Deep-AI
I'm Deep's AI assistant. Feel free to ask me about his work and background. I'm here to help with any questions you have.
I'm Deep's AI assistant. Feel free to ask me about his work and background. I'm here to help with any questions you have.
Jan 2024 – Present (GPA: 3.83/4)
Aug 2021 – Dec 2023 (GPA: 3.82/4)
Aug 2017 – May 2021 (GPA: 8.11/10)
Python
MATLAB
C++
TensorFlow
PyTorch
NumPy
Matplotlib
Pandas
Scikit-Learn
VS Code
Simulink
Git
LaTeX
Jira
Confluence
MS Office
Arduino IDEThis work in progress research-to-practice study describes the development of a new undergraduate research training site on Quantum Machine Learning (QML), hosted at Arizona State University, a large Hispanic-Serving Institution. The objectives of this project are to a) recruit and prepare students from diverse pathways to increase representation of those traditionally underrepresented in QML research, b) increase awareness of career opportunities in the QML field, c) engage students in theoretical and experimental quantum information processing and machine learning (ML), d) motivate students to continue QML research into graduate school, and e) provide professional development training including presenting to stakeholders, developing publications/patents, and building an awareness on social implications, ethics, and privacy. The project adopts an integrative theory, application, and hands-on training approach by immersing undergraduate students in ML algorithm and quantum computing studies with hands-on quantum circuit design tasks. Participants are embedded in research labs, guided by graduate students and faculty mentors on quantum computing research studies. The program is evaluated by both the Center for Evaluating the Research Pipeline (CERP) and an independent evaluator. Formative and summative assessments include pre- and post-surveys, a mid-point check-in survey, and a document review of program deliverables. Findings are described in a final evaluation report. This paper describes the importance of introducing QML research at the undergraduate level, methods for recruiting a diverse group of participants, program format, research projects, and preliminary program evaluation results.
Utility-scale solar arrays require specialized inspection methods for detecting faulty panels. Photovoltaic (PV) panel faults caused by weather, ground leakage, circuit issues, temperature, environment, age, and other damage can take many forms but often symptomatically exhibit temperature differences. Included is a mini survey to review these common faults and PV array fault detection approaches. Among these, infrared thermography cameras are a powerful tool for improving solar panel inspection in the field. These can be combined with other technologies, including image processing and machine learning. This position paper examines several computer vision algorithms that automate thermal anomaly detection in infrared imagery. We demonstrate our infrared thermography data collection approach, the PV thermal imagery benchmark dataset, and the measured performance of image processing transformations, including the Hough Transform for PV segmentation. The results of this implementation are presented with a discussion of future work.
Real-time monitoring and control of individual solar panels in a photovoltaic (PV) array can improve efficiency and reduce the probability of hazardous situations. This paper proposes the design and implementation of a real-time Intelligent Monitoring and Control Device (IMCD) to measure PV parameters such as temperature, voltage, current, and irradiance from individual solar panels. The proposed IMCD uses an embedded machine-learning (ML) algorithm for detecting and classifying four key conditions of the solar panel: partial shading, soiling, extreme soiling, and the standard test condition (STC). IMCDs connect with solar panels and with a control center using an integrated data transceiver. The hardware consists of an Arduino UNO transmitter that is used for data collection, an Arduino Nano BLE 33 Sense receiver for PV measurement, and an integrated processor for real-time fault detection using embedded ML. The training of the embedded ML models and their real-world testing and validation are presented in this paper. Comparisons with cloud-based ML algorithms and the use of bagging ensemble techniques to increase fault detection accuracy are also discussed in this paper.
This Innovative Practice Work in Progress Paper describes the development and assessment of a web-based simulation lab exercise introducing basic quantum computing concepts in a sophomore signals and systems course. Specifically, students make the connection between quantum computing and signals and systems theory through a comparative study of using Quantum Fourier Transforms and Fast Fourier Transforms for a speech analysis-synthesis application. In addition, quantum noise models are introduced and simulated to show their effect on computation performance. Statistics from pre/post quizzes show that there is significant knowledge improvement by completing the lab exercise.
Continuous monitoring and fault detection for solar arrays can increase overall power efficiency and prevent hazardous situations. This paper proposes an intelligent monitoring and control device (IMCD) that can measure relevant photovoltaic (PV) parameters such as temperature, voltage, current, and irradiance. Our system design is based on an Arduino microcontroller that acquires and processes data from a PV panel. The design is such that data collected from the PV panels can be transmitted to a networked computer using an integrated Zigbee wireless transceiver. The design has provisions for embedded machine learning (ML) and then cloud-based ML algorithms trained to detect and classify PV faults in real time. The IMCD design can reconfigure the connection topology of a PV array using relays. These relays can switch from series to parallel connection upon command and hence reconfigure the PV connection topology to optimize power output for different shading conditions.
Continuous real-time solar system monitoring for fault detection and classification can improve solar panel efficiency and overall output. In this study, we developed and implemented a real-time PV fault detection system based on machine learning. The system was implemented on an 18kW testbed facility which consists of 104 solar panels located at the ASU Research Park. Each solar panel is connected to a smart monitoring device (SMD) which obtains real-time voltage and current measurements. SMDs are attached to each panel and transmit all the acquired data to a server that is connected to the internet. We implement fault detection using real-time measurements and various neural network architectures. We train and test both fully connected and dropout neural networks with different dropout regularization. We use both a real-time dataset and a synthetic dataset and present comparative results. We train and classify for the following conditions: soiled panels, shaded and degraded panels, and standard test conditions.
This paper aims to improve the efficiency of the garbage collection process by developing a system for monitoring waste levels in garbage bins using ultrasonic sensors and connecting them to Arduino Uno board for sending the measurements like the amount of waste level to the user. Two smart dustbins were designed for home use and public use which are monitored in real-time using the mobile applications. Notification alerts are also sent when the amount of waste exceeds a certain threshold level. These dustbins are connected wirelessly using a Zigbee based transceiver in the form of a mesh network to facilitate the transfer of the amount of waste present in these dustbins to the nearest garbage collection truck and an optimized shortest route to be followed by the garbage collector truck is calculated. The proposed system is user friendly, compact and cost-effective requiring minimum human intervention.
Environment monitoring and control has become a vital part of present-day to control air pollution and help in agriculture, fishery, shipping and military operations. Environment monitoring using traditional manually operated Weather Monitoring Stations requires skilled technicians for operation, is not scalable, and demands human intervention, which increases the cost of the Weather Monitoring Station. To address these issues, authors have attempted to design and develop E-Sense, an Internet of Things based environment monitoring system. E-Sense measures important environment parameters like temperature, humidity, air quality index, CO concentrations, rain and light. The data collected from the sensors is transmitted to ThingSpeak using the ESP8266 Wi-Fi module. The ThingSpeak helps in analyzing the data and present it in graphical and tabular forms. Additionally, E-Sense creates a heat map of the monitored area. A testbed implementation and experimentation show that the system works without human intervention, is user friendly, compact, and is cost-efficient.
Built a cycle-accurate Python simulator for systolic array accelerator, parameterized by matrix/array dimensions and dataflows; computed per-layer & end-to-end cycle counts, PE utilization, and bandwidth utilization.
Presented my research work on real-time PV fault detection and topology optimization with embedded ML at the 2025 SenSIP Industry Consortium, ASU.
Shared the practical side of my Ph.D. research with REUs and RETs at the ASU Research Park's SenSIP solar facility.
Started reviewing research papers in my field, beginning with a manuscript in Solar Energy and Machine Learning from the reputed journal Solar Energy (Elsevier).
Presented my research work on topology reconfiguration under partial shading at a collaborative workshop with Ss. Cyril and Methodius University, Skopje, Macedonia.
Represented the ASU-SenSIP REU site to discuss strategies for REU student engagement and pathways to graduate studies.
Received the ASU-SenSIP Certificate from my professor Dr. Andreas Spanias at the Arizona State University for my work and learning in the field of Sensor, Signal, and Information Processing.
Demonstrated our custom hardware and embedded ML research for solar monitoring to visiting professors from Tecnológico de Monterrey.
Presented research on using embedded machine learning for real-time fault detection in photovoltaic systems at SenSIP Industry Consortium 2024.
Shared work on applying embedded ML for the immediate detection of faults in solar panels at the Arizona Student Energy Conference 2024.
Engineered and presented a custom PCB and C++ driver solution for USB to SPI bridge board during my summer internship at Skyworks Solutions.
Outlined the design and implementation of a the photovoltaic monitoring device at the annual SenSIP Industry Consortium 2023.
Presented the design of a new intelligent device for monitoring and controlling photovoltaic systems to optimize energy output.
Mentored K-12 teachers in the basics of MATLAB, Python, and Machine Learning as part of the NSF-funded RET program.
Guided undergraduate students in the REU and IRES programs through the fundamentals of MATLAB and Python for their research projects.
"During my time as an undergraduate research intern at ASU-SenSIP, Deep Pujara was my mentor for a project in machine learning. He provided me with a solid foundation in classical machine learning, sharing his knowledge and expertise to help me quickly get up to speed on the background research. His leadership and technical guidance created a highly productive and collaborative environment. I am pleased to recommend Deep for his professionalism and dedication to fostering the growth of his mentees."
"I found Deep to be an excellent team player, self-starter, great communicator of ideas, and precise scientific thinker about and quick implementer of highly complex ideas. He has made extremely valuable contributions to our team. I highly recommend him!"
"During bachelor's program, Deep worked under my guidance for several projects, specifically in the domain of EM & antennas. I found him dedicated and sincere towards the work. I have noticed a good quality of proactiveness while working with him in collaboration. He always try to see the problem as a big picture and apply planned approach for the successful closure. I wish him a great success and happy to recommend his name for any future references."
"My interaction with Deep has been during his BTech study at Nirma University. He did a number of academic projects with me. During the project work, Deep demonstrated the ability to work independently with great creativity and enthusiasm. Deep is well organized, diligent, and a fast learner. He is one of the most dedicated student that I’ve worked with and is always willing to put extra efforts in all of the work assigned to him. I wish him success for all his endeavors."
"Deep is knowledgeable, articulate, and innovative person to work with. I have known him for quite a time during our educational journey and being a colleague and friend of him, he is someone I trusted and always looked forward to work with him. I have seen him closely working on different projects and have always analyzed his ability to bring integrity, innovation and intelligence to his work and also his managing skills. I believe his overall presence positively impacted the work and the people around him. I am very much sure that he will be an asset for any organization he will work with."
"Deep is one of the strongest technologist I have worked with. He has a quick grasping power, excellent deep dive and troubleshooting ability.He is a fast learner and even as an undergraduate student he was involved in some really challenging and innovative projects related to communications and programming. His enthusiasm is contagious. It was a pleasure to work with him and brainstorm any new idea."
"During my time as an undergraduate research intern at ASU-SenSIP, Deep Pujara was my mentor for a project in machine learning. He provided me with a solid foundation in classical machine learning, sharing his knowledge and expertise to help me quickly get up to speed on the background research. His leadership and technical guidance created a highly productive and collaborative environment. I am pleased to recommend Deep for his professionalism and dedication to fostering the growth of his mentees."
"I found Deep to be an excellent team player, self-starter, great communicator of ideas, and precise scientific thinker about and quick implementer of highly complex ideas. He has made extremely valuable contributions to our team. I highly recommend him!"
"During bachelor's program, Deep worked under my guidance for several projects, specifically in the domain of EM & antennas. I found him dedicated and sincere towards the work. I have noticed a good quality of proactiveness while working with him in collaboration. He always try to see the problem as a big picture and apply planned approach for the successful closure. I wish him a great success and happy to recommend his name for any future references."
"My interaction with Deep has been during his BTech study at Nirma University. He did a number of academic projects with me. During the project work, Deep demonstrated the ability to work independently with great creativity and enthusiasm. Deep is well organized, diligent, and a fast learner. He is one of the most dedicated student that I’ve worked with and is always willing to put extra efforts in all of the work assigned to him. I wish him success for all his endeavors."
"Deep is knowledgeable, articulate, and innovative person to work with. I have known him for quite a time during our educational journey and being a colleague and friend of him, he is someone I trusted and always looked forward to work with him. I have seen him closely working on different projects and have always analyzed his ability to bring integrity, innovation and intelligence to his work and also his managing skills. I believe his overall presence positively impacted the work and the people around him. I am very much sure that he will be an asset for any organization he will work with."
"Deep is one of the strongest technologist I have worked with. He has a quick grasping power, excellent deep dive and troubleshooting ability.He is a fast learner and even as an undergraduate student he was involved in some really challenging and innovative projects related to communications and programming. His enthusiasm is contagious. It was a pleasure to work with him and brainstorm any new idea."
Outside of my professional work, I find joy in traveling to new places and experiencing diverse cultures. I carry my camera and drone wherever I go, capturing landscapes, moments, and stories that might otherwise go unnoticed. For me, photography is more than just taking pictures—it’s about preserving emotions and perspectives that words alone cannot express. I also enjoy editing my captures to bring out the essence of each scene, curating them into visual stories that I love to share with others. Traveling and creating in this way fuels my curiosity, inspires fresh perspectives, and keeps me connected to the beauty of the world around me.
I'm always open to discussing new opportunities and innovative ideas. Feel free to reach out.
deeppujara27@gmail.com