My Data Science & Engineering Portfolio
My Story
Data Science Meets Engineering
I am a student of technology. Born and raised in the city of Shanghai, I grew up with real-life examples of how engineering products evolved around daily activities and transformed people's lifestyle. My early experience in life allowed me to develop an open mindset for innovative engineering ideas and ideologies. I believe that in the future of today's technology-driven world, data science and engineering would serve as backbones in pushing human race one step further into information era.
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Together with my true passion in data science and engineering, I keep my foot on the ground, constantly update my knowledge base and analytics methods across different fields, and upkeep with the state-of-the-art. And hopefully such dedication will one day benefit the living quality of human beings.
Image Source: nuro.ai
Insight
Following the recent success of electric vehicles, a trend of traditional car manufacturers acquiescently premiering their self-developed EV has, optimistically speaking, signaled a tip-over point of transforming household-owned gasoline combustion engines into clean energy motor. In the automobile industry, frontline technology startups are currently focusing on using advanced computer vision algorithms, such as environment recognition and perception, to implement Level 5 autonomous driving and has successfully conducted controlled-environment testing. Implementation of self-driving vehicles has already seen progress in solving the last-mile delivery problem which troubled the logistic industry for years.
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It is not difficult to see that there has been a tremendous amount of research focusing on human transportation aiming to provide full automation. From my perspective, the idea of full automation should and will be further expanded in other industry as well. The rising concept of Web 3.0 (Semantic Network) and already prevailing idea of Industry 4.0 (Smart Factory) have more or less overlapped with each other in reshaping status quo with enhanced cloud computing and cognitive computing.
Vision
The ideal blueprint of smart factories, from my point of view, essentially takes in nationwide or sometimes worldwide live manufacturing data from production lines, industrial robots and microelectronics sensors. Well-constructed data warehouse infrastructure is the foundation of the workflow, as current machine learning technology, surprisingly, still suffers from insufficient data and relies on hand engineering. The well-ordered structure data would feed into advance deep learning algorithms in a computation-ready way. Artificial intelligence can use the outputs for hardware maintenance purposes and workloads distribution across multiple factory locations. By utilizing data science and engineering, the future of supply chain will be less susceptible to unpredictable hurdles, such as COVID-19 and global chip shortage, as well-established automation process is more likely to be optimized and resilient.
Image Source: internetofbusiness.com
Image Source: NASA HQ PHOTO
Commitment
Academy Awards actor Denzel Washington has one of my favorite quotes: 'Curiosity makes you start, and consistency makes you finish'. It is not uncommon for any engineering person or data science person to come up with a novel idea, seemingly approachable, and could potentially become one of the most promising innovation. However, once diving deep into the subject matter, forwarding such ambition by iterating through prototypes constructively require an immense amount of consistency.
This is why I believe that it is crucial to find the right project, career and cause that we truly see a future in it. When interviewer asked Elon Musk whether he would regret his investment if Falcon rockets eventually ended up not succeeding, he denied as he believed that it was the right thing to do for the future of human race. Adopting such mindset, together with abandoning failure mindset and effective schedule management , helped me focus on the quality of solutions instead of personal gain or loss, foster systematic approaches to explore alternatives in business problem solving, and remain calm and detached when handling technical difficulties and bottlenecks. As complexity of the data science and engineering projects I engaged with increase over the years, I value consistency of the execution. As I continue to achieve self-realization in data science and engineering, my envisioned entrepreneurial endeavor and professionalism will promise sustainability of my commitment and dedication.