John Rachlin
(he/him/his)
Associate Teaching Professor
Research interests
- Astronomical data mining
- Artificial intelligence
- Bioinformatics
- Large-scale computing
Education
- PhD in Computer Science, Boston University
- MS in Computer Science, Johns Hopkins University
- BA in English and Physics, Cornell University
Biography
John Rachlin is an associate teaching professor in the Khoury College of Computer Sciences at Northeastern University.
Rachlin's research is focused on developing scalable multi-objective optimization and decision-support systems using evolutionary algorithms. He is based in Flagstaff, Arizona, where he is an active volunteer for the Lowell Observatory. He is the former chair of Northeastern's AI Curriculum Working Group, a university-wide initiative to reimagine the Northeastern curriculum in the age of AI.
Before joining Northeastern, he was a staff software engineer at Fitbit and a principal software engineer at Optum Analytics. At Optum, he developed tools and technologies to integrate and analyze patient electronic medical record (EMR) data as part of the company's transition to Hadoop-based distributed computing. Rachlin is also the co-founder of Diatom, LLC, a software consultancy specializing in bioinformatics, health care, and big-data solutions.
Recent publications
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COMBREX-DB: an experiment centered database of protein function: knowledge, predictions and knowledge gaps
Citation: Chang YC, Hu Z, Rachlin J, Anton BP, Kasif S, Roberts RJ, Steffen M (2016). COMBREX-DB: an experiment centered database of protein function: knowledge, predictions and knowledge gaps. Nucleic Acids Res. 44(DB):D330-5. -
COMBREX: a project to accelerate the functional annotation of prokaryotic genomes
Citation: Roberts R.J., Chang Y-C, Hu Z., Rachlin J., et al.; 2010. COMBREX: a project to accelerate the functional annotation of prokaryotic genomes. Nucleic Acid Res. 39, D11-D14. -
Multi-Node Graphs: A Framework for Multiplexed Biological Assays
Citation: Alon N., Asodi V., Cantor C., Kasif S., and Rachlin J.; 2006. Multi-node graphs: A framework for multiplexed biological assays. Journal of Computational Biology 13(10):1659-1672.
