Current Students & Alumni

Current Students

Beulah Agyemang-Barimah (Mezey) My research is currently centered around human genetics. I use programming languages (mainly R) to write algorithms and code simulations of phylogenetic trees. The point of this research is to estimate the probability of observing the data present and track specific alleles backward in time.

Siddharth Avadhanam (Williams) Interested in developing probabilistic models and inference techniques to better understand human genetic variation and demographic history, and to build faster and more accurate tools for annotating genome data. Currently working on building software tools to address ancestry inference problems.

Ian Caldas (Clark/Messer) Interested in applying computational methods, statistics and machine learning to questions about the evolutionary history of populations and species.

Justin Cha (Rotating)

Sam Champer (Messer) I am working to create high fidelity gene-drive models and am interested in exploring the dynamics of large-scale populations modeled in realistic spaces. I am also interested in augmenting realistic population models with machine-learning meta-models to allow for in-depth assessments of computationally complex models. Vrushali Fangal (rotating)  

Ben Fehr (Buckler)

Taylor Ferebee (Buckler) Interested in both using principles of data fusion, statistical learning theory, and network science to develop cross-tissue, cross-genera, and cross-species methods for modelling transcriptional regulation in the Andropogoneae tribe of grasses, and in exploring biologically-informed priors for utilization in interpretable machine learning models.

Shagun Gupta (Yu) Interested in using statistical and computational methods that leverage data generated from mass spectrometers to gain insight into protein-protein interaction networks.  

Adam He (Danko) My current research focuses on identifying how ba­sic transcriptional processes such as initiation, pausing, and stability are encoded in the genetic sequences of transcriptional regulatory elements (TREs). At present, I am working on developing interpretable models that predict how TRE sequence impacts proximal transcription initiation.

Isabel Kim (Messer) 

Olivia Lang (Pugh)  I build tools for analyzing large genomics datasets and study the relationship between DNA organization and gene expression. More specifically, I investigate the mechanisms of transcription initiation around transcription start sites to develop models for global patterns of gene regulation.

Ian Lee (Rotating)

Yilin Liu (Rotating)

Madhav Mantri (De Vlaminck) Interested in developing computational methods for analysis of single cell transcriptomics data, particularly to understand biological processes like development, immunity, infection, and injury at a single cell resolution.

Nathan Oakes (Messer) Characterizing the dynamics of rapid evolution in complex demographics.

Runxi Shen (Messer) Runxi's research focuses on investigating the pesticide resistance alleles in mosquitoes through a novel method called bulk segregant analysis and building up computational and mathematical models for exploring population genetics.

 Parker Singleton (Kuceyski) Interested in studying the effects of pharmacology and disease on human brain activity/connectivity in order to inform theories of the brain and to better understand, diagnose and treat mental disorders. 

Michael Wang (De Vlaminck) To better understand immune cell dynamics and viral infections through the development of high throughput single cell sequencing technologies and analysis. Shayne Wierbowski (Yu) Interested in studying protein networks, including protein-protein interactions, the structural features relevant to protein interactions, and their functional implications. Particular interest in determining the functional role gene-fusion events in cancer play in re-wiring protein networks.

Weilin Xu (Gu) Interested in studying the genetics of mitochondrial DNA in the context of human diseases with the development of high-throughput sequencing approaches as well as the application of computational and statistical methods.

Hao Xue (Rotating)

Li Yao (Yu) Using statistical and machine learning techniques to study mechanisms of transcription regulation from biological datasets, such as run-on sequencing. Junke Zhang (Yu) Interested in using computational and statistical methods to study protein functions and protein-protein interactions in human diseases. Currently working on questions about gene fusions in cancer.   

Manqi Zhou (Rotating)

TRI-I COMPUTATIONAL BIOLOGY & MEDICINE CURRENT STUDENTS IN ITHACA

Daniel Seidman (Williams) Developing a computational method that finds and gives statistical confidence to IBD segments by comparing their probabilities under an IBD vs nonIBD model using a compressed reference genome set.