Previously, Rohit was a Research Scientist at MIT CSAIL. He have also spent substantial time in industry. He received his PhD at MIT under the guidance of Prof. Bonnie Berger and his dissertation on algorithms for analyzing protein interaction networks, is available here. One of the key problems addressed there was the global alignment of protein interaction networks-- this allows the combination of PPI information across species and establish functional orthology relationships. Rohit and his collaborators received the Test of Time Award at RECOMB 2019 for the IsoRank algorithm for global network alignment.

Rohit has spent about a decade working in quantitative trading (LinkedIn profile) and is the co-founder of martini.ai, a company aiming to make it easier for small and mid-sized private businesses to obtain credit and manage their business risk. Previously, he was the CEO of Tech Square Trading, a Boston-based quantitative hedge fund that he co-founded. Before that, he worked at Merrill Lynch and Cubist Systematic. He believes that the fields of quant investing and computational biology require surprisingly similar skills: an expertise in working with large amounts of very messy data and an ability to design robust models that can integrate the data into a coherent picture.

Service: He has served on the program committee for RECOMB 2023 and on the proceedings committee for ISMB/ECCB 2023.

Kapil (homepage) is interested in developing methods for analyzing protein interactions, both through graph-theoretic and deep learning approaches. He.received his PhD from Tufts University.

Hanchen has a background in quantum physics and is interested in how PLMs can help us gain deeper physical insights.

Huan is interested in developing methods to understand the relationship between the genome 3D structure and gene expression, possibly by building and adapting foundation models. He received his undergraduate degree from Univ of Chicago and a Masters from Univ of Wisconsin, Madison.

Yueshan is interested in developing methods to prioritize disease-relevant genes in rare diseases. She received her undergraduate degree from Univ of California, San Diego.

Sinan is interested in developing methods for single-cell genomics. He received his undergrad and masters degrees from Princeton Univ.

Aditya is interested in developing deep learning tools for protein design and single-cell genomics and making them broadly accessible. He received an undergraduate degree from ASU and a masters from CMU.

Ady is broadly interested in developing methods for analyzing single-cell genomic data, with a particular interest in multimodal data. He received his PhD from Univ of Virginia and has spent some time in industry.

Carl and Chris are interested in PLMs. Prannav is interested in studying single-cell genomics.