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Brian Dolhansky


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Education

  • MS Electrical Engineering, Drexel University 2012 (summa cum laude)
  • BS Electrical Engineering, Drexel University 2012 (summa cum laude)
  • Triton Regional High School 2007


Bio

I graduated from Drexel University with a BS/MS in 2012. During my time there, I worked in the METlab as a research assistant. Currently I am a PhD student studying Machine Learning at the University of Pennsylvania.

I enjoy playing the guitar and violin, along with creating electronic compositions and remixing popular music. I also enjoy teaching, programming and game design. I am currently teaching an online class on rigid body physics.


Awards and Fellowships

  • National Science Foundation Graduate Research Fellowship (NSF GRFP) Award , 2012
  • American Society for Engineering Education Science, Mathematics and Research for Transformation (ASEE SMART) Award, 2012 (Declined)
  • Deutscher Akademischer Austausch Dienst Research Internships in Science and Engineering (DAAD RISE) Fellow, 2011
  • Drexel University Undergraduate TA Award (2012)
  • Drexel University Presidential Scholarship (2007-2012)
  • New Jersey Robert C. Byrd Scholarship Recipient (2007-2011)


    • Research Interests

    • Artificial Intelligence
    • Deep Learning
    • Instrument Design and Implementation
    • Instrument Expressiveness
    • Interface Design and HCI
    • Machine Learning


    Past Projects

    • Musical Ensemble Classification and Instrument Identification Using Real Audio
    • Electronic Instrument and New Music Interface Design
    • Study and Parameterization of Musical Instrument Expressiveness
    • Causal Beat Tracker for Real Audio
    • MusiCube (iPhone, iPad Application)
    • PhillyKingDay 2011 (iPhone, iPad Application)
    • Multi-touch Device-Agnostic Library
    • SMT 2011 Analog and Digital Lesson
    • Monochord Implementation


      • Presented Posters


      Publications

      • Schmidt, E. M., Prockup, M., Scott, J., Dolhansky, B., Morton, B. and Kim, Y. E. (2012). Relating perceptual and feature space invariances in music emotion recognition. Proceedings of the International Symposium on Computer Music Modeling and Retrieval, London, U.K.: CMMR. Best Student Paper. [PDF] [Oral Presentation]
      • B. Dolhansky, A. McPherson, Y. E. Kim, "Designing an Expressive Virtual Percussion Instrument," in Proceedings of the 2011 Sound and Music Computing (SMC) Conference, 2011., July 2011.
      • Y. E. Kim, A. M. Batula, R. Migneco, P. Richardson, B. Dolhansky, D. Grunberg, B. Morton, M. Prockup, E. M. Schmidt, and J. Scott, “Teaching STEM concepts through music technology and DSP,” in IEEE 14th Digital Signal Processing Workshop and 6th IEEE Signal Processing Education Workshop, 2011. DSP/SPE 2011., January 2011.