Data Science for Science

  Drug development for the treatment of various diseases is the cornerstone of medicine for human health. Drug development is an expensive and time-consuming process. Most drugs developed up to the human-trials stage end up having no effect at all or too many side-effects. These drugs are thus discarded resulting in wasted time, money and effort. The cost of these drugs end up on the price tags of drugs that do become successful. Before drug development can even take place the drug discovery process must occur. Briefly, drug discovery involves a process of finding promising drug-like molecules that can bind or "dock" properly onto certain protein targets. After successfully docking to the protein, the binding drug, also known as the ligand, can stop a protein from functioning. If this happens to an essential protein of a bacterium, it can kill the bacterium, conferring protection to the human body. Drug discovery, the process of discovering new candidate medications, involves

Hubble Deep Field. Courtesy: Encyclopedia Britannica Space is vast. The number of objects in the Universe is literally astronomical. The first step toward exploring these objects is to identify them aka classify them. Objects could be stars, galaxies, quasars, supernovae etc. Due to the sheer number and complexity of objects the task is very challenging to do manually. The further away a source of light is, the harder it is to distinguish its features and thus classify it. Astronomers have now sought the aid of AI in this task. A machine learning pipeline called SHEEP has been developed. SHEEP first extracts the photometric redshift of celestial objects and then uses this as one of the data features fed into an ML model for training. SHEEP combines several ML algorithms: XGBoost , LightGBM , and Catboost to obtain better classification performance. SHEEP contains two distinct classification methodologies: (i) Multi-class and (ii) one versus all with correction by a meta-learner.

Courtesy: UNDP Moldova Civilian drones today are flown under very specific and safe weather and wind conditions. This is because these drones have adaptive control algorithms that are lacking. This applies to remote-controlled drones as well as autonomous ones. The direct and specific effect of various wind conditions on aircraft dynamics, performance, and stability cannot be accurately characterised as a simple mathematical model. Engineers at Caltech have developed Neural-Fly, a deep-learning method that can help drones cope with new and unknown wind conditions in real time. Existing Machine Learning methods require a huge amount of data to train the model and adapting these large models in real-time is all but impossible. To overcome this, Neural-Fly was developed with a "separation strategy". Only a few parameters of the neural network must be updated in real time and this is achieved with a new meta-learning algorithm, which pre-trains the neural network so that only the

  "Power of the sun in the palm of my hand" Many may remember this line from Toby Maguire's Spiderman 2 uttered by Dr. Otto Octavius. Well, the technology of the power of the sun is not far off in real-life. Usable fusion power has been dreamed of for decades and we get closer to it every year. Practical fusion reactors of today apply heat to atoms to generate fusion plasma. This plasma, when heated to the requisite temperatures (hundreds of millions of degrees), begin to cause atoms to fuse and release large amounts of energy. One day, we hope, the amount of energy used to run the reactor will be surmounted by the energy released, thus providing a clean, unlimited, eco-friendly power source, one to match the sun itself. In order to contain the fusion plasma (after all, earthly materials are not going to be capable of withstanding the heat), magnetic containment is necessary. To help control the delicate process of confinement of ultra-hot plasma, AI techniques are now be

Courtesy: CERN Photolab Physicists have found evidence of mysterious particles known as "X" particles, which were first thought to form just after the Big Bang. "X" particles, called so because of their mysterious unknown inner structure, were created millionths of a second after the Big Bang. In a trillion degree sea of quarks and gluons that randomly collided, "X" particles were formed before the plasma cooled down and such stable particles as protons and neutrons were created. Today, X particles are extremely rare.  X (3872) was first discovered in 2003 by the Belle experiment, a particle collider in Japan that smashes together high-energy electrons and positrons. Within this environment, however, the rare particles decayed too quickly for scientists to examine their structure in detail.  Evidence of X particles in the quark-gluon plasma produced in the Large Hadron Collider (LHC) at CERN based near Geneva, Switzerland has now been found. The LHC's

Courtesy: University of Tennessee Knoxville Hey Folks, We have an intriguing application of Machine Learning (ML) today: Knots and Symmetries in Mathematics! ML, as you're probably aware, works when we have a whole lot of data. In fact, the more data we have, the more likely we are to use ML or its more sophisticated brethren Deep Learning (DL). So without further ado, let's dive in! What are knots and symmetries in mathematics? A knot in mathematics is, as you would expect, inspired from real-life knots but isn't so exactly. Simply put a knot is a 2-D closed loop that exists in 3-D space. This concept could of course be extended to n-dimensional loops in m-dimensional space. Knots can be described mathematically in various ways. Sometimes two seemingly different descriptions are actually of the same knot. This can be determined by comparing properties of knots known as invariants . One of the questions surrounding invariants is: Are two invariants related? To answer this

 Hello Fellow Internet User! How has your day been? If you're like me, you're thinking "Um...good or bad or something in between. It is still a great time to be alive!" Welcome to this blog!  Data Science is everywhere! Because data is everywhere! Data Science is fascinating as it is our very human yearning to know  what all of this data tells us . If not out of curiosity then to find out how those stories can help us in various endeavours. Science and Engineering can also benefit from Data Science, maybe even recursively ;-) This blog is meant to bring together a curated set of advancements on how Data Science has been used to further science and engineering endeavours. Apart from drawing your attention to the substance of these advancements, I shall add my own commentary. Should be a fun ride! Hope you enjoy it! Joie de vivre pour toi! Rahul PS: I am open to comments and suggestions. Please let me know what you think of this blog, its posts or anything else of relev