star twitter facebook envelope linkedin instagram youtube alert-red alert home left-quote chevron hamburger minus plus search triangle x
Leman Akoglu

Leman Akoglu

Dean's Associate Professor of Information Systems

Leman Akoglu joined the Heinz College faculty as an Assistant Professor in Fall 2016. She also holds a courtesy appointment in the Computer Science Department (CSD) and the Machine Learning Department (MLD) of School of Computer Science (SCS).

Akoglu is the Heinz College Dean's Associate Professor of Information Systems. Prior to joining Heinz College, she was an Assistant Professor in the Department of Computer Science at Stony Brook University since receiving her Ph.D. from CSD/SCS of Carnegie Mellon University in 2012. 

Dr. Akoglu’s research interests span a wide range of data mining and machine learning topics with a focus on algorithmic problems arising in graph mining, pattern discovery, social and information networks, and especially anomaly mining; outlier, fraud, and event detection. At Heinz, Dr. Akoglu directs the Data Analytics Techniques Algorithms (DATA) Lab. 

Dr. Akoglu's research has won 7 publication awards; Best Research Paper at SIAM SDM 2019, Best Student Machine Learning Paper Runner-up at ECML PKDD 2018, Best Paper Runner-up at SIAM SDM 2016, Best Research Paper at SIAM SDM 2015, Best Paper at ADC 2014, Best Paper at PAKDD 2010, and Best Knowledge Discovery Paper at ECML PKDD 2009. She also holds 3 U.S. patents filed by IBM T. J. Watson Research Labs.

Dr. Akoglu is a recipient of the National Science Foundation CAREER award (2015) and US Army Research Office Young Investigator award (2013). Her research has been supported by the NSF, US ARO, DARPA, Adobe, Facebook, Northrop Grumman, PNC Bank, and PwC. 

Please also see Dr. Akoglu’s webpage and DATA Lab research group page for more on active research topics, ongoing projects, publications, and other up to date information.

Courses Taught

  • 95-828 - Machine Learning for Problem Solving
  • 95-869 - Big Data and Large-scale Computing


Additional Information