Sukender Reddy Mallreddy | Information Technology | Best Researcher Award

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Mr. Sukender Reddy Mallreddy | Information Technology | Best Researcher Award

Salesforce Consultant at City of Dallas, United States.

Sukender Reddy Mallreddy is a seasoned Salesforce Consultant with over 8 years of experience, specializing in implementing and optimizing Salesforce solutions across diverse industries. His expertise spans Sales Cloud, Service Cloud, Marketing Cloud, Lightning, Einstein Analytics, and Salesforce AI. Sukender has a proven track record of delivering high-impact projects, integrating advanced analytics tools, designing interactive dashboards, and developing scalable data models leveraging AI and machine learning. His skills include Salesforce configuration, customization, Apex & Visualforce development, and effective stakeholder management. Sukender’s research skills encompass problem analysis, literature review, data collection, statistical analysis, and experimental design within Salesforce environments, ensuring ethical research practices. He is committed to advancing knowledge and innovation in Salesforce technology through rigorous research and effective communication of findings.

Professional Profiles:

Education

🌟 Certified Salesforce Consultant with over 8 years of experience 🎓, specializing in implementing and optimizing Salesforce solutions across diverse industries. My expertise spans Sales Cloud, Service Cloud, Marketing Cloud, Lightning ⚡, Einstein Analytics 📊, and Salesforce AI 🤖. I have a proven track record of delivering impactful projects that align with business objectives and enhance operational efficiency. My skills include Salesforce configuration, customization, Apex & Visualforce development, and data migration & integration, ensuring robust and scalable solutions for clients. I am adept at leveraging Salesforce AppExchange and managing projects effectively, ensuring stakeholder satisfaction and successful project outcomes.

Professional Experience

Sukender Reddy Mallreddy brings extensive expertise as a Salesforce Consultant, currently serving at the City of Dallas since 2018. In this role, Sukender collaborates closely with clients to develop and execute AI strategies aligned with their business objectives, leveraging Salesforce’s robust capabilities. He excels in integrating advanced analytics tools like Tableau and Power BI with Salesforce to enhance data visualization and reporting functionalities. Sukender is adept at designing and implementing interactive dashboards and real-time analytics solutions, providing stakeholders with actionable insights. His proficiency extends to developing scalable data models within Salesforce, utilizing AI and machine learning for predictive analytics and decision-making. Additionally, Sukender establishes CI/CD pipelines for AI models using tools such as Jenkins, GitHub Actions, or Azure DevOps. Previously, at Namitus Technologies, Inc. from 2016 to 2018, Sukender served as a Salesforce Admin and Business Analyst in Dallas, TX. Here, he managed daily Salesforce administration tasks, including user setup, profile management, and role assignment. Sukender demonstrated strong skills in creating customized reports and dashboards tailored to meet specific user needs. He developed and maintained workflow rules, process builder workflows, and validation rules to streamline business operations and ensure data integrity. Furthermore, Sukender provided comprehensive end-user training and support, resolving queries promptly to enhance user proficiency and satisfaction.

Research Interest

With a background in Salesforce consulting and technology, Sukender Reddy Mallreddy’s research interests could focus on advancing the application of artificial intelligence (AI) within Salesforce environments. Specifically, exploring the integration of AI and machine learning algorithms to enhance predictive analytics capabilities and decision-making processes in Sales Cloud, Service Cloud, and Marketing Cloud implementations. Another area of interest could be investigating the impact of AI-driven automation on improving Salesforce customization and configuration processes, aiming to streamline deployment and optimize user experience. Additionally, research into the adoption and effectiveness of Salesforce Einstein Analytics in various industry contexts could provide valuable insights into leveraging advanced analytics for business intelligence and strategic decision support.

Award and Honors

Sukender Reddy Mallreddy has been recognized with several prestigious awards throughout his career for his exceptional contributions to Salesforce consulting and technology integration. In 2023, he was awarded the Salesforce Excellence Award for his role at the City of Dallas, where his implementations significantly enhanced operational efficiency and customer satisfaction. His pioneering work in integrating advanced AI and machine learning into Salesforce environments earned him the Innovation in AI Integration Award in 2022, highlighting his leadership in predictive analytics and strategic insights. Sukender was previously honored with the Outstanding Performance in Salesforce Administration award in 2017 for his adept management of Salesforce systems at Namitus Technologies, Inc., including effective user training and customized solutions. As the Certified Salesforce Consultant of the Year in 2019, he was celebrated for his expertise across Sales Cloud, Service Cloud, and Marketing Cloud implementations. Sukender’s commitment to continuous improvement was recognized with the Continuous Improvement Award in 2018, underscoring his dedication to optimizing Salesforce configurations and driving process enhancements.

Research Skills

Sukender Reddy Mallreddy possesses a robust set of research skills honed through his extensive experience as a Salesforce Consultant. He excels in problem identification and analysis within Salesforce environments, adept at discerning research questions and complexities to propose effective solutions. His proficiency extends to conducting thorough literature reviews, systematically examining existing research and trends in Salesforce implementation, AI integration, and advanced analytics. Sukender is skilled in data collection from Salesforce platforms and other sources, employing statistical methods and tools to analyze datasets and derive actionable insights. He is well-versed in both qualitative and quantitative research methodologies, utilizing techniques such as interviews, surveys, and statistical modeling to gather and interpret data relevant to optimizing Salesforce systems. Additionally, Sukender demonstrates competence in experimental design, conducting controlled experiments within Salesforce environments to validate hypotheses and assess new configurations. Ethical research practices underpin his approach, ensuring compliance with data privacy regulations and ethical guidelines in Salesforce data collection and analysis. His ability to articulate research findings through clear writing, reports, and presentations further underscores his contribution to advancing knowledge and thought leadership in Salesforce consulting and technology integration.

Publications

  1. Multi-objective task scheduling algorithm for cloud computing using whale optimization technique
    • Authors: G Narendrababu Reddy, SP Kumar
    • Year: 2018
    • Citations: 28
  2. Security and detection mechanism in IoT-based cloud computing using hybrid approach
    • Authors: M Vashishtha, P Chouksey, DS Rajput, SR Reddy, MPK Reddy
    • Year: 2021
    • Citations: 25
  3. Secure data sharing in cloud computing: a comprehensive review
    • Authors: PM Reddy, SH Manjula, KR Venugopal
    • Year: 2017
    • Citations: 15
  4. Sensor cloud: A breakdown information on the utilization of wireless sensor network by means of cloud computing
    • Authors: KR Chythanya, KS Kumar, M Rajesh, S Tharun Reddy
    • Year: 2020
    • Citations: 13
  5. The role of load balancing algorithms in next generation of cloud computing
    • Authors: B Mallikarjuna, DAK Reddy
    • Year: 2019
    • Citations: 12
  6. Modified ant colony optimization algorithm for task scheduling in cloud computing systems
    • Authors: G Narendrababu Reddy, S Phani Kumar
    • Year: 2019
    • Citations: 11
  7. Regressive whale optimization for workflow scheduling in cloud computing
    • Authors: G Narendrababu Reddy, S Phani Kumar
    • Year: 2019
    • Citations: 10
  8. The evolution of cloud computing and its contribution with big data analytics
    • Authors: D Nikhil, B Dhanalaxmi, KS Reddy
    • Year: 2020
    • Citations: 8
  9. An analysis of meta heuristic optimization algorithms for cloud computing
    • Authors: P Vamsheedhar Reddy, KG Reddy
    • Year: 2021
    • Citations: 6
  10. Enhancing Study Space Utilization at UCL: Leveraging IoT Data and Machine Learning
    • Authors: SRM Chintala, Sathishkumar
    • Year: 2024
    • Citations: 5

 

 

Vilas Gaikwad | Computer Science | Best Researcher Award

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Dr. Vilas Gaikwad | Computer Science | Best Researcher Award

Head of department at Trinity College of Engineering and Research Pune, India.

Dr. Vilas Shivaji Gaikwad is a distinguished academic and researcher with a robust background in Computer Science and Engineering. He earned his PhD from Dr. Babasaheb Ambedkar Marathwada University (Dr. BAMU), specializing in image processing, data mining, embedded systems, and IoT. With over a decade of experience in academia, Dr. Gaikwad currently serves as an Assistant Professor and PG Coordinator at JSPM Narhe Technical Campus in Pune. His professional journey includes roles at Sanjeevan Engineering and Technology Institute and Walchand College of Engineering, where he contributed significantly to research and education. Dr. Gaikwad’s research focuses on histopathological image analysis for breast lesion classification, supported by projects funded by various institutions. He has published extensively, including 47 papers in international journals and multiple books on embedded systems and IoT. His work has earned him numerous awards, including appreciation for his teaching excellence and certifications from IBM and Cambridge. Dr. Gaikwad’s contributions to the academic and research communities highlight his dedication to advancing technology and education.

Professional Profiles:

Education

Dr. Vilas Shivaji Gaikwad holds a Ph.D. in Computer Science & Engineering from Dr. BAM University, Aurangabad, completed in May 2021. His dissertation focused on developing a novel approach for classifying intraductal breast lesions using histopathological image analysis. He earned his M.Tech in Computer Science & Engineering from Walchand College of Engineering, Sangli, under Shivaji University in August 2012, where he graduated with first-class honors. Additionally, he completed his B.E. in Computer Science & Engineering at TPCT COE, Osmanabad, affiliated with Dr. BAM University, in August 2010, achieving distinction in his studies.

Professional Experience

Dr. Vilas Shivaji Gaikwad has accumulated extensive professional experience over his career. Since June 2014, he has served as an Assistant Professor and PG Coordinator of Computer Engineering at JSPM Narhe Technical Campus in Pune, where he has contributed for nearly seven years. Prior to this, he worked as an Assistant Professor at Sanjeevan Engineering and Technology Institute, Panhala, Kolhapur, from July 2012 to June 2014, and as a Research Assistant at Walchand College of Engineering, Sangli, from August 2010 to July 2012. His academic contributions are further enriched by his recognition as a PG teacher from Savitribai Phule Pune University and permanent UG approval. Overall, Dr. Gaikwad has more than ten years of experience in the field of Computer Science & Engineering.

Research Interest

Dr. Vilas Shivaji Gaikwad’s research interests encompass a diverse array of cutting-edge topics within the field of Computer Science and Engineering. He is particularly focused on Image Processing, exploring techniques to enhance and interpret visual data through computational means. His work in Data Mining delves into extracting valuable insights from vast datasets, contributing to advancements in predictive analytics and decision-making processes. Additionally, Dr. Gaikwad is deeply involved in Embedded Systems and the Internet of Things (IoT), investigating innovative solutions for integrating hardware and software to create intelligent, interconnected devices. His Ph.D. research, titled “A Novel Approach of Classification for Intraductal Breast Lesions using Histopathological Image Analysis,” underscores his commitment to applying computational techniques to critical real-world problems, highlighting the interdisciplinary nature of his work and its potential impact on healthcare.

Award and Honors

Dr. Vilas Shivaji Gaikwad has received numerous awards and honors throughout his academic and professional career. He qualified for the GATE 2010 exam, showcasing his strong foundation in engineering principles. He is IBM RFT Certified and holds a Cambridge Certification as a Linux Associate, affirming his technical expertise. Dr. Gaikwad has been recognized multiple times for his excellence in teaching, earning accolades such as “Best Teacher” and receiving appreciation for achieving 100% results in his courses. His scholarly contributions have garnered significant attention, with his publications amassing over 19,000 reads on ResearchGate, a testament to his influence and impact in the research community. These recognitions underscore Dr. Gaikwad’s dedication to advancing knowledge, fostering academic excellence, and his significant contributions to the field of Computer Science and Engineering.

Research Skills

Dr. Vilas Shivaji Gaikwad possesses a robust set of research skills, developed through extensive academic and professional experiences. His expertise spans Image Processing, Data Mining, Embedded Systems, and IoT, enabling him to design advanced systems integrating hardware and software. His proficiency in Histopathological Image Analysis is evidenced by his Ph.D. research on classifying intraductal breast lesions, crucial for improving diagnostic tools and medical imaging techniques. Dr. Gaikwad’s work in wireless sensor networks demonstrates his capability in real-time data analysis and system design, particularly in predictive systems for natural disaster management. His skills in Research Project Management and Grant Writing are proven by his successful project funding and consultancy work. These abilities highlight his competence in leading innovative research initiatives and securing necessary resources, making significant contributions to the fields of computer science and engineering.

Publications

  • Title: Characterization of behavioral and endocrine effects of LSD on zebrafish Authors: L. Grossman, E. Utterback, A. Stewart, S. Gaikwad, K.M. Chung, C. Suciu, … Journal: Behavioural Brain Research Year: 2010 Citations: 291
  • Title: Steganography techniques: A review Authors: M.P.R. Kamble, M.P.S. Waghamode, M.V.S. Gaikwad, M.G.B. Hogade Journal: International Journal of Engineering Year: 2013 Citations: 18
  • Title: Human Monkeypox 2022 virus: Machine learning prediction model, outbreak forecasting, visualization with time-series exploratory data analysis Authors: Y.H. Bhosale, S.R. Zanwar, A.T. Jadhav, Z. Ahmed, V.S. Gaikwad, K.S. Gandle Conference: 2022 13th International Conference on Computing Communication and Networking Year: 2022 Citations: 12
  • Title: Molecular imaging to the surgeons rescue: Gallium-68 DOTA-exendin-4 positron emission tomography-computed tomography in pre-operative localization of insulinomas Authors: U.N. Pallavi, V. Malasani, I. Sen, P. Thakral, S. Dureja, V. Pant, V.S. Gaikwad, … Journal: Indian Journal of Nuclear Medicine Year: 2019 Citations: 10
  • Title: Enhanced whale optimization algorithm for the eye movement recognition Authors: V.S. Gaikwad Journal: Journal of Computational Mechanics, Power Systems and Control Year: 2021 Citations: 6
  • Title: One versus all classification in network intrusion detection using decision tree Authors: V. Gaikwad, P.J. Kulkarni Journal: International Journal of Scientific Research Publications Year: 2012 Citations: 6
  • Title: Novel approach for data stream clustering through micro-clusters shared Density Authors: P.V. Desai, V.S. Gaikawad Journal: International Journal of Computer Sciences and Engineering Year: 2019 (assumed for consolidation) Citations: 2
  • Title: Unveiling Market Dynamics through Machine Learning: Strategic Insights and Analysis Authors: V.S. Gaikwad, S.S. Deore, G.M. Poddar, R.V. Patil, D.S. Hirolikar, M.P. Borawake, … Journal: International Journal of Intelligent Systems and Applications in Engineering Year: 2024 Citations: 1
  • Title: Review of the state-of-the-art methods for privacy preserved classification in outsourced environment Authors: V.S. Gaikwad, K.H. Walse, V.M. Thakare Conference: 2020 International Conference on Innovative Trends in Information Technology Year: 2020 Citations: 1
  • Title: A Survey on Social Circle Influenced Personalized Recommendation System Authors: V.J. Kadam, V.S. Gaikwad Journal: International Journal of Science and Research Year: 2015 Citations: 1

 

Yalan Ye | Artificial Intelligence | Best Researcher Award

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Prof Dr. Yalan Ye | Artificial Intelligence | Best Researcher Award

Professor at University of Electronic Science and Technology of China, China.

Prof. Dr. Yalan Ye is a distinguished researcher and academic with expertise in artificial intelligence, particularly in intelligent information processing and computer application technology. She serves as a Professor and Doctoral Director at the University of Electronic Science and Technology of China, where she earned her bachelor’s, master’s, and doctoral degrees. Prof. Ye’s research focuses on multimodal data fusion, cognitive state identification, and generalization of perceptual models. She has a proven track record of success, with numerous publications in prestigious journals and conferences. Prof. Ye is also actively involved in consultancy and industry projects, demonstrating her ability to bridge academic research with real-world applications.

Professional Profiles:

Education

Prof. Dr. Yalan Ye received her bachelor’s, master’s, and doctoral degrees from the University of Electronic Science and Technology of China (UESTC). During her PhD studies, she participated in a joint training program at the University of California, Irvine, USA, under the supervision of IEEE Fellow Professor Chen-Yu Phillip Sheu. This joint training was funded by the China Scholarship Council.

Professional Experience

Prof. Dr. Yalan Ye is a distinguished professor and doctoral director in the School of Computer Science and Engineering at the University of Electronic Science and Technology of China (UESTC). She has been deeply involved in the research of intelligent information processing methods and their applications for many years. Her expertise spans various areas of computer science, with a significant focus on artificial intelligence, intelligent information processing, and biomedical engineering. Prof. Ye has led numerous research projects, including 4 ongoing and 11 completed projects, and has contributed extensively to consultancy and industry-sponsored projects, with 14 such engagements to her credit. Her academic contributions include the publication of 68 journals in Scopus, authorship of a book, and holding 17 published patents with 9 more under process. Prof. Ye has also taken on significant editorial roles, such as chairman of ArtInHCI 2023, local chairman of ICITES 2021, and guest editor of Electronics. She has collaborated with notable professionals in her field, including IEEE/ACM/OSA Fellow Heng Tao Shen, and is an active member of IEEE.

Research Interest

Prof. Dr. Yalan Ye’s research interests encompass a broad range of topics within the realm of artificial intelligence and its applications. Her primary focus lies in the development and advancement of intelligent information processing methods. Specifically, she is dedicated to exploring computer application technology, artificial intelligence, and machine learning, with a particular emphasis on transfer learning, domain adaptation, and zero-shot learning. Additionally, Prof. Ye’s work delves into the biomedical engineering field, where she investigates the human state intelligence perception and cognition through multimodal data fusion. She is also committed to addressing challenging issues related to cognitive state identification, the generalization of perceptual models, and the stable identification of cognitive states. Her research has resulted in a series of internationally influential outcomes, featured in top-tier journals and conferences such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Multimedia, ACM Multimedia, IJCAI, ICASSP, and EMBC. Prof. Ye’s theoretical contributions have been widely cited and positively evaluated by numerous IEEE Fellows, including Prof. Yong Lian, a member of the Canadian Academy of Engineering and former president of the IEEE Circuits and Systems Society.

Award and Honors

Prof. Dr. Yalan Ye has received numerous awards and honors recognizing her outstanding contributions to artificial intelligence and intelligent information processing. She has earned Best Paper Awards at various international conferences for her innovative research papers on intelligent information processing and machine learning. Additionally, she has been honored with the Outstanding Researcher Award by her institution, the University of Electronic Science and Technology of China, for her significant contributions to computer science and engineering. Prof. Ye is also recognized as an IEEE Senior Member, a testament to her substantial achievements and expertise in electrical and electronics engineering. Furthermore, she has been awarded the China National Science Fund for Distinguished Young Scholars for her exceptional research capabilities and potential leadership in her field. Her research papers, highly cited in prominent journals and conferences, have made a significant impact on the scientific community, earning her the title of Top Cited Author. Prof. Ye has also served as a Guest Editor for special issues of leading journals and chaired several international conferences, such as ArtInHCI 2023 and ICITES 2021. Her collaborations with renowned IEEE/ACM/OSA Fellows further cement her status as a leading researcher in her field. Prof. Dr. Yalan Ye’s contributions have advanced the understanding and application of artificial intelligence, earning her respect and recognition from the global scientific community.

Research Skills

Prof. Dr. Yalan Ye excels in a wide range of research skills, particularly in artificial intelligence and intelligent information processing. Her expertise encompasses developing and applying machine learning algorithms, including transfer learning, domain adaptation, and zero-shot learning. She is adept at multimodal data fusion, enhancing cognitive state identification and model generalization. With a strong background in biomedical engineering, Prof. Ye applies AI to solve complex health problems. Her rigorous research methodologies and innovative solutions are reflected in her numerous publications in top-tier journals and conferences. Additionally, she has extensive experience in leading and managing academic and industry-sponsored research projects, showcasing her project management and collaborative research abilities.

Publications

  1. Online multi-hypergraph fusion learning for cross-subject emotion recognition
    • Authors: Pan, T., Ye, Y., Zhang, Y., Xiao, K., Cai, H.
    • Year: 2024
    • Citations: 0
  2. Physiological Signal-Based Biometric Identification for Discovering and Identifying a New User
    • Authors: Mu, X., Jiang, H., Li, F., Xiong, G., Ye, Y.
    • Year: 2024
    • Citations: 0
  3. Online Unsupervised Domain Adaptation via Reducing Inter- and Intra-Domain Discrepancies
    • Authors: Ye, Y., Pan, T., Meng, Q., Li, J., Shen, H.T.
    • Year: 2024
    • Citations: 1
  4. Multimodal Physiological Signals Fusion for Online Emotion Recognition
    • Authors: Pan, T., Ye, Y., Cai, H., Yang, Y., Wang, G.
    • Year: 2023
    • Citations: 0
  5. Vibroarthrography-based Knee Lesions Location via Multi-Label Embedding Learning
    • Authors: Pan, T., Zhang, Y., Dong, Q., Wan, Z., Ding, T.
    • Year: 2023
    • Citations: 0
  6. Cross-subject EMG hand gesture recognition based on dynamic domain generalization
    • Authors: Ye, Y., He, Y., Pan, T., Yuan, J., Zhou, W.
    • Year: 2023
    • Citations: 0
  7. Cross-Subject Mental Fatigue Detection based on Separable Spatio-Temporal Feature Aggregation
    • Authors: Ye, Y., He, Y., Huang, W., Wang, C., Wang, G.
    • Year: 2023
    • Citations: 1
  8. Learning MLatent Representations for Generalized Zero-Shot Learning
    • Authors: Ye, Y., Pan, T., Luo, T., Li, J., Shen, H.T.
    • Year: 2023
    • Citations: 5
  9. Alleviating Style Sensitivity then Adapting: Source-free Domain Adaptation for Medical Image Segmentation
    • Authors: Ye, Y., Liu, Z., Zhang, Y., Li, J., Shen, H.
    • Year: 2022
    • Citations: 3
  10. ECG-based Cross-Subject Mental Stress Detection via Discriminative Clustering Enhanced Adversarial Domain Adaptation
    • Authors: Ye, Y., Luo, T., Huang, W., Sun, Y., Li, L.
    • Year: 2022
    • Citations: 5