We present a system called ALIAS, that is designed to search for duplicate authors from Microsoft Academic Search Engine dataset. Author-ambiguity is a prevalent problem in this dataset, as many authors publish under several variations of their own name, or different authors share similar or same name. ALIAS takes an author name as an input (who may or may not exist in the corpus), and outputs a set of author names from the database, that are determined as duplicates of the input author. It also provides a confidence score with each output. Additionally, ALIAS has the feature of finding a Top-k list of similar authors, given an input author name. The underlying techniques heavily rely on a mix of learning, mining, and efficient search techniques, including partitioning, clustering, supervised learning using ensemble algorithms, and indexing to perform efficient search to enable fast response for near real time user interaction. While the system is designed using Academic Search Engine data, the proposed solution is generic and could be extended to other problems in the category of entity disambiguation. In this demonstration paper, we describe different components of ALIAS and the intelligent algorithms associated with each of these components to perform author name disambiguation or similar authors finding.