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As a Project Manager - Solar at Mortenson, I lead the planning, budgeting, and execution of utility, community, and rooftop solar projects, ensuring customer satisfaction, quality control, and safety standards. I have successfully managed multiple projects of moderate to high complexity, ranging from 5 MW to 200 MW, with a total value of over $200 million. I have a strong background in project management , operations and maintenance , and industrial engineering, with certifications in product design, Six Sigma, and OSHA. I leverage my skills and knowledge to optimize the project schedule, scope, and cost, using various tools such as Microsoft Dynamics, Procore, Oracle , Power BI, Smart Sheets, MS Project, and Bluebeam. I also collaborate and communicate effectively with internal and external stakeholders, including vendors, subcontractors, engineers, and clients. I am passionate about learning new technologies and best practices, and applying them to deliver innovative and sustainable solutions. My goal is to contribute to the transition to a clean and green energy future. Chinmay Pingulkar is an accomplished Project Manager and Engineer with extensive experience in solar energy, logistics, and supply chain management. Currently pursuing a PMBA in Logistics and Supply Chain from Campbellsville University, he holds a Master’s in Industrial Engineering from Texas A&M University Kingsville and a Bachelor’s in Mechanical Engineering from the University of Mumbai. His expertise spans project management, engineering, and construction with a strong foundation in Six Sigma, lean manufacturing, and quality control methodologies such as PFMEA, DMAIC, 5S, and Kaizen. He is proficient in various software tools, including Bluebeam, Procore Unifier, Power BI, Microsoft Project, and programming in Oracle and Excel. Chinmay is certified in Six Sigma Green Belt, Advanced Engineering Project Management, OSHA 30-hour Construction Safety, Procore Project Management, and NABCEP Advanced PV Training. His professional journey includes leadership roles in major companies such as M.A. Mortenson, Solar Landscape, CS Energy, and Wanzek Construction, where he has successfully managed large-scale solar projects from initiation to completion. As a Project Manager at M.A. Mortenson, he has led EPC contract negotiations, vendor procurement, budget management, and project execution while ensuring compliance with safety, quality, and regulatory standards. At Solar Landscape, he played a critical role in planning, budgeting, and executing photovoltaic projects, managing design, permitting, subcontracting, material procurement, quality control, and site safety. His tenure at CS Energy as an Assistant Project Manager involved overseeing large-scale solar installations, monitoring material usage, optimizing workflows, managing subcontractors, and ensuring efficient resource allocation. Previously, as a Field Engineer at Wanzek Construction, he was instrumental in managing logistics, material tracking, RFI processing, drone-based site inspections, and construction progress reporting. Chinmay’s early career in India saw him as a Senior Maintenance Engineer at Ratnadeep Metal & Tubes Ltd., where he implemented lean strategies to optimize manufacturing processes, and as a Project Engineer at ION Exchange Skill Capital Limited, where he spearheaded water plant commissioning projects. His technical acumen, problem-solving skills, and leadership abilities have enabled him to drive efficiency, cost reduction, and operational excellence across his roles. Passionate about innovation in renewable energy and sustainable infrastructure, Chinmay has consistently demonstrated his ability to manage complex projects, streamline workflows, and foster collaboration among cross-functional teams. His academic pursuits include applying lean methodologies to real-world scenarios, showcasing his commitment to continuous improvement and professional growth. With a strong track record in project execution, vendor management, safety compliance, and strategic planning, Chinmay Pingulkar is a dynamic leader in the solar energy and engineering industries, dedicated to advancing renewable energy initiatives and optimizing project performance through his expertise in process improvement and technology-driven solutions.

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Skills

Experience

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Project Manager - Solar

Mortenson, Minneapolis

Nov-2023 to Present

Education

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Campbellsville University (CU)

MBA in Logistics, Materials, and Supply Chain Management

Passout Year: 2024
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Texas A&M University-Kingsville (TAMUK)

MS Industrial Engineering in Industrial Engineering

Passout Year: 2019

Publication

Research Article
  • dott image January, 2025

Using PFMEA to Enhance Safety and Reliability in Solar Power Systems

Journal : IJRMEET - International Journal of Research in Modern Engineering & Emerging Technology

In recent years, the growing demand for renewable energy has propelled solar power systems to the forefront as a critical solution to sustainable energy generation. However, the comple...

  • dott image September, 2024

Integrating Drone Technology For Enhanced Solar Site Management

The integration of drone technology into solar site management represents a transformative approach to optimizing operations and enhancing efficiency in the renewable energy sector. Drone...

  • dott image September, 2024

Implementing Lean Principles in Solar Project Management

Implementing Lean principles in solar project management can significantly enhance efficiency, reduce waste, and streamline operations, contributing to the overall success of solar energy...

Research Article
  • dott image June, 2024

Risk Mitigation Strategies for Solar EPC Contracts

The solar energy sector has witnessed significant growth, leading to an increasing number of Engineering, Procurement, and Construction (EPC) contracts. However, the complexities inher...

  • dott image December, 2021

AI AND DATA ANALYTICS FOR PREDICTIVE MAINTENANCE IN SOLAR POWER PLANTS

Predictive maintenance in solar power plants is increasingly recognized as a crucial strategy for enhancing operational efficiency and minimizing downtime. This approach leverages artificia...

  • dott image June, 2021

DEVELOPING EFFECTIVE COMMUNICATION STRATEGIES FOR MULTI TEAM SOLAR PROJECT MANAGEMENT

Effective communication is crucial in managing multi-team solar projects, where diverse stakeholders must collaborate seamlessly to achieve common objectives. This study explores the develo...

Peer-Reviewed Articles

DESIGN AND IMPLEMENTATION OF Wi-Fi DEAUTHENTICATION SYSTEM USING NODEMCU ESP8266

Network security is seriously threatened by Wi-Fi de-authentication attacks, which frequently lead to data interception, illegal access, and service interruption. The mechanics and ramifications of these assaults are explored in detail in this research study, which highlights how they could jeopardize network availability, secrecy, and integrity. To bridge theoretical understanding with actual experimentation, the paper presents a practical implementation of a Wi-Fi deauther utilizing the NodeMCU ESP8266 microcontroller platform. With the use of programs like the Arduino IDE and NodeMCU Flasher, the Wi-Fi deauther was created and put through testing to identify and stop de-authentication threats instantly. The system's high detection accuracy, quick response times, and little effect on network performance as a whole are demonstrated by the experimental findings. The NodeMCU ESP8266 platform demonstrated good resource management by managing the detection and countermeasures while keeping CPU use below 70% and guaranteeing less than 5% reduction in network performance and latency. This study advances wireless network security by demonstrating a scalable, affordable method of thwarting de-authentication attacks and by suggesting further improvements that would include machine learning integration and wider assault coverage. For network managers, cybersecurity experts, and researchers looking to strengthen wireless network defenses, the findings offer insightful information and useful recommendations.

Blockchain using Virtual TRY-ON

In today’s dynamic retail environment, the shift towards online shopping necessitates innovative solutions that enhance customer engagement and satisfaction. This project introduces a virtual try-on clothing platform designed to revolutionize the online shopping experience by merging cutting-edge augmented reality (AR) and machine learning technologies. The platform enables users to visualize how garments will fit and appear on their unique body shapes without the need to visit a physical store. By offering a user-friendly interface, the website allows customers to upload personal images or utilize real-time video features, facilitating an interactive and personalized shopping experience. Key functionalities include accurate size recommendations tailored to individual measurements, as well as curated fashion suggestions that align with users' personal styles. These enhancements aim to minimize return rates—a significant challenge in e-commerce—while simultaneously boosting customer satisfaction and driving sales. Additionally, the platform fosters social interaction through built-in sharing capabilities, allowing users to solicit feedback from friends and family, thus enriching the decision-making process. This aspect not only enhances the shopping experience but also builds a sense of community around fashion choices. By integrating advanced technology with a seamless and engaging user experience, this virtual try-on website represents a substantial advancement in online fashion retail. It sets the stage for a more personalized and interactive shopping journey, ultimately redefining how consumers engage with fashion in the digital age. As we look to the future, this platform aims to become a cornerstone of online retail, reflecting the evolving needs and preferences of today’s consumers.

The Power of AI and Machine Learning in Cybersecurity: Innovations and Challenges

Networks and sensitive data are no longer adequately protected by traditional security methods due to the ongoing evolution and sophistication of cyber-attacks. Cybersecurity can be enhanced with the exploitation of machine learning and artificial intelligence techniques, which make threat detection more effective and efficient. This article, while giving an outline of the field's present position, discusses the difficulties in adapting machine learning and artificial intelligence (ML) to cybersecurity. The research discusses machine learning methods that are applied to tasks like malware classification, anomaly detection, and network intrusion detection. Lastly, the necessity for sizable labeled datasets, the adversarial attacks on machine learning models, and the adversity of deciphering models of black-box ML are some of the boundaries and challenges that are also covered.

Design of 4-bit ALU for low-power and High-speed Applications.

This paper presents a novel design and optimization of a 4-bit Arithmetic Logic Unit (ALU) utilizing 90nm CMOS technology, specifically addressing the longstanding carry-out issue prevalent in existing architectures. Notably, our proposed 4-bit ALU architecture successfully minimizes delay and power consumption by incorporating an optimized carry-out design employing AND gates. A comprehensive comparison of three logic styles - Pass Transistor Logic (PTL), Complementary Metal-Oxide-Semiconductor (CMOS), and Transmission Gate Logic (TGL) - is conducted, yielding significant improvements in power-delay tradeoffs. Simulation results validate the efficacy of our design in resolving the carry-out issue, making it an attractive solution for low-power, high-speed digital applications.

Deep Learning for Polymer Classification: Automating Categorization of Peptides, Plastics, and Oligosaccharides

Polymers represent a diverse and vital class of materials across numerous industries, each with unique structural characteristics and functional properties. Traditional methods of polymer classification rely heavily on labor-intensive techniques prone to subjectivity and human error. The emergence of deep learning has significantly transformed material science by enabling automated analysis and classification of complex polymers. In this study, we focus on leveraging deep learning models to classify three distinct classes of polymers: peptides, plastics, and oligosaccharides. Peptides, plastics, and oligosaccharides represent significant subsets of the polymer family, each with distinct structural features and applications. Our research explores the effectiveness of various deep learning architectures, including deep learning to classify peptides, plastics, and oligosaccharides, achieving perfect accuracy with neural networks, K-Nearest Neighbors, and Random Forest classifiers. Principal Component Analysis enabled visualization of sample distribution, demonstrating deep learning's potential to automate and enhance polymer classification, reducing reliance on traditional, labor-intensive methods.

Detecting Fake Reviews in E-Commerce: A Deep Learning-Based Review

E-commerce platforms are increasingly vulnerable to fake reviews, which can distort product ratings and mislead consumers. Detecting these fraudulent reviews is critical to maintaining trust and transparency in online marketplaces. This review provides a comprehensive analysis of deep learning techniques used for fake review detection. Key models such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models like BERT are explored for their ability to analyze textual data and detect linguistic anomalies. Additionally, behavioral analysis using Convolutional Neural Networks (CNNs) and hybrid models combining textual and behavioral features are discussed. The review also highlights the role of Graph Neural Networks (GNNs) for network analysis and unsupervised learning methods like autoencoders for anomaly detection. Despite advances, challenges such as evolving fake review tactics, data imbalance, and cross-platform adaptability remain. The paper concludes by discussing future research directions, including enhancing model interpretability and combining deep learning with blockchain for more secure and verified review systems.

Harnessing Deep Learning for Precision Cotton Disease Detection: A Comprehensive Review

Cotton cultivation plays a critical role in global agriculture, yet its productivity is significantly hindered by various plant diseases that impact yield and quality. Conventional disease detection methods often fall short due to their reliance on manual inspection and limited accuracy. This comprehensive review explores the application of deep learning techniques beyond Convolutional Neural Networks (CNNs) in enhancing cotton disease detection. The paper covers a range of deep learning methodologies, including CNNs, Recurrent Neural Networks (RNNs), and hybrid models that combine different neural network architectures. It examines how these techniques can improve the precision and efficiency of disease diagnosis for common cotton ailments such as boll rot, leaf spot, cotton wilt, and bacterial blight. By reviewing current research and case studies, the paper provides insights into the effectiveness of various deep learning approaches and their integration into practical agricultural systems. It also addresses the challenges faced in implementing these technologies and suggests future directions for advancing disease management strategies through deep learning. This review aims to offer a holistic perspective on the potential of deep learning to transform cotton disease detection and contribute to more sustainable agricultural practices.

Face Recognition : Diversified

This paper presents a novel lightweight hybrid architecture for face recognition, combining the strengths of MobileNet and attention mechanisms to enhance performance under challenging conditions such as facial occlusions (e.g., masks), varied illumination, and diverse expressions. The proposed model is evaluated against popular baseline models, including MobileNetV2, EfficientNetB2, and VGG16, on the Yale Face Dataset and a Simulated Masked Yale Dataset. On the Yale Dataset, the hybrid model achieved superior results with an accuracy of 93.78%, precision of 94.45%, recall of 93.33%, and F1-score of 93.89%, outperforming the baseline models in all key metrics. Additionally, when tested on the Simulated Masked Yale Dataset, the hybrid model exhibited increased resilience to occlusion with an accuracy of 63.45% and F1-score of 64.22%, significantly surpassing the other architectures.

Blockchain in Cybersecurity: Enhancing Trust and Resilience in the Digital Age

Blockchain technology has emerged as a transformative tool in the field of cybersecurity, offering a decentralized, immutable, and transparent framework to enhance trust and resilience in digital systems. This review explores the various applications of blockchain in cybersecurity, focusing on its ability to mitigate key security challenges such as data tampering, unauthorized access, and identity fraud. By analyzing the integration of blockchain in areas like secure data sharing, IoT security, and identity management, this paper highlights the strengths and limitations of blockchain-based solutions. Furthermore, it examines consensus mechanisms and cryptographic techniques that ensure the integrity and confidentiality of information. Despite its potential, blockchain faces challenges such as scalability, regulatory hurdles, and susceptibility to attacks like 51% and Sybil attacks. This review aims to provide a comprehensive understanding of blockchain's role in enhancing cybersecurity, while also identifying future research directions to overcome current limitations.

Deep Fakes and Deep Learning: An Overview of Generation Techniques and Detection Approaches

The rapid evolution of deep learning has fueled the rise of deep fakes, artificially generated media that can convincingly mimic real human faces, voices, and actions. These fabricated images, videos, and audio clips are created using sophisticated neural networks, posing significant threats to privacy, security, and public trust in digital content. This paper presents a comprehensive review of the key deep learning techniques driving both the creation and detection of deep fakes. On the generation side, methods such as Generative Adversarial Networks (GANs), autoencoders, and Recurrent Neural Networks (RNNs) are examined for their role in producing realistic manipulated media. GANs, particularly, have revolutionized deep fake creation by enabling the development of highly convincing facial expressions and motion sequences. Autoencoders are widely employed for face swapping and video manipulation, while RNNs, including Long Short-Term Memory (LSTM) networks, are critical in voice cloning and generating realistic speech patterns. In response to the escalating concerns over deep fakes, substantial research has focused on detection methodologies. This paper reviews the latest advancements in detection, particularly the use of Convolutional Neural Networks (CNNs) for image and video analysis, as well as hybrid models that combine CNNs with RNNs for more effective detection of spatial and temporal inconsistencies. Moreover, the paper explores emerging strategies such as adversarial training, transfer learning, and blockchain-based solutions that aim to strengthen detection robustness against increasingly sophisticated deep fakes.Finally, the paper addresses the broader ethical and societal challenges posed by deep fakes, including their use in disinformation campaigns, identity theft, and other malicious activities. The need for transparent, interpretable detection models and the importance of interdisciplinary collaboration to mitigate these risks are emphasized. By providing an in-depth analysis of both creation and detection techniques, this review aims to contribute to the development of more secure and reliable digital ecosystems in the face of this growing threat.

Voice Assistant System with Object Detection Technology for Visually Impaired

Navigation, object recognition, obstacle avoidance, and reading provide substantial obstacles for visually impaired people, impeding their independence and day-to-day functioning. Current solutions, including standard voice assistants and white canes, either offer little help or cause privacy issues since they rely too much on the cloud. In order to fully address these issues, we suggest an inventive offering an object detection system and voice assistant powered by Android to assist the blind with problems they face on a daily basis The system incorporates the Arduino Uno, YoloV7 for object detection, and Android for the camera module. The complete apparatus is small and light, making it easy to mount anywhere. The assessments are carried out in controlled settings that replicate situations that a blind person could face in the real world. The suggested device allows visually impaired people to be more accessible, comfortable, and able to navigate more easily than the white cane, according to the results. People with visual impairments sometimes struggle to traverse complicated situations effectively. It is also no easy assignment to assist them in becoming perceptive navigators. In ocular individuals, cognitive maps derived. The system finds potential obstructions in the user's path, calculates the user's trajectory, and provides navigational data. Two experimental scenarios have been used to assess the solution. While the data is currently insufficient to draw firm conclusions, it does show that the technology can effectively assist visually impaired individuals in navigating an unfamiliar built environment.

Quantum-Enhanced Machine Learning for Real-Time Ad Serving

This paper presents a groundbreaking approach to addressing the growing computational challenges in real-time ad serving by leveraging quantum computing to accelerate machine learning (ML) algorithms. We propose a hybrid framework, the Quantum AdServer, which utilizes quantum algorithms alongside classical computing to reduce the time complexity of critical ML tasks in programmatic advertising. We explore both Variational Quantum Circuits (VQC) for near-term implementation on noisy intermediate-scale quantum (NISQ) devices and the Harrow-Hassidim-Lloyd (HHL) algorithm for future scenarios where more advanced quantum hardware is available. Our approach demonstrates significant improvements in both speed and scalability of personalized ad delivery, potentially revolutionizing the field of computational advertising. Through comprehensive theoretical analysis, simulations, and a detailed comparison of quantum methods, we showcase the potential of quantum-enhanced ML in ad tech while discussing practical challenges, including current hardware limitations and integration with existing ad-serving systems.

VIDEO TO VIDEO TRANSLATION USING MBART MODEL

There are many languages are spoken in India due to different diversities and different regions,so it is difficult to understand the global languages such as English ,Spanish ,French ,German. so this paper aims to translation of one of the global language English to their regional languages such as Tamil.So what our project does is it takes the Youtube url as an input in which the video should be in English and then save the video and perform the Machine Learning libraries as gTTS and Whisper model,Mbart50 model etc.Through this we do Audio Extraction,Speech-To-Text-Conversion,Text-Translation,Text-To-Speech-Synthesis. Through this we had Integrating language translation and audio synthesis and break down the the Linguistic barriers.

INTEGRATING ARTIFICIAL INTELLIGENCE INTO CYBERCRIME INVESTIGATION: CHALLENGES AND FUTURE DIRECTIONS

Computer and social networking whereby criminals use the Internet to propagate criminal activities are some of the major challenges faced by existing policing strategies. Modern-day crimes include hacking into computer systems and stealing money from consumers, ransomware, identity theft cases, and hacking, all of which use the dark web and encryption. In this regard, artificial intelligence (AI) is the most efficient solution for improving the manner of cybercrime investigation. This paper also analyses how AI technologies such as machine learning natural language processing, and deep learning can be incorporated in cybercrime investigations and how they can assist in dealing with difficulties concerning data volume, complexity, and encryption. The advantages of utilizing AI are numerous from pattern recognition to repetitive tasks cutting down the investigation time. However, the paper recognizes that applying AI in business brings legal, technical, and ethical concerns including; privacy, bias, and legal constrictions. This research analyses existing legal frameworks of India, the EU, and the United States while looking at how it would be possible to incorporate AI into cybercrime investigations without violating the rights of a citizen. Further, it reveals infringement and possible bias, as well as unlawful use for violations, and recounts drawbacks related to the lack of resources and expertise that police departments confront. In the final section of the paper, directions for future research focusing on the use of AI in the fight against cybercrime are given in addition to that, the practice of cooperation with different countries, legal regulation of such activities, protection of ethical issues, and training of personnel are described. They are useful in making sure that the levels of artificial intelligence benefits are achieved fully without compromising security and the basic rights of an individual.

Advanced Machine Learning Techniques for Water Quality Prediction and Management: A Comprehensive Review

The incorporation of IoT, machine learning, and geospatial technologies has rapidized pace in data-driven approaches in water quality monitoring. The approach calls for data-driven methods in water quality assessment that would ensure such a process makes it not only accurate but cost-efficient and within the constraints of applied growing environmental challenges. The IoT sensors allow for real-time data generation, and the machine learning models, such as support vector machines, neural networks, and regression techniques, have changed the index of water quality prediction and analysis. Applications of GIS provide spatial visualization and management of water resources. This collection of papers constitutes the constraints in classical measuring techniques, advanced solutions through automation technologies based on sensor powers, and hybrid algorithms. However, the integration of these technologies solves the complexities associated with water quality measurement apart from having a basis for the supportive suggestions for sustainable water management to provide actionable insights for decision-makers. Hence, this review underlines the potential of such future integration of IoT, AI, and GIS technologies to revolutionize the monitoring of water quality, ensuring clean water through global environmental changes.

Projects

Dec-2024 to Present

Solar Star 3 and 4

BESS / PV project

Certificates

Issued : Nov 2020
  • dott image By : OSHA Safety Tra...
osha 30 hour construction safety

Membership

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Senior Membeship

IEEE - Institute of Electrical and Electronics Engineers

From year 2024 to Present

Honours & Awards

dott image
Globee Awards Judge for Customer Excellence
Awarded by:

Globee Awards

 Year: 2025

Patent

  • dott image Business Administartion
Flexi Solar Array
Assignee:

WHITES SCIENCE INNOVATION PVT LTD

Filing Country:

United States

Filing Month:

Nov 2024

Application No:

6401882

Patent No:

6401882

Publication status:

Published

Publication Date:

Feb 2025

Inventor(s): Mr. Chinmay Milind Pingulkar

Scholar9 Profile ID

S9-102024-0406200

Publication
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(30)

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Academic Identity

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Authors

Chinmay Pingulkar
Chinmay Pingulkar

Project Manager Sola...

Questions I Asked

Does UGC scrape the USG CARE list for journals? Now what should be the parameters to choose journal for publication? What steps should the Indian academic community take to ensure transparency and credibility in journal? What is transparent peer review, and how does it differ from traditional peer review? What are the key benefits of implementing transparent peer review in academic publishing? How can journals implement a transparent peer review process effectively? What ethical considerations should be addressed in transparent peer review? How can journals encourage peer reviewers to participate in transparent peer review? How does transparent peer review impact research credibility and reproducibility? What challenges do journals face when adopting transparent peer review, and how can they be overcome? Which journals currently use transparent peer review, and what are their policies?