Bio


I am a PhD student in computational and mathematical engineering (ICME) at Stanford University. My research interests is mainly reinforcement learning and deep learning. I did my undergrad in Mechanical Engineering and minor of Economics in Sharif University of Technology, Tehran.
I enjoy hiking, reading about music theory and evolutionary biology.

Honors & Awards


  • iCME departmental fellowship, Stanford University (2015)
  • Silver medal of group competition at 5th IOAA, IOAA (2011)
  • Bronze medal of 5th International Olympiad in Astronomy and Astrophysics, IOAA (2011)

Education & Certifications


  • BSc, Sharif University of Technology, Mechanical Engineering, Minor of Economics (2015)

Service, Volunteer and Community Work


  • PSA Board Member, Persian Student Assosciation (April 1, 2015 - Present)

    The Persian Student Association (PSA) is a non-political voluntary student organization whose objective is to sponsor Persian social and cultural activities and events, to promote an understanding of Persian culture, to help foster friendship among different cultural groups, and to provide a source of union and support for the Persian community at Stanford.

    Location

    Stanford

Personal Interests


Music Theory, Evolutionary Biology, Hiking

Current Research and Scholarly Interests


Reinforcement Learning, Deep Learning, Data Efficiency

Work Experience


  • Data Science Intern, StitchFix (June 15, 2016 - September 9, 2016)

    Location

    San Fransisco

All Publications


  • Molecular dynamics modeling of a nanomaterials-water surface interaction JOURNAL OF APPLIED PHYSICS Pishkenari, H. N., Keramati, R., Abdi, A., Minary-Jolandan, M. 2016; 119 (16)

    View details for DOI 10.1063/1.4947189

    View details for Web of Science ID 000375929900020

  • Dynamics of the nanoneedle probe in trolling mode AFM NANOTECHNOLOGY Abdi, A., Pishkenari, H. N., Keramati, R., Minary-Jolandan, M. 2015; 26 (20)

    Abstract

    Atomic force microscopy (AFM), as an indispensable tool for nanoscale characterization, presents major drawbacks for operation in a liquid environment arising from the large hydrodynamic drag on the vibrating cantilever. The newly introduced 'Trolling mode' (TR-mode) AFM resolves this complication by using a specialized nanoneedle cantilever that keeps the cantilever outside of the liquid. Herein, a mechanical model with a lumped mass was developed to capture the dynamics of such a cantilever with a nanoneedle tip. This new developed model was applied to investigate the effects of the needle-liquid interface on the performance of the AFM, including the imaging capability in liquid.

    View details for DOI 10.1088/0957-4484/26/20/205702

    View details for Web of Science ID 000354540200015

    View details for PubMedID 25915451