Khan Academy

Khan Academy is a non-profit educational organization created in 2006 by educator Salman Khan with the aim of providing a free, world-class education for anyone, anywhere. The organization produces short lectures in the form of YouTube videos. In addition to micro lectures, the organization's website features practice exercises and tools for educators. All resources are available for free to anyone around the world. The main language of the website is English, but the content is also available in other languages.

History

The founder of the organization, Salman Khan, was born in New Orleans, Louisiana, United States to immigrant parents from Bangladesh and India. After earning three degrees from the Massachusetts Institute of Technology (a BS in mathematics, a BS in electrical engineering and computer science, and an MEng in electrical engineering and computer science), he pursued an MBA from Harvard Business School.

In late 2004, Khan began tutoring his cousin Nadia who needed help with math using Yahoo!'s Doodle notepad.When other relatives and friends sought similar help, he decided that it would be more practical to distribute the tutorials on YouTube. The videos' popularity and the testimonials of appreciative students prompted Khan to quit his job in finance as a hedge fund analyst at Connective Capital Management in 2009, and focus on the tutorials (then released under the moniker "Khan Academy") full-time.

Computer science

Computer science is the scientific and practical approach to computation and its applications. It is the systematic study of the feasibility, structure, expression, and mechanization of the methodical procedures (or algorithms) that underlie the acquisition, representation, processing, storage, communication of, and access to information. An alternate, more succinct definition of computer science is the study of automating algorithmic processes that scale. A computer scientist specializes in the theory of computation and the design of computational systems.

Its fields can be divided into a variety of theoretical and practical disciplines. Some fields, such as computational complexity theory (which explores the fundamental properties of computational and intractable problems), are highly abstract, while fields such as computer graphics emphasize real-world visual applications. Still other fields focus on challenges in implementing computation. For example, programming language theory considers various approaches to the description of computation, while the study of computer programming itself investigates various aspects of the use of programming language and complex systems. Human–computer interaction considers the challenges in making computers and computations useful, usable, and universally accessible to humans.

Machine learning

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.

Machine learning is closely related to and often overlaps with computational statistics; a discipline which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR),search engines and computer vision. Machine learning is sometimes conflated with data mining, where the latter sub-field focuses more on exploratory data analysis and is known as unsupervised learning.

David J

David John Haskins (born 24 April 1957, Northampton, England), better known as David J, is a British alternative rock musician, producer, and writer. He was the bassist for the gothic rock band Bauhaus and Love and Rockets.

He has composed the scores for a number of plays and films, and also wrote and directed his own play, Silver for Gold (The Odyssey of Edie Sedgwick), in 2008, which was restaged at REDCAT in Los Angeles in 2011, and The Chanteuse and The Devil's Muse, 2011. His artwork has been shown in galleries internationally, and he has been a resident DJ at venues such as the Knitting Factory.

David J has released a number of singles and solo albums, and in 1990 he released one of the first No. 1 hits on the newly created Modern Rock Tracks charts, with "I'll Be Your Chauffeur." He released the single "Tidal Wave of Blood" in 2010 with Shok, and he has a new album, An Eclipse of Ships, due out in April 2014. He is currently touring with his new band, The Gentleman Thieves.

Early life

David Malan (computer scientist)

David J. Malan /deɪvɪd dʒeɪ meɪlɛn/ is a professor of Computer Science at the Harvard School of Engineering and Applied Sciences. His research interests include cybersecurity, digital forensics, botnets, computer science education, distance learning, collaborative learning, and computer-assisted instruction.

Malan teaches Computer Science 50, the largest course in Harvard College, known as CS50x at edX. He also teaches at Harvard Extension School and Harvard Summer School.

Professional career

During undergraduate study at Harvard, Malan worked part-time for the Middlesex District Attorney's Office as a forensic investigator, after which he founded his own two startups. On the side since 2003, he volunteered as an emergency medical technician (EMT-B) for MIT-EMS. He continues to volunteer as an EMT-B for the American Red Cross.

He professionally worked for Mindset Media, LLC during 2008 – 2011 as Chief Information Officer (CIO), where he was responsible for advertising network’s scalability, security, and capacity-planning. Designed infrastructure for collection of massive datasets capable of 500M HTTP hits per day with peaks of 10K per second. Later Mindset Media was acquired by Meebo, Inc.