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Headshot of Seif Eldawlatly, Associate Professor, Department of Computer Science and Engineering

Seif Eldawlatly

  • Position: Associate Professor
  • Department: Department of Computer Science and Engineering
Brief Biography

Seif Eldawlatly joined the Department of Computer Science and Engineering at The American University in Cairo (AUC) as an associate professor in the fall 2022 semester. He received his PhD in electrical and computer engineering from Michigan State University, East Lansing, Michigan, in 2011. He received his MSc and BSc degrees in Electrical Engineering (Computer and Systems Engineering Department) from Ain Shams University, Cairo, Egypt, in 2006 and 2003, respectively. He has been affiliated with the Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University (June 2011 to present) and the Faculty of Media Engineering and Technology, German University in Cairo (February 2019 to August 2022).

His research focuses on utilizing data science, machine learning and signal processing techniques to develop invasive and non-invasive Brain-Computer Interface (BCI) applications. He has been the principal investigator of multiple research projects aiming at using machine learning techniques to develop visual prosthetic systems and diagnose neurodegenerative diseases. He has also been the principal investigator of multiple projects that aim at developing applications to enable disabled subjects to interact with computer devices using their brain electroencephalography (EEG). He has strong experience in data science, neural engineering, signal processing, electrophysiology and neuroscience. He has more than 70 refereed publications in these research areas.

Research Interest
  • Data Science
  • Neural Engineering
  • Machine Learning
  • Signal Processing
  • Electrophysiology
  • Neuroscience
Education
  • PhD in Electrical Engineering, Michigan State University, East Lansing, Michigan, USA, May 2011
  • MSc in Electrical Engineering, Computer and Systems, Faculty of Engineering, Ain Shams University, Cairo, Egypt, June 2006
  • BSc in Electrical Engineering, Computer and Systems, Faculty of Engineering, Ain Shams University, Cairo, Egypt, June 2003

Book Chapters

  • S. Elgohary, M. I. Khalil and S. Eldawlatly, A Two-stage Classification Framework for Epileptic Seizure Prediction using EEG Wavelet-based Features, in Big Data in Psychiatry & Neurology, ed. A. Moustafa, Academic Press, Elsevier, pp. 263-286, 202.
  • S. Eldawlatly, Optimizing Electrical Stimulation for Closed-loop Control of Neural Ensembles: A Review of Algorithms and Applications, in Computational Models of Brain and Behavior, ed. A. Moustafa, Wiley, pp. 439-451, 2017.
  • S. Eldawlatly and K. Oweiss, Graphical Models of Functional and Effective Neuronal Connectivity, in Statistical Signal Processing for Neuroscience and Neurotechnology, ed. K. Oweiss, Academic Press, Elsevier, pp. 129-174, 2010.

Journal Articles

  •  R. H. Elnabawy, S. Abdennadher, O. Hellwich and S. Eldawlatly, PVGAN: A Generative Adversarial Network for Object Simplification in Prosthetic Vision, Journal of Neural Engineering, Vol. 19, No. 5, 056007, 2022
  • H. ElMohandes, S. Eldawlatly, J. M. C. Audí, R. Ruff, and K. Hoffmann, A Multi-Kalman Filter-based Approach for Decoding Arm Kinematics from EMG Recordings, BioMedical Engineering OnLine, Vol. 21, No. 1, pp. 1-18, 2022
  • E. Mounier, B. Abdullah, H. M. K. Mahdi and S. Eldawlatly, A Deep Convolutional Visual Encoding Model of Neuronal Responses in the LGN, Brain Informatics, Vol. 8, No. 11, 2021
  • M. Gamal, M. H. Mousa, S. Eldawlatly, and S. M. Elbasiouny, In-silico Development and Assessment of a Kalman Filter Motor Decoder for Prosthetic Hand Control, Computers in Biology and Medicine, Vol. 132, 104353, 2021
  • S. Eldawlatly, M. Abouelhoda, O. S. Al-Kadi, T. Gojobori, B. Jankovic, M. Khalil, A. H. Khandoker, and A. Morsy, Biomedical Computing in the Arab World: Unlocking the Potential of a Growing Research Community, Communications of the ACM, Vol. 64, pp. 108–113, 2021
  • A. Gaballa, H. Swelim, A. Abd El-Aal and S. Eldawlatly, Neuroprotective Effect of Chrysin in Rat Model of Parkinson's Disease: Histopathological Evidence, The Egyptian Journal of Experimental Biology (Zoology), Vol. 17, No. 1, pp. 67-78, 2021
  • A. Jawwad, H. H. Abolfotuh, B. Abdullah, H. M. K. Mahdi and S. Eldawlatly, Modulating Lateral Geniculate Nucleus Neuronal Firing for Visual Prostheses: A Kalman Filter-based Strategy, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 25, No. 10, pp. 1917-1927, 2017
  • A. S. Elsawy, S. Eldawlatly, M. Taher, G. M. Aly, MindEdit: A P300-based Text Editor for Mobile Devices, Computers in Biology and Medicine, Vol. 80, pp. 97-106, 2017
  • S. Eldawlatly and K. G. Oweiss, Temporal precision in population - but not individual neuron - dynamics reveals rapid experience-dependent plasticity in the rat barrel cortex, Frontiers in Computational Neuroscience, Vol. 8, No. 155
  • K. Y. Kwon, S. Eldawlatly and K. G. Oweiss, NeuroQuest: A Comprehensive Analysis Tool for Extracellular Neural Ensemble Recordings, Journal of Neuroscience Methods 204(1), pp. 189-201, 2012
  • S. Eldawlatly and K. G. Oweiss, Millisecond-Timescale Local Network Coding in the Rat Primary Somatosensory Cortex, PLoS ONE 6(6): e21649
  • M. Aghagolzadeh, S. Eldawlatly, and K. Oweiss, Synergistic Coding by Cortical Neural Ensembles, IEEE Transactions on Information Theory, Vol. 56, No. 2, pp. 875-889, 2010
  • S. Eldawlatly, Y. Zhou, R. Jin and K. Oweiss, On the Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles, Neural Computation 22(1), pp. 158-189, 2010
  • S. Eldawlatly, R. Jin and K. Oweiss, Identifying Functional Connectivity in Large Scale Neural Ensemble Recordings: A Multiscale Data Mining Approach, Neural Computation 21(2), pp. 450-477, 2009

Conference Proceedings

  • R. H. Elnabawy, S. Abdennadher, O. Hellwich, and S. Eldawlatly, A YOLO-based Object Simplification Approach for Visual Prostheses, Proc. of the IEEE 35th International Symposium on Computer Based Medical Systems (CBMS), Shenzhen, China, to appear, 2022.
  • Y. Abdelghaffar, A. Hashem and S. Eldawlatly, Generative Adversarial Networks for Augmenting EEG Data in P300-based Applications: A Comparative Study, Proc. of the IEEE 35th International Symposium on Computer Based Medical Systems (CBMS), Shenzhen, China, to appear, 2022.
  • M. Gamal and S. Eldawlatly, Modeling Natural Visual Stimulus Encoding in the Lateral Geniculate Nucleus using Deep Learning, Proceedings of the Society for Neuroscience Annual Meeting, San Diego, CA, to appear, 2022.
  • A. Elbadry and S. Eldawlatly, Majority-vote Over Multiple ECG Segments for Risk Assessment (MOMESRA): A Machine Learning Approach for Predicting Cardiovascular Events, Proc. of the 21st IEEE International Conference on BioInformatics and BioEngineering (BIBE 2021), Kragujevac, Serbia, pp. 1-6, 2021.
  • R. H. Elnabawy, S. Abdennadher, O. Hellwich, and S. Eldawlatly, Electrode Dropout Compensation in Visual Prostheses: An Optimal Object Placement Approach, Proc. of the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2021), pp. 6515-6518, 2021.
  • M. Gamal, E. Mounier and S. Eldawlatly, On the Extraction of High-level Visual Features from Lateral Geniculate Nucleus Activity: A Rat Study, In: Mahmud M., Kaiser M.S., Vassanelli S., Dai Q., Zhong N. (eds) Brain Informatics. The 14th International Conference on Brain Informatics (BI2021), Lecture Notes in Computer Science, vol 12960, pp. 35-45, 2021.
  • A. Y. Abdelaal, M. H. Mousa, M. Gamal, M. I. Khalil, S. M. Elbasiouny, and S. Eldawlatly, Spiking motoneurons effective connectivity as an early electrophysiological biomarker of amyotrophic lateral sclerosis, Program No. 292.04. 2021 Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience, 2021.
  • Y. M. Hussain and S. Eldawlatly, An Ensemble Classification Approach for Recognizing Steady-state Visually Evoked Potentials Frequencies, 2021 16th IEEE International Conference on Computer Engineering and Systems (ICCES), pp. 1-6, 2021.
  • H. Elhilbawi, S. Eldawlatly and H. Mahdi, The Importance of Discretization Methods in Machine Learning Applications: A Case Study of Predicting ICU Mortality, The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2021), pp. 214-224, 2021.
  • A. ElSherif, A. Karaman, O. Ahmed, O. Magdy, R. Shouman, R. El-Noumier, A. M. Hamed, H. Eldawlatly and S. Eldawlatly, Monitoring and Predicting Driving Performance using EEG Activity, Proceedings of 2020 15thInternational Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, pp. 1 – 6, 2020.
  • M. Heikal and S. Eldawlatly, An Ensemble Classification Technique of Neurodegenerative Diseases from Gait Analysis, Proceedings of 2020 15th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, pp. 1 – 6, 2020.
  • A. Y. Abdelaal, M. H. Mousa, M. Gamal, M. I. Khalil, S. M. Elbasiouny, and S. Eldawlatly, A Classification Approach to Recognize the Firing of Spinal Motoneurons in Amyotrophic Lateral Sclerosis, Proc. of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020), pp. 3680 – 3683, 2020.
  • R. H. Elessawy, S. Eldawlatly and H. M. Abbas, A Long Short-term Memory Autoencoder Approach for EEG Motor Imagery Classification, 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates, pp. 79-84, 2020.
  • H. Elhilbawi, S. Eldawlatly and H. Mahdi, A Taxonomy of Discretization Techniques based on Class Labels and Attributes' Relationship, Proceedings of 2019 14th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, pp. 316-321, 2019.
  • E. Mounir, B. Abdullah, H. M. K. Mahdi and S. Eldawlatly, Encoding models of visual and electrical stimuli in rat lateral geniculate nucleus: A deep learning approach, Program No. 577.10. 2019 Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience, 2019.
  • A. El-Amin, A. Attia, O. Hammad, O. Nasr, O. Ghozlan, R. Raouf, A. M. Hamed, H. Eldawlatly, M. El-Moursy and S. Eldawlatly, Brain-in-Car: A Brain Activity-based Emotion Recognition Embedded System for Automotive, 2019 IEEE International Conference of Vehicular Electronics and Safety (ICVES), Cairo, Egypt, pp. 1-5, 2019.
  • M. Gamal, M. H. Mousa, S. Eldawlatly and S. M. Elbasiouny, Automated Cell-Type Classification and Death-Detection of Spinal Motoneurons, 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), Cairo, Egypt, pp. 57-60, 2018.

Second Best Student Paper Award

  •  M. Essam, S. Eldawlatly and H. Abbas, Contextualized Word Representations for Self-Attention Network, 2018 13th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, pp. 116-121, 2018.

  • E. Mounir, B. Abdullah, H. M. K. Mahdi and S. Eldawlatly, Visual Encoding in Rat Lateral Geniculate Nucleus: An Artificial Neural Network Approach, 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), Tunis, Tunisia, pp. 24-29, 2018.
  • N. N. Nashed, S. Eldawlatly and G. M. Aly, A Deep Learning Approach to Single-trial Classification for P300 Spellers, 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), Tunis, Tunisia, pp. 11-16, 2018.

Best Student Paper Award

  • H. Elmohandes, S. Eldawlatly, J. M. C. Audí, R. Ruff and K-P. Hoffmann, Decoding Arm Kinematics from EMG Signals using Kalman Filter, Proceedings of 2018 8th International Conference on Biomedical Engineering and Technology (ICBET 2018), Bali, Indonesia, pp. 96-102, 2018.
  • E. Mounir, B. Abdullah, H. M. K. Mahdi and S. Eldawlatly, An Encoding Model of Visual and Electrical Stimuli in Rat Lateral Geniculate Nucleus: A Deep Learning Approach, Artificial Vision 2017, Aachen, Germany, 2017.
  • A. G. Yehia, S. Eldawlatly and M. Taher, WeBB: A Brain-Computer Interface Web Browser Based on Steady-State Visual Evoked Potentials, 2017 12th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, pp. 52-57, 2017.
  • M. R. Meshriky, S. Eldawlatly and G. M. Aly, An Intermixed Color Paradigm for P300 Spellers: A Comparison with Gray-scale Spellers,2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Thessaloniki, Greece, pp. 242-247, 2017.
  • H. H. Abolfotuh, A. Jawwad, B. Abdullah, H. M. K. Mahdi and S. Eldawlatly, BNEL_VP: An Image Processing Toolbox for Visual Prostheses, 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), Shanghai, China, pp. 461-464, 2017.
  • G. Soliman, A. El-Nabawy, A. Misbah and S. Eldawlatly, Predicting All Star Player in the National Basketball Association Using Random Forest, 2017 Intelligent Systems Conference (IntelliSys), United Kingdom, pp. 706-713, 2017.
  • M. A. Helal, S. Eldawlatly and M. Taher, Using Autoencoders for Feature Enhancement in Motor Imagery Brain-Computer Interfaces, 2017 13th IASTED International Conference on Biomedical Engineering (BioMed), Innsbruck, Austria, pp. 89-93, 2017.
  • S. Elgohary, S. Eldawlatly and M. I. Khalil, Epileptic Seizure Prediction using Zero-Crossings Analysis of EEG Wavelet Detail Coefficients, 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Chiang Mai, Thailand, pp. 1-6, 2016.
  • A. Jawwad, H. H. Abolfotuh, B. Abdullah, H. M. K. Mahdi and S. Eldawlatly, Tuning Electrical Stimulation for Thalamic Visual Prosthesis: An Autoencoder-based Approach, Proc. of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016), pp. 5431 – 5434, August 201​​​​6.
  • H. H. Abolfotuh, A. Jawwad, B. Abdullah, H. M. K. Mahdi and S. Eldawlatly, Moving Object Detection and Background Enhancement for Thalamic Visual Prostheses, Proc. of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016), pp. 4711 – 4744, August 201​​​​​6.
  • A. G. Yehia, S. Eldawlatly and M. Taher, Principal component analysis-based spectral recognition for SSVEP-based Brain-Computer Interfaces, 2015 Tenth International Conference on Computer Engineering & Systems (ICCES), pp. 410-415, 2015.
  • A. Jawwad, H. H. Abolfotuh, B. Abdullah, H. M. K. Mahdi and S. Eldawlatly, A Kalman-based Encoder for Electrical Stimulation Modulation in a Thalamic Network Model, Proc. of the 15th IEEE International Conference on Bioinformatics & Bioengineering (BIBE 2015), pp. 1-5, 2015.
  • S. Eldawlatly and A. Jawwad, Optimizing electrical stimulation parameters in a lateral geniculate nucleus (LGN) model, Program No. 148.02. 2015 Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience, 2015.
  • B. Elasuty and S. Eldawlatly, Dynamic Bayesian Networks for EEG Motor Imagery Feature Extraction, Proc. of 7th Int. IEEE EMBS Conf. on Neural Engineering (NER), pp. 170-173, 2015.
  • A. S. Elsawy and S. Eldawlatly, P300-based Applications for Interacting with Smart Mobile Devices, Proc. of 7th Int. IEEE EMBS Conf. on Neural Engineering (NER), pp. 166-169, 2015.
  • A. S. Elsawy, S. Eldawlatly, M. Taher and G. M. Aly, Performance Analysis of a Principal Component Analysis Ensemble Classifier for Emotiv Headset P300 Spellers, Proc. of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014), pp. 5032 – 5035, August 2014.
  • A. S. Elsawy, S. Eldawlatly, M. Taher and G. M. Aly, A Principal Component Analysis Ensemble Classifier for P300 Speller Applications, Proc. of the Image and Signal Processing and Analysis (ISPA), 2013 8th International Symposium on, pp. 444-449, September 2013.
  • A. Fathy, A. S. Fahmy, M. Elhelw and S. Eldawlatly, EEG Spectral Analysis for Attention State Assessment: Graphical versus Classical Classification Techniques, Proc. of the 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES 2012), pp. 888 - 891, December 2012.
  • S. Eldawlatly, A. Eleryan, A. Mohebi, M. Aghagolzadeh and K. G. Oweiss, Beyond single neuron responses: networks capture ensemble coding properties and re-organization in cortex, Program No. 413.07. 2012 Neuroscience Meeting Planner. New Orleans, LA: Society for Neuroscience, 2012.
  • S. Eldawlatly and K. Oweiss, Inferring Stimulus-specific Network Dynamics Associated with Experience-dependent Plasticity in the Rat Somatosensory Cortex Following Sensory Deprivation, Program No. 74.22. 2011 Neuroscience Meeting Planner. Washington, DC: Society for Neuroscience, 2011.
  • S. Eldawlatly and K. Oweiss, Stimulus-specific Neuronal Circuits in the Rat Somatosensory Cortex, Program No. 782.19. 2010 Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2010.
  • S. Eldawlatly and K. Oweiss, Causal Neuronal Networks Provide Functional Signatures of Stimulus Encoding, Proc. of 32nd IEEE Eng. in Medicine and Biology, pp. 5460-5463, August 2010.
  • M. Aghagolzadeh, S. Eldawlatly and K. Oweiss, An Information Theoretic Approach to Identify the Role of Higher-order Interactions between Cortical Neurons in Stimulus Coding Proc. of the 43rd Annual Asilomar Conference on Signals, Systems and Computers (Asilomar'09), pp. 1085-1089, Nov. 2009.
  • S. Eldawlatly, S. Fujisawa, G. Buzsaki and K. Oweiss, Identifying Position-specific Functional Networks in the Rat Medial Prefrontal Cortex, Program No. 192.9. 2009 Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience, 2009.
  • S. Eldawlatly, M. Aghagolzadeh and K. Oweiss, Decoding Spike Train Ensembles using the Cooperative Interaction between Task-dependent Cortical Neurons, BMC Neuroscience 2009, 10(Suppl 1):P96, July 2009.
  • M. Aghagolzadeh, S. Eldawlatly and K. Oweiss, Coding Stimulus Information with Cooperative Neural Populations, IEEE International Symposium on Information Theory (ISIT), pp. 1594 – 1598, 2009.
  • S. Eldawlatly, Y. Zhou, R. Jin and K. Oweiss, Inferring Functional Cortical Networks from Spike Train Ensembles using Dynamic Bayesian Networks, Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), pp. 3489-3492, April 2009.
  • M. Aghagolzadeh, S. Eldawlatly and K. Oweiss, Identifying Functional Connectivity of Motor Neuronal Ensembles Improves the Performance of Population Decoders, Proc. of 4th Int. IEEE EMBS Conf. on Neural Engineering, pp. 534-537, 2009.
  • K. Y. Kwon, S. Eldawlatly and K. G. Oweiss, NeuroQuest: A Comprehensive Tool for Large Scale Neural Data Processing and Analysis, Proc. of 4th Int. IEEE EMBS Conf. on Neural Engineering, pp. 622-625, 2009.
  • S. Eldawlatly, Y. Zhou, R. Jin and K. Oweiss, A Two-stage Paradigm for Reconstructing Functional Neuronal Circuits from Spike Train Ensembles, Program No. 862.15. 2008 Neuroscience Meeting Planner. Washington, DC: Society for Neuroscience, 2008.
  • S. Eldawlatly, Y. Zhou, R. Jin and K. Oweiss, Reconstructing Functional Neuronal Circuits Using Dynamic Bayesian Networks, Proc. of 30th IEEE Eng. in Medicine and Biology, pp. 5531 - 5534, August 2008.
  • K. Oweiss, M. Shetliffe and S. Eldawlatly, Revamping Signal Processing for Adaptive, Real Time, Bi-directional Brain Machine Interface Systems, Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), pp. 5197-5200. Digital Object Identifier 10.1109/ICASSP.2008.4518830, April 2008.
  • M. Shetliffe, S. Eldawlatly and K. Oweiss, Multiscale Compressed Sensing of Neuronal Response Properties for Brain Machine Interfaces, Program No. 770.2. Neuroscience 2007 Abstracts. San Diego, CA: Society for Neuroscience, 2007.  
  • S. El Dawlatly and K. Oweiss, Clustering Synaptically-Coupled Neuronal Populations under Systematic Variations in Temporal Dependence, Proc. of 29th IEEE Eng. in Medicine and Biology, Vol. 1, pp. 1445-1448, 2007.
  • S. El Dawlatly and K. Oweiss, Identifying Spike-timing Dependent Plasticity in Spike Train Models of Synaptically-Coupled Neuronal Ensembles, BMC Neuroscience 2007, 8(Suppl 2):P193, July 2007.
  • S. El Dawlatly and K. G. Oweiss, Tracking Plasticity in Probabilistic Spike Trains Models of Synaptically-Coupled Neural Population, Proc. of 3rd Int. IEEE EMBS Conf. on Neural Engineering, pp. 498-501, 2007. 
  • F. Chen, S. El Dawlatly, R. Jin and K. Oweiss, Identifying and Tracking the number of independent clusters of functionally interdependent neurons from biophysical models of population activity, Proc. of 3rd Int. IEEE EMBS Conf. on Neural Engineering, pp. 542-545, 2007.
  • S. El-dawlatly, H. Osman, and H. Shahein, Enhanced SVM versus Several Approaches in SAR Target Recognition, Proc. of ICCES06, pp. 266-270, Cairo, Egypt, 2006.
  • S. El-dawlatly, H. Osman, and H. Shahein, SVM Enhancement with Application to SAR Imagery Classification, Proc. of ICSP06, vol. 3, Guilin, China, Nov 2006.
  • S. El-dawlatly, H. Osman, and H. Shahein, A New Spatial FCM Approach with Application to SAR Target Clustering, Proc. of ICSP06, vol. 3, Guilin, China, Nov 2006.