Ashish Nanda

Seattle, Washington, United States Contact Info
1K followers 500+ connections

Join to view profile

About

Engineering Manager having over 8 years of experience, with 3.5 years of leading multiple…

Activity

Join now to see all activity

Experience & Education

  • Amazon Web Services (AWS)

View Ashish’s full experience

See their title, tenure and more.

or

By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.

Licenses & Certifications

Publications

  • Extreme Learning Machines in the Field of Text Classification

    IEEE Computer Society Press. Proceedings of the 16th IEEE/ACIS SNPD International Conference

    The World Wide Web serves as a huge repository of information that is highly dynamic, diverse and growing at an exponential rate in a lightening speed. In order to speed-up and further improve tasks like information search and retrieval, personalization etc; it is highly important to develop techniques to classify text documents more accurately and efficiently than before. This paper is an effort in that direction, where the effectiveness of Extreme Learning Machines(ELM) in the domain of text…

    The World Wide Web serves as a huge repository of information that is highly dynamic, diverse and growing at an exponential rate in a lightening speed. In order to speed-up and further improve tasks like information search and retrieval, personalization etc; it is highly important to develop techniques to classify text documents more accurately and efficiently than before. This paper is an effort in that direction, where the effectiveness of Extreme Learning Machines(ELM) in the domain of text classification is studied and compared with many of the existing relevant techniques like Support Vector Machines(SVM), which are currently one of the most popular and effective techniques for classifying text documents. Ours is one of the few works that highlight the high performance of ELM in the field of text classification, by implementing classifiers based on different interpretations of ELM, analyzing their performance, and studying which feature selection techniques are most suited to improve their accuracy. In our multi-class classification problem, we studied a single ELM classifier based on the one-against-all scheme, and a multi-layer ELM classifier inspired from deep networks, and then perform extensive experiments on different datasets to demonstrate the applicability and effectiveness of our approach. Results show that ELM based classifiers can outperform many of the traditional classification techniques including the most powerful state-of-the-art technique such as SVM.

    Other authors
    See publication

  • Personalized Web Page Recommendation Using a Graph-Based Approach to Implicitly Find Influential Users

    Springer. Proceedings of the 2014 International Conference on Intelligent Computing, Communication and Devices

    In this paper, we propose a novel graph-based approach for modeling the browsing data of Web users in order to understand their interests and their relationship with other users in the network. The aim was to identify users who are more influential while recommending pages to a network of users with similar interests. We call these users influential users and assign them an influence score that indicates the extent to which similar users follow their recommendations. By monitoring the browsing…

    In this paper, we propose a novel graph-based approach for modeling the browsing data of Web users in order to understand their interests and their relationship with other users in the network. The aim was to identify users who are more influential while recommending pages to a network of users with similar interests. We call these users influential users and assign them an influence score that indicates the extent to which similar users follow their recommendations. By monitoring the browsing activity of influential users, we can identify their interest profiles as well as relevant pages quickly, and recommend these pages to users with similar interests. We call our proposed graph-based model a recommendation network. In this graph, nodes represent users and an edge between users u and v expresses the fact that u and v have similar interests, in particular the weight of the edge is the degree to which the user interest profiles match. Based on the graph, we build a recommendation system for Web pages, taking into account the influence of users in a network. Experimental results that measure the precision, with which recommended Web pages are visited by users, indicate that our system performs significantly better than traditional collaborative filtering-based recommender systems.

    Other authors
    • Rohit Omanwar
    • Bharat Deshpande
    See publication

  • Implicitly Learning a User Interest Profile for Personalization of Web Search Using Collaborative Filtering

    IEEE Computer Society Press. Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

    The increasing abundance of content on the web has made information filtering even more important in helping users find information related to their interests. Personalization of web search is one such effort, that aims at improving the efficiency with which a user finds results relevant to his query. This is done by keeping track of a user's individual interests, and taking it into account while returning search results. We propose a robust user modeling technique that implicitly creates a…

    The increasing abundance of content on the web has made information filtering even more important in helping users find information related to their interests. Personalization of web search is one such effort, that aims at improving the efficiency with which a user finds results relevant to his query. This is done by keeping track of a user's individual interests, and taking it into account while returning search results. We propose a robust user modeling technique that implicitly creates a Dynamic Category Interest Tree (DCIT), using a general ontology of the web and a set of web pages collected over time that give an insight into a user's interests. The DCIT is designed to use a fuzzy classification technique to keep track of what topics a user is interested in, his amount of interest in a topic, as well as reflect his changing interests overtime. The DCIT consists of a general ontology of the web, where each node represents a topic and consists of keywords that are usually used to describe that topic or category. Additional keywords that the user frequently associates with a topic, such as names of important people, organizations, or a specialized terminology, etc. Are also incorporated into the relevant topic. We use the Apriori Algorithm to extract these associated words from the user's web history in order to more accurately define the user's categories of interest. The DCIT is initially created by a content based approach using only the browsing history of the user, and is later further enhanced through collaborative filtering using the k-nearest neighbour-based algorithm. We propose a technique to re-rank the results from a search engine according to their relevance to a user, based on his implicitly learned DCIT. According to experimental results, our DCIT based ranking often outperforms search engines such as Google when it comes to retrieving web pages that are more relevant to a user's interest.

    Other authors
    • Rohit Omanwar
    • Bharat Deshpande
    See publication

Courses

  • Advanced Big Data Analytics

    -

  • Advanced Machine Learning

    -

  • Analysis of Algorithms

    -

  • Cloud Computing & Big Data

    -

  • Financial Computing

    -

  • Internet Technology and Economics

    -

  • Machine Learning

    -

  • Product Management and Tech Innovation

    -

  • Serverless Cloud Applications

    -

  • Tech Entrepreneurship

    -

Projects

  • GitRepo Bot: A Facebook Messenger Chatbot for GitHub Repositories

    Built a Facebook Messenger Chatbot using Node.js & the Serverless framework, that helps users search for relevant repositories on GitHub. It can also return software development themed jokes if the user requests them.

    See project

  • Palate: An App for Restaurant Recommendations & Analytics

    Developed an app for restaurant recommendations & analytics using Python, Flask, AWS EBS & Spark that leveraged Point of Sale (POS) data to provide instant feedback, booking & intelligent analytics to restaurant managers, along with personalized recommendations & rewards to customers based on ordering history.

    See project

  • Predicting Virality of Social Media Content

    Analyzed a large dataset from the online news outlet, Mashable using Python, R & Spark, and developed an interactive visualization app to show the influential users in each content category. Used sentiment analysis to track the emotional response & the Random Forests classifier to predict which set of features can result in the article going viral.

    See project

More activity by Ashish

View Ashish’s full profile

  • See who you know in common
  • Get introduced
  • Contact Ashish directly
Join to view full profile

Other similar profiles

Explore more posts

Explore collaborative articles

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.

Explore More

Others named Ashish Nanda in United States

6 others named Ashish Nanda in United States are on LinkedIn

See others named Ashish Nanda

Add new skills with these courses

See all courses