Shishir Tewari | Big Data Analytics | Outstanding Contribution Award

Mr. Shishir Tewari | Big Data Analytics | Outstanding Contribution Award

Mr. Shishir Tewari | Big Data Analytics – Procore Technologies | United States

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πŸ‘¨β€πŸ’Ό Professional Summary

Shishir Tewari is a highly accomplished Senior Engineering Manager with over 19 years of experience in software development, data engineering, and business intelligence. He has a proven track record of building and leading high-performing teams to deliver scalable, cloud-native data platforms that power enterprise decision-making. Shishir is known for his deep expertise in data warehousing, real-time analytics, and AI/ML-driven data solutions, consistently aligning technical strategy with business goals to drive innovation and operational excellence.

🏒 Work Experience

At Procore Technologies, Shishir leads the design and implementation of next-generation Master Data Management (MDM) and Marketing Analytics platforms, integrating AI/ML technologies to ensure data accuracy and accessibility. Previously at Google, he managed the Finance Data Universe on GCP, leading a global team to build data pipelines that processed over 100 petabytes of data, optimizing financial reporting and revenue forecasting. At Amazon, he engineered high-scale advertising data infrastructure, reducing costs and improving system performance significantly. His experience also spans financial giants like Morgan Stanley and JP Morgan Chase, where he built robust data ecosystems for compliance and regulatory reporting.

☁️ Technical Expertise

Shishir brings strong technical acumen across modern data stacks. He is proficient in cloud platforms like AWS (S3, Redshift, Lambda) and GCP (BigQuery, Composer, IAM), and skilled in big data technologies such as Spark, Hive, HDFS, and Databricks. He is also experienced in ETL tools like Informatica, SSIS, and BI platforms like Tableau and SAP BO. His programming knowledge includes Python, SQL, and C#, and he regularly applies data science libraries such as Pandas, Scikit-learn, and TensorFlow to solve complex business challenges.

πŸ” Special Projects & Achievements

Shishir led the migration from Snowflake to Databricks at Procore, boosting performance and enabling real-time data capabilities. At Amazon, his cost optimization strategies reduced monthly data storage costs by 80%. He also developed financial and statistical models for liquidity stress testing at Morgan Stanley using Python, and optimized advertising pipelines that supported over $100M in deals.

πŸŽ“ Education

Shishir holds a specialization in Data Science and Analytics from Rutgers University, NJ, completed in 2019. He earned his Bachelor of Technology in Information Technology from UPTU, India in 2006 with a strong academic record.

🀝 Leadership & Collaboration

As a passionate leader, Shishir emphasizes team empowerment, cross-functional collaboration, and mentorship. He has successfully managed and scaled engineering teams across geographies, fostering a culture of continuous improvement and agile delivery. His collaborative approach ensures alignment between technical solutions and strategic business objectives.

πŸ› οΈ Core Competencies

His key skills include Data Architecture, Cloud Data Platforms, ETL Development, Machine Learning Integration, Technical Strategy, and Agile Methodologies. Shishir is also well-versed in data governance, compliance, and process automation, which makes him a well-rounded engineering leader.

πŸ“š Selected Publications

Title: AI Powered Data Governance – Ensuring Data Quality and Compliance in the Era of Big Data
    • Authors: S. Tewari

    • Journal: Journal of Artificial Intelligence General Science (JAIGS), ISSN: 3006-4023

    • Year: 2025

Title: Operationalizing Explainable AI in Business Intelligence: A Blueprint for Transparent Enterprise Analytics
    • Authors: A. Chitnis, S. Tewari

    • Journal: Iconic Research and Engineering Journals

    • Year: 2024

Title: AI and Multi-Cloud Compliance: Safeguarding Data Sovereignty
    • Authors: S. Tewari, A. Chitnis

    • Journal: Iconic Research and Engineering Journals

    • Year: 2024

Title: Scalable Metadata Management in Data Lakes Using Machine Learning
    • Authors: S. Tewari

    • Journal: Iconic Research and Engineering Journals

    • Year: 2023

Title: AI-Powered Financial Forecasting: Enhancing Accuracy with Machine Learning in Enterprise System
    • Authors: S. Tewari

    • Journal: Journal of Emerging Technologies and Innovative Research

    • Year: 2023

Abeer abdelhalim | Big Data Analytics | Best Researcher Award

Prof. Abeer abdelhalim | Big Data Analytics | Best Researcher Award

Prof. Abeer abdelhalim | Big Data Analytics – king faisal university | Saudi Arabia

Abeer M. M. Abdelhalim is a Full Professor of Accounting at King Faisal University, KSA, specializing in managerial accounting with over 30 years of academic and professional experience. He has contributed significantly to the field with numerous publications in peer-reviewed journals and has secured prestigious research grants. Prof. Abdelhalim is highly skilled in project management, scientific research, and organizing academic events. He has worked in various academic and leadership roles across multiple institutions, and his research focuses on management accounting, cost accounting, and financial disclosure. A dedicated educator, he fosters a stimulating learning environment for students and has led various training programs in accounting. His passion for continuous self-learning and development keeps him at the forefront of modern educational practices and technologies.

Profile

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Education

Prof. Abeer M. M. Abdelhalim earned his Bachelor’s in Accounting in 1995 from the Faculty of Commerce, Suez Canal University, Egypt. He then completed his Master’s in Cost & Managerial Accounting in 2000, followed by a Ph.D. in Managerial Accounting in 2006 from the same institution. His academic background laid the foundation for his distinguished career in both teaching and research. Prof. Abdelhalim’s education has empowered him to contribute significantly to the field of accounting, particularly in managerial and cost accounting, through various publications and applied research. His educational journey continues to inspire his teaching and consultancy work today.

Professional Experience

Prof. Abdelhalim has held several key positions throughout his career. Currently, he is a Full Professor of Accounting at King Faisal University, KSA, where he has also served as an Associate Professor and Assistant Professor. Prior to this, he worked at Imam University and Suez Canal University, where he taught various accounting and financial subjects. He has designed and implemented numerous accounting systems and led educational development projects. Additionally, Prof. Abdelhalim has extensive experience in training and consultancy, having conducted numerous workshops and courses in financial and managerial accounting. His leadership roles include heading committees in strategic planning, accreditation, and student affairs.

Awards and Recognition

Prof. Abeer M. M. Abdelhalim has earned several prestigious awards throughout his career. Notably, he won the First Place in Economic Studies from Prince Rashid bin Humid for Culture and Sciences in 2018. He also received the Distinguished Publication Award from the Scholarly Council at King Faisal University in 2023 for his significant contributions to research. His work in academia and research has been widely recognized, making him a leading figure in his field. His dedication to excellence in teaching and research continues to earn him accolades and recognition from both academic institutions and professional organizations.

Research Skills

Prof. Abdelhalim possesses advanced skills in scientific research, particularly in the fields of managerial and cost accounting. He has authored numerous articles in high-impact journals and has a strong track record of securing research grants. His research interests include financial reporting, digital transformation in accounting, big data analytics, and corporate sustainability. Prof. Abdelhalim is highly skilled in conducting applied research that addresses practical challenges in the accounting profession, particularly in relation to modern technologies such as blockchain and artificial intelligence. He is also experienced in leading research teams and contributing to global academic conferences.

Publications

From the Internet of Things to the Internet of Ideas: The Role of Artificial Intelligence
πŸ‘€ Abeer Abdelhalim | πŸ“– Springer International Publishing | 🌍 2023 | DOI: 10.1007/978-3-031-17746-0 | ISBN: 9783031177453, 9783031177460 | ISSN: 2367-3370, 2367-3389 πŸŒŸπŸŽ“

The Moderating Role of Digital Environmental Management Accounting in the Relationship between Eco-Efficiency and Corporate Sustainability
πŸ‘€ Abeer Abdelhalim | πŸ“– Sustainability | 🌍 2023 | April 23 | πŸŒŸπŸŽ“πŸ’Ό

The Influence of Audit Committee Chair Characteristics on Financial Reporting Quality
πŸ‘€ Abeer Abdelhalim, Abdalwali Lutfi, Saleh Zaid Alkilani, Mohamed Saad, Malek Hamed Alshirah, Ahmad Farhan Alshirah, Mahmaod Alrawad, Malak Akif Al-Khasawneh, Nahla Ibrahim | πŸ“– Journal of Risk and Financial Management | 🌍 2022 | November 29 | DOI: 10.3390/jrfm15120563 πŸŒŸπŸŽ“πŸ’Ό

The Relationship between Risk Disclosure and Firm Performance: Empirical Evidence from Saudi Arabia
πŸ‘€ Abeer Abdelhalim | πŸ“– The Journal of Asian Finance, Economics and Business | 🌍 2021 | June 30 | DOI: 10.13106/JAFEB.2021.VOL8.NO6.0255 πŸŒŸπŸŽ“πŸ’Ό

Naiwei Lu | Data Analysis | Best Researcher Award

Assoc Prof Dr. Naiwei Lu | Data Analysis | Best Researcher Award

Changsha University of Science of Technology, China

Author Profiles

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πŸ‘¨β€πŸ« Associate Prof. Dr. Naiwei Lu

Assoc. Prof. Dr. Naiwei Lu is an Associate Professor in Civil Engineering at Changsha University of Science and Technology (CSUST), China. His research primarily focuses on reliability and safety in bridge engineering. He has a strong background in system reliability evaluation, fatigue analysis, and structural health monitoring, which he applies to the engineering and maintenance of long-span bridges. Dr. Lu is an active member of various international research communities and has been involved in multiple national and international projects.

πŸŽ“ Education Background

Dr. Naiwei Lu earned his Ph.D. in Civil Engineering from Changsha University of Science and Technology (CSUST) in 2015, under the supervision of Prof. Yang Liu. Prior to that, he completed his Master’s degree in Civil Engineering in 2011 and his Bachelor’s degree in Bridge Engineering in 2008, all at CSUST, Changsha, China. His academic journey laid a strong foundation for his expertise in bridge engineering and reliability analysis.

πŸ’Ό Employment and Research Experience

Dr. Lu’s academic career began in 2015 when he became a Postdoctoral Researcher at Southeast University in Nanjing, China, where he worked under Prof. Mohammad Noori. He also held a Postdoctoral Researcher position at Leibniz University Hannover, Germany, working with Prof. Michael Beer. Since 2020, Dr. Lu has been serving as an Associate Professor at CSUST, where he also previously worked as a Lecturer from 2017 to 2019. His diverse academic and international experiences contribute to his deep expertise in bridge engineering.

πŸ’° Research Funding

Dr. Lu has been the Principal Investigator (PI) of several funded research projects, including the National Science Funding in China for research on structural health monitoring of suspension bridges and the fatigue reliability evaluation of steel bridge decks. He has received significant funding support for his work in system reliability and dynamic analysis, with total funding exceeding Β₯1 million for his various research initiatives. These projects aim to develop advanced methods for assessing the reliability and safety of long-span bridges.

πŸ” Research Interests

Dr. Lu’s research spans several cutting-edge topics in structural engineering. His main research interests include system reliability evaluation of long-span bridges using intelligent algorithms πŸ€–, fatigue reliability of stay cables and orthotropic steel bridge decks πŸŒ‰, and probabilistic modeling using data from structural health monitoring πŸ“Š. He is also focused on intelligent maintenance and management of in-service bridges βš™οΈ, as well as uncertainty quantification in structural engineering πŸ› οΈ.

πŸ“š Teaching Experience

As an educator, Dr. Lu teaches undergraduate courses such as Principle of Structural Design (in both Chinese and English) and Building Information Modeling (BIM) Technology (in Chinese). He has supervised 24 undergraduate students and 4 postgraduate students between 2017 and 2020, guiding them in various aspects of civil engineering, particularly related to bridge design, maintenance, and reliability.

πŸ—οΈ Engineering Activities

In addition to his academic role, Dr. Lu is a Registered Construction Engineer and a Registered Inspection Engineer in Municipal Engineering and Bridge & Tunnel Engineering, respectively. His engineering activities include overseeing the structural safety of long-span bridges during construction πŸ—οΈ, conducting structural health monitoring of suspension bridges πŸŒ‰, and contributing to the detection and reinforcement of aging bridges πŸ› οΈ. He has also been involved in the advance geological forecasting and monitoring of tunnels during construction, working on over five extra-long tunnels.

Dr. Naiwei Lu’s work integrates advanced intelligent algorithms, data-driven methods, and structural health monitoring to enhance the safety, reliability, and longevity of bridges and infrastructure systems. His research and engineering expertise continue to make significant contributions to the field of civil engineering.

πŸ“–Top Noted PublicationsΒ 

Structural damage diagnosis of a cable-stayed bridge based on VGG-19 networks and Markov transition field: numerical and experimental study

Authors: Naiwei Lu, Zengyifan Liu, Jian Cui, Lian Hu, Xiangyuan Xiao, Yiru Liu

Journal: Smart Materials and Structures

Year: 2025

Coupling effect of cracks and pore defects on fatigue performance of U-rib welds

Authors: Yuan Luo, Xiaofan Liu, Fanghuai Chen, Haiping Zhang, Xinhui Xiao, Naiwei Lu

Journal: Structures

Year: 2025

A Time–Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen

Authors: Naiwei Lu, Yiru Liu, Jian Cui, Xiangyuan Xiao, Yuan Luo, Mohammad Noori

Journal: Sensors

Year: 2024

A Novel Method of Bridge Deflection Prediction Using Probabilistic Deep Learning and Measured Data

Authors: Xinhui Xiao, Zepeng Wang, Haiping Zhang, Yuan Luo, Fanghuai Chen, Yang Deng, Naiwei Lu, Ying Chen

Journal: Sensors

Year: 2024

Experimental and numerical investigation on penetrating cracks growth of rib-to-deck welded connections in orthotropic steel bridge decks

Authors: Zitong Wang, Jun He, Naiwei Lu, Yang Liu, Xinfeng Yin, Haohui Xin

Journal: Structures

Year: 2024

Fatigue Reliability Assessment for Orthotropic Steel Decks: Considering Multicrack Coupling Effects

Authors: Jing Liu, Yang Liu, Guodong Wang, Naiwei Lu, Jian Cui, Honghao Wang

Journal: Metals

Year: 2024