Robi Polikar, Ph.D.

Robi Polikar, Ph.D.

Robi Polikar, Ph.D.
Electrical & Computer Engineering Professor and Department Head

Robi Polikar, Ph.D.
Electrical & Computer Engineering

Contact Info
(856) 256-5372
Engineering Hall 346B




Ph.D., Electrical Eng. and Biomedical Eng., Iowa State University, 2000
M.S.,  Electrical Eng. and Biomedical Eng, Iowa State University, 1995
B.S., Electrical Engineering, Istanbul Technical University, 1993

Research Expertise:
The overarching scope of my research is development of fundamental computational intelligence approaches and statistical learning models for challenging problems in machine learning. Under this main umbrella, my work has focused on four primary areas:

1) adversarial machine learning with a focus on versatile defensive mechanisms agains a broad spectrum of malicious attacks;

2) incremental (continuous) learning from streaming or batches of data obtained from dynamic, nonstationary, drifting, uncertain or even adversarial environments where the learning model may possibly be under malicious attack;

3) development and adaptation of ensemble based approaches for a broad spectrum of underexplored areas of machine learning, such as missing data, data fusion and concept drift in recurrent nonstationary environments,  and

4) applications of above mentioned approaches to practical problems, such as early diagnosis of neurological disorders or species identification and classification in metagenomics.

Honors and Awards:
  • IEEE Computational Intelligence Magazine Best Paper Award
  • IEEE Computational Intelligence Society Service Award for Chair of Technical Committee
    on Data Mining and Big Data Analytics
  • Electrical and Computer Eng. Dept. Heads Association, Innovative Program of the Year Award
  • Professional Progress in Engineering Midcareer Alumni Award, Iowa State Univ., Ames, IA
  • Rowan University Research and Scholarly Activity Achievement Award
  • IEEE Senior Member Elevation
  • National Science Foundation CAREER award
  • New Century Scholars Fellowship, Stanford University, Palo Alto, CA
  • Biomedical Engineering Society Young Investor Travel Award
  • National Effective Teaching Institute, American Society of Engineering Education

Professional Memberships & Service

  • ABET, Program Evaluator
  • Member of Editorial Board, Neural Networks
  • Associate Editor, IEEE Transactions on Neural Networks and Learning Systems              
  • IEEE, COmputational Intelligence Society, Signal Processing Society   
  • International Neural Network Society                                              
Recent Publications

  • Ozdogan E., Sabin N., Gradie T., Portley S., Halac M., Coard T., Trimble W., Sokhansanj B., Rosen G., Polikar R., Incremental and Semi-Supervised Learning of 16S-rRNA Genes For Taxonomic Classification, IEEE Symposium Series on Computational Intelligence (SSCI 2021), Orlando, FL, 2021, DOI: 1109/SSCI50451.2021.9660093
  • Umer M., Polikar R., “Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models,” Joint Conf. on Neural Networks (IJCNN 2021), Virtual, 2021. DOI: 10.1109/IJCNN52387.2021.9533400
  • Dawson G., Polikar R., “OpinionRank: Extracting Ground Truth Labels from Unreliable Expert Opinions with Graph-Based Spectral Ranking,” Joint Conf. on Neural Networks (IJCNN 2021), Virtual, 2021. DOI:10.1109/IJCNN52387.2021.9533320
  • Binaco R., Calzaretto N., Epifano J., McGuire S., Umer M., Emrani S., Wasserman V., Libon J. and Polikar R., “Machine Learning Analysis of Digital Clock Drawing Test Performance for Differential Classification of Mild Cognitive Impairment Subtypes versus Alzheimer’s Disease,” Journal of the International Neuropsychological Society, vol. 26, no. 7, pp. 690-700, 2020. DOI: 10.1017/S1355617720000144.
  • Umer M., Dawson G., Polikar R., “Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks,” Joint Conf. on Neural Networks (IJCNN 2020), Glasgow, Scorland, UK, 2020. DOI: 10.1109/IJCNN48605.2020.9206809
  • Umer M., Frederickson C., Polikar R., “Vulnerability of Covariate Shift Adaptation Against Malicious Poisoning Attack,” Int. Joint Conf. on Neural Networks (IJCNN 2019), Budapest, Hungary, 2019. DOI: 10.1109/IJCNN.2019.8851748
  • Frederickson C., Moore M., Dawson G., Polikar R., “Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning,” Int. Joint Conf. on Neural Networks (IJCNN 2018), Rio de Janerio, Brazil, 2018. DOI: 10.1109/IJCNN.2018.8489495
  • G. Ditzler, J. LaBarck, J. Ritchie, G. Rosen and R. Polikar, “Extensions to Online Feature Selection Using Bagging and Boosting,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, pp. 4504-4509, 2018. DOI: 10.1109/TNNLS.2017.2746107
  • G. Ditzler, R. Polikar, and G. Rosen, “A Sequential Learning Approach for Scaling up Filter-Based Feature Subset Selection,” IEEE Transactions on Neural Networks & Learning Systems, vol. 29, no. 6, pp.2530-2544, 2018. DOI: 10.1109/TNNLS.2017.2697407