Sadia Shakil

A Little About Sadia

My Story

I am an Assistant Professor at the Institute of Space Technology since September 2016, and an Adjunct Research Associate at Monash Uinversity (one year from September 2019). My research focus is on human bio-signals. I completed my PhD in Electrical and Computer Engineering from Georgia Institute of Technology with Dr. Chin-Hui Lee and Dr. Shella Keilholz in 2016 after securing a Fulbright Scholarship. My focus during my PhD was on resting-state functional connectivity dynamics. I used algorithms and methods from various domains such as signal processing, machine learning, information theory, and pattern recognition to study functional magnetic resonance imaging (fMRI) data during. I completed a one-year postdoctoral fellowship (February 2019) in ‘Computational Connectomics’ with Dr. Randy McIntosh at the Rotman Research Institute in Toronto after securing a Schlumberger Faculty for the Future Fellowship. During my postdoc, my research focus was to understand the relationship between brain and naturalistic behavior using EEG data collected during music listening. I worked on extracting the topologies in brain and behavior data using manifold learning, partial least squares and topological data analysis. My current research involves the design and development of low-cost hardware devices/systems for monitoring and rehab with patient populations. I am an Assistant Professor at the Institute of Space Technology since September 2016. I completed my PhD in Electrical and Computer Engineering from Georgia Institute of Technology with Dr. Chin-Hui Lee and Dr. Shella Keilholz in 2016 after securing Fulbright Scholarship award. My focus during PhD was to analyze the dynamics of resting-state functional connectivity. I used algorithms and methods from various domains such as signal processing, machine learning, information theory, and pattern recognition to study functional magnetic resonance imaging (fMRI) data during my PhD. I finished my one-year postdoctoral fellowship (in February 2019) in ‘Computational Connectomics’ with Dr. Randy McIntosh after securing Schlumberger Faculty for the Future Fellowship award. During Postdoc, my research focus was to understand the relationship between brain and behavior using EEG data collected during music listening. I worked on extracting the topologies in brain and behavior data using manifold learning, partial least square and topological data analysis for this purpose.

Research Interests

 Dynamics of functional connectivity.

 

Brain networks change configuration when the subject is at rest (not performing an explicit task). These network changes are different in healthy and disordered brain functions and their comprehension may assist in finding the cure of brain disorders. 

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Brain and Behavior

Our brain functionality changes with our behavior under different tasks/conditions. For example, music influences our moods but the influence of same music on different people may be different. Study of the relationship between brain and behavior is challenging but the results may be useful in understanding the role of behavior changes to improve brain functionality.

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Prosthetics Control

Accidents and other disasters result in amputation of different body parts such as hands and legs, which are replaced by artificial/prosthetic limbs. Even though these limbs and their controls are available in the market but they are mostly inefficient and very expensive, especially in the developing and under-developed countries. There is a need to design and manufacture low-cost and efficient electronic control systems for prosthetics.

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Stress Effects

The stress levels due to social and economic conditions are on the rise all over the world and the situation is worse in developing/under-developed countries due to lack of awareness. There is a need to understand the causes and effects of stress on people from different domains of the society. This comprehension will assist in finding the ways to reduce the stress levels for mentally healthy societies.

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Research Interests

Dr. Anthony Randal McIntosh

Dr. Anthony Randal McIntosh

Senior Scientist, Rotman Research Institute, Canada.

Dr Adeel Raza

Dr Adeel Raza

Senior Research Fellow, Monash University, Australia

Dr. Shella Keilholz

Dr. Shella Keilholz

Associate Professor, Georgia Institute of Technology and Emory University, USA

Publications
Journal (Peer-reviewed)

1. Shakil S., Billings J., Keilholz SD., Lee C.-H.,” Parametric Dependencies of Sliding Window Correlation”, IEEE Transactions on Biomedical Engineering, 2018.
2. Billings J., Medda A., Shakil S., Shen X., Kashyap A., Chen S., Abbas A., Zhang X., Nezafati M., Pan W-J., Berman G., Keilholz S., “Instantaneous Brain Dynamics Mapped to a Continuous State Space”, NeuroImage, November 2017.
3. Keilholz SD., Pan WJ., Billings JCW., Nezafati M., Shakil S., “Noise and non-neuronal contributions to the BOLD signal: Applications to and insight from animal studies”, NeuroImage, Dec, 2016
4. Shakil S., Lee C.-H., and Keilholz SD., “Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states”, NeuroImage, June 2016.
5. Pan WJ., Billings JCW., Grooms JK., Shakil S., Keilholz SD., “Considerations for resting state functional MRI and functional connectivity studies in rodents”, Frontiers in neuroscience, 2015.
6. Thompson GJ., Pan WJ., Billings JCW., Grooms JK., Shakil S., Jaeger D., Keilholz SD., “Phase-amplitude coupling and infraslow (< 1 Hz) frequencies in the rat brain: relationship to resting state fMRI”, Frontiers in neuroscience, 2014.

Journal (Peer-reviewed)

1. Shakil S., Billings J., Keilholz SD., Lee C.-H.,” Parametric Dependencies of Sliding Window Correlation”, IEEE Transactions on Biomedical Engineering, 2018.
2. Billings J., Medda A., Shakil S., Shen X., Kashyap A., Chen S., Abbas A., Zhang X., Nezafati M., Pan W-J., Berman G., Keilholz S., “Instantaneous Brain Dynamics Mapped to a Continuous State Space”, NeuroImage, November 2017.
3. Keilholz SD., Pan WJ., Billings JCW., Nezafati M., Shakil S., “Noise and non-neuronal contributions to the BOLD signal: Applications to and insight from animal studies”, NeuroImage, Dec, 2016
4. Shakil S., Lee C.-H., and Keilholz SD., “Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states”, NeuroImage, June 2016.
5. Pan WJ., Billings JCW., Grooms JK., Shakil S., Keilholz SD., “Considerations for resting state functional MRI and functional connectivity studies in rodents”, Frontiers in neuroscience, 2015.
6. Thompson GJ., Pan WJ., Billings JCW., Grooms JK., Shakil S., Jaeger D., Keilholz SD., “Phase-amplitude coupling and infraslow (< 1 Hz) frequencies in the rat brain: relationship to resting state fMRI”, Frontiers in neuroscience, 2014.

Conference (Full papers, Peer-reviewed)

1. Mohammad S., Shakil S., “Hand Electromyography Circuit and Signals Classification Using Artificial Neural Network”, 14th IEEE International Conference on Emerging Technologies, Islamabad, Pakistan, 2018.
2. Mohammad AH., Shakil S., Jangsher S., “Stress Effects on Exam Performance using EEG”, 14th IEEE International Conference on Emerging Technologies, Islamabad, Pakistan, 2018.
3. Shakil S., Keilholz SD., and Lee C.-H., “Adaptive Change Point Detection of Dynamic Functional
4. Connectivity Networks”, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016.
5. Keilholz SD., Billings JCW., Wang K., Abbas A., Hafeneger C., Pan WJ., Shakil S., Nezafati M., “Multiscale network activity in resting state fMRI”, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016.
6. Shakil S., Keilholz SD., and Lee C.-H., “On Frequency Dependencies of Sliding Window Correlation”, IEEE International Conference on Bioinformatics and Biomedicine, 2015.
7. Shakil S., Magnuson M., Keilholz SD., and Lee C.-H., “Cluster-based analysis for characterizing dynamic functional connectivity,” 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014.

Conference (Abstracts only)

1. Shakil S., Faber S., McCulloch AR., Brown T., Shen K., McIntosh AR., “Relationship of Brain States and Music Perception using Uniform Manifold Approximation and Projection” Society for Neuroscience conference, 2018.
2. Shakil S., Faber S., McCulloch AR., Brown T., Shen K., McIntosh AR., “Relationship of Brain States and Music Perception using Uniform Manifold Approximation and Projection” Sixth Biennial Conference on Resting State and Brain Connectivity, 2018.
3. Shakil S., Faber S., McCulloch AR., Brown T., Shen K., McIntosh AR., “Relationship of EEG and music features through low dimensional manifolds” INCF, 2018.
4. Shakil S., Billings J., Lee C.-H., and Keilholz SD., “Low Dimensional Visualization of Sliding Window Correlation results using t-SNE”, OHBM conference, 2016.
5. Shakil S., Lee C.-H., and Keilholz SD., “Functional Connectivity Adaptive Change Point Detection Algorithm”, OHBM conference, 2016.
6. Shakil S., Lee C.-H., and Keilholz SD., “Filter dependence of dynamic functional connectivity”, in OHBM conference, 2015.
7. Shakil S., Lee C.-H., and Keilholz SD., “The identification of state transitions and durations in resting state functional connectivity”, in International Society of Magnetic Resonance in Medicine conference, 2015.
8. Shakil S., Lee C.-H., and Keilholz SD., “Evaluation of dynamic analysis methods for characterization of time-varying functional connectivity”, in Resting State /Brain Connectivity conference, 2014.

Journal (Peer-reviewed)

1. Shakil S., Billings J., Keilholz SD., Lee C.-H.,” Parametric Dependencies of Sliding Window Correlation”, IEEE Transactions on Biomedical Engineering, 2018.
2. Billings J., Medda A., Shakil S., Shen X., Kashyap A., Chen S., Abbas A., Zhang X., Nezafati M., Pan W-J., Berman G., Keilholz S., “Instantaneous Brain Dynamics Mapped to a Continuous State Space”, NeuroImage, November 2017.
3. Keilholz SD., Pan WJ., Billings JCW., Nezafati M., Shakil S., “Noise and non-neuronal contributions to the BOLD signal: Applications to and insight from animal studies”, NeuroImage, Dec, 2016
4. Shakil S., Lee C.-H., and Keilholz SD., “Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states”, NeuroImage, June 2016.
5. Pan WJ., Billings JCW., Grooms JK., Shakil S., Keilholz SD., “Considerations for resting state functional MRI and functional connectivity studies in rodents”, Frontiers in neuroscience, 2015.
6. Thompson GJ., Pan WJ., Billings JCW., Grooms JK., Shakil S., Jaeger D., Keilholz SD., “Phase-amplitude coupling and infraslow (< 1 Hz) frequencies in the rat brain: relationship to resting state fMRI”, Frontiers in neuroscience, 2014.

Conference (Full papers, Peer-reviewed)

1. Mohammad S., Shakil S., “Hand Electromyography Circuit and Signals Classification Using Artificial Neural Network”, 14th IEEE International Conference on Emerging Technologies, Islamabad, Pakistan, 2018.
2. Mohammad AH., Shakil S., Jangsher S., “Stress Effects on Exam Performance using EEG”, 14th IEEE International Conference on Emerging Technologies, Islamabad, Pakistan, 2018.
3. Shakil S., Keilholz SD., and Lee C.-H., “Adaptive Change Point Detection of Dynamic Functional
4. Connectivity Networks”, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016.
5. Keilholz SD., Billings JCW., Wang K., Abbas A., Hafeneger C., Pan WJ., Shakil S., Nezafati M., “Multiscale network activity in resting state fMRI”, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016.
6. Shakil S., Keilholz SD., and Lee C.-H., “On Frequency Dependencies of Sliding Window Correlation”, IEEE International Conference on Bioinformatics and Biomedicine, 2015.
7. Shakil S., Magnuson M., Keilholz SD., and Lee C.-H., “Cluster-based analysis for characterizing dynamic functional connectivity,” 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014.

Conference (Abstracts only)

1. Shakil S., Faber S., McCulloch AR., Brown T., Shen K., McIntosh AR., “Relationship of Brain States and Music Perception using Uniform Manifold Approximation and Projection” Society for Neuroscience conference, 2018.
2. Shakil S., Faber S., McCulloch AR., Brown T., Shen K., McIntosh AR., “Relationship of Brain States and Music Perception using Uniform Manifold Approximation and Projection” Sixth Biennial Conference on Resting State and Brain Connectivity, 2018.
3. Shakil S., Faber S., McCulloch AR., Brown T., Shen K., McIntosh AR., “Relationship of EEG and music features through low dimensional manifolds” INCF, 2018.
4. Shakil S., Billings J., Lee C.-H., and Keilholz SD., “Low Dimensional Visualization of Sliding Window Correlation results using t-SNE”, OHBM conference, 2016.
5. Shakil S., Lee C.-H., and Keilholz SD., “Functional Connectivity Adaptive Change Point Detection Algorithm”, OHBM conference, 2016.
6. Shakil S., Lee C.-H., and Keilholz SD., “Filter dependence of dynamic functional connectivity”, in OHBM conference, 2015.
7. Shakil S., Lee C.-H., and Keilholz SD., “The identification of state transitions and durations in resting state functional connectivity”, in International Society of Magnetic Resonance in Medicine conference, 2015.
8. Shakil S., Lee C.-H., and Keilholz SD., “Evaluation of dynamic analysis methods for characterization of time-varying functional connectivity”, in Resting State /Brain Connectivity conference, 2014.

Highlights

  1. Signal Processing
  2. Machine Learning
  3. Pattern Recognition
  4. Manifold Learning
  5. Topological Data Analysis

Student Supervision

  Adeel collected EEG data from students and explored the influence of stress on students’ exam performance using this data.

He used machine learning and deep learning algorithms in his study and published a paper in an IEEE conference.

 

 

Shahzaib designed and manufactured low cost EMG device, collected the data from healthy subjects with it, and successfully classified this data to control prosthetic arm. He used neural networks in his research in published a conference in an IEEE conference. We are extending Shahzaib’s project to collect the data from amputees and to process it. The goal is to commercialize this low-cost device.

Projects
Start-Up Research Grant from Higher Education Commission, Pakistan in 2017 (USD 5000).

Teaching
Advanced Engineering Math (Graduate)
Digital Signal Processing (Undergrad)
Signals and Systems (Undergrad)

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Contact Sadia

Contact Sadia

sadia.shakil@mail.ist.edu.pk