Lab Members


Graduate Students  

Anisha Datta

Immunotherapy has shown great promise as a cancer therapeutic; however, its efficacy is limited to a subset of patients, necessitating further study of the mechanisms at play. One mechanism of particular interest is the recruitment and infiltration of T cells into the tumor, as studies have shown that tumors that have been infiltrated by these adaptive immune cells respond positively to immunotherapies as compared to tumors that do not have T cell infiltration. Given that there is cross-talk between innate immune cells and adaptive immune cells - in that the former help prime and activate the latter - there has been great interest in the factors that contribute to and determine whether innate immune cells carry out this vital function. My project focuses on a family of receptor tyrosine kinases known as the TAM receptors, and I am specifically studying Axl. Studies have demonstrated that targeting Axl and blocking it from carrying out its functions yields an immunostimulatory state in innate immune cells and decreases aggression and invasion in cancer cells. Thus, it is likely that anti-Axl therapies may synergize with immunotherapies. My work aims to understand how anti-Axl treatment alters the tumor immune landscape, thereby allowing for a better understanding of which immunotherapies it would synergize with as well as which patients would benefit most from such a combination.

Anisha Datta

Christine (Davis) Wiggins

My research interest is in using high-throughput data generation methods, large datasets, and computational models to investigate infectious disease impact, progression, and biomarkers. I am particularly interested in intrinsic variation in immune response within populations in addition to differences between disease severities. Currently, I am researching immune response and mechanisms of protection for tuberculosis.

Christine Davis

Maddie Dery

IgA antibodies play a multifaceted role in the immune system. In the presence of pathogens, IgA immune complexes form which can trigger an inflammatory response. This response is desirable in the treatment of infectious diseases and cancer but has also been linked to many autoimmune diseases when it becomes overactive, potentially due to excessive recruitment of neutrophils. My research aims to better understand what factors control the nature of the IgA immune response using high-throughput assays to measure neutrophil-mediated effector functions with the goal of informing the development of therapies for autoimmune diseases and cancer.

Maddie Dery

Diana Gong

I am interested in understanding the heterogeneity of response of different cell types and different patients to inflammatory and infectious disease. Currently, I am analyzing how different mutations impact the immune environment in colorectal cancer using single-cell RNA sequencing. Moving forward, I will also be studying correlates of protection in tuberculosis by using a multi-omics approach and developing computational models.


Meelim Lee

Systems biology analysis of immune mechanisms in neurodegenerative disease

While amyloid and tau pathology are defining characteristics of Alzheimer's Disease (AD), they do not robustly correlate with disease severity. Nonetheless, amyloid and tau dysregulation are key features in a majority of AD mouse models used for preclinical evaluation of therapies. To address this limitation, I am applying a computational framework developed in the lab for inter-species translation of molecular/phenotype relationships, taking a consensus-driven approach across diverse mouse models that recapitulate different biochemical and cellular aspects of human AD. Specifically, this project aims to identify features of AD mouse model transcriptomic signatures predictive of human disease outcomes via a principal component regression. Identifying translatable signatures could provide a framework for rationally selecting multiple mouse models for preclinical testing.

Additionally, I am following up on a subset of computationally-identified translatable signatures related to the vascular endothelial growth factor (VEGF) signaling pathway and angiogenesis response. I am using a mechanistic modeling framework combined with quantitative experiments to understand subcellular changes related to predicted, translatable signatures with an initial focus on signaling and trafficking disruption in neurons.

Meelim Lee

Nikolaos Meimetis

I am broadly interested in understanding how different biological systems work and what causes them to differentiate, via computational modeling, in order to better understand diseases and their therapeutic solutions. Currently, I am exploring Artificial Intelligence approaches to identify important features that can explain several conditions across different biological systems, what connects them and how they can be used to make predictions in a therapeutic context.


Krista Pullen

Vaccines are one of the most cost-effective medical interventions for combating infectious disease. That being said, efficacious vaccines have yet to be developed for some of the deadliest of these diseases, including HIV/AIDS, Malaria, and Influenza. Currently vaccine development is limited by our knowledge of correlates of immunity against infection. I am interested in utilizing machine learning techniques to reveal antibody and immune cell effector functions predictive of disease protection. This information may lead to better understanding of disease mechanisms and allow us to rationally design more effective vaccines.


Daniel Zhu

I am broadly interested in applying machine learning techniques alongside more traditional experimental methods to gain insight into problems rooted in biology and medicine. In particular, I am currently interested in studying the highly complex networks of interactions involving immune cells in a pathological context, to develop a deeper understanding of the mechanisms by which the immune system controls and evolves correlates of protection against disease. New insights uncovered by this process could then guide the development of targeted therapeutics against the long-standing threat posed by maladies such as tuberculosis and HIV/AIDS. At the moment, these efforts face challenges in the form of the scale of the networks involved and the frequent lack of direct translatability of insights discovered in animal models to the human context. In the near future, I hope to leverage machine learning to find and predict relationships in the interactome that might not be immediately obvious, intuitive or easily assayable, and in doing so, construct hypotheses that can then be tested experimentally, identify animal features that are predictive of human disease outcomes, and develop a deeper understanding of the systems-level components that underpin progression towards said outcomes.

Daniel Zhu

Tomer Zohar

I am interested in how immunological systems evolve during responses to disease and other perturbations, and what determines differences in outcomes. Specifically, I research discrepancies in innate immunity and antibody function between different patient/population cohorts at different time points using a myriad of the high-through assays and computational analyses.

Tomer Zohar

Research Professionals


Lauren Baugh (Postdoctoral Associate)

Endometriosis is the presence of endometrium outside of the uterus. This painful and debilitating disease has been estimated to affect around 10% of all women, but has been largely understudied. In the context of a 3D hydrogel, disease model system developed in collaboration with Linda Griffith’s lab, we are able to study primary patient cell responses of those with and without endometriosis. By gathering both proteomic and transcriptomic data, we can study both cell to cell interaction and cell to extracellular matrix interactions. Using these approaches, we aim to developed a better sense of endometriosis disease mechanisms and search for novel diagnostic approaches.

Lauren Baugh

Brian Joughin (Koch Institute Research Scientist)

Mechanistic interpretation of phosphoproteomic data.

I am interested in ways to use publically accessible information to aid in the mechanistic interpretation of phosphoproteomic (e.g., mass spec) data. More generally, I am available for collaboration with researchers with the Integrative Cancer Biology Program at MIT who would like help turning their data into models.

Brian Joughin

Avlant Nilsson (Postdoctoral Associate)

The purpose of my research is to enable computer-designed therapies against disease. Currently, drug development is challenged by the complexity of intracellular signaling. This could be overcome by computer models that predict cellular responses, however, current models are limited in scope, generalizability and ease of parameterization. I am developing a method, based on artificial neural networks, to model cellular signal-integration on a mechanistic level. The model integrates data from cells that are stimulated with many combinations of ligands. I am particularly interested in using it to better understand the role of macrophages in cancer and tuberculosis.

Avlant Nilsson

Chuangqi Wang (Postdoctoral Associate)

Correlation analysis between survival rate and pathogen genomics in infectious diseases.

I am interested in developing computational and statistical methods to reveal hidden mechanisms in heterogeneous patient samples using genetics, genomics, and imaging. My current research lies in applying machine learning methods in pathogen genome sequences and single-cell transcriptomics to identify pathogenic features that could best predict the survival state of individuals with infectious diseases (such as tuberculosis, HIV and Ebola).

Chuangqi Wang
Lab Manager  
Thomas Donaghey  

Administrative Assistant


Lindsay King


Information Systems Administrator  

Aran Parillo