Kemafor Anyanwu (Ogan)

Associate Professor at North Carolina State University

Lecture Information:
  • March 19, 2021
  • 2:00 PM
  • Zoom: Contact SCIS coordinator (Hadi Amini) for zoom credentials if you did not receive the email.
Kemafor Anyanwu

Speaker Bio

Dr. Kemafor Anyanwu (Ogan) is an Associate Professor of Computer Science at North Carolina State University’s department of Computer Science where she has been since 2007. Her research contributions have focused on techniques for Big Data management in different application contexts ranging from Search on the Web to Blockchain applications. She has multiple papers with best paper awards as well as award nominations. Her research is supported by multiple NSF grants as well as industry funding such as IBM faculty awards. Her students (Ph.D. and MS) have been well placed in industry at companies such as Microsoft, IBM, Oracle, E-Bay Amazon AWS and so on.


Fundamental to automating decision making in many application domains is the ability to analyze Big Data. However, maximizing the value of analyzing big data is the need for analytics techniques to be able to “see” beyond” just syntactic representations of data. Rather, the meaning of data or data semantics must be represented in a machine-processible manner so that the exact nature of relationships in data can be exploited effectively. In particular, many applications require the assembly of different kinds of heterogeneous data to be analyzed to solve important problems. This is impossible without the ability to automatically reason about the way in which the different data are related. Data semantics is also being considered as a critical component of enabling “interpretability” of the outcomes of machine and deep learning techniques which currently are mostly semantics-oblivious.

Significant strides have been made in the area of standardizing the modeling and expression of semantically-enriched data which are based on the use of formal ontologies and ontological reasoners for inferencing. However, beyond modeling is the challenge of scalable and efficient processing techniques for such enriched data which will underpin analytics techniques but still requires further investigation. In this talk, I will present some of our work on scalable processing of knowledge graphs and ontological data. A current theme in our work is developing succinct data representations and algebraic transformations of tasks in a manner that renders them more amenable to parallelized processing. I will also highlight emerging big data applications and their needs for semantic data representations such as decentralized marketplaces on blockchains and review the directions of our efforts in those areas as well.

Watch this Lecture on Youtube