jonathan kaldor

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Education

Cornell University 2004 - Present
Ph.D. Student in Computer Science
Advisors: Steve Marschner and Doug James

Amherst College, Amherst 1999 - 2003
Bachelor of Arts, Double Major in Computer Science and Mathematics
Summa Cum Laude

Research Experience

Cloth Simulation
Graduate Research Assistant, Cornell Univ. with Steve Marschner and Doug James
2005-present

Worked on simulating the motion of knitted fabrics by simulating the motion of the constituent yarns. Models of this type are important because the commonly used models for cloth in computer graphics typically approximate the cloth as an elastic sheet with typically linear isotropic behavior inspired by the construction of woven fabrics. However, they do a poor job of predicting the behavior of knits, which are driven by the complex interactions of yarn loops pulled through each other.

In our simulations, yarns in the fabric are modeled as inextensible but flexible cubic B-spline curves. Yarn dynamics are dictated by both energy terms and hard constraints defined directly on the curves, and the simulation is stepped forward in time using a combination of an explicit integrator and constraint enforcement using efficient projections. Friction interactions, a critical component of correct yarn behavior, is approximated using a velocity filter that penalizes locally non-rigid motion. We performed qualitative evaluation of our model to the predictions of an elastic model, as well as observed deformations of hand-knitted samples in the laboratory, and showed that our model predicts the key mechanical properties of different knits. In addition, we showed that this simulator can scale up to knit structures of substantial size in a computationally reasonable amount of time.



Linear Programming for NP-Hard Problems
Undergraduate Thesis, Amherst College, with Lyle McGeoch
2002 - 2003

Studied the application of linear programming (LP) to the solutions for select NP-Hard problems, in particular the Traveling Salesman Problem (TSP) and the Graph Coloring problem. An extensive survey of the state-of-the-art in TSP research was performed, focusing in particular on the LP-relaxation formulation of the problem. The TSP can be expressed as an integer LP problem; however, this problem is also NP-Hard. By treating it as an LP with rational solutions instead of integral, the problem can be solved, but it may not be a solution to the TSP (the salesman takes a fractional path, for instance). By introducing additional constraints that all valid TSP solutions must satisfy but fractional solutions might not, the hope is to eventually find a solution to the LP-relaxed problem that is also a solution to the TSP. The second part of the thesis focused on a similar approach to solving graph coloring problems. An LP formulation for the problem was discussed, along with a set of basic constraints to eliminate some fractional solutions.

Publications

Jonathan Kaldor, Doug James, and Steve Marschner. Efficient Yarn-based Cloth with Adaptive Contact Linearization. Accepted to SIGGRAPH 2010

Jonathan Kaldor, Doug James, and Steve Marschner. Simulating Knitted Cloth at the Yarn Level. SIGGRAPH 2008

Jonathan Kaldor. Cutting-plane Algorithms Applied to Traveling Salesman and Graph Coloring Problems. 2004. Undergraduate Thesis, Amherst College (advised by Lyle McGeoch)

Teaching

CS3220: Introduction to Scientific Computing
Instructor
Summer 2008
(6 week summer course)



CS322: Introduction to Scientific Computing.
Teaching Assistant (TA Excellence Award)
Spring 2007
Taught two sections.



CS465: Computer Graphics I.
Teaching Assistant (Part Time) Fall 2006



CS212: Programming Practicum, a one credit class accompanying the undergraduate course on Data Structures
Teaching Assistant (TA Excellence Award)
Fall 2005, Spring 2006

Awards

Phi Beta Kappa (Junior year); Sigma Xi member; Addison Brown Scholarship (senior with highest GPA after junior year); Computer Science Award; TA Excellence Award (CS212, CS322)

Professional Activities

Member of ACM / ACM SIGGRAPH.

Graduate Course Work

Advanced Programming Languages, Advanced Computer Systems, Matrix Computations, Numerical Solution of Differential Equations, Physically Based Rendering, Analysis of Algorithms, Advanced Design and Analysis of Algorithms