journal of the International Measurement Confederation (2025), this article has no citations yet.

Initializing the weights of a multilayer perceptron for activity and emotion recognition – Published in Expert Systems with Applications (2024), this article has no citations yet.

Introducing the ReaLISED Dataset for Sound Event Classification – Published in Electronics (2022), cited by two articles.

Linear detector and neural networks in cascade for voice activity detection in hearing aids – Published in Applied Acoustics (2021), cited by one article.

A wrapper feature selection algorithm: An emotional assessment using physiological recordings from wearable sensors – Published in Sensors (2020), this open-access article focuses on emotion assessment using physiological data from wearable sensors.

Jing Wang | Artificial Intelligence | Best Researcher Award

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Dr. Jing Wang | Artificial Intelligence | Best Researcher Award

Assistant Professor, Southeast University, China

Jing Wang is an assistant researcher at the School of Computer Science and Engineering, Southeast University, China. With a Ph.D. from Southeast University under Prof. Xin Geng, Jing has made significant strides in machine learning, focusing on multi-label learning and explainable machine learning. Jing is a recognized contributor to multiple esteemed journals and conferences, with impactful research on label distribution learning.

Publication Profile

ORCID

Strengths for the Award:

  1. Solid Academic Background: The candidate has pursued advanced degrees in Computer Science from reputable institutions, including a Ph.D. from Southeast University under the supervision of renowned professors.
  2. Focused Research Interests: The candidate’s research concentrates on machine learning, with a particular emphasis on multi-label learning and explainable machine learning—fields of significant current interest.
  3. Prolific Publication Record: The candidate has authored numerous high-quality journal and conference papers, many in well-regarded venues such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, and AAAI Conference on Artificial Intelligence.
  4. Academic Service and Leadership: The candidate has served as a lead guest editor and guest editor for special issues in reputable journals and has been a program committee member and reviewer for major conferences and journals, showcasing their commitment to advancing their field.
  5. Collaboration and Recognition: The candidate’s work involves collaboration with other established researchers, and they have published in leading journals and conferences, reflecting their recognition and influence in the research community.

Areas for Improvement:

  1. Research Impact and Application: While the candidate has published extensively, there is limited information on the real-world impact and applications of their research. Emphasizing how their work has been applied or can be applied to solve practical problems in industry or society could strengthen their profile.
  2. Awards and Honors: Although the candidate has made notable academic contributions, there is no mention of individual awards or recognitions, which could further validate their research impact and excellence.
  3. International Collaboration and Diversity of Research Areas: Expanding collaborations beyond their current network, potentially with international researchers from diverse fields, could enhance their research’s global reach and interdisciplinary impact.

 

🎓 Education

Ph.D. in Computer Science from Southeast University, China, supervised by Prof. Xin Geng. M.Sc. in Computer Science from Northeast University, China, supervised by Prof. Xingwei Wang. B.Sc. in Computer Science from Suzhou University of Science and Technology, China.

🏆 Experience

Jing Wang serves as an assistant researcher at the School of Computer Science and Engineering, Southeast University, China. Jing actively contributes to the academic community as a guest editor for renowned journals and as a program committee (PC) member and reviewer for prestigious conferences, including AAAI, UAI, and ECML.

🔍 Research Focus

Jing Wang’s research delves into machine learning, with a particular emphasis on multi-label learning and explainable machine learning. Jing’s work is notable for pioneering approaches in label distribution learning, leveraging common and label-specific feature fusion spaces, and developing innovative methodologies for driver distraction detection and open-world few-shot learning.

🏅 Awards and Honors

Lead Guest Editor for IEEE Transactions on Consumer Electronics on “When Consumer Electronics Meet Large Models: Opportunities and Challenges.” Guest Editor for the International Journal of Machine Learning and Cybernetics on “Reliable and Interpretable Machine Learning: Theory, Methodologies, Applications, and Beyond.” Program Committee Member for AAAI-23, UAI-24, and ECML-24.Reviewer for several high-impact journals, including IEEE TNNLS, IEEE TMM, IEEE TAI, IEEE JBHI, and Medical Image Analysis (MIA).

📚 Publications Top Notes

Jing Wang has authored numerous high-impact papers in top-tier journals and conferences. Key publications include works on label distribution learning in Pattern Recognition and IEEE Transactions on Neural Networks and Learning Systems, contributing to the understanding of label-specific feature fusion and fuzzy label correlation in machine learning. Jing’s research on “Driver Distraction Detection Using Semi-supervised Lightweight Vision Transformer” has been recognized for its innovative application in Engineering Applications of Artificial Intelligence.

Jing Wang, Fu Feng, Jianhui Lv, and Xin Geng. “Residual k-Nearest Neighbors Label Distribution Learning.” Pattern Recognition (PR), 2024, in press.

Zhiyun Zhang, Jing Wang†, and Xin Geng. “Label Distribution Learning by Utilizing Common and Label-Specific Feature Fusion Space.” International Journal of Machine Learning and Cybernetics, 2024, in press.

Jing Wang, Zhiqiang Kou, Yuheng Jia, Jianhui Lv, and Xin Geng. “Label Distribution Learning by Exploiting Fuzzy Label Correlation.” IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2024, in press.

Zhiqiang Kou, Jing Wang, Yuheng Jia, and Xin Geng.* “Inaccurate Label Distribution Learning.” IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), 2024, in press.

Jing Wang and Xin Geng. “Explaining the Better Generalization of Label Distribution Learning for Classification.” SCIENCE CHINA Information Sciences (SCIS), 2024, in press.

Conclusion:

The candidate demonstrates a strong research profile with a solid foundation in machine learning, a prolific publication record, and active involvement in the academic community. Their focused research in multi-label learning and explainable AI aligns well with contemporary challenges and advancements in artificial intelligence. To strengthen their candidacy for the Best Researcher Award, they could emphasize the practical impact of their research, seek additional recognitions or awards, and pursue more diverse and international collaborations. Overall, the candidate is highly suitable for the award, with a promising future in their research career.