Lawrence Udeigwe

Associate Professor, Mathematics

Research Interests: Differential Equations; Dynamical Systems; Computational Neuroscience; Mathematics Pedagogy; Interfacing Mathematics and Jazz.

Education

PHD, University of Pittsburgh, 2014
MA, University of Pittsburgh, 2008
MS,  University of Delaware, 2006
BS & BA, Duquesne University, 2004

Curriculum Vitae

Courses Taught

Undergraduate
Math 096: Bridge Course for Business Students 
Math 100: Precalculus 
Math 158: Introduction to Mathematical Computations
Math 185: Calculus I 
Math 186: Calculus II 
Math 285: Calculus III 
Math 272: Linear Algebra 
Math 286: Differential Equations  
Math 386: Partial Differential Equations 
Math 387: Analysis I 
Math 491: Topics in Mathematics - Dynamical Systems 

Graduate
Matg 511: Computational Methods for Analytics 
Matg 557: Machine Learning
Matg 692: Topics in Mathematics - Theory of Differential Equations
Matg 692: Topics in Mathematics - Dynamical Systems
Matg 699: Research in Mathematics

  • Research
    Research areas
    Differential Equations
    Dynamical Systems
    Computational Neuroscience
  • Publications and Scholarly Activities
     
    Select Publications
    1. L.C. Udeigwe. “Using Phase Portraits to Analyze Relationship Dynamics.” SIMIODE, (2019). https://www.simiode.org/resources/6263
    2. L.C. Udeigwe. An analysis of the cluster-detecting property of the BCM neuron. International Journal of Pattern Recognition and Artificial Intelligence, 2019 under review. [PDF HERE]
    3. L.C. Udeigwe, P.W. Munro, G. Bard Ermentrout. Emergent Dynamical Properties of the BCM Learning Rule. Journal of Mathematical Neuroscience.  Vol 7:2, (2017), DOI: 10.1186/s13408-017-0044-6
    4. L.C. Udeigwe and G.B. Ermentrout. Waves and patterns on regular graphs. SIAM Journal on Applied Dynamical Systems Vol. 14(2), 1102-1129, 2015
    5. L.C. Udeigwe, Dynamical systems of the BCM learning rule: emergent properties and application to clustering (PhD dissertation). University of Pittsburgh, 2014
    6. L.C. Udeigwe and G.B. Ermentrout, and P.W. Munro. Oscillation and chaos in the dynamics of the BCM learning rule. BMC Neuroscience Vol. 14 (1), 2013 
    7. D.B. Smith, L. C. Udeigwe, and J. Rubin. Physical interactions between D1 and NMDA receptors as a possible inhibitory mechanism to avoid excessive NMDA currents. BMC Neuroscience Vol. 8 (2), 2007 
    8. L.C. Udeigwe. Identification of objects in an acoustic waveguide: Numerical results and an introduction to an alternate approach via the method of images (Masters thesis). University of Delaware, 2006 

    Select presentations

    1. L. C. Udeigwe. A Single-Neuron Data Classification Algorithm. MAA New York Sectional Meeting. May 4, 2019
    2. L. C. Udeigwe. An Unsupervised Clustering Algorithm Based On Sensory Neuronal Selectivity. Lafayette College. April 17, 2019
    3. L. C. Udeigwe. A single-neuron data classication algorithm. Manhattan College,Department of Mathematics Seminar Series Riverdale, NY. April 11, 2018
    4. L.C. Udeigwe. Synaptic Plasticity as a function of Homeostatic time-scales. Sense to Synapse Conference New York, NY. April 7-8, 2017
    5. L. C. Udeigwe. “The Effect of Homeostatic Time-Scale Adjustment of the BCM Learning Rule”. Sense to Synapse Conference, Rockefeller University, New York. 2016
    6. L. C. Udeigwe. “The Perceptron: Overview, Convergence Theorem, Self-Supervision” Manhattan College, Department of Mathematics Seminar Series Riverdale, NY. April 13, 2016.
    7. L. C. Udeigwe. Oscillators and patterns on a sphere. Manhattan College, Department of Mathematics Seminar Series Riverdale, NY. April 22, 2015
    8. L.C. Udeigwe, G.B Ermentrout, and P. W. Munro. Emerging Dynamics of cortical neuronal selectivity. Temporal Dynamics of Learning Center, USDC, Annual spring retreat La Jolla, Ca. February 6th, 2014
    9. L.C. Udeigwe, G.B Ermentrout, and P. W. Munro. Dynamics of cortical plasticity: oscillatory, toroidal, and chaotic properties of the BCM learning model. Complex Biological Systems seminar, University of Pittsburgh Pittsburgh, Pa. October 10th, 2013
    10. L.C. Udeigwe, G.B Ermentrout, and P. W. Munro. Exploring the dynamics of the BCM learning rule. Temporal Dynamics of Learning Center, USDC, Annual Fall retreat La Jolla, Ca. August 26th, 2013
    11. L. C. Udeigwe, G. B. Ermentrout, and P. W. Munro. Oscillatory and chaotic dynamics of the BCM learning rule. Annual Computational Neuroscience Meeting, Paris, France. July 17th, 2013 
    12. L.C. Udeigwe and G.B Ermentrout. Non-synchronous oscillations on graphs. SIAM Conference on Applications of Dynamical Systems. May 24th, 2011 
    13. L.C. Udeigwe and G.B Ermentrout. Coupled neural oscillators on the surface of a sphere. Complex Biological Systems seminar, University of Pittsburgh Pittsburgh, Pa. Sept 14th, 2010
    14. L.C. Udeigwe and G.B Ermentrout. Application of non-synchronous oscillations on graphs. SIAM Conference on the Life Sciences, Pittsburgh, Pa. July 14th, 2010

    Student Publications / Presentations

    1. N. Colon and A. Gonzalez. Image recognition using autoencoding in multilayer neural networks and multi-value neurons. The Manhattan Scientist, Series B Volume 4, Fall 2017 (mentored by I. Aizenberg and L. C. Udeigwe)

    2. N. Colon and A. Gonzalez. Image recognition using analysis of discrete Fourier transform by multilayer neural networks with multi-valued neurons. The Manhattan Scientist, Series B Volume 4, Fall 2017 (mentored by I. Aizenberg and L. C. Udeigwe)

  • Professional Experience and Memberships
    Experience:
    • Assistant Professor of Mathematics, Manhattan College (2016 -- present)
    • Visiting Assistant Professor of Mathematics, Manhattan College (2014 -- 2016)
    • Adjunct Instructor of Mathematice, CCAC Pittsburgh, PA (2012-2014)
    • Instructor of Mathematice, Penn States, Fayette (2009-2012)
    Membership:

    - Sigma Xi (2016--present)
    - Mathematical Association of America (2004—present)
    - Organization for Computational Neuroscience (2012—present)
    - Society of Industrial and Applied Mathematician (2004—present)