"Overcoming my fear of poetry"

No! I won't! I won't write a poem! 

You can't make me! Nor will I succumb to my desires. No, no, no...

I'm a researcher. That's right. I seek knowledge and certainty. I seek soundness and completeness. I seek objective truths. 

For I see the world in black and white. Atop a ship in a sea of gray,

In absolutes, in truth and beauty I can describe the world.

Still, the mighty roar of the foggy ocean, surrounds me on all sides, 

through its cloudy mist light cannot penetrate. I fear what lies beneath the surface.

Einstein was the wisest man alive, as science gives us answers,

or Aristotle, a thinker like no other, with philosophy, more questions, 

I search for land, refuge from an infinite sea. I won't read Coleridge or Whitman or Thoreau. 

I'll remain willfully blind to what can't be described or learned.

I choose not to forsake my judgement in rhetoric and logic,

lest I should become overpowered by my desires within.

I won't. I'll lock the chest and throw away the key.

You won't get a poem out of me. 

Stoicism to improve your life

“All cruelty springs from weakness.” - Seneca the Younger
Philosophy, not solely confined to the writing of academics, can, in some ways, be seen as a way of living. For people to turn to philosophy for the answers to their common struggles and for ways of improving their life isn't as far-fetched as it would initially seem when one studies the role philosophy had for the Ancient Greeks and Romans. For many Americans, Stoicisms holds answers, yet still remains an incomplete explanation of an individuals' relationship with emotions.  

Examining the current paradigm of artificial intelligence (with help from philosopher Thomas Kuhn)

"Normal science, the activity in which most scientists inevitably spend almost all their time, is predicated on the assumption that the scientific community knows what the world is like. Normal science often suppresses fundamental novelties because they are necessarily subversive of its basic commitments." - Thomas Kuhn, The Structure of Scientific Revolutions
My doubts have been getting to me. My questions about the nature of existence and science itself have left me disillusioned and detached from many aspects of my day-to-day routine. I began to slowly and steadily believe that my work as a scientist wouldn't be valued nor would it be worth doing in any sense. I could barely find answers questions and concerns I wrestled with as a scientist. As I walked through one of the buildings of the National Institutes of Health, I listened to music to drain out the sounds of the world. 

From my view, something caught my eye. A bookshelf. In the middle of the hallway. I stared at it in disbelief took the headphones out of my ears. Anyone could take a book and leave one for others to read. In the middle of the top shelf sat philosopher Thomas Kuhn's The Structure of Scientific Revolutions. Interesting, I thought. Looks like the scientists at the NIH aren't completely aloof to the philosophical underpinnings of science. In this post, I hope to discuss the nature of Kuhn's paradigm shifts and their relevance in the field of Artificial Intelligence (AI).

A modern-day paradigm for computational approaches to psychiatric illness

Source: https://www.youtube.com/watch?v=lQLsyf64xak
My research in computational neuropsychiatric genomics on the zebrafish has lead me to investigate what sort of methods and inquiries I could put forward using statistics and algebra. Though zebrafish are an inherently helpful model organism for psychiatric disease I want to extend the nature of zebrafish research such that we can achieve the full potential of psychiatric disorders no matter what species we are studying. The results of artificial intelligence in particular hold promising techniques that extend into biology and neuroscience. As scientists peek into the architectures and algorithms like hierarchical filtering and supervised learning, they can create more detailed and elaborate explanations of biological phenomena. OpenSource platforms in particular need to establish a framework or fundamental principles by which scientists can draw conclusions on the nature of psychiatric disease itself through accounting for the limits of experimental observation. In this post, we'll discover some of the latest findings in computational neuroscience as they relate to the questions we'd like to answer.

Getting the best of both worlds with data journalism

Under Your Skin: Molecules and Cells for Touch and Pain

How to maintain intellectual character in an era of fake news

"Truth Coming Out of Her Well" - Jean-Léon Gérôme (1896)
To speak of our own intellectual character requires a tremendous amount of humility and generosity. To speak broadly, intellectual character is all about embracing truth, criticism, and ideas in a way that's justified, fair, and ethical. It's very easy for many scholars and students to treat their own intellect in arrogant, bodacious ways that only serve to satisfy our selfish desires. In today's era of fake news and post-truth, we find abundant examples of trying to win arguments out of sheer pride and vengeance or spreading misleading or false information to make one's self appear better. To determine the ways these intellectual conflicts and conversations reflect our moral character means understanding what intellectual character is and how to maintain it in today's society.

How war shapes a country: a review of Nora Krug's "Belonging"

Pondering difficult questions of her own cultural background, German author Nora Krug asks the questions of what belonging is and what that means to her. To belong to a culture of Germans responsible for the unspeakable atrocities of World War II meant Krug was challenging the very idea that she should belong to that culture. Though she was born several dozen years after the fall of the Nazis, the actions would cast a shadow on her life. Searching for answers, Krug's graphic memoir wrestles with home and her self.

Memories of Claude Shannon, father of information theory

"My greatest concern was what to call it. I thought of calling it 'information,' but the word was overly used, so I decided to call it 'uncertainty.' When I discussed it with John von Neumann, he had a better idea. Von Neumann told me, 'You should call it entropy, for two reasons. In the first place your uncertainty function has been used in statistical mechanics under that name, so it already has a name. In the second place, and more important, no one really knows what entropy really is, so in a debate you will always have the advantage.'" - Claude Shannon, Scientific American

Encoding and decoding with stochastic neuron models

In the spring of 2018, I worked on an encoding and decoding project for a course about machine learning in Python. I studied the ways neuroscientific data can be analyzed to form predictions of how humans perceive the world. For this set of data, we rely on voxels (the unit of array elements in neuroimaging) and can measure various characteristics from them such as intensity, size, and shape. With these sorts of issues, supervised learning can be used to decode images to relate brain images to behavioral or clinical observations. The predictions made by these supervised forms of learning can then be cross-validated to assess how those results from these individual experiments can be generalized to broader patterns in neuroscience data. We'll be using a Python module called nilearn for this analysis.

In this post, I'd like to explain how to apply different statistical methods to real data in the field of neuroscience. Biophysical models can be made to estimate empirical evidence brought by the biology and neuroscience, and using descriptions of neurons (complete with dendrites, synapses, and axons). These mathematical frameworks are common throughout various problems in genetics and embryology, but I'd like to focus on their role in neuron models. Through various methods such as sodium and potassium ion channels (which activate through spiking at threshold potentials), we can uncover predictive models based on the causality of this data.

"What's so great about classical music?" A personal and philosophical perspective

"The aim and final end of all music should be none other than the glory of God and the refreshment of the soul." - Bach
Arguably moreso than other forms of music, classical can enrich and and stir the soul in ways that require incredibly lengths of comprehension and description to truly appreciate. To put in words what the body and mind experience when listening to a classical composition would be to draw lines between what is and what isn't music in these complex theoretical forms that we give life to. Looking at definitions of aesthetics, pleasure, symmetry, and other elements that go into all forms of music, we can create an idea of what classical music is and why it's so important to many of us. I write this as I listen to Ravel's "Pavane for a Dead Princess", a piece I had the honor of playing in my high school orchestra class. I see various YouTube videos promising that their playlists of classical music are beneficial to brain power. I'm incredibly dubious of those claims, but I believe we can find a sort of intellectual enlightenment with a philosophical appreciation or reflection of the music itself.

On the nature of causation and correlation: elections and cancer

Credit to Randall Munroe.
With election season approaching, everyone wants to know how the future of the United States' leadership will shape up. As we turn to data, we can make predictions through inferences of the past and present, especially as statisticians such as Nate Silver would explain. As the title would suggest, in this post I discuss under what conditions, exactly, can we use experimental data to deduce a causal relationship between two or more variables?

"What is art?" Turning to philosophy for answers

This post is introduces definition of what art is. I'll introduce different theories art and consider their respective merits and pitfalls. To start we will need to have a clear idea on what we hope to achieve with a definition of art and what sort of thing that definition would need to be.

Physician Rita Charon on how stories matter to medicine

Tasks like discerning difference between modern and postmodern illness would prove difficult for anyone without appropriate training in the arts and humanities. What is and what isn’t a fact has never been obvious or uncontroversial. There was no golden age of truth. Given the present day notions of post-truth in an era of decreasing trust towards authorities, physicians and other professionals in the field of health care find themselves faced with understanding humanity's struggles in several different points of view. As I sat in the crowded audience of the Warner Theatre in downtown Washington D.C., I was lost in thought. Staring at the paintings that physician and literary scholar Rita Charon discussed, I reflected upon their aesthetic and moral value as they related to medicine. According to Charon, the field of narrative ethics seeks to address these issues. 

The role of neural networks in machine learning

Neural networks (NN) are algorithms used to detect information and conclusions from large sets of data by recognizing underlying relationships in sets of data the same way a human brain does. They hold a tremendous amount of potential for deep learning, part of a broader family of machine learning  (ML) methods based on learning data representations, as opposed to task-specific algorithms. NN and deep learning are now computationally feasible due to GPUs, it shows unbeatable power on complex prediction problems that have very high dimensionality and millions-billions of samples.

An insight into philosopher Paul Feyerabend, an imaginative maverick

    "...And it is of course not true that we have to follow the truth. Human life is guided by many ideas. Truth is one of them. Freedom and mental independence are others. If Truth, as conceived by some ideologists, conflicts with freedom, then we have a choice. We may abandon freedom. But we may also abandon Truth." - "How to Defend Society Against Science", Paul Feyerabend

    Paul Feyerabend (1924-1994), having studied science at the University of Vienna, moved into philosophy for his doctoral thesis, made a name for himself both as an expositor and (later) as a critic of Karl Popper's “critical rationalism”, and went on to become one of the twentieth century's most famous philosophers of science. An imaginative maverick, he became a critic of philosophy of science itself, particularly of “rationalist” attempts to lay down or discover rules of scientific method.

    Meditative thoughts on symmetry in relation to the nature of beauty

    Tunga, Untitled, 2011, ink on paper, 29 7⁄8 × 20". From the series “La voie humide,” 2011–16.
    In approaching the topic of symmetry (in its many forms through nature, philosophy, music, and even logic), we find many different expressions of beauty. Symmetry itself becomes a feature that almost defines beauty in the way we can craft elegant equations in mathematics and physics to our own perceptions of facial features. In symmetry, we find a similarity among all these myriad forms of beauty, and, within symmetry itself, the repetition of a feature creates a sort of rhythm that invokes aesthetic pleasure. In searching for unifying principles among several different perceptions, subjective experiences, and even more objective forms of reasoning, we can view this sort of unity as something that creates defined, certain meaning among many forms. Symmetry becomes a rhythm, like the equality on both sides of an equals sign in a mathematical equation. And, in creating these uniformities among observations, judgements, and perceptions we can deepen our senses of the world and create discoveries in science and philosophy that we couldn't have done before. Unity would seem to be a moment's reflection will show us that unity cannot be absolute and be a form; a form is an aggregation, it must have elements, and the manner in which the elements are combined constitutes the character of the form. A perfectly simple perception, in which there was no consciousness of the distinction and relation of parts, would not be a perception of form; it would be a sensation. This sensation is the key to understanding the relation between moral value and aesthetic pleasure that the arts and sciences invoke within us.

    The story of how I won

    This is the story of how I won. This is the story of how I spoke out against wrongdoing that sought to hurt me fundamentally as a human being. I overcame these struggles with the fearlessness that has been given to me. The world is full of moral ambiguities and existential horrors. Yet I made the right decisions at the right time in such a way that I found success and happiness.

    How to become who you are, according to Nietzsche

    In Hiking with Nietzsche: On Becoming Who You Are, American professor of philosophy John Kaag shows how important and salient philosophy's role in everyday life is. By hiking through mountains and experiencing what the Swiss Alps have to offer, Kaag illustrates a view of Nietzsche's life that provides an intimate understanding of the challenges Nietzsche for which the German philosopher sought answers. Comparing himself to Zarathustra and Dionysus, Nietzsche actualizes his true potential in a way that other philosophers may face struggles with. He's able to overcome the limits and disadvantages of discourse and rumination and, instead, write about the urgency of addressing issues of his time - many of which persist in the present day.

    As I finish reading this book, it's time for me to become who I am.

    Methods of description and explanation in neuroscience

    In my current research on the zebrafish brain, I'm creating a mapping of parts of the brain to the genes which are expressed using mathematics and statistics. This method of devising theoretical models carries difficulties and issues in the way the accuracy and precision of these models. This model of the zebrafish neuroscience holds insight for our methods of using the organism for studying psychiatric disorders. In understanding phenomena of the brain, neuroscientists have various methods of referring to how to both explain and describe the causal mechanisms of the brain. The way our brain interacts with things like stimuli (such as visual imagery or sounds) and creates its own effects (such as neuronal responses in the brain) need to be precise to determine the nature of those phenomena we empirically observe. The 3M (model-mechanist-mapping) constraint is one such method. 

    Reinforcement Learning: Super Mario, AlphaGo and beyond

    There are generally two types of machine learning. Supervised learning is where we have a labeled dataset. This means we already have data from which to develop models using algorithms such as Linear Regression, Logistic Regression, and others. With this model, we can make predictions like, given data on housing prices, what will the cost of a house with a given set of features be. Unsupervised learning, on the other hand, doesn’t have a labeled dataset. The model we create in this setting just needs to derive a pattern amongst the data. We do this with algorithms such as K Means Clustering, K Nearest Neighbors, etc. to solve problems like grouping a set of users according to their behavior in an online shopping portal. But what if we don’t have much data? What if we are dealing with a dynamic environment and the model needs to gather data and learn in real time? Enter reinforcement learning. In this post, I'll take a look at the basics of what reinforcement learning is, how it works and some of its practical applications.