Alexander Friedman

Assistant Professor, Biological Sciences, University of Texas, El Paso

Dr. Friedman joins UTEP after completing his post-doctoral training and working as a research scientist at MIT for 10 years. Dr. Friedman uniquely combines computation and the development of tools for big data analysis with behavioral physiology to study computation principles and underlying psychiatric and neurological disorders. Dr. Friedman earned his doctoral degree in brain physiology (summa cum laude) from Bar-llan University (BIU) in Israel. While at BIU, he developed deep brain stimulation methods to treat depression and addiction in rodent models. Dr. Friedman demonstrated the first evidence about the physiological role of the striosomal compartment of the striatum in decision-making, and how the striosomal activity and decision-making are affected by chronic stress, aging, and Huntington disorder. His work resulted in solving a 42-year old mystery that led him to publish three major publications in the Cell journal during his time as a post-doctoral researcher.

Dr. Friedman continually works on the development of novel computational, electrophysiological, and optical methods to record and analyze neuronal ensembles. At MIT, he facilitated hands-on research engagement workshops for more than 50 undergraduate and graduate students where they were active participants and contributors while learning state-of-the-art techniques and computational methods. He is excited to undertake laboratory classes at UTEP where students can acquire the latest computational methods, physiological recording methods, and behavior physiology via active participation in the research. Dr. Friedman’s lab will uniquely combine the development of computational methods and physiological recording methods with the development of natural behavior assets to study disorders such as PTSD, addiction, chronic stress, and age-related disorders.

Keywords

  • Key words should be limited to six words or phrases related to your areas of expertise.
  • Decision making
  • Learning
  • Neuronal nets
  • Clustering
  • Machine learning
  • HMM