Neshat Jalali Heravi’s curiosity for material properties led her to explore the frontiers of machine learning and AI has been saved
Neshat Jalali Heravi’s curiosity for material properties led her to explore the frontiers of machine learning and AI
Deloitte AI Institute is proud to introduce a series profiling AI warriors who are pushing the boundaries of what’s possible in the search for new and innovative uses of AI.
Can you share the most interesting part of your career journey?
The most fascinating aspect of my professional journey has been the trajectory of my intellectual evolution. As a chemical engineer working in the field of advanced materials and researching on solid oxide nuclear fuel, I was intrigued by the complex interplay between material properties and their reactions. This sparked my interest in data-driven optimization and process engineering. After working in the medical devices industry, I soon realized that the traditional statistical models I had been using were no longer sufficient to capture the underlying complexity of the systems I was working with. This realization led me to explore the field of data science, where I learned about the power of machine learning and AI.
Through my work here at Deloitte, I have seen the impact that smart operation practices can have on supply chain management and performance.
What I find most interesting about my journey is the way in which my initial curiosity about material properties has led me to explore the frontiers of machine learning and AI. In many ways, these fields represent a new kind of material science, where the “materials” are data and the “reactions” are complex systems. By bringing these two worlds together, I have been able to gain a unique perspective on the power and potential of data-driven optimization.
Through my work here at Deloitte, I have seen the impact that smart operation practices can have on supply chain management and performance. By automating and optimizing processes, we can not only save costs but also improve efficiency and reduce waste. This is a truly exciting time to be working in the field of AI and data engineering, and I am grateful to be part of a community that is pushing the boundaries of what is possible.
What excites you most about working with data and AI?
The most exciting aspect of working with data and AI is the potential for these fields to spur new waves of creativity and innovation. I consider AI a natural extension of the human curiosity and desire to understand and explore the world around us. By developing and improving the algorithms and models, we are able to unlock insights and patterns that were previously unknown to us.
The potential applications of data and AI are virtually limitless. These tools allow us to push beyond the boundaries of traditional approaches.
However, we must also be mindful of the ethical implications of these technologies and work to ensure that their benefits are distributed fairly and equitably. By doing so, we can fully harness the power of data and AI to drive positive change and create a better world for all.
Describe an interesting project that you have worked on.
I’m working on an exciting project that involves transforming a major aerospace company’s approach to quality management. Our team is implementing a state-of-the-art statistical process control system to enable smart operations and aim for zero-defect manufacturing. At the core of this transformation is the use of data and AI for real-time digital data collection and analysis. By collecting and analyzing data on key product characteristics, we can apply various techniques, such as deterministic, prescriptive, and predictive analytics, to optimize performance and reduce defects.
However, achieving these goals requires a significant shift in organizational culture and behavior. That’s why we’re establishing a standardized digital platform that can handle a wide range of data types and formats based on the buyer’s priorities and needs.
By harnessing the power of AI and data science, we aim to achieve unprecedented levels of quality and efficiency in the manufacturing process.
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