Difference between revisions of "Ashutosh Jadhav"

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**Played a vital role in enhancing efficiency (speedup) of the meta-search engine by replacing existing linear search with perfect hashing technique for table look-up
 
**Played a vital role in enhancing efficiency (speedup) of the meta-search engine by replacing existing linear search with perfect hashing technique for table look-up
  
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== Contact Information ==
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= Contact Information =
 
*Ashutosh Jadhav
 
*Ashutosh Jadhav
 
**Email: ashutosh@knoesis.org
 
**Email: ashutosh@knoesis.org

Revision as of 20:18, 15 May 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


Research Projects

Search Intent Mining

Objective of this project is to identify users’ search intent within a domain by developing a semantic framework leveraging the domain specific background knowledge. We have selected health domain for the evaluation of the framework. We developed a semantic driven intent framework for identification of health seeking intent in agnostic manner. The framework is based on rich background knowledge from UMLS and utilizes UMLS semantic types and concepts for the search intent classification. Using the intent framework and data analysis techniques, we demonstrated that device choice (personal computers: desktop, laptop and smart devices: smartphone, tablets) influences online health information seeking. We are working on integration of this framework at Mayo Clinic for personalized health information intervention and targeted advertisements.

Social Health Signals

Objective this project is to understand and satisfy users need for keeping track of new information in healthcare and well-being domain. The project harvest collective intelligence to identify high quality, reliable and informative healthcare content shared over social media based on following analysis: Text Analysis: Structural, syntactic and linguistic analysis; Semantic analysis: Concept identification, annotation using UMLS and Wikipedia; Reliability analysis: Website (from the message) credibility based on Google PageRank; Popularity (social engagement) Analysis: Twitter (tweets, retweets), Facebook (like, share, comment), Google plus (share, +1) for the message

Twitris- social media analysis and research platform

I have been one of the mainstays of the Twitris project at Kno.e.sis. Twitris facilitates understanding of social perceptions using semantic processing of massive amounts of event-centric data. Twitris addresses challenges in large scale processing of social data and information overload in social media. Twitris 2.0 also covers context based semantic integration of multiple Web resources and expose semantically enriched social data to the public domain. Specifically, I coordinated the development, analysis and UI design for Twitris and Twitris 2.0. I contributed to text analysis, information extraction and information integration components of the system. Kno.e.sis won a NFS research grant and filed a patent based on the Twitris work. Articles on Twitris has been featured on semanticweb.com, mashable.com etc.

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.

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)

(Due to confidentiality reasons, limited information is provided)
During 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.


Technical Skills

  • Programming Languages: JAVA, C, PHP, HTML, XML, Scheme
  • DBMS  : MySQL, MS Access, Virtuoso triple store
  • Semantic Web  : OWL, RDF, SPARQL
  • Tools  : Weka, Protege
  • Operating Systems  : Mac OS, Linux, MS Windows


Publications

Google Scholar Bibliography on NCBI PubMed

  • Empowering Personalized Medicine with Big Data and Semantic Web Technology: Promises, Challenges, Pitfalls, and Use Cases
    • Maryam Panahiazar, Vahid Taslimi, Ashutosh Jadhav, Amit Sheth, and Jyotishman Pathak
    • IEEE International Conference on Big Data (IEEE BigData 2014), October 27-30, 2014, Washington DC, USA
  • 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


Professional Activities


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 the database transaction integrity project. During the internship, I developed macros in TACL programming language for HP NonStop Servers that are used to handle SSA’s credit card transaction and database integrity.
  • Graduate Research Assistant, Data Mining Lab, Wright State University (Jan 2008 - June 2008)
    • Advisor: Prof. Guozhu Dong
    • Played a vital role in enhancing efficiency (speedup) of the meta-search engine by replacing existing linear search with perfect hashing technique for table look-up

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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