Be Part of the Team


We are always on the lookout for driven and independent students and postdocs. See below for more information.

Scientific Environment


ur lab is about evenly split between two areas: the intersection of machine learning and molecular modeling, and the intersection of molecular modeling and systems biology. Roughly speaking, the former focuses on method development of biophysically-grounded machine learning models for molecular problems, including the prediction of protein structure and protein-ligand interactions; the latter focuses on studies that leverage the biophysical and molecular perspective (enabled by the first arm) to address basic and translational questions in systems biology. In this area, we focus on signal transduction networks: how they are organized and how their logic is effected (emphasizing the biophysics and structure of signaling proteins), how they have evolved across metazoa, and how they are dysregulated in disease, particularly cancer. We believe that the biophysical and molecular perspectives provide a particularly powerful lens through which to investigate the genotype-phenotype map.

The boundaries between method development and basic science are not firm however. For example, our systems biology work requires the development of dynamical systems modeling techniques that scale to entire proteomes. Conversely, our machine learning models push the boundaries of the core science of machine learning, for example by broadening learning from Euclidean spaces to general manifolds.

Current Interest Areas

Machine Learning of Biomolecules
  • Protein structure and dynamics
  • Protein-protein and protein-X interactions
  • Representations for proteins, genomes, and systems
  • Integration of cryoEM and cryoET data
  • Protein design
Organization of Signaling Networks
  • Abstraction and pattern reuse in signaling logic
  • Network evolution and evolution of higher-order patterns
  • Computing paradigms employed by signaling networks
  • Mesoscale simulation methods (1000s of chemical species)
Variation & Dysregulation in Signaling
  • Effects of genetic variation on signaling behavior
  • Variability of signaling networks within and across tumors
  • Modulation using small molecules and biologics

Prospective Members


e welcome students and postdocs from diverse personal and scientific backgrounds as we ourselves are a motley crew. Our focus is computational but we do host experimentalists jointly with other labs. If you are interested in method development and machine learning, then prior programming experience is required and some exposure to machine learning is preferred. Domain knowledge in a natural science (biology, chemistry, physics or one of the in-betweens) can also be very helpful but is not a requirement. If you are interested in our basic or translational biological research, then a background in life sciences is highly recommended. Programming experience at the level of scripting and data analysis/munging is also helpful. As no two people (or projects) are alike, if you have an eclectic background that doesn't quite fit the above recipes but you think has prepared you well, then do not hesitate to reach out. Our only real requirements are high integrity, collegiality, love of science, and a sense of adventure.

Graduate Students

If you are a graduate student at Columbia interested in rotating, feel free to contact us directly—we are able to take students from many programs at Columbia. If you are a prospective student interested in applying for a Ph.D. program at Columbia, we recommend the Systems Biology Track in the Integrated program, or alternatively the Computer Science Ph.D. program.

Postdoctoral Fellows

Please send your CV, references, and a description of your interests and what you would like to work on. Relevant past experience is helpful but you do not need to be an expert in all areas of the lab—the desire to learn new topics is expected and encouraged.

Undergraduate Students

We welcome undergraduate student researchers and have had great past experience with students producing first-author papers in excellent venues. As the commitment to mentor is a substantial one, we do require that students commit to at least one semester and a minimum of 10-20 hours per week (thus displacing commensurate coursework). We find that this is what is typically necessary to gain meaningful research experience and for the student to hit their stride. Multi-semester engagements are encouraged and can result in serious research work. If you are interested please free to contact us with your CV or resume.