In 1946, he joined the Ballistics Research Laboratory Weapon Systems Lab, which would later become AMSAA. As a production engineer in the Office of the Chief of Ordnance, he computed the Ordnance materiel requirements needed to equip 100 divisions - the necessary number believed to fight WWII. Golub's distinguished military and civilian career began in 1941, at the beginning of World War II. "This facility is a true representation of Golub's never ending desire to take the Operations Research field to unprecedented heights and the AMSAA workforce has and will continue to embody Golub's legacy." "Abe Golub took a discipline that was virtually unheard of and made it part of the Army's standard processes," Amato said. The professionalism, commitment and dedication of all our analysts, whether deployed in theater or working back at home station, are truly impressive."Īmato cited that same innovative spirit in Golub's legacy. "AMSAA's analysts are the true talent behind the great analytic efforts that we have provided over the decades. "The people in this building will enjoy the resources they need to critically think, to analyze, to brainstorm, to execute, and to take the field of Operations Research and Systems Analysis to a whole other level," Amato said. The facility is intended to be the hub of AMSAA's "wireless campus." It contains state-of-the-art conference capabilities, along with ergonomically designed workstations. James Amato, director of AMSAA, hosted the ribbon-cutting and the unveiling of a plaque describing Golub's career. Army Materiel Systems Analysis Activity dedicated a newly renovated facility to the late Abraham Golub, a pioneer in the Operations Research Community, at a ceremony here June 3. Pictured in front of the new building after the ceremony (from.ĪBERDEEN PROVING GROUND, Md. Army) VIEW ORIGINAL 2 / 2 Show Caption + Hide Caption – James Amato, Director, AMSAA (pictured left) joins Abraham Gol. The video below will be illuminating for beginners, and confirmatory for people with experience.The U.S. Andrew Ng who focus on the practical aspects of making things work in the real world. In Deep Learning we are lucky to have people like Dr. Once you are confident about the quality and consistency of your data, have a go at optimizing your model and improving the accuracy by a couple of percentage points. This should be the first step, and this is the easiest way you will get big wins. It's dirty work but it is crucial to train a good model. medical data.)Ī good data scientist is a good data janitor. This is especially true for small datasets and also for datasets where the engineers do not have the expertise to evaluate results (e.g. It is a common mistake to spend time optimizing the model instead of digging deeper into your data, finding the sources of error, cleaning up the dataset, or taking steps to resolve ambiguity in data.