Alborz Rezazadeh is a distinguished leader in the fields of AI, machine learning (ML), and computer vision (CV). In his current role as the Director of Applied CV at Recursion, a biotechnology company dedicated to revolutionizing drug discovery through AI, he has developed the applied CV/ML department, overseeing a team of over 10 ML scientists and engineers. Under his visionary leadership, the team has achieved remarkable success in foundational CV models, video analysis, multimodal learning, and the integration of large language models to expedite drug discovery processes. Alborz's leadership has fostered innovation and cost savings in the biotechnology sector. Before joining Recursion, Alborz spent two years as the Team Lead and Staff AI Scientist at LG AI Research Lab, where he led a team of eight ML scientists and engineers, successfully delivering projects in object detection, image classification, real-time pose estimation, and few-shot learning for various applications and LG products. Prior to his time at LG, Alborz contributed to pioneering deep-learning research for smartphones and appliances during his two-year tenure at Samsung AI Research Center. He also published multiple papers in prestigious AI conferences. Before entering the world of AI and ML, from 2012 to 2014, Alborz served as an Electrical Engineer at North Inc, where he was one of the initial two employees and played a pivotal role in the company's transformation into a unicorn startup. Alborz holds a PhD in Biomedical Eng. from the University of Toronto (2014-2018), where he developed groundbreaking brain-computer interfaces. His research earned him the "Best PhD Thesis" award from the University. He obtained his master's degree in Electrical Eng. (EE) from the University of Waterloo (2010-2012) and his B.Sc. degree in EE from Sharif University. Throughout his career, Alborz has demonstrated a passion for technology, visionary leadership, and a commitment to pushing the boundaries of AI.

Alborz Rezazadeh, Ph.D.

Leader in Applied Machine Learning and A with 11 years of research and product experience | 8 years of experience in Healthcare, Drug Discovery and Biotechnology | Author of over 20 peer-reviewed papers and patents with over 500 citations and an h-index of 10.

Research Expertise

Brain-computer Interfaces

Artificial Intelligence

Machine Learning

Computer Vision

Electrical and Electronic Engineering

Computational Mathematics

Acoustics and Ultrasonics

Computer Networks and Communications

Biomedical Engineering

Cellular and Molecular Neuroscience

Behavioral Neuroscience

Human-Computer Interaction

Psychiatry and Mental health

Neuropsychology and Physiological Psychology

Neurology

Biological Psychiatry

About

Publications

EEG Classification of Covert Speech Using Regularized Neural Networks

IEEE/ACM Transactions on Audio, Speech, and Language Processing / Dec 01, 2017

Rezazadeh Sereshkeh, A., Trott, R., Bricout, A., & Chau, T. (2017). EEG Classification of Covert Speech Using Regularized Neural Networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(12), 2292–2300. https://doi.org/10.1109/taslp.2017.2758164

Online EEG Classification of Covert Speech for Brain–Computer Interfacing

International Journal of Neural Systems / Nov 02, 2017

Sereshkeh, A. R., Trott, R., Bricout, A., & Chau, T. (2017). Online EEG Classification of Covert Speech for Brain–Computer Interfacing. International Journal of Neural Systems, 27(08), 1750033. https://doi.org/10.1142/s0129065717500332

Online classification of imagined speech using functional near-infrared spectroscopy signals

Journal of Neural Engineering / Nov 16, 2018

Rezazadeh Sereshkeh, A., Yousefi, R., Wong, A. T., & Chau, T. (2018). Online classification of imagined speech using functional near-infrared spectroscopy signals. Journal of Neural Engineering, 16(1), 016005. https://doi.org/10.1088/1741-2552/aae4b9

Development of a ternary hybrid fNIRS-EEG brain–computer interface based on imagined speech

Brain-Computer Interfaces / Oct 02, 2019

Rezazadeh Sereshkeh, A., Yousefi, R., Wong, A. T., Rudzicz, F., & Chau, T. (2019). Development of a ternary hybrid fNIRS-EEG brain–computer interface based on imagined speech. Brain-Computer Interfaces, 6(4), 128–140. https://doi.org/10.1080/2326263x.2019.1698928

VASTA

Proceedings of the 25th International Conference on Intelligent User Interfaces / Mar 17, 2020

Sereshkeh, A. R., Leung, G., Perumal, K., Phillips, C., Zhang, M., Fazly, A., & Mohomed, I. (2020, March 17). VASTA: a vision and language-assisted smartphone task automation system. Proceedings of the 25th International Conference on Intelligent User Interfaces. https://doi.org/10.1145/3377325.3377515

Exploiting error-related potentials in cognitive task based BCI

Biomedical Physics & Engineering Express / Dec 20, 2018

Yousefi, R., Sereshkeh, A. R., & Chau, T. (2018). Exploiting error-related potentials in cognitive task based BCI. Biomedical Physics & Engineering Express, 5(1), 015023. https://doi.org/10.1088/2057-1976/aaee99

Development of a robust asynchronous brain-switch using ErrP-based error correction

Journal of Neural Engineering / Nov 11, 2019

Yousefi, R., Rezazadeh Sereshkeh, A., & Chau, T. (2019). Development of a robust asynchronous brain-switch using ErrP-based error correction. Journal of Neural Engineering, 16(6), 066042. https://doi.org/10.1088/1741-2552/ab4943

RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) / Jun 01, 2023

Sypetkowski, M., Rezanejad, M., Saberian, S., Kraus, O., Urbanik, J., Taylor, J., Mabey, B., Victors, M., Yosinski, J., Sereshkeh, A. R., Haque, I., & Earnshaw, B. (2023, June). RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw59228.2023.00451

Online detection of error-related potentials in multi-class cognitive task-based BCIs

Brain-Computer Interfaces / Apr 03, 2019

Yousefi, R., Rezazadeh Sereshkeh, A., & Chau, T. (2019). Online detection of error-related potentials in multi-class cognitive task-based BCIs. Brain-Computer Interfaces, 6(1–2), 1–12. https://doi.org/10.1080/2326263x.2019.1614770

A pipeline of spatio-temporal filtering for predicting the laterality of self-initiated fine movements from single trial readiness potentials

Journal of Neural Engineering / Oct 20, 2016

Zeid, E. A., Sereshkeh, A. R., & Chau, T. (2016). A pipeline of spatio-temporal filtering for predicting the laterality of self-initiated fine movements from single trial readiness potentials. Journal of Neural Engineering, 13(6), 066012. https://doi.org/10.1088/1741-2560/13/6/066012

DATNet: Dense Auxiliary Tasks for Object Detection

2020 IEEE Winter Conference on Applications of Computer Vision (WACV) / Mar 01, 2020

Levinshtein, A., Sereshkeh, A. R., & Derpanis, K. G. (2020, March). DATNet: Dense Auxiliary Tasks for Object Detection. 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv45572.2020.9093325

A Ternary Brain-Computer Interface Based on Single-Trial Readiness Potentials of Self-initiated Fine Movements: A Diversified Classification Scheme

Frontiers in Human Neuroscience / May 24, 2017

Abou Zeid, E., Rezazadeh Sereshkeh, A., Schultz, B., & Chau, T. (2017). A Ternary Brain-Computer Interface Based on Single-Trial Readiness Potentials of Self-initiated Fine Movements: A Diversified Classification Scheme. Frontiers in Human Neuroscience, 11. https://doi.org/10.3389/fnhum.2017.00254

A novel concept for post-fabrication tuning of microwave filters

2013 IEEE MTT-S International Microwave Symposium Digest (MTT) / Jun 01, 2013

Sereshkeh, A. R., Attar, S., Azizi, M., & Mansour, R. R. (2013, June). A novel concept for post-fabrication tuning of microwave filters. 2013 IEEE MTT-S International Microwave Symposium Digest (MTT). https://doi.org/10.1109/mwsym.2013.6697748

Isolated Persian digit recognition using a hybrid HMM-SVM

2008 International Symposium on Intelligent Signal Processing and Communications Systems / Feb 01, 2009

Hejazi, S. A., Kazemi, R., & Ghaemmaghami, S. (2009, February). Isolated Persian digit recognition using a hybrid HMM-SVM. 2008 International Symposium on Intelligent Signal Processing and Communications Systems. https://doi.org/10.1109/ispacs.2009.4806757

Education

University of Toronto

Ph.D., Biomedical Engineering / August, 2018

Toronto, Ontario, Canada

University of Waterloo

M.Sc., Electrical and Computer Engineering / October, 2012

Waterloo, Ontario, Canada

Sharif University of Technology

B.Sc., Electrical Engineering / July, 2009

Tehran

Experience

Amazon

Senior Applied Scientist (L6) / January, 2024 — Present

Recursion

Director of Applied Machine Learning / January, 2022 — October, 2023

Drug Discovery, Biotechnology, Machine Learning, Deep Learning

LG

Team Lead - Staff AI Research Scientist / January, 2020 — January, 2022

AI, Machine Learning, Home Appliances

Samsung

AI Research Scientist / July, 2018 — January, 2020

AI, Machine Learning, Home Appliances, Mobile Devices

Bloorview Research Institute

Graduate Research Assistant / May, 2014 — July, 2018

Brain-computer interfaces, AI, ML, Rehabilitation, Biomedical Engineering

Thalmic Labs (North Inc)

Electrical Engineer / November, 2012 — May, 2014

Signal Processing, Electronics, Circuits

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