Whether you’re familiar with the D. E. Shaw Research group, or this is the first you’re hearing them, their recruiting talk will offer you an opportunity to stay current with their research, drug discovery, machine learning, and engineering projects. Food will be served!
D. E. Shaw Research (DESRES), based in New York City, develops and uses advanced computational technologies to understand the behavior of biologically and pharmaceutically significant molecules at an atomic level of detail, and to design precisely targeted, highly selective drugs for the treatment of various diseases. Among its core technologies is Anton, a proprietary special-purpose supercomputer that DESRES designed and constructed to vastly accelerate the process of molecular dynamics simulation. DESRES uses Anton machines and high-speed commodity hardware, together with machine learning methods and other computational techniques, in both internal and collaborative drug discovery programs.
Join them for an overview of their work and to hear about the current full-time and internship opportunities in the group.
About the Speakers
Nathan Miller
Nathan holds a B.S. in engineering from Harvey Mudd College and a Ph.D. in medical engineering and medical physics from the Harvard-MIT Program in Health Sciences and Technology at MIT. As a graduate student in Ram Sasisekharan’s group, Nathan computationally investigated epitope-paratope interactions between viruses and antibodies, including the Nipah virus, Influenza A, and SARS-CoV-2, with an emphasis on factors driving epitope complexity and broad neutralization. In tandem with his research, Nathan participated in a clinical clerkship at Harvard Medical School with rotations in radiology, cardiology, and anesthesiology; and as a course coordinator, he guided other graduate students through the process of biotech startup conceptualization.
Avery Parr
Avery received an A.B. in Physics and Mathematics from Harvard University. As an undergraduate researcher in Kang-Kuen Ni’s lab at Harvard, Avery worked on simulating interactions between highly excited alkali metals to exploit Rydberg interactions for quantum computing and quantum science applications. Additionally, as a summer intern at IBM Quantum, he improved the core evaluation functionality of an automatically differentiable Hamiltonian simulation package using JAX.