Lab Members

Sneha Pandya

Research Associate

Department of Radiology

Weill Cornell Medicine, New York, NY

Sneha is a research associate in the field of biomedical engineering and currently working for Weill Cornell Medicine, NY. Over the past 8 years Sneha has worked closely with RajLab, WCM Multiple Sclerosis Center, and Sumit Niogi’s Lab serving both radiology and neurology departments while applying problem-solving techniques to current clinical problems in the imaging, diagnosis and treatment of major brain diseases. CoCo Lab’s initiative to use quantitative methods and machine learning on multi-modal neuroimaging data to map brain-behavior relationships has mainly inspired her to be part of this lab. Predominant drive of Sneha’s academic career has been to apply these techniques to current issues in neuroimaging. Sneha plans to expand her research pursuits by developing quantitative and machine learning models in understanding structural-functional relationships and predicting early onset of varying brain diseases.

Co-Mentored Graduate Students

Meenakshi Khosla (Mert Sabuncu, Cornell ECE)

Gia Ngo (Mert Sabuncu, Cornell ECE)

Suniyya Waraich (Jonathan Victor, WCM Neuroscience)

Undergraduate Students

Siddhi Balamurali (Cornell)

Catherine Cai (Cornell)

Jason Chen (Cornell)

Yiran Li (Cornell)

Nate Roy (Cornell)

Nicholas Vartanian (UVM)

Danny Viera (Cornell)

Elaine Wu (Cornell)

High School Students

Georgia Russello


Sarah Dennis (Sarah Lawrence College Mathematics)

Sharon Dang (Cornell)

Alex Lin (Cornell)

Elvisha Dhamala (Weill Cornell Medicine)

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Keith Jamison, PhD

Research Associate

Department of Radiology

Weill Cornell Medicine, New York, NY

Keith Jamison is a staff associate in the CoCo Lab. He has a Bachelors of Science in computer science and a masters in Biomedical Engineering from Cornell University. Through his education and training, he has developed the broad range of skills and expertise necessary to discern scientifically and clinically relevant patterns from large neuroimaging datasets. While working for the Human Connectome Project at the University of Minnesota, he helped implement and adapt preprocessing and analysis pipelines for a large number of anatomical, functional, and diffusion MRI scans. He also helped design and test new scanning protocols and modalities for some of the HCP-related studies whose data we now propose to analyze. Since joining the CoCo lab in 2017, he has built upon this expertise in neuroimaging acquisition and processing to help develop modeling approaches that use this neuroimaging data to better understand the relationship between functional and structural connectivity, and how connectivity relates to both healthy brain function and neurological damage or disease.

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Ceren Tozlu, PhD

Postdoctoral Fellow

Department of Radiology

Weill Cornell Medicine, New York, NY

Ceren is a post-doctoral associate at Department of Radiology of Weill Cornell Medicine and at Department of Statistics and Data Science and Computational Biology of Cornell University. She received her Bachelor’s degree at Galatasaray University in mathematics in 2013, and M.S. and Ph.D. degrees at Biostatistics, Biomathematics, Bioinformatics and Health department (3B-H) of Université Claude Bernard Lyon 1 in 2014 and 2018, respectively. She gave lectures for the M.S. students of Cancer, Neuroscience, Biostatistics and Public Health at Université Claude Bernard Lyon 1 and for the medical students at École Santé des Armées (Army Health School of France) for 4 years.

Her M.S. research project focused on the application of various machine learning methods on the voxel-based conventional human imaging data to predict the infarction risk of 3-dimensional brain tissue in acute stroke patients. Her Ph.D. thesis focused on modeling the disease evolution of stroke and Multiple Sclerosis (MS) patients based on cross-sectional and longitudinal clinical and imaging data plotted over 5 years.

Her post-doctoral research focuses on (1) modeling of the disease evolution in the neurological diseases, particularly in patients with stroke, MS and Posterior Fossa Syndrome (PFS), using statistical and machine learning methods based on their demographic, clinical, regional and pair-wise functional&structural connectivity measurements, and (2) the identification of the best biomarkers of the disease evolution including the particular structural and functional connections that contributes to the differences in patients with a particular neurological disease. Her post-doctoral study aims to develop a novel and personalized model to be used by the clinicians to predict individual disease evolution via an application or a software, thus to decide personalized treatment.


Biotechnology Building, 101D

526 Campus Road

Ithaca, NY 14850

Amy Kuceyeski, PhD

Associate Professor

Department of Radiology

Weill Cornell Medicine, New York, NY


Adjunct Associate Professor
Department of Statistics and Data Science & Computational Biology
Cornell University, Ithaca, NY

For the past decade, Amy has been interested in understanding how the human brain works in order to better diagnose, prognose and treat neurological disease and injury. The CoCo lab's main focus is on using quantitative methods, including machine learning, applied to multi-modal neuroimaging data to map brain-behavior relationships. The lab's overall goal is to develop individualized therapies that can boost natural recovery mechanisms and support recovery after neurological disease or injury.

Amy is also the founder and co-director of the cross-campus working group Machine Learning in Medicine, which aims to bring together ML researchers in Cornell-Ithaca and clinicians and researchers at WCM to address medicine's toughest problems. See the group's website here.

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Emily Olafson

PhD Student, Neuroscience

Department of Radiology

Weill Cornell Medicine, New York, NY

Emily is a first-year PhD student in Neuroscience at Weill Cornell. She completed her BSc in Neuroscience at McGill university where she studied cortical phenotypes of autism spectrum disorder from structural brain images. In the CoCo Lab, she is studying how the brain’s functional and structural networks change after an ischemic stroke. She hopes that her research will be used to better understand how brain activity changes that occur following a stroke impact patient outcomes and to identify potential targets for noninvasive therapies.

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Lisa Iatckova

PhD Student, Physiology, Biophysics, and Systems Biology

Weill Cornell Medicine, New York, NY

Lisa is a second-year student at Weill Cornell in the Physiology, Biophysics & Systems Biology Graduate Program and a recipient of an NSF Graduate Research Fellowship in Bioengineering. After three years of studying Economics in Construction in her hometown in Russia, she immigrated to the U.S., and graduated with a BA in Neurobiology from Hunter College (CUNY) in 2019. At Hunter, Lisa also conducted research in the Goldfarb lab, where she tricked brain cancer cells into expressing mutant sodium channels and then poked them with electrodes to learn how the mutations affected voltage-dependent fast inactivation, a key property of the channel that is altered in disease phenotypes. Lisa’s strongest aspiration in life is to contribute to the development of brain-computer interfaces that improve the quality of life for people with impaired physical and cognitive function (and maybe even augment natural brain capabilities of healthy people). Lisa is an avid reader (sci-fi is obviously her favorite genre), a mom of two rescue kitties Synna and Shishka, and a wife to the most supportive husband on Earth.

Zijin Gu

PhD Candidate, Electrical and Computer Engineering

School of Electrical and Computer Engineering

Cornell University, Ithaca, NY

Zijin is currently a first-year PhD student at Department of Electrical and Computer Engineering of Cornell University. She received her B.Eng degree in Electrical Engineering from Zhejiang University, China.

Her research focuses on the intersection of machine learning and neuroscience. Particularly, she is interested in applying innovative machine learning algorithms to brain connectivity network analysis. Her project is about developing a noninvasive, spatially unconstrained and personalized method for neuromodulation, which involves creating deep neural networks for stimuli and brain activation patterns mapping. She hopes that manipulating brain connectivity networks will help alleviate symptoms or boost recovery after neurologic injury.

Parker Singleton

PhD Candidate, Computational Biology

Cornell University, Ithaca, NY

Parker is a PhD candidate and NSF Graduate Research Fellow in Computational Biology at Cornell. He transferred to us from the department of Chemistry and Chemical Biology where he obtained his MS in 2017 before spending 3 years as a high school and community college science teacher. Originally from South Carolina, he received his BS in Chemistry at the University of South Carolina in 2015. He is studying the effects that potent serotonergic compounds have on brain activity/connectivity and the development of neural-mass models to drive targeted brain therapies. He hopes his research can be used to inform theories of consciousness and to better understand,  diagnose, and treat mental disorders.