BSIA Lab Members

Present Graduate Students

 

Murtadha Hssayeni is pursuing his Ph.D. degree in Computer Engineering at Florida Atlantic University. His research interests are signal processing and machine learning. He is currently working on developing an automated method to estimate the severity of Parkinson's Disease using motion analysis and deep learning. He is also working on an algorithm for Intracranial Hemorrhage detection and segmentation. Murtadha received his M.S. degree in Computer Engineering form Rochester Institute of Technology (RIT) and his B.S. in Computer Engineering from University of Technology, Iraq. Contact: Murtadha Hssayeni

 

lillian_2 Lillian Boettcher is an undergraduate student working towards a bachelor's degree in Computer Science at Florida Atlantic University. She is a dual-enrolled high school student through the school's FAU High School program. As an undergraduate researcher she assists and learns under Dr. Ghoraani's graduate students in machine learning. She is primarily working on the deep learning aspect of the Parkinson's Disease research project.

 

 

Syed Qasim Gilani is pursuing his Ph.D. degree in Electrical and Computer Engineering at Florida Atlantic University. His research interests are biomedical signal processing and machine learning. Qasim received his MS degree in Electrical Engineering from the National University of Science and Technology (NUST), Pakistan and his BS in Electronics Engineering from Comsats Institute of Information Technology, Abbottabad.Contact: Syed Qasim Gilani

 

 

Past Students

Prasanth Ganesan Ph.D. Electrical Engineering degree, Florida Atlantic University (2019). Dissertation: Development of an algorithm to guide a multi-Pole diagnostic catheter for identifying the location of atrial fibrillation sources.

Sathyashree Basavaraju MS Electrical Engineering degree, Rochester Institute of Technology (2018). Thesis: Segmentation of the left atrium from the LGEMRI using graph cuts.

Anthony Salmin BS/MS Electrical Engineering degree, Rochester Institute of Technology (2016). Thesis: Determining the locations of Atrial Fibrillations using simulated electrograms and multipolar sensing catheters.

Vignesh Ramji MS degree in Electrical Engineering with a specialization in Communications and Signal Processing at Rochester Institute of Technology (2016). Thesis: Tensor approach of multi-channel sensors for Parkinson's Disease assessment.

Murtadha Hssayeni M.S. Computer Engineering student at the Rochester Institute of Technology (2016). Thesis: Developing an automated method to estimate medication fluctuation of Parkinson's Disease using motion analysis and deep learning.

Miguel Dominguez, M.S. Electrical Engineering student at the Rochester Institute of Technology (2016). Thesis: Developing a higher-quality and lower-bitrate vocoders for voice communications applications.

Prasanth Ganesan, M.S. Electrical Engineering student at the Rochester Institute of Technology (2015). Thesis: Characterization of cardiac electrogram signals during atrial fibrillation.

Kristina Shillieto B.Sc. Electrical Engineering, Rochester Institute of Technology (RIT). Project: Simulataion of a Lasso multi-pole diagnostic catheter on a 3D atrial endocardium for virtual cardiac testing and analysis of an Atrial Fibrillation.

Alison Kahn, Biomedical Engineering student at the Rochester Institute of Technology (2016). Project: Quantify EEG signals from children with infantile spasms through the comparison of different aspects of EEGs in both normal and abnormal subjects.

Nicholas DaCosta B.S in Biomedical Engineering, at the Rochester Institute of Technology (RIT). Project: Developing a novel algorithm to automatically detect Parkinson’s Disease patient medication state fluctuations using unobtrusive and easily available sensors.

Isaac Arabadjis B.S in Biomedical Engineering, at the Rochester Institute of Technology (RIT). Project: Developed Matlab Algorithms to determine electroencephalogram (EEG) coherence for infants experiencing infantile spasms (IS).

Ronak Patel, M.S. Electrical Engineering student at the Rochester Institute of Technology (Fall 2015). Project: Characterization of the mechanism of atrial fibrillation using probabilistic and machine learning.

Supachan Traitruengsakul, Electrical Engineering Graduate at the Rochester Institute of Technology (2015). Thesis: Extracted features of hyppsarrthymia EEG from children with infantile spasms.

Jefferson Medel, BS/MS Computer Engineering student at the Rochester Institute of Technology (summer 2015). Project: Dictionary Learning and its versatility and applicability regarding different modes of classification.

Sriram Kumar , M.S. in Electrical Engineering at Rochester Institute of Technology (summer 2015). Project: Discovering discriminative dictionaries for robust signal classification.

Ryan Selby, Electrical Engineering student at the Rochester Institute of Technology, (Summer 2015). Project: The examination of cardiac surface unipolar electrograms in conjunction with ECG T-wave alternans.

Anup Jonchhe, Biomedical Engineering undergraduate student at the Rochester Institute of Technology (Summer 2015). Project: The Design and construction of an ECG data acquisition chamber for ex vivo rabbit hearts sustained through the Langendorff ​system.

Erik Messier, Biomedical Engineering undergraduate at the Rochester Institute of Technology (summer 2015). Project: Analysis of the relationship between Sudden Cardiac Death, and abnormalities in T-wave configurations.

Rachel Baumgarten, Biomedical Engineering undergraduate at the Rochester Institute of Technology (summer 2014). Project: Developing a sonification system that alerts patients of Paroxysmal Atrial Fibrillation through music.

Daniel Sinkiewicz, M.S. in Electrical Engineering at Rochester Institute of Technology (2013-2014). Thesis: Developed a novel method for extraction of neural response from cochlear implant auditory evoked potentials.

Mark Sterling, Postdoc, Rochester Institute of Technology, (2013-2014). Project: Characterization of cardiac arrhythmias by applying novel signal processing and learning analyses to physiological signals including the surface ECG.

Steven Ladavich, M.S. in Electrical Engineering at Rochester Institute of Technology (2013-2014). Thesis: Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity.

Amy Zeller, Biomedical Engineering undergraduate, Rochester Institute of Technology (summer 2013). Project: Analysis of body surface mapping of T-wave alternans distribution in patients with myocardial scarring.

Baabak Mamaghani, M.S. in Electrical Engineering, Rochester Institute of Technology (2013). Thesis: Lead optimization for accurate atrial fibrillation detection using an ambulatory ECG recording device.

Andrew Tock, Biomedical Engineering undergraduate, Rochester Institute of Technology (summer 2013). Project: Minimization of cochlear implant stimulus artifact in deaf and hard-of-hearing individuals.

Alexander Martin, M.S. in Health Systems Administration, Rochester Institute of Technology (2012-2013). Project: Investigation of thermal imaging for Atrial Fibrillation treatment.

Brandon Micale, Biomedical Engineering undergraduate, Rochester Institute of Technology (fall 2012). Project: Evaluation of atrial fibrillation organization in left atrium.

Suba Seevaratnam, Biomedical Engineering undergraduate, Ryerson University (2008-2009). Project: Performed MATLAB coding for detection of Twave alternans in body surface mapping ECG recordings.

Mohammad Parahoo, Biomedical Engineering undergraduate, Ryerson University and Sunnybrook Hospital (2009-2010). Thesis: Developed a Graphical User Interface (GUI) for the Research Interface Box (RIB2 Box) in cochlear implant research.

Angela Ip, Biomedical Engineering undergraduate, Ryerson University (summer 2008). Project: Developed C programing for Matching Pursuit analysis for detection of Twave alternans.