Last updated: Aug 18, 2023
My main career interest is in the development, deployment, and commercialization of autonomous and intelligent systems from prototypes to the real world. Below, I list some of my career-related opinions.
- I prefer working on pragmatic uses of intelligent systems to augment utility in the real world.
- For complex and realistic problems, I believe that open-minded and interdisciplinary approaches tend to work better.
- It seems to me that highly narrow approaches tend to perform better on idealized and artificial problems.
Working as a Group
- For R&D, I enjoy project planning, multidisciplinary collaboration, and other social aspects.
- Without honest and fluid communication between collaborators, research projects fail.
- An organized and well-managed research environment improves the risk/reward ratio of projects.
- Stubborn, inflexible, and infallible attitudes are harmful and wasteful (especially for R&D).
- I prioritize listening to people to better understand them and to learn from them.
- I enjoy partnering with focused individuals and contributing in ambitious teams.
Nuances in Problem-Solving
- First, attempt established methods; second, survey existing projects and/or research literature; third, identify what’s missing (if at all).
- When it comes to technical approaches, there are no silver bullets: It is important to draw inspiration from various sources.
- It is equally important to retain focus and restrict to the approaches relevant to the problem.
- Sometimes, an integrative approach is necessary (e.g., combining perception, planning, control, and learning).
- Sometimes, exploiting the problem structure can be more apt (e.g., passive mechanical design, mechatronic solutions).
- Sometimes, ‘classical’ can be more apt than ‘learning-based’; and direct ‘automation’ can be more apt than ‘robotics’.
- Biologically-driven fields (e.g., Neuroscience, Cognitive Science, Biomechanics, Medicine) can be terrific sources of ideas.
- Bear in mind the constraints and the economics of automation: eventually the intelligent system must generate value for business, finance, and government.
Skill and Technique
- Basics before exotics: Master the rudiments (mathematical, computational, software, …) before proceeding to technical niches.
- Respect mathematical formalisms (e.g., dynamical systems, optimization theory, linear systems, stochastic processes, …) even in their applied form.
- Get to know a software construct (language, library, API, …) before using it.
- Get acquainted with the underlying components (distributed computing, networking, hardware, ICs, actuators, sensors, …) for better context.
- More generally: Build context around both theory and practice.
My long-term aim is to empower people via systems that make hard work easier. I think social progress is aided by reducing scarcity (via technologial and economic progress). Societies have (in part) become more peaceful due to productivity-increasing systems (better cost, quality, and convenience). I like to research, develop, and commercialize such autonomous and intelligent systems along with their mathematical formalisms, technological applications, business value, and social impacts.