Laboratory Directory

Linwei and students in computer lab
 

People

Postdoc Positions Open

The CBL Technology is seeking highly qualified postdoctoral fellows to join the team to lead health-AI research investigations in the areas of:

(1) hybrid physics-based and neural modeling of complex systems, especially cardiovascular systems, and
(2) adaptive/continual time-series forecasting for longitudinal health monitoring, both with applications in cardiac diseases.

They will assist in the project management of large NIH-funded multidisciplinary projects. See detailed applications

PhD Positions Open

The CBL is seeking highly qualified PhD students interested in the development of cutting-edge statistical inference and machine learning methods with applications in medicine and health. BS or MS students with a background in electrical engineering, computer science, computer engineering, applied statistics / mathematics, or related areas are welcome to apply. Candidates are expected to have:

  • Strong background and interest in mathematics and statistics
  • Experience with programming using python, C++, Java or related languages.
  • Good communication and collaboration skills.

Applicants are encouraged to send a copy of  resume and transcripts to Dr. Linwei Wang.

Faculty

Linwei Headshot in front of white board

Linwei Wang

Dr. Linwei Wang is a Professor in the PhD Program of Computing and Information Sciences at the Rochester Institute of Technology in Rochester, NY. Her research interests center around large-scale statistical inference and learning, with an application to biomedical signal and image analysis for improving patient care in heart diseases. Dr. Wang is a recipient of the NSF CAREER Award in 2014 and the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2019.

PhD Students

Maryam Toloubidokhti

Maryam Toloubidokhti
(2019-present)

Maryam received her BS degree in Computer Science from Shahid Beheshti University, Tehran, Iran. She is interested in Bayesian inference and deep learning for multi-modal fusion of structural imaging and functional electrocardiographic data for improving ECG-imaging.

Nilesh Kumar

Nilesh Kumar
(2019-present)

Nilesh received his BS degree in Computer Science from the National University of Computer and Emerging Sciences, Islamabad, Pakistan. He is interested in machine learning and deep learning approaches to transfer knowledge from simulation data to real data domains.

Pradeep Bajracharya

Pradeep Bajracharya
(2019-present)

Pradeep received his undergraduate degree in Electronics and Communication Engineering from Institute of Engineering from Pulchowk Campus, Tribhuvan University, Kathmandu, Nepal. He is interested in Bayesian active learning and its use for uncertainty quantification in multiscale multiphysics models. Personal page.

Xiajun Jiang

Xiajun Jiang
(2019-present)

Xiajun received his undergraduate degree in Electrical Engineering & Automation from the Zhejiang University Hangzhou, China, and master degree in Computer Science from the University of Southern California. He is interested in Bayesian inference and deep learning techniques to make ECG-imaging adaptive and active. 

Ryan Missel Headshot

Ryan Missel
(2020-present)

Ryan received his undergraduate degree in Computer Science from RIT. He is interested in time sequence modeling for risk prediction and the interpretability of the model.

Ruby Shrestha

Ruby Shrestha
(2022-present)

Ruby received her undergraduate degree in Computer Science and Information Technology from Deerwalk Institute of Technology, Tribhuvan University, Nepal. She is interested in fairness and distributional robustness of deep learning models.

Bipin Lekhak Headshot

Bipin Lekhak
(2022-present)

Bipin received his undergraduate degree in Electronics and Communication Engineering from Pulchowk Campus, Tribhuvan University, Nepal. He is interested in modernizing longitudinal disease progression modeling and causal-effect intervention modeling.

PhD students
  • Zhiyuan Li (2018-2023, Computing & Information Sciences, RIT). Dissertation: Unsupervised Progressive and Continual Learning of Disentangled Representations, Computing and Information Sciences, Rochester Institute of Technology, defended April 2023. Currently with Vanderbilt University (postdoc).
  • Parashnna Gywali (2016-2021, Computing & Information Sciences, RIT). Dissertation: Learning with Limited Labeled Data in Biomedical Domain by Disentanglement and Semi-Supervised Learning, Computing and Information Sciences, Rochester Institute of Technology, defended March 2021. Currently with Stanford University (postdoc).
  • Omar Gharbia (2015-2021, Computing & Information Sciences, RIT). Dissertation: Noninvasive Electrocardiographic Imaging (ECGi) to GuideCatheter Ablation of Scar−related Ventricular Tachycardia. Computing and Information Sciences, Rochester Institute of Technology, defended September 2021. Currently with University of Utah (postdoc).
  • Sandesh Ghimire (2015-2020, Computing & Information Sciences, RIT). Dissertation: On Learning and Generalization in Solving Inverse Problem of Electrophysiological Imaging, Computing and Information Sciences, Rochester Institute of Technology, defended August 2020. Currently with Northeastern University (postdoc).
  • Jwala Dhamala (2014-2020, Computing & Information Sciences, RIT). Dissertation: Bayesian Active Learning for Personalization and Uncertainty Quantification in Cardiac Electrophysiological Model, Computing and Information Sciences, Rochester Institute of Technology, defended Feb 2020. Currently with Amazon. (Dissertation PDF)
  • Mohammed Alawad (2016-2019, Computing & Information Sciences, RIT). Dissertation: Transferring Generalized Knowledge from Physics-Based Simulation to Clinical Domains, Computing and Information Sciences, Rochester Institute of Technology, defended April 2019.  (Dissertation PDF)
  • Jingjia Xu (2011-2016, Computing & Information Sciences, RIT). Dissertation:  Bayesian Inference with Combined Dynamic and Sparsity Models: Application in 3D Electrophysiological Imaging, Computing and Information Sciences, Rochester Institute of Technology, defended Feb 2016.  (Dissertation PDF).
  • Azar Rahimi Dehaghani (2010-2015, Computing & Information Sciences, RIT). Dissertation: Uncertainty Quantification and Reduction in Cardiac Electrophysiological Imaging, Computing and Information Sciences, Rochester Institute of Technology, defended April 2015. Currently with Microsoft (Seattle, WA). (Dissertation PDF).
  • Hongda Mao (2009-2015, Computing & Information Sciences, RIT). Co-advisee. Dissertation: Integrated Cardiac Electromechanics: Modeling and Personalization. Dissertation defended February 2015. Currently with Amazon. (Dissertation PDF)

MS Students

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Casey Meisenzahl

Casey is currently working on applied machine learning projects in healthcare, including 1) active learning for real-time guidance in treatment of ventricular arrhythmia, and 2) risk prediction for heart failure readmission.

MS students
  • Jaideep Vital Murkute (MS, CS/RIT, 2023)
  • Pradumna Vilas Suryawanshi (MS, CS/RIT, 2023)
  • Swapnil Shah (MS, CS/RIT, 2023)
  • Sagar Kukreja (MS, CS/RIT, 2019): Capstone project: Localization of Ventricular Tachycardia Origin From 12-Lead Electrocardiograms using Multi-Task Learning in Convolutional Neural Network
  • Vishwanath Raman (MS, CS/RIT, defended 2019): Thesis: Prediction of Treatment Target for Ventricular Tachycardia using Multi-Task Machine Learning
  • Jay Upadhyay (MS, CS/RIT, 2019): Independent study:  Semi-supervised sequence learning methods for improving 12-lead ECG based localization of arrhythmia origin
  • Tappan Ajmera (MS, CS/RIT,2018): Capstone project: Semi-supervised Sequence Learning for ECG Analysis
  • Shubham Bharat Patil (MS, CS/RIT): Independent study: Multi-level variational autoencoders for assigning semantic information to disentangled latent space
  • Sanjay Khatwani (MS, CS/RIT, 2018): Independent study: Deep-learning based semantic segmentation of ECG waveforms
  • Parikshit Prashant (MS, CS/RIT, 2018): Independent study: Deep-learning based semantic segmentation of ECG waveforms
  • Sourabh Khasbag (MS, CS/RIT, 2018): Independent study: Active Learning Techniques for monitoring heart patient
  • Niraj Dedhia (MS, CS/RIT, 2018):Independent study: Using Active Learning to Detect Urgency of Doctor’s Visits
  • Nihar Vanjara (MS, CS/RIT, 2017): Capstone projects:Classification of single lead ECG Signals using Deep Neural Networks
  • Eric Fortunato (MS, CS/RIT, 2017):  Capstone projects: Analysis of Stress in Intelligent Systems Computer Science
  • Varun Mantri (MS, CS/RIT, 2017):  Independent study: Application of deep learning in ECG analysis
  • Darshan Kavathe (MS, CS/RIT, 2017): Independent study: Deep Learning approach to Atrial Fibrillation classification
  • Mounika Alluri (MS, CS/RIT, 2017): Independent study: Noninvasive computational cardiac imaging
  • Savitha Jayasankar (MS, CS/RIT, 2017): Independent study: Deep Learning on ECG Signals
  • Vasudev Bethamcherla (MS, CS/RIT, 2013-2015): Research assistant: focused on the fusion of multiple sensor data, especially facial data, for cognitive load and stress monitoring (co-advisee).
  • Krithika Sairamesh (MS, CS/RIT, 2013-2014): Research assistant & independent study: statistical analysis of multi-modal sensor data for cognitive load and stress monitoring
  • Martin Corraine (MS, CS/RIT, 2011-2012): MS thesis: Analysis of GPU acceleration of transmural electrophysiological imaging (secondary advisee)

BS Students

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Coming soon
BS students
  • 2020-2021. Aditya Khanna. Bayesian active learning for treatment guidance.
  • 2019-2020. Ryan Missel. Bayesian active learning for real-time treatment guidance (through NSF REU supplement). Computer Science, RIT.
  • 2019. Nate Glod. Bayesian active learning for real-time treatment guidance (through NSF REU supplement). Computer Science, RIT.
  • 2018-2020. Cameron Knight. Disentangled representation learning (through NSF REU supplement). Computer Science, RIT.
  • 2017. Erin Coppola. ECG-based Risk Stratification of Sudden Cardiac Death and Classification of Atrial Fibrillation classification (through NSF REU supplement). Biomedical Engineering, RIT.
  • 2017. Dan Giaime. Deep Learning Approach to Atrial Fibrillation classification (through NSF REU supplement). Game Design and Development, RIT.
  • 2017. Scott Eisele, Computational Modeling of ECG Generation (through NSF REU supplement). Mechanical Engineering, RIT.
  • 2017. Dawei Liu, Computational Modeling of ECG Generation (through NSF REU supplement). Mechanical Engineering  and Software Engineering, RIT
  • 2017. Don Phan, Noninvasive electrophysiological imaging of atrial fibrillation (through NSF REU supplement). Biomedical Engineering, RIT.
  • 2017. Rebecca Medina, Attention and behavior of students in online vs. face-to-face learning contexts (through NSF REU Site).
  • 2017. Daniel Carpenter, Attention and behavior of students in online vs. face-to-face learning contexts (through NSF REU Site).
  • 2016. Ashley Edwards, Attention and behavior of students in online vs. face-to-face learning contexts (through NSF REU Site).
  • 2016. Anthony Massicci, Attention and behavior of students in online vs. face-to-face learning contexts (through NSF REU Site).
  • 2016. Ram Longman. Focus on modeling and simulation of electrocardiographic data generation (through NSF REU supplement). Computer Engineering, RIT.
  • 2015-present. Roland Sanford. Focus on computational imaging of atrial fibrillation (through NSF REU supplement). Physics (BS) & Computational and Applied Mathematics (BS/MS) double major. RIT.
  • 2015-2018. Roland Sanford. ECG-imaging of Atrial Fibrillation (through NSF REU supplement). Applied Mathematics & Physics, RIT.
  • 2015-2016. William Hammond. Focus on Simulation-Based Learning (through NSF REU supplement). Computer Science, RIT.
  • 2015. Raymond Bremner. Focus on Computational Imaging of Atrial Fibrillation (through NSF REU supplement). Computer Engineering. RIT.
  • 2014-2016. Will Paul (co-advisor with Cissi Alm). Focusing on the fusion of multiple sensor data, especially eye tracking data, for cognitive load and stress monitoring (through GCCIS Kodak Endowed Chair Funds). Computer Science, RIT.
  • 2014-2015. Dakota Williams. Focusing on developing the software infrastructure supporting physics-based data-driven inference (through NSF REU supplement). Computer Science, RIT.
  • 2014 – 2015. Tom Becker. Focusing on developing the software infrastructure supporting physics-based data-driven inference (through NSF REU supplement). Computer Engineering, RIT.
  • 2013 – 2015. Brendan John (co-advisor with Reynold Bailey). Focusing on the fusion of multiple sensor data, especially eye tracking data, for cognitive load and stress monitoring (through GCCIS Kodak Endowed Chair Funds). Computer Science, RIT.
  • 2013 – 2014. Taylor Kilroy (co-adviser with Cissi Ovesdotter Alm). Focusing on the fusion of multiple sensor data, especially linguistic data, for cognitive load and stress monitoring (through GCCIS Kodak Endowed Chair Funds). Computer Science, RIT.
  • 2013. Karna Priya (co-advisor with Anne Haake). Psycho-Physiological Measures for Assessing Cognitive Overload. (through GCCIS Kodak Endowed Chair funds), Biomedical Sciences, RIT.
  • 2013. Robin Li. Psycho-Physiological Measures for Assessing Cognitive Overload (through McNair Scholars Program). Computer Science, RIT.
  • 2009 - 2010. Team ION (Brian Call, Devin Lane, Peter Lavellee, Brad Pease, Richard Stack). Senior Year Project: Development of a Computational Platform for the Modeling and Simulation of Cardiac Physiology and Pathophysiology. Software Engineering, RIT.

High-school interns

  • 2019. Sharanya Parvathaneni, Pittsford High-School Career Internship Program. Pittsford Mendon High School
  • 2018. Kyle Owlett. Pittsford High-School Career Internship Program. Pittsford Mendon High School
  • 2017. Katie Glance. Pittsford High-School Career Internship Program. Pittsford Mendon High School
  • 2016. Zainab Shar. Pittsford High-School Career Internship Program. Pittsford Mendon High School
  • 2015. Adisree Ankolekar. Pittsford High-School Career Internship Program. Pittsford Mendon High School
  • 2015. Shalei Kumar. Pittsford High-School Career Internship Program. Pittsford Mendon High School
  • 2014. Jason Han (high-school junior). Pittsford High-School Career Internship Program. Pittsford Sutherland High School
  • 2012-2013. Grace Shi (high-school senior). Pittsford High-School Career Internship Program. Pittsford Mendon High School