Marina Bendersky
Postdoctoral Fellow
Marina Bendersky received her BSc in Chemical Engineering from the Technion Israel Institute of Technology in 2006. She received her PhD in the same field from the University of Massachusetts Amherst in 2013. Her PhD thesis focused on the development of computational models to predict colloidal interactions between particles and collectors that are heterogeneous at the nanoscale. Marina's research interests include machine learning algorithms for medical image processing, content-based image retrieval and distributed statistical modeling. Her research is focused on the development of statistical models and infrastructure that will facilitate computations with distributed private data. One of the main project objectives is to include quantitative imaging data in such distributed statistical models. She is also developing methods to quantify and track cancer lesions over time.
|
Mehmet Ertosun
Postdoctoral Fellow
 Mehmet Ertosun graduated from Bilkent Üniversitesi (Bilkent University) with a B.S. degree in Electrical & Electronics Engineering in 2004. During his undergraduate studies at Bilkent, he conducted research with Prof. Haldun Ozaktas in the fields of optics and signal processing. He received an M.S. degree in Electrical Engineering from Stanford University in 2006, where his research focused on optoelectronic & semiconductor devices, and carbon nanotubes. He received a second M.S. degree in Financial Mathematics from Stanford in 2009. In the field of quantitative finance, his interests include credit risk & modeling, financial instrument pricing, and computational modeling with applications to finance, including simulation of stochastic processes and asset pricing models. In 2010, he earned his Ph.D. degree from Electrical Engineering department of Stanford University. Between 2010 and 2012, he worked as a Senior Member of Technical Staff -- Research Engineer in the industry, and conducted research in the field of novel & emerging memory technologies, where he was focused on modeling, optimization and engineering of emerging memories such as resistive switch memories. In 2012, he joined the Molecular Imaging Instrumentation Laboratory at Stanford School of Medicine as a postdoctoral scholar, where he worked on development, modeling and simulation of novel medical imaging technologies, with a special interest in photon-counting CT technology with aspects ranging from semiconductor-level novel detector studies to quantitative imaging and biomedical informatics studies. Since 2014, Dr. Ertosun has been a fellow in the Stanford Cancer Imaging Training (SCIT) Program, supported by the National Cancer Institute, and has been working on applications of artificial intelligence and machine learning for detection and diagnosis of cancer.
|
Vanessa Sochat
Graduate Student - Biomedical Informatics
Vanessa Sochat is a PhD student in Biomedical Informatics. She received a BA in Psychology and Neuroscience from Duke University, and was head RA in a neuropsychology lab at Duke for two years before coming to Stanford. She aims to use multimodal neuroimaging data and methods from machine learning and natural language processing to identify patterns of structure and function that predict disorder. She is currently working on reproducibility metrics and standards in cognitive neuroscience in the laboratory of Russ Poldrack.
|
Hugh Zhang
High School Student
Hugh Zhang is a student at Valley Christian High School. His interests include applying statistics and machine learning to solve difficult real world problems. In the Rubin lab, he is working on analyzing texture features of perfusion (bloodflow) images of brain cancer patients in order to predict patient survival. He was in the lab for a summer in 2014.
|
Mustafa Safdari
Graduate Student - Computer Science
Mustafa has a Bachelor's in Computer Science from Birla Institute of Technology and Science - Goa Campus. At Stanford, he completed his Master's in Computer Science in the Artificial Intelligence Track. His areas of interest include probabilistic topic models, and his current work includes implementing these models for classification and semantic searching in images. He graduated in 2013 and is currently a Software Development Engineer at IMDb.com.
|
Dana Yeo
College Student
Dana Yeo is completing her undergraduate studies at Stanford University majoring in BioMedical Computation. She is interested in increasing the efficacy and precision of medical diagnostics using computational methods. Her previous research project focused on using zinc finger technology to target the sickle cell mutation in patient-derived IPS lines. In her spare time she enjoys a pint of green tea ice cream and a good game of Settlers of Catan.
|
Daniel Golden
Postdoctal Fellow
Daniel Golden received his B.S. in Electrical and Computer Engineering from Cornell University in 2005 and his Ph.D. in Electrical Engineering from Stanford University in 2011. Daniel's interests involve using machine learning and statistics in order to predict response to therapy for patients with known cancerous lesions. His current project is focused on using quantitative measures from dynamic contrast-enhanced MRI images to predict the response of triple-negative breast cancer patients to neoadjuvant chemotherapy. This predictive capability will allow patients and physicians to more effectively tailor custom therapies to individual patients. He graduated in 2013 and is currently Recognition Expert, CellScope, Inc.
|
Jiajing Xu
Doctoral Student - Electrical Engineering
Jiajing Xu completed his PhD in the Department of Electrical Engineering at Stanford University in 2013. His research interests include machine learning and content-based image retrieval for medical applications. He received his bachelor's and master's degree in EE from California Institute of Technology and Stanford University in 2006 and 2008. His work is in methods for content-based image retrieval (CBIR) in radiology for decision support, automated segmentation, and computerized methods to evaluate temporal imaging studies to quantify tumor response to treatment.
|
Raghav Pasari
Graduate Student - Computer Science
Raghav Pasari received his B. Tech in Information Technology from Indian Institute of Information Technology, Allahabad. Currently he is a first year Masters student in the Computer Science program at Stanford. He has worked as a intern at Google India for about 5 months and will be interning at eBay San Jose this summer. Currently he is working on liver lesion classification and detection using a novel visual dictionary of words approach. His interests include Image Processing and application of Machine Learning approaches to solve biomedical problems.
|
Witi Sachchamarga
Graduate Student - Management Science & Engineering
Witi is a Ph.D. student in Management Science & Engineering at Stanford University. He received his BS in Mechanical Engineering and his MS in Industrial and System Engineering from Texas A&M University. His research interests are in decision and risk analysis, probabilistic modeling and artificial intelligence. He is currently researching how to aggregate a number of expert opinions (network structure, probability and evidence) for decision support systems in medical diagnostic domains.
|
Mina Ghaly
Medical Student
Mina Ghaly is a medical student at Dartmouth Medical School. He received his B.S. in Biochemistry and Molecular Biology/Biological Sciences from the University of Massachusetts-Amherst. His current interests include bioinformatics, radiology decision support and machine learning capabilities.
|
Alan Snyder
Scientific Research Developer
Alan Snyder received his Bachelors from the University of Illinois in Engineering Physics. He previously did lithography research for Intel, where patented work related to phase-shifted reticles. Since then he has been a software developer working at start-ups, working on distributing video in the Internet. He has worked at worked at Live365, Macrovision, Azureus/Vuze among others. At Macrovision he received a patent for "Techniques for Watermarking and Distributing Content" using a peer-to-peer network protocol. While at Azureus he wrote the Network Monitor plug-in, which is used to determine which ISP is throttling connections, in addition to working on the client and writing the data-center management applications. Alan is currently working on the ePad project (http://epad.stanford.edu) using the latest features of HTML5 for semantically annotating medical images for clinical and research purposes.
|
Lior Weizman
Graduate Student - Computer Science
Lior Weizman received his B.Sc and M.Sc. degrees in Electrical Engineering from the Ben-Gurion University of the Negev, Israel in 2002 and 2004, respectively. He is currently a Ph.D. student in the School of Engineering and Computer Science in the Hebrew University of Jerusalem, and a summer intern at Stanford University. His main research interests are image segmentation, biomedical image processing and statistical signal processin
|
Christina Hung
High School Student
Christina Hung is a student at Piedmont Hills High School. She has a strong interest in graphical models and computerized decision support, as well as the uses of imaging in the field of radiology. She worked on a project involving decision support with mammography during a summer rotation project
|
Ihsan Djomehri
Medical Student
Ihsan received the BA in physics and BS in electrical engineering from UC Berkeley, and the MS and then PhD in electrical engineering from MIT. He has worked on both experimental development and computational physics research of nanotechnology devices and imaging. Now as a medical student at Stanford University, he is interested in bioinformatics and using the semantic web for radiology decision support of focal liver disease as an example.
|
Rohan Bansal
College Student
Rohan Bansal is currently a student at Princeton University. He is a pre-medical Computer Science major. His major interests lie in imaging and its uses in medicine, as well as other computer-science related technologies in medicine. He is working to create distinguish and split regions in an MRI to subregions and superpixels. His previous and current research includes peak-picking and analyzing mass spectrometry data, and creating a user friendly viewer for the plasmodium genome based off of PlasmDB.
|
Chaitanya Malladi
High School Student
Chaitanya Malladi is a high school graduate from the Harker School. He will be a member of the Class of 2016 at Caltech. He is interested in bioimaging analysis, decision support tools, and other bioinformatics applications. He is working on improving the functionality of the biomedical image metadata manager (BIMM) by expanding the available features. One such enhancement is allowing BIMM to interface with PACS.
|
Qiang Chen
Faculty – Computer Science
Qiang Chen received his B.Sc. degree in computer science and Ph.D. degree in Pattern Recognition and Intelligence System from Nanjing University of Science and Technology, China, in 2002 and 2007, respectively. Currently, he is an associate professor with the School of Computer Science and Technology at the Nanjing University of Science and Technology. His main research interests are image segmentation, object tracking, image restoration, image enhancement, and image quality assement.
|
Mia Levy
Faculty in Biomedical Informatics and Medicine, Vanderbilt University
Mia Levy received her PhD in Biomedical Informatics at Stanford in 2010. She is currently Assistant Professor in Biomedical Informatics and Medicine at Vanderbilt University and Cancer Clinical Informatics Officer at Vanderbilt Ingram Cancer Center. Her dissertation was entitled "Rule-Based Response Assessment Framework" in which she developed generalizable methods of representing treatment response to disease. She continues to collaborate with our laboratory in developing methods for using quantitative imaging to evaluate cancer treatment response and tool development to automate these tasks.
|
Pooja Naik
Undergraduate Student
Pooja Naik is a final year student at Birla Institute of Technology and Science, Pilani- Goa Campus pursuing Msc (Tech.) Information Systems. Her research interests include Artificial Intelligence, Machine Learning, Quantitative Imaging and Decision Support. She is currently working on using DCE-MRI to discover imaging biomarkers that predict response to PARP inhibitor therapy in Triple Negative Breast Cancer.
|
Neeraj Agrawal
Graduate Student - Computer Science
Neeraj received a bachelor's degree in computer science from the Indian Institute of Technology Roorkee. He is currently working on image analysis to characterize liver legions for content based image retrieval in radiology.
|
Ankit Gupta
Graduate Student – Computer Science
Ankit received a bachelor's degree in computer science from the Indian Institute of Information Technology and is completing a masters in Computer Science at Stanford. While in the lab, he worked on image analysis to characterize the margin of lesions for content based image retrieval in radiology. He produced a manuscript on his work and accepted submissions to major meetings.
|
Allika Walvekar
College Student
Allika Walvekar is an undergraduate at Caltech. She is interested in electronic encoding of medical knoweledge, and she is working on building a prototype decision support system that will use the knowledge in the Semantic Media wiki to help doctors access current medical knowledge more effectively to make the best diagnostic decisions.
|
Daniel Korenblum
Scientific Research Developer
Daniel Korenblum obtained a B.A. in Biochemistry and Molecular Biology (BMB) from Reed College, an M.S. in Biophysics from Cornell University, and an M.S. in Computational and Mathematical Engineering at Stanford University. His interests include biomedical image and signal processing, biomedical informatics, molecular modeling, and computational biophysics. Daniel is part of the Image Feature effort of the Information Science in Imaging at Stanford (ISIS) group. He was the primary architect and developer of the biomedical image metadata manager (BIMM) and co-developer of the Aim Template Manager (pre-release version only) applications. He is currently working at Genia Technologies Inc., developing protein nanopore single-molecule measurement devices.
|
Cesar Rodriguez
Research Programmer
Cesar received his bachelor’s degree in Biological Science from Florida State University and his MD from Howard University College of Medicine. He created the initial version of iPAD, an application to use ontologies to annotate radiology images to make the semantic content machine-accessible. While he has now moved on to puruse systems biology, he continues to remain engaged in the iPAD project to support its continued development.
|
Katie Hsih
College Student
Katie Hsih received her BSE degree Operations Research & Financial Engineering from Princeton University in 2010. She is interested in a multidisciplinary approach to medical problems and global health policy. Katie currently running a pilot study for iPAD, a computerized tool for annotation of radiological images.
|
Krithin Sitaram
College Student
Krithin Sitaram is a undergraduate student at Princeton interested in Natural Language Processing.
|
Meeta Arora
Graduate Student - Management Science & Engineering
Meeta is pursuing her Master's in Management Science and Engineering at Stanford University. Prior to this, she received her undergraduate degree in Computer Engineering from Mumbai University and worked as a Software Engineer developing web applications. She is developing Web-based applications to enable viewing large pre-coordinated terminologies for radiology and to create integrated data warehouses linking clinical data (through the I2B2 platform) to radiology data (in BIMM).
|
Brett Lullo
College Student
Brett Lullo is a senior at Princeton University where he is a Computer Science major on the Pre Med track. Brett's Junior Independent Work has examined the crossroads of medicine and technology. In the Fall he worked on the AphasiaFox extension for firefox, a picture, sound and video pop-up dictionary for people suffering from the language disorder aphasia. In the Spring he worked on the MotifDigger project in order to identify let-7 miRNA target sequences in the 3'UTR region of the human genome. Currently he is working at the American College of Radiology Imaging Network, updating the iPAD annotation tool for radiological images.
|
Jithun Nair
Graduate Student - Electrical Engineering
Jithun Nair received his B. Tech in Electronics and Electrical Engineering from Indian Institute of Technology Kharagpur. He's pursuing his Masters in Electrical Engineering at Stanford. He is currently working on developing a web-based version of the iPad, a computerized tool for annotation of radiological images. His interests include developing Rich Internet Applications (RIA) and Computer Graphics.
|
Abhik Lahiri
Graduate Student - Computer Science
Abhik Lahiri is pursuing a Master's in Computer Science at Stanford. He received his Bachelor's in Computer Science from Birla Institute of Technology and Science, Pilani - Goa Campus. He recently worked for 5.5 months in HP Labs, Bangalore. His interests are in AI, Machine Learning, Information Retrieval. Abhik is currently working on information extraction of semantic information from radiology reports to enable publishing and computerized reasoning over these data on the Semantic Web.
|
Stephanie Chan
Radiology Resident
Stephanie received her MD from Stanford and is currently a radiology resident at UCSF. She is interested in creating models to help physicians integrate diverse data in decision making. She is currently developing a probabilistic model of breast imaging that incorporates the results of biopsy to help radiologists evaluate whether negative biopsy is due to sampling error.
|
Irene Liu
Radiology Resident
Irene received her MD and PhD at Stanford. Her PhD is in biomedical informatics, and she is interested in creating decision support applications to improve the consistency in radiology interpretation. She is developing Bayesian Network models of thyroid imaging to help radiologists evaluate thyroid nodules and improve decision making about when to biopsy these lesions.
|
Debra Willrett
Scientific Research Developer
 Debra Willrett received her BS in mathematics and BS in computer science from Iowa State University. She received her MS in electrical engineering from Stanford University. She has designed and built web applications for business and education. These sites sell services and products, manage memberships, offer member services, publish online member directories, manage group calendars, and process online event registration and payments. In addition, she has provided litigation support to law firms for software intellectual property cases. Debra designed and developed the award-winning project management application MacProject which was awarded the Readers Choice Software of the Year Award for many years running by MacWorld magazine, and was the first third-party application to be release for the Macinotsh. MacProject was the follow on product to Debra's LisaProject, the first graphical project management software, which was distributed by Apple for the Lisa. Debra worked on the ePad project (http://epad.stanford.edu), using cutting-edge features of GWT-P and HTML5 to create Web application-based front end for image viewing and semantic image annotation. She is currently in the CEDAR group in Dr. Musen's laboratory at Stanford University.
|
Sadhika Malladi
High School Student
Sadhika Malladi was a high school student while in the lab and enjoys working to use Computer Science in medical applications. In particular, her interests include building a plugin to ePad's environment and modeling patient responses to chemotherapy.
|
Rebecca Sawyer
Graduate Student - Biomedical Informatics
Rebecca Sawyer completed her M.S. in Electrical Engineering and PhD in Biomedical Informatics. She received her B.S. in Electrical Engineering at UNC Charlotte. She is interested in image processing and machine learning for content-based image retrieval and computer-aided diagnosis. She is currently working on mammography feature extraction methods for computer-aided diagnosis of breast cancer.
|
Evani Radiya-Dixit
High School Student
Evani Radiya-Dixit was a high schools student at The Harker School while in the lab. Her interests include the applications of mathematics and computer science to solve real world issues centered around education and medicine. She is currently working on developing statistical natural language processing methods to extract information from mammographic reports. The goal of the project is to improve diagnosis and treatment of breast cancer.
|
David Liang
Graduate Student - Electrical Engineering
David is a second-year masters student in Computer Science who received his B.S. in Computer Science with honors and a B.S. in Mathematics (Pure) from the University of Wisconsin-Madison in 2018. He is interested in the applications of deep learning to medical image analysis and is currently involved with projects working with Mammograms and Video EEG data.
|
Jared Dunnmon
Postdoctoral Scholar
Jared Dunnmon received a PhD in Mechanical Engineering from Stanford (2017) after earning a B.S. in Mechanical Engineering from Duke University (2011) and both an MSc. in Mathematical Modeling and Scientific Computing (2012) and MBA (2013) from the University of Oxford, where he studied as a Rhodes scholar. Jared's PhD research focused on novel applications of low-contrast X-ray CT, while his current work centers around leveraging techniques from weak supervision to enable scalable creation of clinically viable machine learning models across a variety of sensing modalities and use cases.
|
Sheryl John
Research Software Developer
Sheryl John received her Bachelor of Technology (B.Tech) degree in Computer Science and Engineering from the National Institute of Technology, Tiruchirappalli, India and her M.S degree in Computer Science from University of Southern California. She worked as a Software Engineer at Children’s Hospital of Los Angeles, where she developed data processing workflows using the Hadoop stack to build clinical decision support systems. She has also designed responsive web applications, using JavaScript frameworks like React.js and Backbone.js, to serve as an user interface to retrieve data from Electronic Health Records. Her interests and experience include big-data processing and visualization of medical data to meet the challenges of clinical research needs. She is working on the front-end architecture and developing the user interface design for the ePad project (http://epad.stanford.edu). She currently works for a software company in San Francisco.
|
Siddharth Sharma
High School Student
Siddharth Sharma is a high school senior at BASIS Independent Silicon Valley who is passionate about automating the modern world and researching the applications of machine learning. Under the mentorship of Dr. Rubin and Dr. Lee-Messer, his current research focuses on object detection models and video algorithms for seizure detection. He has been accepted to 4 IEEE conferences as the only high school student, co-authored a published book on AI, and has been recognized as a finalist in the Junior Science and Humanities Symposium, a qualifier to the California State Science Fair, and a 1st award winner at the Synopsys Championship. He plans to continue his work with state-of-the-art deep learning techniques for the purposes of medical imaging and diagnosis.
|
Natasha Sheybani
Postdoctoral Scholar
Natasha Sheybani received her Ph.D. in Biomedical Engineering from the University of Virginia (2020) after earning her B.S. in Biomedical Engineering (with Honors) from Virginia Commonwealth University (2015). Her graduate research in the laboratory of Dr. Richard Price centered on leveraging focused ultrasound and multi-modality imaging to potentiate immunotherapy for primary and disseminated solid cancers. She is a former recipient of the NSF Graduate Research Fellowship and Robert R. Wagner Fellowship, and she currently holds the NCI F99/K00 Predoctoral to Postdoctoral Fellow Transition Award. As a postdoctoral research fellow, she is being jointly mentored by Dr. Daniel Rubin and Dr. Ash Alizadeh to pursue research interests spanning quantitative imaging processing, radiomics/radiogenomics and advanced AI/ML approaches for novel image biomarker discovery, clinical risk stratification and personalized therapy in the setting of lymphoma.
Stanford Profile: https://profiles.stanford.edu/natasha-sheybani
LinkedIn: https://www.linkedin.com/in/natasha-sheybani-024439123/
|
Karissa Cyn-Yun Yau
Stanford Undergraduate - Computer Science
Karissa Cyn-Yun Yau is an undergraduate student at Stanford University studying Computer Science. She is interested in the application of artificial intelligence and machine learning in the medical field. Currently, she is working on Rubin Lab's FASR system, a software that aids radiologists in decision support.
|
Zeshan Hussain
Undergraduate Student in Computer Science
Zeshan was a coterminal M.S. student in Computer Science as well as a B.S. candidate in Computer Science at Stanford University. His research deals with building convolutional neural networks for mammography classification and designing generative methods to generate artificial mass lesions on mammograms. In general, he is passionate about the intersection of CS and healthcare and has previously worked at a population health startup called Acupera. He is currently a medical student at Harvard.
|
Anthony Barthell
Undergraduate Student
Anthony Barthell is currently pursuing a double major in Computer Science and Applied Mathematics at the University of Wisconsin–Madison. He grew up in the Bay Area and graduated from Saratoga High School. He is interested in the applications of Natural Language Processing and Deep Learning. His current research involves creating NLP algorithms to detect malignant tumors from radiology impressions.
|
Blaine Rister
Graduate Student - Electrical Engineering
Blaine received his B.S. in Electrical Engineering and B.A. in Philosophy from Rice University in 2014. He is currently pursuing his PhD in Electrical Engineering at Stanford University. His research is primarily in medical image analysis, including local feature extraction, registration, pattern recognition, and content-based retrieval.
Personal webpage: http://web.stanford.edu/~blaine/
|
Aaron Abajian
Medical Student
Aaron is a medical student at Yale University. He earned a B.S. in Computer Science & Engineering, a B.S. in Mathematics, and a B.S. in Biological Sciences from the University of California, Irvine. He spent two years following graduation as a full-time high school chemistry teacher as part of Teach for America. He previously worked as a PACS software engineer with Candelis, Inc. His research interests include the development of automated algorithms for the segmentation and extraction of metadata from solid tumor images. His current project entails the comparative analysis of novel response to treatment criteria.
|
Hakan Bulu
Postdoctoral Fellow
Hakan recieved his M.Sc. degree in computer engineering from 9 Eylul University, Turkey in 2007. Hakan Bulu completed his Ph.D. in 2013 and was a Research Assistant at the same department. In his graduate research he worked on developing an ontology based medical image annotation and retrieval system for mammographic examinations to make possible case-based retrieval for mammography. In his Postdoc work at Stanford he is developing NLP methods for mammography reports and the AIM API for the ePAD system. He currently works for IBM Almaden.
|
Daniela Lee
High School Student
Daniela Lee was a high school student at The Harker School while in the lab. Her research interests are centered around the medicine field as a whole, but especially the imaging aspect. She is currently working on the standardization of image data into AIM files as well as on the analysis of image features to create a treatment response model to neoadjuvant chemotherapy.
|
Jocelyn Barker
Doctoral Student - Biophysics
Jocelyn Barker received her PhD in Biophysics in 2015. Her main research interests are image analysis of medical data for biological and medical relevance. Previously in her undergraduate career, she did research in genomic bioinformatics while getting her degrees in Biochemistry and Molecular Biology and Mathematics. For her thesis project, she developed novel computational methods for analyzing pathology image data incancers to define disease subtypes and to correlate image-defined phenotypes with molecular, radiological, and survival outcomes data. She currently works for Microsoft.
|
Avinash Thangali
High School Student
Avinash is a student at Lynbrook High School in San Jose. He likes working with software in general and is an avid programmer, but is especially interested in software development and data mining applied within a medical context. His current project involves decision support by implementing content based image retrieval (CBIR) and relevance feedback mechanisms into the ePad environment using the biomedical image metadata manager (BIMM) system.
|
Kaushik Shivakumar
High School Student
Kaushik Shivakumar is a rising senior at the Harker High School and joined the Rubin Lab as an intern in June 2018. He is interested in the use of advanced machine learning techniques for clinical applications. Kaushik is currently working on automated segmentation and diagnosis of lung nodules from thoracic CT scans, in attempts to facilitate overall diagnosis, prognosis, and treatment.
|
Imon Banerjee
Research Scientis
 Imon Banerjee received her Master of Technology degree from National Institute Technology Durgapur, India and completed the master thesis from the European Organization for Nuclear Research (CERN), Switzerland. She reeived her PhD thesis in computer science from Department of Informatics, Bioengineering, Robotics, and System Engineering (DIBRIS) at University of Genova, Italy and worked as a research fellow in the 3D Shape modeling group of the National Council of Research (CNR), Italy. During her Ph.D., Imon was awarded the Marie Curie European fellowship. Her research is primarily focused on realizing the integration between patient-specific 3D data and formalized bio-medical knowledge by means of 3D shape characterization and analysis, statistical modeling and machine learning techniques, that can create a scope for bringing the 3D patient-specific model more into clinical reasoning and correlation tasks. She developed original approaches for part-based annotation of 3D patients’ data and the representation of this semantics in a machine-readable way. After completing her PhD, she became a postdoctoral fellow in the Rubin laboratory, and is currently a Research Scientist in that lab. Her current research focusts on artifiical intelligence methods for analyzing medical records and image data for making clinical predictions by leveraging narrative text. She developed innovative word embedding-based methods for deep learning with text for inforamtion extraction and clinical prediction. She developed a novel semantic word embedding (SWE) approach that she is pursuing to enable automated annotation of large collections of clinical images in the PACS for AI applications. Imon is currently a faculty at Emory University.
|
Yan-Ran (Joyce) Wang
Postdoctoral Scholar
Yan-Ran (Joyce) Wang received her Ph.D. in computer science at Northwestern University (Evanston) in 2019. Her research lies in the broad category of computer vision and biomedical image analysis. Her Ph.D. research focused on the fundamental problem of image and video segmentations, while her current work centers on the use of deep learning models for medical image analysis. She is particularly interested in the combination of machine learning frameworks with underlying physiology for solving medical image problems.
|
Okyaz Eminaga
Postdoctoral Scholar
 Okyaz passed his medical examination (Staatsexamen) and received his Ph.D. in Medicine (major topic: medical informatics) under the supervision of Dr. Semjonow and Dr. Dugas from University of Muenster (Germany) in 2009 and 2010 respectively. His residency in Urology was completed in the University Hospital of Cologne (Germany) with a major focus on uro-oncology. He was a research fellow in Prostate Center of University Hospital Muenster, doing research in biomarkers, biobanking infrastructure, and histopathology. During his residency fellowship, he further evaluated the role of different miRNAs in prostate cancer under the supervision of Dr. Warnecke-Eberz. After his residency, he was a postdoctoral scholar at the laboratory of Dr. Brooks, doing genomic research and bioinformatics. His current interests expand to medical imaging informatics and integrative data analysis. Recently, he has been selected for the early-investigator research award for prostate cancer managed by DOD. As part of this award, he will be jointly working in the laboratories of Dr. Daniel Rubin and Dr. Brooks and use advanced machine learning techniques and clinical data to develop decision-aided tools for diagnosis and prognosis of prostate cancer.
|
Darvin Yi
Graduate Student - Biomedical Informatics
Darvin Yi is a Ph.D. student in Biomedical Informatics. He received a A.B. in physics at Princeton University, and he's interested in the union of low bias computer vision methods and radiology. And like the other moths attracted to the hype flame, he is one of many students working on Deep Learning. Darvin's current projects involve studying the limitations of Deep Learning in radiology, using neural networks to visualize features on data, and trying to survive in the over-priced Bay Area.
|
Vivian Zhu
High School Student
Vivian Zhu is a senior at Saint Francis High School in Mountain View. She enjoys developing software and is interested in the applications of machine learning in the medical field. Her current research involves facilitating queries in chest imaging annotations as well as incorporating XNAT to the CWT distributed deep learning platform. In her free time, she enjoys oil painting, taking photos, and skateboarding.
|
Jean-Benoit Delbrouck
Postdoctoral Scholar
Jean-Benoit Delbrouck received a PhD in Electrical Engineering from the University of Mons (2020) after earning a Master in Computer Science from the University of Lille. He is now a postdoctoral research fellow in Prof. Daniel Rubin's lab at Stanford. His research aims to develop deep-learning methods for multi-modal problems at the intersection of vision and language. He focuses on multi-modal medical data, combining information from multiple images (such as x-rays) and familial or patient histories (clinical reports) to provide diagnostics that are accurate but also semantically rich. His other interests are about out-of-distribution detection and incremental learning.
Website : http://web.stanford.edu/~jbdel
|
David Cohn
Data Scientist and Data Curator
David Cohn received his Bachelor’s degree in Biology from Stanford University in 2015, and a Master’s degree in Biomedical Informatics from Stanford in 2017. His projects involved applying machine learning and data mining to features extracted from histopathology image data, as well as data derived from an Interventional Radiology database. David graduated the Stanford BMI program in 2019.
|
Maximilian Pfau
Visiting Postdoctoral Scholar
 Maximilian earned his Medical Degree from Heidelberg University, Germany with a scholarship from the merit-based “Studienstiftung des deutschen Volkes”. During this time, he worked on a basic science project on SNARE-mediated membrane fusion in Dr. Söllner’s lab at the Heidelberg University Biochemistry Center. Following graduation, he completed a short-term research fellowship at the Doheny Eye Institute, UCLA under the supervision of Dr. SriniVas R. Sadda, and then entered the research-intensive ophthalmology residency program at the University Eye Hospital Bonn, Germany. His research there - under the supervision of Dr. M. Fleckenstein, Dr. F.G. Holz, and Dr. S. Schmitz-Valckenberg - primarily focused on structural and functional endpoints for non-exudative late-stage age-related macular degeneration. He was awarded for his work with the young investigator award of the German Retina Society as well as the clinical research award of the PRO RETINA foundation. In Dr. D.L. Rubin’s lab, Maximilian will now work on artificial intelligence methods for the assessment and prediction of structural disease progression in retinal degenerative diseases.
|
Nipun Agarwala
Masters (coterm) Student - Electrical Engineering
Nipun Agarwala graduated as a Coterm in Electrical Engineering. He did a B.S. in EE, specializing in Hardware and Computer Systems. His M.S. is focusing on Machine Learning and Optimization. His primary research interests are building hardware for deep learning, developing and understanding novel machine learning models and ensembles, and applying machine and deep learning to health care and clinical applications. In prof. Rubin lab, Nipun focuses on developing novel techniques for lesion segmentation.
|
Luis de Sisternes
Postdoctoral Fellow
Luis de Sisternes is a Postdoctoral Scholar in the Department of Radiology at the Laboratory of Quantitative Imaging. His main research interests include image processing, quantitative imaging, 3-D modeling and CAD. He received his Ph. D. in Electrical Engineering at Illinois Institute of Technology, where he investigated the application of novel Phase-Contrast X-Ray imaging methods in breast diagnosis while researching at the Medical Imaging Research Center. An important part of his previous research work includes the development of a versatile mathematical model that enables the generation of a large number of realistic three-dimensional breast tumor simulations. His current projects are focused on the development and evaluation of novel quantitative imaging features of disease both in retinal imaging using optical coherence tomography and for cancer characterization in computed tomography images. He currently works for Carl Zeiss Inc.
|
William Du
Undergraduate Student
William Du is an undergraduate student at Stanford University who recently took part in the Stanford Institute of Medical Research Summer Program (SIMR). He is interested in radiogenomic analysis as a way to expand his understanding of biomedical informatics as a whole. He is currently working on radiogenomic analysis of glioblastoma patients in order to gain insights into medical features.
|
Tiffany Ting Liu
Postdoctoral Fellow
Tiffany Ting Liu completed her Ph.D. in Biomedical Informatics. She received her Bachelor of Computer Science with Bioinformatics Option at the University of Waterloo in Canada. She is interested in integrated analysis of molecular and imaging data using methods in machine learning and image analysis. Her thesis project is on analyzing multiscale glioblastoma data to identify molecular and imaging biomarkers for personalized cancer treatment. A major paper on her work was published in Neuroinformatics. She is currently a Bioinformatics Scientist at Veracyte, Inc.
|
Billy Bloomquist
High School Student
Billy Bloomquist was a high schol student at the Harker School while in the lab. He is passionate about science and technology, and he loves solving problems. Billy is working on ISPY2 data of patient outcomes from medical trials which will be used to rapidly change treatment for subsequent trial participants. He hopes this work will have a measurable and positive impact on patient quality of life.
|
Abra Jeffers
Doctoral Student - Management Science & Engineering
Abra Jeffers is a PhD student in Management Science & Engineering. She received an MPhil in Management Science from Cambridge University. She received her BA and BS in Chemistry and Economics from Michigan State University. Her research interests are decision and risk analysis and probabilistic modeling. She is current researching a breast cancer risk assessment model."
|
Josh Sanyal
High School Student
Josh Sanyal is a rising sophomore at Homestead High School. He is interested in the applications of image analysis and machine learning in the medical field. His current research involves registering and classifying prostate tumors in multiparametric MRI scans.
|
Alfiia Galimzianova
Postdoctoral Fellow
Alfiia Galimzianova received a Diploma in Computer Science from Kazan Federal University, Russia, in 2010,
and a PhD in Electrical Engineering from University of Ljubljana, Slovenia, in 2015. During her PhD studies, she concentrated on development of methods for modeling, estimation and segmentation of normal and pathological structures from brain MR images. Alfiia's current projects involve development of methods for automated annotation of ultrasound images of head and neck area, and segmentation of brain tumors from brain MR images. She currently works for an imaging company in Copenhagen.
|
Jiaming Zeng
Graduate Student - Management Science & Engineering
Jiaming Zeng is a Ph.D. student in the Management Science & Engineering at Stanford University. She received a B.S. in Mathematics with Computer Science from MIT. Her research interests include machine learning, causal inference, and decision analysis. She's currently working on developing artificial intelligence tools for cancer treatment strategies.
|
Anuj Pareek
Postdoctoral Scholar
Anuj Pareek earned his Medical Degree from University of Southern Denmark in 2012, and subsequently completed his PhD studies at Aarhus University Denmark in 2018. His previous research has concentrated on clinical application of lung ultrasound in the NICU, and the use of iron oxide nanoparticles for characterization of pediatric tumors with PET/MRI. He is jointly working in the laboratories of Dr. Heike E. Daldrup-Link and Dr. Daniel Rubin with the purpose of conducting research at the intersection of Artificial Intelligence and Radiology. His main focus is using Machine Learning in Radiology for non-invasive cancer diagnosis, prediction of tumor volume, and enhancing patient scans from scarce data. Anuj is very interested in Artificial Intelligence approaches in Radiology, which can be directly implemented in clinical practice and lead to increased diagnostic accuracy or optimized workflow.
|
John Lambert
Graduate Student - Biomedical Informatics
John Lambert is a coterminal M.S. student in Biomedical Informatics and a B.S. candidate in Computer Science at Stanford University. He is interested in helping those that are sick and suffering by developing medical systems that will improve outcomes in patients and expedite and enhance diagnoses. His current research revolves around the training of convolutional and recurrent neural networks to automatically narrate the contents of radiology images, energy-minimization methods for automatic tumor segmentation, dynamic memory networks, and most importantly, helping the public overcome their widespread fear of AI.
|
Florian Dubost
Postdoctoral Scholar
 Florian Dubost is a postdoctoral research fellow in Prof. Daniel Rubin's lab at Stanford. He develops AI methods for neurology and radiology. His current focus is the prediction of seizures from EEG and video recordings of epileptic patients. His fields of expertise are engineering, computer science, AI, deep learning, weakly supervised learning, self-supervised learning, image generation, registration, and variational autoencoders, with application in dementia, stroke, scoliosis, emphysema, cystic fibrosis, accelerated MRI reconstruction, and brain lesion detection. He holds a PhD from Erasmus University Medical Center, Rotterdam, the Netherlands, a MSc. in Medical Engineering from the Technical University of Munich, Munich, Germany, and another MSc. in engineering from the Ecole Centrale Marseille, Marseille, France.
Top Articles include:
Dubost, F., Adams, H., Yilmaz, P., Bortsova, G., van Tulder, G., Ikram, M.A., Niessen, W., Vernooij, M.W. and de Bruijne, M., 2020. Weakly supervised object detection with 2D and 3D regression neural networks. Medical Image Analysis, 65, p.101767.
Dubost, F., Bortsova, G., Adams, H., Ikram, A., Niessen, W.J., Vernooij, M. and De Bruijne, M., 2017, September. GP-Unet: Lesion detection from weak labels with a 3D regression network. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 214-221). Springer, Cham.
Dubost, F., Yilmaz, P., Adams, H., Bortsova, G., Ikram, M.A., Niessen, W., Vernooij, M. and de Bruijne, M., 2019. Enlarged perivascular spaces in brain MRI: Automated quantification in four regions. NeuroImage, 185, pp.534-544.
More articles here:
https://scholar.google.com/citations?user=_yNBmx8AAAAJ&hl=fr
|
Vignav Ramesh
High School Student
 Vignav Ramesh is a junior at Saratoga High School who is interested in capitalizing AI/ML techniques to automate disease classification, encourage resource stewardship, and enable precision medicine. He is particularly fascinated by the potential for AI to operate in the intersection between evidence based medicine and patient centered care. His current research, under the mentorship of Dr. Rubin, involves automatically quantifying COVID-19 severity by segmenting infected lung lesions on chest X-rays computed as coronal projections of axial CT volumes. His previous research at the QIAI lab involved the creation of medical imaging plugins for the ePAD platform, including PCA-based fusion of multiple DICOM image series of various modalities and the automatic generation of DICOM segmentation objects from image contours. In the past, Vignav has helped lead the unsupervised language learning movement at SingularityNET; he has submitted publications to two IEEE conferences, and his natural language generation research has been presented at an international AI and data science conference. Other projects of his have won top 3 at LAHacks, HackMIT, and COVIDathon, three of the world’s largest collegiate and workplace hackathons. He is also the cofounder and CEO of an AI-based startup providing democratized playtesting services to independent game developers. In his free time, Vignav enjoys playing competitive tennis and practicing the double bass.
|
Hersh Sagreiya
Postdoctoral Fellow
Hersh earned his BA in Biochemical Sciences at Harvard in 2007, his MD at Stanford in 2012, and completed a residency in Diagnostic Radiology at the University of Pittsburgh in 2017. He previously worked in the laboratory of Dr. Russ Altman at Stanford, doing research in pharmacogenomics and medical informatics. His current two greatest areas of interest are medical imaging informatics and molecular imaging. He is jointly working in the laboratories of Dr. Daniel Rubin and Dr. Juergen Willmann. As part of an RSNA Fellow grant, he will use machine learning and texture analysis to develop a quantitative tool for the early detection of ovarian cancer. This study will specifically use BR55, a novel molecular imaging agent that targets sites of neoangiogenesis. He is interested in additional opportunities to apply deep learning techniques, as well as correlating radiologic and pathologic data. He is currnetly a faculty at UPenn.
|
Cavit Altindag
Scientific Research Developer
Cavit Altindag received his B. Sc. from San Francisco State University in Computer Science. He has many years experience in web development. His main focus is back-end development. He has skills in various computer languages such as java , javascript, c, c++. For several years he has been working on the ePad project as a QA developer and tester. He is actually taking role in back-end and front-end development in java and javascript .
|
Haque Ishfaq
Graduate Student - Statistics
Haque Ishfaq completed his masters (coterm) student in the Statistics department at Stanford. Prior to this, he was an undergraduate student in Mathematical and Computational Science at Stanford. His research interests are in the intersection of deep learning, statistical modeling and healthcare. In prof. Rubin lab, Haque focuses on improving the metric learning technique. Apart from deep learning, he is an avid follower of the Bangladesh Cricket Team.
|
Selen Bozkurt
Postdoctoral Fellow
 Selen Bozkurt received her PhD in the Department of Biostatistics and Medical Informatics at Akdeniz University, Turkey in 2015. She received her M.Sc. degree in Medical Informatics at same department and B.S. in Statistics at 9 Eylul University, Turkey. She is also a member of RSNA Radiology Reporting Committee since 2009. Her interests are Structured Reporting in Radiology and providing decision support for radiologists using methods in natural language processing and machine learning. Her dissertation work was entitled "A Real Time Decision Support System for Mammography Interpretations" in which she developed an automated system for deep information extraction from mammography reports and an approach for real-time decision support driven by analysis of dictated radiology reports. Recently she was Instructor, Department of Biostatistics and Medical Informatics, Akdeniz University Faculty of Medicine, Antalya, Turkey. Her current work is in NLP projects in imaging informatics.
|
Dev Gude, M.S.E.E.
Software Consultant, Research
Dev Gude has been a software consultant in Silicon Valley for several years and has many years experience designing and developing web applications and enterprise systems, particularly backend, server and system software. Applications have included business and financial systems, process control systems, document management systems and product lifecycle management systems, most of them written in Java (for more information see http://www.dw-systems.com). He is working on the ePad project (http://epad.stanford.edu), focusing on back end services and plug-in architecture design.
|
Assaf Hoogi
Postdoctoral Fellow
Assaf Hoogi received his MSc and PhD degrees in biomedical engineering from the Technion (Israel) in 2008 and 2013 respectively with specialization in image processing and computer vision. In his PhD research, Assaf developed quantitative method to evaluate the risk of stroke by using Contrast Enhanced Ultrasound images of Carotid artery plaques. The research included video analysis - objects detection, segmentation and object tracking. During his PhD Assaf was an intern fellow in the image processing group in Thorax center, Rotterdam, Netherlands. His current projects focus on developing new mathematical models for 1) automatic segmentation of tumors and 2) texture-features extraction for tumors classification. He is currently a Research Scientist at the Hewbrew University in Israel.
|
Franco Lamping
College Student
Franco is a computer science student at the University of Sao Paulo, Brazil, previously attending North Carolina State University. His team won second place in the 2012 Google Apps Developer Challenge. Franco won a bronze medal in The Brazilian Olympiad in Informatics 2008. He has co-authored multiple papers in the Semantic Web field.
|
Francisco Gimenez
Graduate Student - Biomedical Informatics
Francisco Gimenez is a Ph.D. student in Biomedical Informatics. He received his B.S. in Electrical Engineering and Computer Sciences at UC Berkeley, and was a researcher at UCSF for 2 years. He is interested in providing decision support for radiologists using methods in computer vision, natural language processing, and machine learning. His current projects are focused on developing validation systems for radiological reporting to increase accuracy and reproducibility.
|
Allison Tam
High School Student
Allison is a senior at Lynbrook High School. She joined the Stanford Institute of Medical Research Summer Program (SIMR) in 2014. Her research interests include image processing and artificial intelligence applications in the medical field. She is currently working on image normalization for big data studies and regional pattern recognition in pathology images for tumor microenvironment modeling.
|
Mete Ugur Akdogan
Scientific Research Developer
Mete received his B.Sc. from Istanbul Technical University, M.Sc. and Ph.D. in Computer Engineering from Dokuz Eylul University, Izmir, Turkey. His main research subjects are high performance computing, big data analytics and parallel algorithms. He worked as a Research Assistant at Dokuz Eylul University where he instructed and assisted several courses, such as Parallel Programming, Operating Systems, Data Organization & Management and Data Structures. He also contributed to and supervised several research projects prior to joining Laboratory of Quantitative Imaging at Stanford University School of Medicine in June 2018.
|
Xuerong Xiao
Graduate Student - Electrical Engineering
Xuerong Xiao is a Ph.D. student in Electrical Engineering at Stanford University. She received her B.S. in Engineering Science from Pennsylvania State University. Her research focuses on automated detection, segmentation and characterization of volumetric images using methods in machine learning and computer vision.
|
Aalok Patwa
Junior, Archbishop Mitty High School
Aalok Patwa is a junior at Archbishop Mitty High School with an interest in data science and machine learning for biomedical tasks. His most recent research, done under the mentorship of Dr. Yamashita and Dr. Rubin, involves predicting recurrence in triple-negative breast cancer patients. It has been recognized as a qualifier to the Regeneron International Science and Engineering Fair and a Grand Prize winner at the Santa Clara Synopsys Championship. In the past, his research involved creating a deep learning model and processing pipeline for handwritten survey response recognition and nuclear segmentation of gigapixel pathology images.
|
Minhaj Alam
Postdoctoral Scholar
Minhaj Alam is a Postdoctoral Scholar at the Stanford Department of Biomedical Data Sciences, with a research focus on Medical AI/ML applications and quantitative image processing (Ophthalmology and Radiology). He has extensive experience in quantitative image biomarker development and incorporating machine learning algorithms for computer aided diagnosis/classification in Ophthalmology and Radiology. He holds a PhD in Bioengineering (CV/AI applications in Ophthalmology) from University of Illinois at Chicago.
Stanford bio link: https://profiles.stanford.edu/minhaj-nur-alam
LinkedIn: https://www.linkedin.com/in/minhaj-nur-alam-bioe/
|
Rikiya Yamashita
Postdoctoral Scholar
Rikiya Yamashita received his Medical Degree from Kyoto University, Japan, in 2006. He completed his residency in Diagnostic Radiology and clinical fellowship in Body Imaging at Kyoto University in 2012. He then completed a PhD in Medical Science from Kyoto University in 2017. He previously worked in the laboratory of Dr. Hedvig Hricak at Memorial Sloan Kettering Cancer Center, doing research in 1) the development and validation of a diagnostic algorithm for liver cancer using deep learning and 2) reproducibility of quantitative imaging biomarkers in pancreatic cancer. His current interests involve the development of decision-support tools to improve diagnostic accuracy and clinical effectiveness by applying advanced machine learning techniques to a wide variety of large clinical data, particularly radiologic and pathologic data.
|
Andy Nguyen Vu
Undergraduate Student in Computer Science
Andy is a computer science student at Stanford University. He is interested in computation as a tool to improve medical exams and diagnostics. He is currently building an interface for the Rubin lab's FASR program, which aids radiologists in reading mammograms.
|
Tara Kapoor
High School Student
Tara Kapoor is a junior at Palo Alto High School. Her interests include using human-centric AI and deep-learning for medical applications. Her current research, under the mentorship of Dr. Yamashita and Dr. Rubin, involves classification of cancerous thyroid nodules in ultrasound cine clips utilizing state-of-the-art deep-learning techniques.
|
Carson Lam, MD
Graduate Student - Biomedical Informatics (and Resident in Ophthalmology)
Carson Lam has a BS in Biomedical Engineering and MD from Northwestern University, and Ophthalmology residency from Stanford. He is working on human interpretable and teachable ConvNets, is interested in Computer Vision, Natural Language Processing, Deep Learning, <insert buzz word here> and a happy transition to using AI in our daily care.
|
Lewis Hahn
Radiology Resident
Lewis Hahn is a radiology resident at Stanford Hospital and Clinics. He completed his undergraduate studies in Biochemistry and Engineering at Harvard and received his MD from Yale. He is interested in developing tools for radiologists that improve diagnostic accuracy and speed. His current work is on using quantitative texture analysis to characterize lesions seen on prostate MR.
|
Siyi Tang
Graduate Student - Electrical Engineering
Siyi Tang (see website) is a Ph.D. student in Electrical Engineering at Stanford University. She received her B.Eng. in Electrical Engineering (Highest Distinction Honors) from National University of Singapore. Siyi is broadly interested in leveraging machine learning techniques for medical applications. Her current research focuses on spatiotemporal modeling of EEG data with graph neural networks, developing clinically interpretable models using weak supervision techniques, as well as quantifying data quality in large-scale medical imaging datasets.
|
Timon Dominik Ruban
Graduate Student - Electrical Engineering
Timon Ruban completed his M.S.in Electrical Engineering. He got his B.S. also in EE at ETH Zurich in Switzerland. In his undergrad he focused on digital communication and signal processing. At Stanford he applies deep learning methods such as variational auto-encoders for challenging clinical applications.
|
