Contact: 349 Gates Hall, Cornell University
Ithaca, NY, 14853-7501
Email At: (my-first-name)[at]cs[dot]cornell[dot]edu
- Invited talk on Learning with Humans in the Loop at NIPS-2014 Personalization workshop. Slides online.
- Paper on Bayesian Ordinal Peer Grading accepted into ACM L@S 2015 conference. An older version that was presented at the NIPS-2014 workshop on Human-Propelled Machine Learning can be found here. Short set of slides available here.
- Journal paper on Understanding Intrinsic Diversity in Web Search published in ACM TOIS Journal (Oct 2014 issue).
- Invited talk on Interactive Machine Learning with Humans in the Loop at Los Alamos National Laboratory. Slides online.
- Paper on machine learning methods for ordinal peer-grading at scale at KDD 2014. Datasets and Code available. Recording of talk available here. Slides available here.
- Launched web service for peer-grading at scale using machine learning techniques. Details of our method can be found in our Paper. Our toolkit can also be downloaded as a software.
- Won the Best Student Paper Award at SIGIR 2013 for work on Intrinsic Diversity in Web Search. Paper and Slides are online. A recording of a longer version of the talk (by Paul) is also online.
- Paper on online learning of socially optimal information systems at ECML 2013. Slides and Poster are online.
- Awarded Google PhD Fellowship.
- Paper on improving inference in big data pipelines at KDD 2013. Slides and Poster are online.
- Paper on stable coactive learning at ICML 2013. Slides and Poster are online.
- Awarded Yahoo! Key Scientific Challenge Award.
- Machine Learning, Structured Prediction, Online Learning
- Web Search, Information Retrieval, Learning to Rank, Rank Aggregation, Recommender Systems
- Educational Analytics, Peer Grading, Machine Learning for MOOCs
- Data Science, Big Data Analytics, Data Pipelines, Data Mining
- I am also interested in Natural Langauge Processing, Information Networks and Game Theory
- PeerGrading-Toolkit: Python-based toolkit for the peer-grading problem. We also have a web-service: peergrading.org.
- SVM-Dyn: Python-based Structural SVM-based approach used to predict two-level dyanmic rankings.
- HotelRev-Scrape: Python module for scraping hotel reviews from popular travel websites.
- RateProf-Scrape: Python module for scraping review information from ratemyprofessor.com.
- I manage the experimental text search engine for arxiv.org.
- I also develop algorithms for the recommendation engine at my.arxiv.org.
You can find a complete list of my publications along with additional resources including presentations, posters, bibtex and talk videos here.
- Bayesian Ordinal Peer Grading
: Karthik Raman and Thorsten Joachims, ACM L@S 2015, Vancouver, Canada, March 2015
- Understanding Intrinsic Diversity in Web Search: Improving Whole-Session Relevance
: Karthik Raman, Paul N. Bennett and Kevyn Collins-Thompson, ACM TOIS, October 2014 Issue
- Methods for Ordinal Peer Grading
: Karthik Raman and Thorsten Joachims, KDD 2014, New York, USA, August 2014
- Learning Socially Optimal Information Systems from Egoistic Users
: Karthik Raman and Thorsten Joachims, ECML 2013, Prague, Czech Republic, September 2013
- Beyond Myopic Inference in Big Data Pipelines
: Karthik Raman, Adith Swaminathan, Johannes Gehrke and Thorsten Joachims, KDD 2013, Chicago, USA, August 2013
- Toward Whole-Session Relevance: Exploring Intrinsic Diversity in Web Search
: Karthik Raman, Paul N. Bennett and Kevyn Collins-Thompson, SIGIR 2013, Dublin, Ireland, July 2013
[Best Student Paper Award]
- Stable Coactive Learning via Perturbation
: Karthik Raman, Thorsten Joachims, Pannaga Shivaswamy and Tobias Schabel, ICML 2013, Atlanta, USA, June 2013
- Learning from Our Mistakes: Towards a Correctable Learning Algorithm
: Karthik Raman, Krysta M. Svore, Ran Gilad-Bachrach and Chris Burges, CIKM 2012, Maui, USA, October 2012
- Online Learning to Diversify from Implicit Feedback
: Karthik Raman, Pannaga Shivaswamy and Thorsten Joachims, KDD 2012, Beijing, China, August 2012
- Structured Learning of Two-Level Dynamic Rankings
: Karthik Raman, Thorsten Joachims and Pannaga Shivaswamy, CIKM 2011, Glasgow, Scotland, October 2011
- Multilingual Relevance Feedback: Performance Study of Assisting Languages
: Manoj Chinnakotla, Karthik Raman and Pushpak Bhattacharyya, ACL 2010, Uppsala, Sweden, July 2010.
- Multilingual PRF: English Lends a Helping Hand
: Manoj Chinnakotla, Karthik Raman and Pushpak Bhattacharyya, SIGIR 2010, Geneva, Switzerland, July 2010
- On Improving Pseudo-Relevance Feedback using Pseudo-Irrelevant Documents
: Karthik Raman, Raghavendra Udupa, Abhijit Bhole and Pushpak Bhattacharyya, ECIR 2010, Milton Keynes, UK, March 2010
You can also find me on Google Scholar, DBLP and ACM.
- Ph.D. in Computer Science (Minor in Applied Mathematics) at Cornell University (Expected: June 2015)
- M.S. in Computer Science at Cornell University (2013)
- B.Tech at IIT Bombay (2010)
- SIGIR 2013 Best Student Paper Award
- Google PhD Fellowship in Search and Information Retrieval 2013
- Yahoo Key Scientific Challenge Award 2011
- Cornell Olin Fellowship 2010-2011
- WSDM 2015 Outstanding Reviewer Award
- Cornell Teaching Assistance Excellence Award 2011, 2013
- CBSE Merit Scholarship (2006)
- National Talent Search Scholarship (2004)
- Intern at Google: May-August 2014
- Research Intern at MSR, Redmond: May-August 2012
- Research Intern at MSR, Redmond: May-August 2011
- Research Intern at MSR,Bangalore: May-July 2009
- TA: CS4780/5780 (Machine Learning) Fall 2011, Fall 2013, Fall 2014
- Lectures: CS4780/5780 (Machine Learning) on 9/11/12
- Students Mentored/Co-Advised: Ashueep Singh (Fall 2014), Ziyu Fan (Fall 2013, Spring 2014), Akhilesh Potti (Fall 2013, Spring 2014), Tobias Schnabel (Spring 2012), Diego Accame (Spring 2012)
- PC Member: WWW (2014, 2015); WSDM (2015); ICML (2014, 2015); KDD (2015); SIGIR (2014); ECML (2013, 2014); CIKM (2013), MOD (2015); IKDD (2014); CaRR (2013)
- Reviewer: KDD(2014); ICML (2013); CIKM (2012); SIGIR (2012); AAAI (2012); IJCNLP (2011)
- Journal Reviewing: JMLR, Machine Learning, ACM TOIS