# Teaching

- Pennsylvania State University
Guest Lecturer: CMPSC 443: Introduction to Computer Security (Spring 2022)

- Purdue University:
Guest Lecture: CS 59000-DSP Data Security And Privacy (Spring '20)

CS 180: Problem Solving and Object-Oriented Programming (Fall '18, Spring '19): Primitive data types and strings, Standard operations on primitives and strings, If, switch, while, for, and do-while statements, Single- and multi-dimensional arrays, Finding and fixing bugs, Object-Oriented Programming, Exception handling, Concurrency, Input/Output (I/O), Graphical User Interfaces (GUIs), Dynamic Data Structures, Recursion.

- Daffodil International University (Fall '17, Spring '18):
CSE 131: Discrete Mathematics: Set theory; Relations; Functions; Graph theory; Propositional calculus and predicate calculus; Mathematical reasoning: induction, contradiction, and recursion; counting; Principles of inclusion and exclusion; Recurrence relations; Algebraic structures: rings and groups.

CSE 122: Problem Solving Lab ( C Programming Lab): data types, operators, expressions, control structures; Functions and program structure: parameter passing conventions, scope rules and storage classes, recursion; Header files; Preprocessor; Pointers and arrays; Strings; Multidimensional array; User-defined data types: structures, unions, enumerations; Input and Output: standard input and output, formatted input and output, file access; Variable-length argument list; Command line parameters; Error Handling; Graphics; Linking; Library functions. This is Laboratory work based on CSE 121.

CSE 414: Artificial Intelligence Lab: Introduction to old and new AI techniques; Knowledge representation; Propositional and first-order logic, inference in first-order logic; Frame problem; Search techniques in AI; Game playing; Planning; Probabilistic reasoning; Learning in symbolic and non-symbolic representation; Natural language processing. Introduction to an expert system. This is Laboratory work based on CSE 413.

CSE 415: Simulation And Modeling: Simulation modeling basics: systems, models and simulation; Classification of simulation models; Steps in a simulation study; Concepts in discrete-event simulation: event-scheduling vs. process-interaction approaches, time-advance mechanism, organization of a discrete-event simulation model; Continuous simulation models; Combined discrete-continuous models; Monte Carlo simulation; Simulation of queuing systems.

Building valid and credible simulation models: validation principles and techniques, statistical procedures for comparing real-world observations and simulated outputs, input modeling; Generating random numbers and random variates; Output analysis. Simulation languages; Analysis and modeling of some practical systems.