Career Profile

I am a Ph.D. candidate at Wright State University in Dayton, OH. My research interests include Artificial Intelligence, Cognitive Architectures, Automated Planning, Perception and Machine Learning.


Research Assistant

2015 - Present
WSU Collaboration and Cognition Laboratory, Dayton, OH

Investigated autonomous agents operating in dynamic environments.

Developed an interface between the MIDCA cognitive architecture and a Baxter humanoid robot.

Studied the relationship between Perception, Planning, and Interpretation.

Research Assistant

2013 – 2015
Kno.e.sis Center, Dayton, OH

Conducted research in areas of cloud computing, big data and machine learning in distributed frameworks.

Worked on an approach to scale up existing Euclidean embedding algorithms for Big Data.

Software Developer

2012 - 2013
Agah Co

Developed a customized online shopping store using Grails and My SQL.

Software Developer

2011 – 2012

Developed a Valuation system to estimate share prices of banks.

Developed an Excel Add-in that shows on-line trading information.

Developed a duplex WCF service that pushes new data to subscribed clients.

Software Developer

2009 – 2011
Arish Co

Developed apps to read utility meters remotely using varied technologies (GPRS, etc.)


  • [AIC-18] Dannenhauer, Z. A., & Cox, M. T. (2018). “Rationale-based Perceptual Monitors.” In AI Communications Journal 31.2, pp. 197–212.
  • [ACS-17] Cox, M. T., & Dannenhauer, Z. A. (2017). “Perceptual goal monitors for cognitive agents in changing environments.” In The Fifth Annual Conference on Advances in Cognitive Systems (pp. 1-16). Palo Alto, CA: Cognitive Systems Foundation.
  • [FLAIRS-17] Dannenhauer, Z. A., & Cox, M. T. (2017).“Rationale-based visual planning monitors for cognitive systems.” In V. Rus & Z. Markov (Eds.), Proceedings of the 30th International FLAIRS Conference (pp. 182-185). Palo Alto, CA: AAAI Press.
  • [IJCAI-16 Goal Reasoning Workshop] Alavi, Z. and Cox, M.T. (2016). “Rationale-based Visual Planning Monitors.” In Working Notes of the 4th Workshop on Goal Reasoning. New York, IJCAI-16.
  • [AAAI-16] Cox, M. T., Alavi, Z., Dannenhauer, D., Eyorokon, V., & Munoz-Avila, H. (2016). “MIDCA: A metacognitive, integrated dual-cycle architecture for self-regulated autonomy.” Proceedings of the 30th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press.
  • [CLOUD-15] Alavi, Z., Sharma, S., Zhou, L., & Chen, K. (2015, June). “Scalable Euclidean Embedding for Big Data.” In Cloud Computing (CLOUD), 2015 IEEE 8th International Conference on (pp.773-780). IEEE.
  • [VLDB-14] Alavi, Z., Zhou, L., Powers, J., &Chen, K. (2014). “RASP-QS: Efficient and Confidential Query Services in the Cloud.” Proceedings of the VLDB Endowment, 7(13).
  • [ICCAE-10] Rahbarinia, B., Pedram, M. M., Arabnia, H., & Alavi, Z. (2010, February). “A multi-objective scheme to hide sequential patterns.” In Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on(Vol. 1, pp. 153-158). IEEE.
  • [EJSR-10] Djahantighi, F. S., Feizi-Derakhshi, M. R., Pedram, M. M., & Alavi, Z. (2010). “An effective algorithm for mining users behaviour in time-periods.” European Journal of Scientific Research, 40(1), 81-90.


MIDCA (METACOGNITIVE INTEGRATED DUAL CYCLE ARCHITECTURE) - My work focused on integrating Perception, Planning and Interpretation to help an agent to respond to unexpected changes in dynamic world. We claim that an intelligent agent should actively watch for what can go wrong and anticipate mistakes before they occur. Some world changes affect the agent’s goals and some affect the agent’s plan. The agent should be able to respond to these changes appropriately to successfully accomplish its tasks.
( Video of MIDCA Controlling a Robot ); In this demo, someone makes a change in the world while Baxter is planning. Using plan monitors, Baxter can observe the change and adapt its plan for the new situation.
RASP-QS : Efficient and Confidential Query Services in the Cloud ( Query Processing Demo and Visualization )


Distributed Computing - MapReduce, Pig, Hadoop
Machine Learning - Deep Learning (RNN), Deep Learning Platforms (TensorFlow), SVM, Dimensionality Reduction Techniques, Classification and Clustering Algorithms.
Programming - JAVA, C#, Python, Prolog, Scheme
Databases - MY SQL, SQL Server
Knowledge Representation - RDF, RDFS, OWL
Robotics - ROS, Gazebo

Professional Activities

Served as a reviewer for IJCAI-16 GR Workshop, AAAI-16, ACS-17, and ICAPS-19.