Moises Sudit

PhD

Moises Sudit

PhD

Moises Sudit

PhD

Research Topics

Information fusion, military operations research

Contact Information

B20B Lockwood

Buffalo NY, 14260

Phone: (716) 645-2423

sudit@buffalo.edu

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Courses Taught

University at Buffalo

EAS140 Engineering Solutions: A first course in engineering. Introduces students to engineering design used to solve technologically based problems in the various fields of engineering, and develops computer skills for problem solving using MAPLE, spreadsheets, network file transfer, remote login, e-mail, UNIX, and algorithmic problem-solving approaches.

IE 373 Introduction to Operations Research: Undergraduate course that introduces students to deterministic mathematical models such as linear programming and network optimization.

IE460C/560C Introduction to Artificial Intelligence: Undergraduate/Graduate course that familiarizes the class with propositional logic, knowledge representation and reasoning using LISP.

IE553 High Level Information Fusion: Graduate level course that gives an introduction to a number of Fusion Models and how their different subcomponents interact with each other. The course focuses on Information Fusion as it relates to Situational Awareness, Understanding, Impact/Threat Assessment and Process Refinement.

IE 572 Linear Programming: Graduate level course in linear programming.

IE 573 Discrete Optimization: Graduate level course that introduces integer programming, combinatorics, network optimization and computational complexity.

IE 670 (2000) Design and Analysis of Algorithms: Doctoral level course presenting computational complexity, efficient design of algorithms and heuristic optimization.

IE670 (2014) Big Data Optimization: Doctoral level course presenting workflow and methods to deal with problems that have large amounts of data as input,

IE 674 Integer Programming: Doctoral level course dealing with theoretical aspects of integer programming.

IE 677/684 Graph Theory: Doctoral level course introducing graphical structures, algorithms and related applications.

Rochester Institute of Technology

IE 401 Introduction to Operations Research I: An introduction to the deterministic optimization methodology of mathematical problem formulation and solution strategies.

IE702 Integer and Nonlinear Programming: Graduate course addressing mathematical foundations of integer programming and nonlinear optimization techniques.

IE 703 Supply Chain Management: Graduate course presenting concepts in transportation, inventory control, facility location, information systems and their interaction.

IE 742 Artificial Intelligence Applications: Graduate course in the development and application of "intelligent" (knowledge-based) systems.

IE 787 Systems Optimization: Executive Graduate course dealing with the interaction of operations management and systems.

Purdue University

IE 335 Introduction to Operations Research I: Introduction to deterministic optimization modeling and algorithms in operations research. 

IE 490G Computer Graphics: Undergraduate/Graduate course exposing students to Computer Graphics and a number of applications in Industrial Engineering.

New Courses Developed

Big Data Optimization (University at Buffalo). This course will introduce the definition of “Big Data” (Volume, Velocity, Veracity and Variety) as input to optimization problems. The course will look at the workflow of heterogeneous data types (structured and unstructured) as they progress in a flow of phases (i.e. semantic augmentation, homogenization and analytics). Both, batch and streaming analytics will be studied and create an understanding on the type of methods that will be proven successful for certain characterized problems.

High Level Information Fusion (University at Buffalo). This course will introduce High Level Information Fusion concepts and methods to give students a good understanding of this new area of research that is the combination of multidisciplinary classical fields of research. This course will give a brief introduction of a number of Fusion Models and how its different subcomponents interact with each other. The course will focus on Information Fusion as it relates to Situational Awareness/Understanding, Impact/Threat Assessment and Process Refinement (which is called High Level Fusion in one of the models we will be studied).

Discrete Optimization (University at Buffalo). This course is a natural continuation to Linear Programming to give students the basic tools in the areas of Integer Programming and Network Optimization. The course was developed with the intention of given students in the areas of production, manufacturing and operations research the necessary tools to solve discrete phenomena that arise in a number of practical problems in industry.

Design and Analysis of Algorithm (University at Buffalo). This course was design for advanced Master Students and Ph.D. students. Different than Computer Science courses in this area, the emphasis is Computational Complexity and Heuristic Optimization rather than exact Algorithms.

Introduction to Artificial Intelligence (University at Buffalo). The course was developed for seniors and graduate students interested in the principles of Artificial Intelligence and Expert Systems. The course used LISP language as a learning tool for Artificial Intelligence concepts.

Supply Chain Management (Rochester Institute of Technology). The course was developed for undergraduate and graduate students in Industrial Engineering as well as part of a graduate program in Product Development. This course introduces students to the concept of logistic management and its integration with applied operations research.

Computer Graphics (Purdue University). This course was developed for senior undergraduate and graduate students with interests in interfacing Computer Graphics with a number of Industrial Engineering applications.

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