Engineer, Researcher
Designer, Learner
I am a Mechanical Engineer and AI Researcher, having recently graduated with a Ph.D. in Mechanical and Industrial Engineering at the University of Toronto. With a strong background in engineering, I am passionate about leveraging technology, specifically AI and digitalization, to enhance product design and development processes. Throughout my academic and professional journey, I have developed a keen ability to tackle complex problems and find innovative solutions. Proficient in Python, CAD, statistical analysis, and modern AI frameworks (PyTorch, TensorFlow), I have led and contributed to collaborative academic–industry projects focused on design automation, digital threads, and predictive analytics. I am eager to contribute to fast-paced, innovation-driven environments where I can apply research, coding, and systems thinking to solve impactful engineering challenges. Alongside my research, I bring over five years of teaching and mentorship experience. As a teaching assistant at U of T, I have led tutorials and lectures for core engineering courses, designed and graded assessments, and provided tailored feedback to help students master complex technical subjects. I’ve also mentored undergraduate researchers and high school students on AI-focused projects, curriculum design, and academic writing—earning recognition for both impact and accessibility in my teaching.
University of Toronto - 2024
Selected Coursework: CSC2515 – Introduction to Machine Learning, MIE1402 – Experimental Methods in Human Factors Research, APS1023- New Product Innovation, MIE1720 Creativity in Conceptual Design
The Pennsylvania State University - 2020
Selected Coursework: IE546 – Design Product Families, IE549 – Design Decision Making, IE402 – Advanced Engineering Economics, IE522 – Discrete Event Simulation, IE570 – Supply Chain Engineering, IE505 – Linear Programming
University of Mumbai - 2017
Selected Coursework: Applied Mathematics, Production Processes, Finite Element Analysis, CAD/CAM/CAE, Production Planning and Control, Industrial Engineering and Management, Machine Design, Design of Mechanical Systems
(Sept 2020 - December 2024)
Under the supervision of Prof. Alison Olechowski at Ready Lab , I worked towards understanding methods, decision-making processes, and strategies, that help researchers and practitioners enhance product design processes, especially through the lens of AI and digitalization.
Collaborated with a UK-based research-industry team to understand the process of implementing a Digital Thread in their product's design process to enhance its product (PDM) data management system with an eye on future AI implementation.
I conducted a post-hoc review to unveil the Digital Thread features, implementation challenges, extensibilities, and best practices for its implementation and application. In this project, I identified the importance of a PDM system to provide data synchronicity, traceability, version control, and interoperability to ensure effective interdisciplinary team collaboration, data management, reference, and future integration of advanced technologies such as AI, AR, and VR in the product design processes.
Collaborated with a Canadian industry partner to understand the process behind the selection, development and implementation of an apt AI framework for improving the efficiency of their product design process.
I drafted a project proposal for the Mitacs Accelerate Research Grant (awarded). In this project, I developed and deployed a Python-based automation framework that enhanced the design and manufacturing-specification-generation processes of FRP flanges, ten times faster than their traditional design process.
Conducted a focused literature review highlighting the status quo of seven different AI-based methods applied across 108 research works focused on five engineering design stages.
In this project, I reviewed several state-of-the-art AI-based design research works —including generative AI (GenAI) models, large-language models (LLMs), Agentic AI models, and Machine Learning— that are specifically deployed in different stages of the engineering design process and demonstrated how these methods assist engineers in the five design stages (problem definition, conceptual design, preliminary design, detailed design, and design communication).
Analyzed the current prevalence of AI courses across mechanical engineering curricula of Canadian accredited universities.
In this project, I developed a novel "web-scraping + keyword-matching" approach to analyze the current prevalence of AI courses across mechanical engineering curricula (~2200 courses) of 28 Canadian accredited universities.
(June 2019 - May 2020)
Under the supervision of Prof. Christopher McComb (now at Carnegie Mellon University) at THRED Group, I worked towards understanding how deep learning algorithms can help accelerate structural evaluations of materials.
Conducted strain field material evaluations of porosity-ridden material microstructures using deep learning-based image colorization algorithm.
In this project, I developed a framework based on a deep neural network (a convolutional neural network) to predict strain fields in aluminum microstructures with 95% accuracy and 15x faster than traditional FEA software (ABAQUS).
In addition to the major projects, I completed the following projects as part of my research and education.
Performed qualitative analysis using T-SNE plots and on these five self-supervised learning algorithms using CIFAR10 dataset and and quantitative analysis of these self-supervised learning (SSL) algorithms by evaluating their models’ transfer learning ability using out-of-distribution testing for image classification.
Analyzed the Behavioral Risk Factor Surveillance System (BRFSS) dataset, using K-means clustering for pattern identification and subsequently logistic regression to predict heart-attack-diagnosis, cancer-diagnosis, and diabetes-diagnosis.
Analyzed and optimized the number of servers required for a drive-through restaurant- chain in the area to improve efficiency according to desired performance standards using discrete event simulations in Python.
Developed two new algorithms for damage detection, localization, and refinement using lamb waves.
Khanolkar, P. M., Gopsill, J., & Olechowski A. (2024). Decoding the Digital Thread Digitalization Approach for Product Design and Development: Benefits, Challenges and Extensions. Under review at Artificial Intelligence for Engineering Design, Analysis and Manufacturing Journal.
Khanolkar, P. M., Vrolijk, A., & Olechowski A. (2024). Discovering the roadmap for selecting, developing, and implementing AI-based automation in product design process: a case study. Under Review at the ASME Journal of Mechanical Design.
Khanolkar, P. M., Vrolijk, A., & Olechowski A. (2023). Mapping artificial intelligence-based methods to engineering design stages: a focused literature review. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 37, e25, 1–18. Link to paper
Khanolkar, P. M., McComb, C. C., & Basu, S. (2021). Predicting elastic strain fields in defective microstructures using image colorization algorithms. Computational Materials Science, 186, 110068. Link to paper
Yelve, N. P., Rode, S., Das, P., & Khanolkar, P. M. (2019). Some new algorithms for locating a damage in thin plates using lamb waves. Engineering Research Express, 1(1), 015027. Link to paper
Khanolkar, P. M., Lu, J., Hurst, A., & Olechowski A. (2024). ASSESSING THE PREVALENCE OF ARTIFICIAL INTELLIGENCE IN MECHANICAL ENGINEERING AND DESIGN CURRICULA. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers, 2024.. Link to paper
Khanolkar, P. M., Vrolijk, A., & Olechowski A. (2023). A Case Study of the Decision-Making Behind the Automation of a Composites-Based Design Process. Proceedings of the Design Society, 3, 49-58. Link to paper
Khanolkar, P. M., Gad, M., Liao, J., Hurst, A., & Olechowski, A. (2021). A PILOT STUDY ON THE PREVALENCE OF ARTIFICIAL INTELLIGENCE IN CANADIAN ENGINEERING DESIGN CURRICULA. Proceedings of the Canadian Engineering Education Association (CEEA). Link to paper
Khanolkar, P. M., Abraham, A., McComb, C., & Basu, S. (2020). Using deep image colorization to predict microstructure-dependent strain fields. Procedia Manufacturing, 48, 992-999. Link to paper
Rode, S., Yelve, N., Khanolkar, P. M., Thube, M., Thampy, A., & Thomas, C., (2017). Development of a Lamb Wave Based Algorithm for Detecting a Damage in Thin Plate Structures. ISSS International Conference on Smart Materials, Structures, and Systems. Link to paper
Olechowski, A., Khanolkar, P. M., Lu, J., & Hurst, A. (2022). Towards a Modern Canadian Engineering Design Curriculum: Balancing Artificial Intelligence and Human Cognition. Link to paper
Jia Sheng (Jerry) Lu, Pranav Milind Khanolkar, Ada Hurst, & Alison Olechowski. (2021). Keyword-Matching-for-Canadian-Mechanical-Engineering-Programs: Second Release (v1.0.1). Zenodo. Link to paper
Khanolkar, P. M., Basu, S., & McComb, C. (2021). Image-based data on strain fields of microstructures with porosity defects. Data in Brief, 34, 106627. Link to paper