PAKDD Early Career Award
Award Rules (Effective for 2018 Award)
This award is based on an individual’s whole body of work in the first 10 years after PhD. The award aims to promote young researchers in KDD fields as they create their career.
- Annual call for nominations and may be awarded to an individual (if there is at least one qualified candidate) and consists of a plaque (and potentially cash) to the awardee. The recipient will receive the award at the annual PAKDD Conference during the awards session.
- The candidate’s PhD should have been awarded no earlier than December 31st, 2007.
- The candidate can be anyone except the current PAKDD Awards Committee.
- The candidate must have published at least one full/long paper in PAKDD, and attended at least one meeting to present his/her results.
- The award is given in recognition of the researcher’s overall research contributions in KDD fields since the awarding of the PhD.
3. Nomination Process
- Anyone in the field can nominate one person (self-nominations are excluded). (Nominated by PAKDD SC members for the 2018 award)
- Nominations should include a proposed citation (up to 25 words) and a detailed statement to justify the nomination (500 words or less).
- In addition to the nomination, there should be an endorsement letter (approximately a page) from at least one other SC member.
- Nominations must be received by March 30th, 2018 to be considered for this year’s award.
- Nominations should be sent to current PAKDD Awards Committee Chair, Jaideep Srivastava (email: firstname.lastname@example.org).
- Nominations that did not result in an award can be resubmitted or updated in subsequent years as long as the eligibility conditions for the award still hold.
The Awards Committee will evaluate all nominations and decide on zero or more winners.
The Steering Committee is pleased to award the PAKDD Early Career Award for 2018 to Prof. Yang Yu, for demonstrating the potential for a bright future as a scholar. He has made significant contributions to semi-supervised learning, as well as to the solutions of high-dimensional multi-objective optimization problems. His exceptionally strong research record includes getting the PAKDD 2008 Best Paper Award.