Signal processing and machine learning in health care for modeling time series medical signals
Jeff is currently a PhD student in the ECE department at JHU. He received his M.S. from Boston University in 2015. He earned a B.S. and B.A. from Virginia Tech in aerospace engineering and English literature, respectively, in 2011. His research focuses on signal processing and machine learning for modeling time series medical signals.
Niharika Shimona D’Souza
Medical image anaylsis; machine learning; applied computer vision
I’m currently a second year PhD student in the ECE Department at JHU, prior to which I received my Bachelor’s degree (B. Tech, Hons.) from the Indian Institute of Technology, Kharagpur in Electrical Engineering along with a minor in Electronics and Electrical Communication Engineering. My research interests are Medical Image Analysis, Machine Learning and Applied Computer Vision.
Fine-grained activity recognition from video kinematics
Robert DiPietro is a PhD student in the Department of Computer Science at Johns Hopkins University. Robert’s current research focuses on learning-based activity recognition, with applications to clinical care. Previously, he was an associate research-staff member at MIT Lincoln Laboratory. He received his B.S. in applied physics and his M.S. in electrical engineering from Northeastern University with highest honors.
Machine learning in clinical medicine; decision support systems; representation learning
Noam is a PhD student in the Computer Science Department. Prior to joining the department he completed a masters in Biostatistics at JHU. He is advised by Dr. Suchi Saria and is working towards developing reliable machine learning systems to assist physicians with diagnostics and treatment plans. Previously he studied philosophy and economics, and worked as a research engineer and a software developer.
Machine Learning; pattern recognition; robotics; computer vision
Yixin Gao is a PhD candidate at the Dept. of Computer Science, Johns Hopkins University. Her research includes machine learning and pattern recognition on complex time series data, especially data from medical and surgical domains that involve intervention on patients. Currently she is working on unsupervised structure discovery from surgical tool motion. Before joining Hopkins, she obtained her Bachelor’s and Master’s degree in Electrical Engineering from Xi’an Jiaotong University, where she worked on video data compression and unified speech and audio coding schemes.
Network modeling, Medical Image Analysis, Machine Learning, Computer Vision
Sayan is a first year doctoral student in the Department of Electrical and Computer Engineering and a member of Neural Systems Analysis Laboratory at Johns Hopkins University. He is currently working in the Malone Centre for Engineering in Healthcare under the supervision of Prof. Archana Venkataraman. His current research interest focuses on functional connectivity analysis of the latent brain networks of Schizophrenia patients using Functional Neuroimaging techniques (fMRI).
Prior coming to Hopkins, Sayan graduated with First Class Honors from the Department of Electronics and Telecommunication Engineering, Jadavpur University. During his undergrad he was involved in a number of research projects at Jadavpur University, India and University of Alberta, Canada.
He is deeply passionate about machine learning, artificial intelligence, human computer interactions and its application in healthcare and different clinical applications.
Machine learning applied to health care; disease prediction; in-patient monitoring
I am an NSF graduate research fellow working with Suchi Saria on acute disease prediction in the ICU. I’ve recently been involved in developing an algorithm to predict septic shock many hours prior to onset. I’m broadly interested in time-series modeling, especially as it applies to in-patient monitoring and disease prediction.
Applications of random processes; dynamical systems; information theory in medical and healthcare domains
I’m a graduate student in ECE advised by Sanjeev Khudanpur. I received the BS degree (Engineering Physics) from Eastern Illinois University and the BS degree (Electrical Engineering) from UIUC in 2014.
Developing models for analysis of complex human activities using video and sensor data
Colin received his B.S. in Mechanical Engineering at the University at Buffalo Honors College in 2011 and has since been in the Computer Science Ph.D. program at Johns Hopkins. He was a National Science Foundation Graduate Research Fellow from 2012 to 2015 and an Intuitive Surgical Research Fellow from 2011 to 2012.
fMRI analysis of different neurological conditions; machine learning and signals processing in neuroscience
Naresh is a first year electrical and computer engineering PhD student working in the Neural Systems Analysis lab. He is on a machine learning and signals processing track. Naresh recently graduated from Vanderbilt University with an electrical engineering major and a mathematics/economics double minor. He has pursued research in neuroimaging analysis techniques and 3D modeling of the brain from structural MRI. His current project involves quantifying neural plasticity effects in brain tumor patients through fMRI analysis. Naresh is extremely interested in applying electrical engineering techniques to better understand neuroscience. He is always eager to meet current or prospective students and discuss research!
Automated skill assessment from surgical tool motion data
I am a second year Ph.D. student in Computer Science working with Austin Reiter, Greg Hager, and Russ Taylor. I work on automated skill assessment from surgical tool motion data. My current project focuses on skill assessment for Septoplasty data using Machine Learning and Deep Network approaches. Specifically I am continuing work in our lab that identifies “strokes” in the surgical data and determines the surgeon’s skill using statistics from these strokes. I am also using Convolutional Neural Networks (CNNs) to identify the surgeon’s skill from a fixed window of tool motion data. The goal for this work is to 1) identify the surgeon’s skill, and 2) give interpretable feedback to trainee surgeons.
Machine Learning in Personalized Medicine, Representation Learning, Approximate Probabilistic Inference
Peter is a PhD student in the Computer Science Department and is affiliated with the Malone Center for Engineering in Healthcare, Institute for Computational Medicine, and the Center for Language and Speech Processing. His research interests lie at the intersection of machine learning, statistical inference, and medicine. He is an NSF Graduate Research Fellow and was previously a Whiting School of Engineering Centennial Fellow.
Signal processing and machine learning in health care
I am a first year PhD student in the department of Electrical and Computer Engineering at JHU. My research interests are mainly in the area of signal processing, machine learning and their application to the health care domain. Prior to joining JHU, I received my undergraduate degree in Electronics and Electrical Engineering from Indian Institute of Technology, Guwahati.
Causal Inference; machine Learning; healthcare including missing data and causal reinforcement learning
Eli Sherman is a PhD student in the Department of Computer Science at Johns Hopkins University. His research interests lie at the intersection of Causal Inference, Machine Learning, and Health Care including Missing Data and Causal Reinforcement Learning. Eli previously worked with Amy Cohn at the Michigan Center for Healthcare Engineering and Patient Safety and Jenna Wiens in the Machine Learning for Data-Driven Decisions lab. Eli earned a B.S. in Computer Science with Honors from the University of Michigan.
Statistical shape modeling; medical image analysis; anomaly/disease detection
I am a fifth year Ph.D. student working with Greg Hager and Russ Taylor. I work on the enhanced endoscopic navigation (sinus) project, and specifically work on improving registration between pre-op computed tomography (CT) images and intra-op endoscopy video data. Topology changes in naturally varying tissue as well as due to administered decongestants cause regular registration methods to fail. I work on building statistical models of the sinuses and structures within the sinuses to account for the deformation that takes place in the sinuses between CT scan and surgery, and using these models to improve registration. I further hope to investigate whether these models can be used to stage diseases within the sinuses.
Machine learning and signal processing in healthcare
I am a PhD student in the Department of Electrical and Computer Engineering on the Department Graduate Fellowship. I am excited about big, challenging problems in healthcare engineering with potential for direct translational impact. I currently work on disease severity models in critical care settings. My formal background spans the fields of signal processing, machine learning, biomedical sciences and physics. I have previously attended Carnegie Mellon University and the Indian Institute of Technology Madras.
Machine learning; causal inference
Adarsh is a PhD student in the computer science department where he is advised by Professor Suchi Saria. His research interests lie at the intersection of causal inference and machine learning with applications to healthcare. Prior to joining Johns Hopkins, he completed his undergraduate studies at Vanderbilt University where he worked on protections against data re-identification threats.
Public health applications of machine learning
I am a PhD student in the Department of Computer Science, advised by Professor Mark Dredze. I received by B.A. from Carleton College in 2014. I am broadly interested in machine learning and causal inference, and currently work on modeling noisy text data for public health applications.
Healthcare systems; machine Learning; mHealth
I am Ph.D student in the Dept. of Computer Science at Johns Hopkins University. My advisers are Prof. Andreas Terzis and Prof. Suchi Saria. My current research interests include mobile health, machine learning and computer networks.