Difference between revisions of "Ashutosh Jadhav"

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==Search Intent Mining==
 
==Search Intent Mining==
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* Understanding intent from search queries is crucial for designing an intelligent search engine. The objective of this project is to identify health information seeking intent from queries.
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* Motivation
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* First, we conducted three qualitative focus group studies to get users’ perspective on online health information seeking. Second, we selected 14 consumer oriented health search intent classes based on inputs from focus group studies and based on analysis of popular health websites, literature and empirical study.
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**Understanding intent from search queries is crucial for designing an intelligent search engine. The objective of this project is to identify health information seeking intent from queries.
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* Finally, I developed a semantic-driven intent framework for the identification of health seeking intent in disease agnostic manner. The framework is based on rich background knowledge from UMLS and utilizes UMLS semantic types and concepts for the search intent classification. The framework can semantically classify health search queries into 14 intent classes such as symptoms, treatments, food and diet, living with prevention.  
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*Approach
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* I developed the framework using Big Data technologies such as Hadoop-MapReduce, Hive and HBase to process millions of search queries efficiently.   
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**First, we conducted three qualitative focus group studies to get users’ perspective on online health information seeking. Second, we selected 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analysis of popular health websites, literature survey, and empirical study.
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* This framework will be integrated at Mayo Clinic to create user interest profiles based on their search history. The user profiling will be further used for personalized health information intervention, content recommendation and targeted advertisements.
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**Finally, I developed a semantic-driven intent framework for the identification of health seeking intent in disease agnostic manner. The framework is based on rich background knowledge from UMLS and utilizes UMLS semantic types and concepts.  
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**I developed the framework using Big Data technologies such as Hadoop-MapReduce, Hive, and HBase to process millions of search queries efficiently.   
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*Real World Application
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**This framework will be integrated at Mayo Clinic to create user interest profiles based on their search history. The user profiling will be further used for personalized health information intervention, content recommendation, and targeted advertisements
  
 
==Semantic Query Expansion==
 
==Semantic Query Expansion==

Revision as of 19:43, 21 July 2015

Ashu-2.png

Graduate Research Assistant - Kno.e.sis - Ohio Center of Excellence in Knowledge-enabled Computing
Ph.D. Candidate - Computer Science and Engineering Wright State University
Advisor - Dr. Amit Sheth

LinkedIn profile
Google Scholar
Bibliography on NCBI PubMed

Education and Work Experience

Education

Work Experience

  • Research Intern, Health Science Research, Mayo Clinic, Rochester, MN (May 2013 - May 2014)
    • 1) Developed a consumer-centric health categorization framework using Semantic Web techniques 2) Semantic Intent mining for health search, based on rich knowledge from UMLS and novel approach for semantic similarity computation 3) Qualitative analysis of online health information seeking using data science techniques
    • Mentor Dr.Jyotishman Pathak
  • Software Developer Intern, Speedway SuperAmerica LLC, Enon, Ohio (June 2008 – Sept 2008)
    • Worked with credit card transaction department on a database transaction integrity project involving the implementation of scripts to automatic verification and integrity/quality checks.


Research Projects

Search Intent Mining

  • Motivation
    • Understanding intent from search queries is crucial for designing an intelligent search engine. The objective of this project is to identify health information seeking intent from queries.
  • Approach
    • First, we conducted three qualitative focus group studies to get users’ perspective on online health information seeking. Second, we selected 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analysis of popular health websites, literature survey, and empirical study.
    • Finally, I developed a semantic-driven intent framework for the identification of health seeking intent in disease agnostic manner. The framework is based on rich background knowledge from UMLS and utilizes UMLS semantic types and concepts.
    • I developed the framework using Big Data technologies such as Hadoop-MapReduce, Hive, and HBase to process millions of search queries efficiently.
  • Real World Application
    • This framework will be integrated at Mayo Clinic to create user interest profiles based on their search history. The user profiling will be further used for personalized health information intervention, content recommendation, and targeted advertisements

Semantic Query Expansion

  • First step in search query processing is to understanding user’s information need from the submitted search query and reformulating a seed query to improve retrieval performance (Query Expansion).
  • Multiple IR studies have shown that search engine’s performance degrades with increase in the complexity of the search query. In this project, I am working on semantic query expansion specifically focusing on complex health search queries (queries with multiple concepts).
  • In this research, I am extending current query expansion approaches by leveraging health domain semantics with a) consumer health vocabulary b) UMLS concept hierarchy c) semantic similarity between concepts


Social Health Signals

  • The objective of this project is to understand and satisfy users’ need for keeping track of new information in the healthcare and well-being domain.
  • The project harvests collective intelligence to aggregate high quality, reliable, and informative healthcare content shared over social media on one platform, Social Health Signals (SHS).
  • We utilized a hybrid approach based on rule-based filtering and supervised Machine Learning classification to facilitate identifying informative health-related information. SHS capabilities includes:
    • to retrieve relevant and reliable health information shared on Twitter in real-time
    • to enable question answering on Twitter data
    • to rank results based on relevancy, popularity and reliability
    • to enable efficient browsing of the results, we semantically categorize the information into health categories such symptom, food and diet, healthy living and prevention


Twitris- social media analysis and research platform


Device Effect on Online Information Seeking

  • Personal Computers (Desktops, Laptops) and Smart Devices (Smartphones, Tablets) have distinct characteristics in terms of readability, user experience, accessibility, etc.
  • As search traffic from Smart Devices is exponentially increasing, it is critical to understand the effects of the device used for online information seeking.
  • Such knowledge can be applied to improve the search experience and to develop more advanced next-generation knowledge and content delivery systems.
  • In this study, we performed comparative analysis of large-scale (more than 100 million) health search queries submitted through Web search engines from both types of devices.


Context-aware Content Recommendation in Enterprise Social Network

Research Internship at HP Services Research Lab, HP Labs, CA
Claudio Bartolini (manager) and Hamid Motahari (mentor)

  • During my HP Labs internship, I worked with HP Services Research team on a context-aware computing research project titled ‘Context-aware content recommendation in enterprise social network’.
  • Objective of the project is to expedite Request For Proposal (RFP) response process by making right content recommendation (past deal documents) at right time.
  • We developed a document recommender system using semantic document similarity approach taking into account information about content (current and past documents), enterprise social network (people and their role) and different stages of enterprise RFP process.

Based on my internship work, HP Labs filed a patent (83138852) on Context-Aware Deal Recommendations with myself being primary inventor.


Disaster Data Informatics for Situational Awareness

  • I lead the research, design and development of Air Force Research Lab (AFRL) funded project to create Geo-Social mash-up for situational awareness in a disaster response situation.
  • The objective of this project was to expedite decision-making process in the disaster situation by identifying location-based actionable information from social media.


Knowledge Acquisition from Community-Generated Content

  • This project aims at generating or extracting a domain model from Wikipedia or other similarly structured knowledge sources.
  • My contribution: I worked on extraction of links (wikipedia links and web URLs) mentioned on whole corpus of Wikipedia articles as a part of Doozer, a tool for automatically creating topic domain model using Wikipedia corpus.


Technical Skills

  • Programming: Java, PHP, HTML, XML, Scheme
  • Big Data Technologies: Hadoop, MapReduce, HBase, Hive
  • Databases: MySQL, MS Access, Virtuoso triple store
  • Semantic Web: OWL, RDF, SPARQL
  • Research Tools: Weka, Protege
  • Operating Systems: Mac OS, Linux, MS Windows


Big & Smart Data Certification from Computer Science Department, Wright State University, Dayton, OH

  • To manage and analyze Big Data and to create Smart Data enabled applications for enterprises and individuals.


Publications

Google Scholar Bibliography on NCBI PubMed

  • Twitris: Socially Influenced Browsing
    • Ashutosh Jadhav, Wenbo Wang, Raghava Mutharaju, Pramod Anantharam, Vinh Nguyen, Amit P. Sheth, Karthik Gomadam, Meenakshi Nagarajan, and Ajith Ranabahu
    • Semantic Web Challenge 2009, 8th International Semantic Web Conference, Oct. 25-29 2009, Washington, DC, USA
  • HP patent on Context-Aware Information Recommendation, filed in January 2013
    • Patent filled based on HP summer 2011 internship work
    • Ashutosh Jadhav, Hamid Motahari, Susan Spence, Claudio Bartolini


Research Grants and Proposals

( significant contribution)


  • NIH-R01 proposal (in resubmission)
    • Modeling Social Behavior for Healthcare Utilization and Outcomes in Depression
    • In collaboration with Mayo Clinic and partly based on my Mayo Clinic internship work


  • Air Force Research Lab (AFRL) proposals
    • SIDFOT (Sensors Integration for Data Fusion in Operations and Training) project
      • Title: Geo-Social mash-up for situational awareness in a disaster response situation
      • Funded project: 2010-2011, Real-time Twitris
    • Information Operations/Cyber Exploitation Research (ICER) Program, City Beat
      • Title: Social media analysis for situational awareness
      • Funded project: 2011-2012
    • WBI's Tec^Edge Innovation and Collaboration Center (Tec^Edge ICC)
      • Funded project: Summer 2010, Summer 2011


  • Mayo Clinic Meritorious Award
    • Healthcare trend surveillance using social networks and health search queries (funded 2013)
    • What makes a health-related tweet informative – patients’ perspective (funded 2014)


Selected Coursework

  • Semantic Web
  • Cloud Computing
  • Data Mining
  • Web 3.0 and Social Semantic Web
  • Machine Learning
  • Knowledge Representation for the Semantic Web
  • Parallel Programming with MPI
  • Distributed Computing Principles
  • Web Information System
  • Data Structures and Algorithms
  • Database Systems and Design
  • Advance Database Systems
  • Computer Engineering Mathematics
  • Computer Vision
  • Advanced Computer Networks
  • Multimedia Coding and Communication
  • Comparative languages
  • Computer Architecture I and II


Mentorship

  • Nishita Jaykumar
    • Computer Science Masters
    • Project in Social Health Signals
    • Jan. 2013 – Apr. 2013
  • Sreeram Vallabhaneni
    • PhD student
    • Project: Semantic understanding of health related post shared on twitter
    • Apr. 2012 – Aug. 2012
  • Michael Cooney
    • CS Undergrad
    • Projects: Twitris, Disaster Situation Awareness
    • 2010 – 2011


Professional Activities

  • Reviewer AMIA Annual Symposium 2015
  • Subreviewer for ICWSM 2011, 2012, 2013, 2014, 2015
  • External sub-reviewer for World Wide Web 2013, ‘Social Networks and Graph Analysis’ track
  • External reviewer for IEEE Internet Computing Magazine
  • External reviewer for Semantic Web Journal


Selected Presentations and Talks

  • Semantic Analysis of Online Health Information Seeking for Cardiovascular Diseases, AMIA Annual Symposium, Washington DC, USA 2014
  • Semantic Intent Mining for Health Search, European Medical Informatics Conference (MIE 2014), Istanbul, Turkey
  • Context-Aware Content Recommendation In Enterprise Pursuit Process, HP Research Labs, 2011
  • Enterprise Social Network: State of the Art, presented at HP Research Labs, 2011


Contact Information

  • Ashutosh Jadhav
    • Email: ashutosh@knoesis.org
    • 380 Semantic Web lab, Joshi Research Center, Wright State University, 3640 Colonel Glenn Hwy, Dayton, Ohio 45435-0001
    • LinkedIn
    • Google Scholar


Curriculum Vitae (CV)

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