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Talk Schedule for Undergraduate and Graduate Mathematics Club
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ISQGD Mathematics Clubs — Eastern Hemisphere and Western Hemisphere
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DateRegion Subject Group / CategorySpeakerAffiliationTalk TitleTalk AbstractAbout the SpeakerU.S. Central TimeIndian Standard Time (IST)Convert to Your Local TimeDuration of Talk (including questions and answers)Zoom LinkStatusNotesVolunteer Coordination
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September 19, 2026Eastern HemisphereApplied Mathematics, Optimization & Mathematical ModelingDr. Diksha GuptaJaypee Institute of Information Technology, Noida, INDIAFuzzy Sets and Fuzzy Logic: From Fundamentals to ApplicationsFuzzy set theory and fuzzy logic provide effective mathematical tools for handling imprecision, vagueness, and uncertainty in real-world problems. This talk introduces the fundamental concepts of fuzzy sets, membership functions, fuzzy operations, linguistic variables, and fuzzy reasoning. The transition from classical logic to fuzzy logic will be discussed through simple examples, along with the basic principles of fuzzification, fuzzy inference, and defuzzification. The talk will also highlight applications of fuzzy techniques in artificial intelligence, decision-making, control systems, clustering, and pattern recognition, providing an overview of their role in developing intelligent systems.Dr. Diksha Gupta is an Assistant Professor (Grade I) at Jaypee Institute of Information Technology (JIIT), Noida, India. She received her Ph.D. from the Department of Mathematical Sciences at IIT (BHU), Varanasi. Her research interests include fuzzy set theory and its applications, analytical fuzzy geometry, fuzzy calculus, fuzzy image processing, and fuzzy fractal geometry.6:30 AM5:00 PMView Your Local Time60 minutes CompletedDr. Megha Pandey
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September 26, 2026Western HemisphereGeometry, Topology & Dynamical SystemsDr. Sharareh SayyadDepartment of Mathematics and Statistics, Washington State University (WSU), USATopTimeNet: Topologically-assisted time-series classification modelClassifying the dynamical regime of time-series data, such as distinguishing periodic from chaotic behavior, is a problem of broad interest across various fields of science. The core difficulty lies in extracting discriminative structure from signals with complex short- and long-range temporal correlations. Deep neural networks address this by jointly solving a representation problem (what features matter?) and a discrimination problem (how do they separate classes?), at the cost of large parameter counts and limited interpretability. In this talk, I argue that for dynamical regime classification, these two problems can be decoupled. Rather than learning representations implicitly from raw signals, I encode the dynamical properties of the data explicitly through topological summary statistics. A lightweight neural network then acts purely as a discriminative head on this structured representation. I show that this approach provides comparable deep learning classification accuracy while reducing model complexity and, crucially, making the learning process more interpretable.Dr. Sharareh Sayyad is a researcher and Teaching Assistant in the Department of Mathematics and Statistics at Washington State University (WSU), USA. Her research lies at the intersection of mathematical physics, computational condensed-matter physics, quantum many-body systems, non-Hermitian physics, and topology. She received her Ph.D. from the University of Hamburg, Germany, and her research has appeared in journals including Physical Review Letters, Physical Review Research, and SciPost Physics.9:00 AM7:30 PMView Your Local Time60 minutesScheduled Ms. Claudia Maria Schmidt
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October 3, 2026Eastern HemisphereGeometry, Topology & Dynamical SystemsMr. Sayandip Pandit PhD student in the Department of Mathematics , IIT Delhi, IndiaQuantization in Countably Infinite Self-Similar Systems: A Counterexample and a Corrected PerspectiveWe reconsider the quantization problem for probability measures generated by countably infinite systems of contracting similarities. Mihailescu and Roychowdhury, Kyoto J. Math. 55 (2015), 857–873, asserted under a strong separation hypothesis and a pressure finiteness condition that the quantization dimension of order r is the number κr determined by ∑︂ j≥1 (pjsr j)κr/(r+κr) = 1. We first isolate a gap in the proof of the upper quantization estimate: a geometric container for the tail of the first-level cylinders is subsequently iterated as though it were an additional self-similar branch, although the required measure identity is not available. We then show that the difficulty is substantive. For every fixed r > 0 we construct a strongly separated countable self-similar probability satisfying the pressure finiteness hypothesis for which Dr(µ) ≥ r 2r +1 >κr. Thus the pressure formula for the quantization dimension does not hold under the hypotheses stated in the 2015 paper. The construction shows that, in a countable system, the spatial distribution of the first-level cylinders may create an additional quantization scale that is invisible to the sequence (pj,sj). We also state a rigorous sufficient condition under which a finite tail replacement is legitimate and the classical finite-system quantization theorem applies. The results indicate that a general quantization theory for countable self-similar systems must control both internal contraction and the metric complexity of the tail geometry.PhD student in the Department of Mathematics , IIT Delhi, India.9:00 AM7:30 PMView Your Local Time60 minutesScheduledDr. Megha Pandey
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