VISHAL KATARIYA, B. Tech.
Vishal Katariya is a fresh engineering physics graduate from the Indian Institute of Technology Madras, India. He is passionate about quantum computing and information theory, and is about to join the Quantum Science and Technologies Group at Louisiana State University to commence research towards a PhD. His area of study will be quantum information, and the application of such constructs in the real world. He has had experience in working on a number of quantum physics projects, stretching from purely theoretical studies of quantum information to experimental realisations of quantum mechanics. He is also passionate about machine learning, recognising a paradigm that will influence much of our modern lives. His passion for mathematics, computer science and physics has drawn him to quantum machine learning, which brings together in a fascinating manner these subjects.
RAUL COTO, Ph.D.
In 2009 Dr. Coto joined the Pontificia Universidad Catolica de Chile and under the supervision of Miguel Orszag he developed his career as a physicist. He successfully completed his physics bachelor's' degree in 2011. In 2015 he completed the physics PhD with a solid foundation in quantum optics and quantum information, after which he joined as a postdoc. He has recently been working on performing quantum information in hybrid systems like spin-mechanics and solid state systems. He is interested in all the benefits that quantum information and quantum computing can bring to our daily life.
CAMERON CROWELL, M. Eng.
Cameron Crowell is a recent materials science and engineering masters graduate from Virginia Tech. During his college career, Cameron was known for solving numerical problems in creative ways and leaving extensive comments throughout his codes. Cameron's passion for programming came in middle school where he programmed his TI-83 graphing calculator to do the redundant algebraic arithmetic for him. He honed his programming skills modelling the natural world on projects related to quantum machine learning as well as personal projects like game servers and computer networking. In his free time he is learning new coding languages and applying them in creative ways. Cameron's immense curiosity, programming capabilities and science background help the team create unique solutions through quantum machine learning.
PRAKASH MADHAVAN, M. Sc. Eng.
Prakash is a Mechanical Engineering Masters graduate from the University of Texas at Austin. Prior to enrolling in graduate program, he completed a dual Physics and Mechanical Engineering undergraduate program from Birla Institute of Technology & Science, Pilani, India. His professional life has been involved in the design and engineering of pipelines/subsea equipment for the offshore industry. His academic experience involved implementation of genetic algorithms to improve the design of a precision mechanism. With his background in numerical simulation and interest in programming, he looks forward to applying and learn the techniques of quantum machine learning.
SANTIAGO SERRANO, B.S.
Santiago is an Electronics Engineer graduate of Universidad Nacional de Colombia and is in the process of obtaining an additional undergraduate degree in physics. For the past five years he has been passionate about quantum computing and quantum information technology, gaining experience in atomistic simulation of quantum systems, NMR to implement quantum circuits and to experimentally simulate arbitrary Hamiltonians, and quantum programming. Santiago is also well versed on the design and implementation of control and instrumentation schemes, and the simulation/visualization of physical systems. Some of the research areas he finds most engaging include the quantum mechanical approach for cryptography, machine learning, and artificial inteligence.
Juan is a senior undergraduate physics student at the National University of Colombia who is interested in quantum computer simulation. He is currently developing new tools to extend the scope of his computer simulation abilities, which include cellular automation, machine learning, PDEs, statistical mechanics, fluid dynamics, and quantum mechanics. In order to develop his abilities as a programmer, Juan has taken many courses in computer simulation. He specializes in the Lattice-Boltzmann method, the finite difference method, the finite element method, and the Monte Carlo method. Juan is always looking for new ways to use the computer in his work, among these, quantum machine learning.