Black Holes and Blackboards: What Theory Research Looks Like

Using math to understand how the universe works has always excited me beyond measure. Before coming to UCSB, however, I had absolutely no idea what research in theoretical astrophysics actually looks like. Would my days consist of aimlessly scribbling equations? Would I spend my time reading stacks of dense papers? Would my hair somehow turn white and disheveled like Doc Brown’s from Back to the Future? As it turns out, some of these are closer to the truth than others. Here is what I actually do as a theoretical astrophysicist studying simulations of black hole accretion disks in Professor Omer Blaes’s Accretion Group.

In “The Lab”

Being a theorist means that my “lab” is pretty much anywhere that grants access to a laptop and plenty of hot coffee. In other words, I essentially wake up already “in the lab”. My day typically begins with a review of the work I did the night before, which means flipping through my notebook and going through whatever code I wrote the previous day. If I have the time, I also make my way through some of the recent literature and write down any technical questions for my advisor.

Next Step: Collaboration

The lack of physical lab space means that in order for me and my advisor to collaborate, we have to specifically set aside time to spend in front of the blackboard discussing the physics and working through problems and questions. These meetings are usually the second portion of my typical research day, and they are possibly the most important.

Our meetings usually have the following progression:

  1. Discuss plots and figures generated from the code since our last discussion
  2. Further develop the context of these problems and explore next steps
  3. Decide what trends we want to investigate more deeply
  4. Address any questions I have about the project/literature

During the meeting, I am always sure to jot equations or vocabulary that I want to explore on my own when I revisit the literature later.

Coding and Calculation

“The Lab”

This aspect of my research is both the most challenging and the most rewarding. After meeting with my advisor, I usually take time to reflect on the ideas we discussed and write details in my notebook. I may even consult the literature again to fill in any gaps in my knowledge or to revisit a concept we discussed. Additionally, I spend time deriving any relevant equations to get a deeper feel for the mathematics, and I begin to work on the code.

The main function of these codes is to calculate and graph solutions to the derived equations. This is the real “meat and potatoes” of my research: deriving equations and then finding the proper way to visualize their solutions so that they reveal the relationships we want to investigate. I then document the method I used to generate these graphs, and I make a note of any issues or difficulties I encounter along the way.


Although most people immediately associate research with working in a lab, research in theory heavily reflects all of the methodology and collaboration one expects to find in an experimental setting. This unique dynamic is actually one of the things that draws me to theoretical physics: the semisweet balance of independence and collaboration. Although the days vary greatly – sometimes with much more time spent meeting with colleagues, and others spent buried in the literature – I learn more about the universe every single day. That is ultimately what I think makes research so great.

Deep Reinforcement Learning

How can we understand something that we have never seen before?

What impels us to think and act?

How do we learn?

Once upon a time, these questions were purely philosophical endeavors — and seemingly hopeless ones at that. Questions too far from reasonable application to be considered with any meaningful consequence.

However, amidst the ever-rising tide of technological innovation, these questions have reemerged from the depths of philosophy to be examined in closer detail by new fields, seemingly far removed from their philosophical origins.

Now, these questions are no longer things merely to ponder, for they have now grown to immense consequence in the young yet highly promising field of Deep Reinforcement Learning.

First, some background: the field of Deep Reinforcement Learning is a natural extension of Deep Learning, which is a natural extension of machine learning, which is a natural extension of statistical inference.

All clear?

If not, then don’t worry… because you are about to be a lot more confused anyways.

But before we get confused by Reinforcement Learning, let’s start with its older brother – Deep Learning.

Deep Learning is a pretty name for the type of machine learning that statistical theory hasn’t yet been able to catch up to. Made possible by a progressive increase in general computational power and public dataset size, Deep Learning is essentially the application of basic machine learning principles to more complicated functions – often called Neural Nets. The complexity of these functions allows them to map the data that they take in to a higher dimensional space that accounts for relationships between elements of the incoming data. During computation, various tricks are used to achieve useful properties, such as the nonlinearity between hidden layers that allows for the approximation of more complex functions, or dropout and entropy reduction in training to approach desirable statistical properties. This mess of engineering tricks fastened onto the underlying structure provided by traditional machine learning has allowed Neural Nets to solve problems that have previously seemed too difficult for traditional statistics.

For example, by seeking to encode the underlying information in a dataset of images and paintings, combinations of Neural Networks called GAN’s can create mesmerizing, psychedelic artwork.

In Deep Learning, the goal is often a simple form of classification: mapping a set of data to what it means. In essence, we want to be able to say that an image of a duck is a duck and that an image of a cat is a cat. From a human perspective, this task seems trivial, yet we can hardly begin to describe a solution. What about this arrangement of data causes us to say that the image it represents truly is a cat?

If, for instance, you laid out the list of pixels that represent the image, you could go through them one by one and verify that every single pixel does not necessarily make this picture a picture of a cat – and the same thing can be done with every set of two pixels. Nevertheless, and however indescribable by mere language, Deep Learning is able to capture underlying patterns in images, and match the image to its meaning with shocking, sometimes superhuman accuracy. Every day, new applications are imagined for this technology, weather it be in the realm of art, medicine, or business.

Reinforcement learning is a fascinating extension to Deep Learning, relying essentially on modification to traditional classification that is simple in premise: instead of seeking the proper name for an image, we seek proper action for an image – as defined by the propensity of the action to maximize total reward. This is most simply interpreted in the context of games, then extrapolated into more consequential scenarios, such as the development of self-driving cars.

The goal in both levels of this problem, in games and in the real world, involves recognizing that a certain action is likely to achieve a desired outcome given an observed state. In essence, this is a variant of the classic classification problem, but clearly, more complexities are introduced by the ability to alter environmental state with action. Nevertheless, much of the existing Deep Learning architecture can remain, and we can simply change the loss function to seek to maximize total reward gained over a task, rather than minimize error in prediction of the proper image label.

As researchers seek to improve performance in their Reinforcement Learning algorithms, they are, whether they know it or not, seeking answers to the philosophical questions we started with. And as we are able to create algorithms that are able to learn more efficiently, we are offered a glimpse at the answers to these questions and insight into the inner workings of the mind in the process.

How can we understand something that we have never seen before?

What impels us to think and act?

How do we learn?




Forging a New Path

With a lack of exposure to a world of different ideas, academia often at times seemed to be too much of a task for me to confront. From deciding upon which field to choose, a suitable learning environment conducive to my well-being and attempting to find a place to call home for the next several years of my life, among many other factors, some moments in the decision making process seemed more strenuous than helpful, granted that this process is key to carving out one’s future in a very fundamental way. Particularly for many of us whom derive from pasts that have lacked personal models, whom have lead a life in higher education, with the ability to guide those that follow. I in turn had carve out my own path. In addition, pursuing an education in the STEM world, in hopes of becoming a scientist presents its own set of challenges. With trial, error, perseverance and slight trepidation, I managed to progress through each course with curiosity and passion for learning as driving motivators, while simultaneously being cognizant of my performance or lack thereof and how this aspect of assessment would grant or deny my participation in certain opportunities. Little did I know that life in a laboratory would require very different modes of thought and resilience.

As a recent scholar of the MARC program, I have learned more in the past few weeks conducting experiments and gaining guidance from my mentor than what I was exposed to during many classes that I have taken before. Walking past each lab bench filled with various reagents, centrifuge tubes, pipettes, and scales, remnants from my childhood memory suddenly remind me of Dexter’s Laboratory and I find myself excited to be in this environment of discovery, that began as an original desire and thought. Although filled with tangible items useful for our ongoing inquiries, my first several weeks also exposed me to the dialogue that takes place between researchers and those aspiring to become so one day. As they probe further into hypotheses that have been revised several times, I have been able to witness firsthand how “messy” science can be in terms of the quest for answers being a never ending endeavor combined with attempting to understand concepts that have never been addressed or encountered. This part of science filled with mystery keeps the “spirit” of inquiry alive while never-ending failed attempts fuel many to consistently re-evaluate where in the scientific process a mishap may have occurred.

As I exit the laboratory each weekday, heading back to my home I find myself glad to have taken this path which had been foreign to me for an extended period of time. I have already encountered numerous lessons with experiments not resulting as expected and having to troubleshoot mistakes made along the way. Confronted with a new task foreign from my past experiences, I now consistently long to unveil the “dilemma” of the day.

The Budding Life of a Researcher

Growing up, I never had a real affinity towards plants. I had no green thumb whatsoever, and neither did anyone in my family. Any attempts made in growing plants whether it be in school projects or for myself usually resulted with a unwatered withered corpse. It was with this mindset that I approached my now mentor Jason Johns in the Hodges Lab. At the time, I saw this as an opportunity to reconnect with nature. With trepidation, I now stepped forward into a project that would have me help to grow ~350 plants known as Aquilegia Jonesii. In hopes of finding the genetic basis of alpine adapted dwarfism in the species. The purpose of this is to one day avoid the problem of lodging. Lodging is something that occurs when a plant grows too tall and tips over itself. As a result, the plant dies and a bunch of resources have now been wasted in helping the plant grow to adulthood.

Now, the big hurdle is to extract the DNA from the plants and to sequence every single one. To do this, I take some leaves from the plant, preferably younger, since it is easier to break down plant tissue in younger plants. Afterwards, I place the leaves in a tube along with some metal balls and freeze them using liquid nitrogen. I break down the tissue with a machine called a bead beater. Then, I add a solution and centrifuge it to place all the plant material at the bottom and suck up the liquid goodies left up top. Unfortunately, this includes DNA, proteins, and others. So, to isolate DNA I add magnetic beads and proceed to hit the solution with a mix of chemical washes in hopes of isolating the DNA. Finally, I add water along with the magnetic beads, which have the negatively charged DNA attached. The water having a higher charge than the magnetic beads, pulls the DNA. Finally, we have our isolated DNA. Afterwards, I take a small amount of DNA and i quantify the amount of DNA in ng per mL of solution. To do this I add a solution to the DNA and take it to a machine which spits out the concentration. This has mainly consisted of my lab work thus far. However, a majority of my time has been spent caring for my plants. I have had to clean them up, re-pot them, and tag them. While this may not seem like a lto this has easily been what I have spent most of my time on. I have washed around ~300 pots and spent hours carefully transporting each plant into bigger pots. I make it a habit to go to the green house once a day to check on them and just to admire their beauty. If I am being completely honest this has been one of the most satisfying parts of my research.

Caring for these plants is truly something special and I would never give it up. Recently, one of my plants have flowered. Knowing that I have aided in their development also makes me feel almost accountable for them. I have this connection to every single one of them, that brings me a peculiar warmth seeing them grow larger everyday. One day, I hope to bloom as beautifully as this plant, and become a full fledged researcher. Until then, I continue to happily work in the Hodges’ lab.


Anshika Bagla ’19, Marine Biology

Sharks are very misunderstood, and being able to work towards conservation efforts definitely keeps me motivated.”

The first thing you notice about Anshika Bagla is her hydro flask.

On UCSB’s campus, hydro flasks are ubiquitous, with students typically adorning their bottles with stickers of all sorts.

But Anshika may have the most unique hydro flask sticker on campus. Near the top of her bottle, the sticker features an image of a hand-drawn shark, with the words “Crazy Shark Lady” written in large font.

“I don’t think I can pinpoint why I love sharks–they’re just awesome,” Anshika said.

Anshika has been surrounded by marine biology her entire life. Growing up in Cerritos, Anshika spent a majority of her time at the library–which featured a full floor-to-ceiling aquarium.

“That was my first experience around marine life, and I thought the fish were really cool,” she said.

Beginning in eighth grade, Anshika became a regular volunteer at the Aquarium of the Pacific–a local aquarium which also happens to be one of the largest aquariums in California. For five years, Anshika was perpetually surrounded by her favorite marine animals.

So when Anshika came to college at UCSB, there was little doubt she wanted to study marine biology. In her free time, Anshika worked at the REEF on campus as a shark aquarist. The more she worked with the sharks in the reef, the more she fell in love with the species.

Starting last January, Anshika has translated that love into action, playing an active role in shark conservation efforts through her research with the McCauley lab. The lab uses dermal denticles that shed off of shark’s bodies and are preserved in sediment in order to determine which sharks used to live in an area. Once the research team figures out which sharks lived in the area, they can use that data to figure out why and how the community transformed.

In particular, Anshika’s job is to determine the shedding rates of sharks in order to get a rough estimate of the number of sharks in an area at a given time. Once that data is ascertained, the team can use data such as fishing statistics to eradicate issues that plague shark populations.

“What we do is put out trays full of sand at The Aquarium of the Pacific, and catch the sharks’ denticles,” Anshika said. “We then have to pick through all of the samples, which is a very intensive process because we have to use a microscope to look at every grain of sand. No one has studied shedding rates, so my goal is to get a rough estimate rather than something super specific.”

Even for a self-described crazy shark lady, however, the research can be exhausting. Anshika spends ten hours a week under a microscope to look at the sand grains–something that can feel mundane after the first few hours. Still, Anshika is driven by the implications behind her work.

“Sharks are very misunderstood, and being able to work towards conservation efforts definitely keeps me motivated,” Anshika said.

Through it all, Anshika’s love for sea life, and especially sharks, has evolved from her early days of volunteering at her local aquarium.

“I used to just spout facts about animals as a kid, but now I’ve learned to think much more critically about sea life,” Anshika said. “For example, I never would have thought [my research] could be used as a way to help sharks, because it seems so obscure.”

And yet, Anshika has done just that, sacrificing her free time to try to save sharks across the world. Just before the end of my interview with Anshika, she makes her stance on sharks abundantly clear.

“I’m known as ‘shark girl’, and I’m ok with that,” she says with a smile. “I’m shark for life”.