amir bahmani

Amir Bahmani, Ph.D.

Amir Bahmani is a Research and Development Lead at Stanford Center for Genomics and Personalized Medicine (SCGPM). He has been working on distributed and parallel computing applications since 2008.

Amir received his Ph.D. in computer science from North Carolina State University. He collaborates with multiple universities (e.g., NC State, Duke University, University of North Carolina) on several computationally intensive applications. In the past, he has also worked on industry cloud computing projects with Impulsonic and Illumina.

In his free time, Amir enjoys volunteering with his wife Christine’s non-profit Compassion Network helping people in need throughout Tri-Cities Fremont/Union City/Newark communities.

Open Positions at SCGPM: Talented individuals looking for internship opportunities, please contact me for more details.



Research Interests
  • Computationally Intensive Medical Applications and Cloud Computing
  • In-Situ Data Analysis of HPC Applications
  • Extreme-Scale, Data-Intensive Computing
  • High Performance Machine Learning
  • Database Management Systems
  • Pervasive and Ubiquitous Computing

  • The objective of this work was to create a cloud-based annotation engine that automatically annotates the user's VCF files, and scale over the cloud. - Stanford Center for Genomics and Personalized Medicine (SCGPM) (In collboration with VA's Million Veteran Program and Google Genomics), Summer 2016 - present.


  • ScalaTrace is an MPI tracing toolset that provides orders of magnitude smaller, if not near-constant size, communication traces regardless of the number of nodes while preserving structural information. Combing intra- and inter-node compression techniques of MPI events, the trace tool extracts an application's communication structure. A replay tool allows communication events recorded by our trace tool to be issued in an order-preserving manner without running the original application code - NCSU, Spring 2013 - 2017.



  • The objective of this work is to create a software framework for highly parallel analytics of medical big data in the cloud. Our longterm idea is to take patient data as it becomes available during MRI imaging as well as DNA testing and consult existing medical databases to uncover potential data correlations that imply specific diseases. - NCSU, Duke, UNC, Spring 2016.


  • Worked as a software consultant (Intern) at illumina Inc. The overarching objective of the project was to develop a system to do literature search indexing for illumina research product automatically and efficiently. Main tasks were 1) Implementing automation system, 2) Regenerating ontology/dictionary files and 3) Improving the indexing process using Spark, Hadoop map/reduce. June 2015 - August 2015.
  • Worked as an HPC engineer (Intern) at Impulonic Corporation. The company released a product for acoustic analysis, called Acoustect SDK. This SDK contains two broad categories of acoustic simulation algorithms: ARD and GA. 1) Deployed Acoustect SDK on the Windows Azure and Amazon EC2 platforms , 2) Adapted the existing C# / WPF front-end in the Acoustect SDK to create a desktop front-end that runs ARD on Azure and EC2, 3) Provided an option in the front-end to launch multiple simulations on multiple compute nodes on Azure and EC2, and 4) Deployed MPARD a cluster-based version of ARD on Azure. May 2014 - August 2014.
  • Worked as a research assistant and JAVA developer on the PERCEPOLIS project, the overarching objective of which is to develop an educational cyberinfrastructure that facilitates resource sharing, collaboration, and personalized learning in higher education. We leverage advances in agent-based software engineering, databases, global information sharing processes, and pervasive computing to create this cyberinfrastructure - Missouri S&T, Fall 2010 - Summer 2012.
  • System anomalies, such as performance bottlenecks, resource hotspots, and service level objective (SLO) violations, constitute major threats to large-scale hosting infrastructures. Handling such anomalies in a dynamic execution environment requires an adaptive anomaly management system. ALERT is a self-evolving, context-aware anomaly prediction scheme capable of raising alerts before an anomaly occurs so that the administrator or an automated anomaly prevention system can apply the necessary counter-measures. The current implementation of ALERT uses decision tree (DT) based classification scheme. The effectiveness of ALERT's prediction model depends on the optimality of the DT. Learning an optimal decision tree is an NP-complete problem, so we have replaced the DT for classification with a Bayesian classifier scheme and tested our implementation on the Google App Engine and PlanetLab wide-area network system testbeds, Spring 2013.
Funded Research Projects

  • A. Bahmani (PI) , Frank Mueller (NCSU), "ElasticMedFlow: Design and Implementation of a Scalable, Adaptable Multistage Pipeline for Medical Applications" , Department of Computer Science, North Carolina State University, Funding level: $5,000, November 2015.
  • A. Bahmani (PI), Frank Mueller (NCSU), Edward Patz (Duke), Kouros Owzar (Duke) "Boosting the War on Cancer with the Amazon AWS Cloud", AWS in Education Grant, Funding level: $10,000, October 2015.
  • A. Bahmani (PI), "High Performance Computing and Computationally Intensive Medical Applications", AWS in Education Grant, Funding level: $800, February 2015.



Past Interns

  • Arash Alavi, Computer Science Ph.D. Candidate, University of California, Riverside, Summer 2018
  • Utsab Ray, Computer Science Ph.D. Candidate, North Carolina State University, Summer 2018
  • Negin Forouzesh, Computer Science Ph.D. Candidate, Virginia Tech, Summer 2018
  • Ziye Xing, Computer Science MS, University of California, Los Angeles, Summer 2018
  • Contact Me

    Stanford Center for Genomics & Personalized Medicine, Stanford Medicine, Stanford University, Palo Alto, CA, USA

    abahman [You know]

    amirbahmani [dot] h [You know] gmail!