Dr. Sonjoy Das
Adjunct Associate Professor, SUNY Buffalo State College
Dr. Sonjoy Das is currently an Adjunct Associate Professor in the Department of Mathematics in SUNY Buffalo State College. His general research interest are Predictive Science, Advanced Mathematical Modeling of Physical and Social Systems, Uncertainty Analysis, Numerical Simulations, and Multiscale and Multiphysics Models. In a myriad of scholarly products (journal publications, invited talks, and conference/workshop presentations), Dr. Das proposed highly novel and innovative methods and approaches that combine an array of methodologies from Random Matrix Theory, Information Theory, Polynomial Chaos Expansion, Machine Learning, Data Science, Extreme Value Theory, Stochastic Optimization, Evolutionary Optimization, Multiscale Material Modeling, and Fluid-Structure Interaction. The proposed methods and attendant algorithms have been successfully applied in a diverse areas of practically significant applications such as 3D Printing; Brain Mechanics; and Aerospace, Mechanical, and Civil Engineering Materials and Structures.
Prior to joining SUNY Buffalo State College, he served as an Assistant Professor for 8 years in the University at Buffalo (UB). His research was funded by National Science Foundation and Industry (owns 100%). Dr. Das mentored 2 PhD students and several master and undergraduate students. Prior to joining UB, he was postdoctoral research associates in Massachusetts Institute of Technology and University of Southern California.
Dr. Sonjoy Das has a strong international visibility in his field of research due to his interdisciplinary research background and substantial intellectual contributions in his research area. He is recognized and respected for his commitment to continually developing his research and teaching approaches. With a passion for discovering new findings and sharing knowledge, he has made a significant impact on his research field and higher education as a whole.
Bio
Destined to be a researcher and educator, Dr. Sonjoy Das is known for the natural ability to explain abstract concepts in engaging, interesting and thought-provoking ways. His compassionate approach sparks curiosity and connects with people at all skill levels, whether it’s within his published works or the courses he teaches.
Scholarly Products
PEER-REVIEWED JOURNAL ARTICLES
J12.) Chakravarty, S., Das, S., Hadjesfandiari, Ali R., and Dargush, Gary F., "Variational inequalities for heterogeneous microstructures based on couple stress theory," International Journal for Multiscale Computational Engineering, v. 16, no. 2, pp. 119-137, 2018, doi:10.1615/IntJMultCompEng.2018022854.
J11.) Wang, J., Das, S., Rai, R., and Zhou, C., "Data-driven Simulation for Fast Prediction of Pull-up Process in Bottom-up Stereo-lithography," Computer-Aided Design, v. 99, pp. 29-42, 2018, doi:10.1016/j.cad.2018.02.002.
J10.) Hang, Y., Venketeswaran, A., Das, S., and Zhou, C., "Investigation of separation force for constrained-surface stereolithography process from mechanics perspective," Rapid Prototyping Journal, v. 23, no. 4, 2017, doi:10.1108/RPJ-06-2016-0091.
J9.) Das, S. and Chakravarty, S., "Predictive Algorithm for Detection of Micro-cracks from Macro-scale Observables," SIAM/ASA Journal on Uncertainty Quantification, v. 4, no. 1, pp. 660-707, 2016, doi:10.1137/15M1037275.
J8.) Das, S., Goswami, K., and Datta, B., "Quadratic partial eigenvalue assignment in large-scale stochastic dynamic systems for resilient and economic design," Mechanical Systems and Signal Processing, v. 72-73, pp. 359-375, 2016, doi:10.1016/j.ymssp.2015.10.001.
J7.) Liravi, F., Das, S., and Zhou, C., "Separation Force Analysis and Prediction Based on Cohesive Element Model for Constrained-Surface Stereolithography Processes," Computer-Aided Design (Special Issue on Geometric and Physical Modeling for Additive Manufacturing), v. 69, pp. 134-142, 2015, doi:10.1016/j.cad.2015.05.002.
J6.) Sovizi, J., Das, S., and Krovi, V., "Random Matrix Approach: Toward Probabilistic Formulation of the Manipulator Jacobian," ASME Journal of Dynamic Systems, Measurement \& Control, v. 137, no. 3, pp. 031003-1--031003-10, 2014, doi:10.1115/1.4027871.
J5.) Das, S., Spall, J.~C. and Ghanem, R., "Efficient Monte Carlo Computation of Fisher Information Matrix using Prior Information," Computational Statistics and Data Analysis, v. 54, no. 2, pp. 272-289, 2010, doi:10.1016/j.csda.2009.09.018.
J4.) Das, S., and Ghanem, R., "A Bounded Random Matrix Approach for Stochastic Upscaling," SIAM Multiscale Modeling and Simulation (Special Issue on Multiscale Modeling of Materials), v. 8, no. 1, pp. 296-325, 2009, doi:10.1137/090747713.
J3.) Das, S., Ghanem, R. and Finette, S., "Polynomial Chaos Representation of Spatio-temporal Random field from Experimental Measurements," Journal of Computational Physics, v. 228, no. 23, pp. 8726-8751, 2009, doi:10.1016/j.jcp.2009.08.025.
J2.) Ghanem, R., and Das, S., "Hybrid Representations of Coupled Nonparametric and Parametric Models for Dynamic Systems," AIAA journal, v. 47, no. 4, pp. 1035-1044, 2009, doi:10.2514/1.39591.
J1.) Das, S., Ghanem, R. and Spall, J. C., "Asymptotic Sampling Distribution for Polynomial Chaos Representation from Data: A Maximum Entropy and Fisher information approach," SIAM Journal on Scientific Computing, v. 30, no. 5, pp. 2207-2234, 2008, doi:10.1137/060652105.
PEER-REVIEWED SOLICITED BOOK CHAPTERS
B2.) Goswami, K., Das, S., and Datta, B., "Robust Control of Stochastic Structures using Minimum Norm Quadratic Partial Eigenvalue Assignment Technique," in: Adhikari, Mahim R. (Eds.), Mathematical and Statistical Applications in Biology, Engineering, Environment and Information Science, Springer, 2017, doi:10.1007/978-981-10-5370-2_2.
B1.) Das, S., and Ghanem, R., "Stochastic Upscaling for Inelastic Material Behavior from Limited Experimental Data," in: Ghosh, Somnath and Dimiduk, Dennis (Eds.), Computational Methods for Microstructure-Property Relationships, Springer, New York, pp. 443-468, 2011, doi:10.1007/978-1-4419-0643-4_12.
Overview of Courses
Knowledge for Every Level
January 5 - June 13, 2026
Introductory Course
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January 5 - June 13, 2026
Workshop Course
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January 5 - June 13, 2026
Graduate Course
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January 5 - June 13, 2026
Introductory Course
This is a summary of your course. Include an overall description of the class, significant concepts that are taught and any other relevant information that would be helpful for a potential student. It’s also a good idea to mention any specific requirements for materials or time commitment outside of class.
January 5 - June 13, 2026
Workshop Course
This is a summary of your course. Include an overall description of the class, significant concepts that are taught and any other relevant information that would be helpful for a potential student. It’s also a good idea to mention any specific requirements for materials or time commitment outside of class.
January 5 - June 13, 2026
Graduate Course
This is a summary of your course. Include an overall description of the class, significant concepts that are taught and any other relevant information that would be helpful for a potential student. It’s also a good idea to mention any specific requirements for materials or time commitment outside of class.
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