Nothing triggers impostor syndrome more than going through the final year of a degree knowing (nearly) nothing beforehand. This is how I felt during my first week as a third-year engineer at Cambridge.
For this academic year, I am doing what the other 200+ students at Cambridge are doing: completing a BA degree in engineering. What is different is that I have not done the first two years of the course. Everything is new to me. In the past term, I took five modules with examinable credits, but I also audited two more related modules at Cambridge and some courses available online to catch up on prerequisite knowledge and deepen my understanding. There will be five more modules next term before I sit for the final exam, and end the year off with two projects.
Information engineering is at the intersection of electrical engineering, computer science, and mathematics. The information engineering modules this year tackle signals and how we process them using statistical tools. I am interested in this field because it provides a perspective of looking at machine learning algorithms as a way to encode and decode the signal and distinguish them from noise. Many well-respected machine learning researchers like Mihaela van der Schaar have backgrounds in this field.
The reason why information engineering has such wide applications is that virtually everything we are concerned with has some form of information. When we measure the information with one metric and observe how it changes with another metric, we get a signal. A signal describes how some variables change with another variable. When we study “how it changes” from the perspective of the sender, we use random process from probability theory. When we study the same how question from the perspective of the receiver, we use estimation and inference from the field of statistics. The distinction between the sender and receiver lies in the fact that only the sender has perfect information of the signal, and the job of the receiver is to infer what the signal is. Receivers may also want to ask “whether there is a signal” with detection theory and recover the information by using noise filters.
Signals contain information, and can eventually influence a system that we are interested in. To study how this interaction works, we investigate the transfer function of the system. Sometimes the signal can destabilize the system. For example, wind can be seen as a signal correlating force with space-time, and it can destroy a poorly-built bridge, which is the system that we are interested in. The field concerns with this are control theory.
Other times, we want to represent one source of information in other forms, so we encode the information in an efficient, compressed way, communicate the information through a channel that may distort our message, and try to decode them probabilistically afterwards. We also want to quantify “how much” information there is. Information theory allows us to do this.
That is pretty much what has been covered in information engineering so far. For many topics, the properties of continuous (e.g. how blood pressure varies with time) and discrete (e.g. how blood pressure varies at 9 am, 9 pm) are different, so we need to study them in parallel.
I completed three projects for information engineering. In the first project, I designed and implemented a compressor using adaptive and contextual compression. In the second project, I studied a few techniques in probability theory, including histogramming & kernel density estimate, Monte Carlo simulation, Jacobian transform, and generation Laplacian and student-t distribution from a scaled normal distribution. In the third project, I studied requirements to stabilize an air-plane and implemented an auto-pilot system.
Biological and medical engineering
Biomaterials (medical device engineering)
From drug delivery, tissue engineering, and implant to device regulation, this covers a wide range of topics in medical technology. Yet, the depth of the topics is by no means compromised, as we learned how to use mathematical models for the mechanical and chemical properties of the device.
In the project, I investigated the gelling, absorptive, and mechanical properties of different types of the hydrogel.
Molecular engineering (synthetic biology)
Initially, I thought this would be an “easy” module given I have studied biochemistry and worked as a geneticist. However, the paradigm is very different in engineering. The focus of the engineering course is on how we can utilize biological systems. Delving deep into using biological systems to build synthetic circuits or to use DNA as information storage is simply fascinating.
In the project, I designed a COVID-19 vaccine and test based on the RNA sequence of the virus.
Overall, it has been a very challenging journey to juggle understanding concepts, finishing problem sets, and completing projects. However, it is invaluable to have developed the confidence in dealing with quantitative subjects, and the perspective of looking at any problem from an engineer’s eyes.