Teaching & Learning

Courses Taught at Various Places

1. HMRITM College New Delhi

• Operating Systems 

• Computer Network

2. LNMIIT Jaipur

• Introduction to Complexity Theory (Computability (TM, undecidability, recursive function, decidability of logical theories, Kolmogorov complexity) and complexity theory(time, space and limits of diagonalization))

• Combinatorics for Computer Science (Classical, Linear Algebraic, Probabilistic, Polynomial)

• JAVA Programming 

• Operating System Lab (fork, pipe, shared memory, thread, synchronization, scheduling)

3. BITS Pilani Hyderabad

• Compiler Design Lab

• Design and Analysis of Algorithms Tutorial

• Discrete Structure for Computer Science (Proof Strategies, Sets, Induction, Counting (including bijective and double), Recurrence, Generating Functions, Number Theory (Congruence, GCD, Prime number, Fermat, Wilson, CRT), Graph Theory, Modern Algebra(Group, Subgroup, Lagrange theorem, Burnside lemma, Group homomorphism, Definitions of Ring, Ideal and Field))

• Theory of Computation (FA, regular language, regular grammar, PDA, CFG, CFL, LBA, CSL, TM, Unrestricted grammar, Chomsky hierarchy, Undecidability)

• Cryptography (Perfect Secrecy, Game-based definition of secrecy, Classical encryption schemes, Pseudorandom generator, LFSR, DES and its Cryptanalysis, MAC, Markele Damgard Construction, Hash Functions, and some classical attacks, AES, RSA, ElGammal, Digital Signature)

4. VIT Bhopal

• Object Oriented Programming in C++

• Object Oriented Programming in JAVA

• Software Engineering

• Database Management Systems

• Design and Analysis of Algorithms(RAM Model, Proving Correctness, Analysis of GCD, Divide and Conquer, Dynamic Programming, Greedy Strategy, Amortized Analysis, Strings, Geometric and Approximation Algorithms)

• Convex Optimization (Only Theory Part of Convex Optimization)

5. SRM University AP

• Formal Languages and Automata Theory

• Compiler Design Theory and Lab

6. Central University of South Bihar, Gaya

• Mathematical Foundation of AI  (Logic, Linear Algebra, Probability and Statistics, Multivariate Function and Partial Derivative)

• Machine Learning (Concept learning, Decision tree, ANN, Evaluation of Hypothesis, Bayesian Learning, Linear Regression and Classification, SVM, Clustering)

• Deel Learning (MLP, ANN, PCA, Autoencoder, CNN, RNN, LSTM, Word2Vec etc)

Used Books

1. Discrete Mathematics by Kenneth Rosen

2. Discrete Mathematics by Tremblay and Manohar

3. Elementary Number Theory by David Burton

4. Problem-Solving Strategies by Arthur Engel

5. A path to Combinatorics for Undergraduates by Titu Andresscu and Zuming Feng

6. Introduction to Linear Algebra by Gilbert Strang

7. Topics in Algebra by IN Herestein

8. Algebra by Michael Artin

9. Contemporary Abstract Algebra by Joseph A. Gallian

10. Algebraic Number Theory and Fermat's Last Theorem by Stewart and Tall

11. Number Field by Marcus

12. Introduction to Commutative Algebra Atiyah and Macdonald

13. Calculus and Analytic Geometry by George B. Thomas and Ross L. Finney

14. Topology of Metric Space by Kumaresan

15. Advanced Calculus by Fitzpatrick

16. Extremal Combinatorics by Stasys Jukna

17. Theory of Computation by Michael Sipser

18. Introduction to formal languages, automata, and theory of computation by Hopraft and Ullman

19. Computational Complexity: A Modern Approach by Sanjay Arora and Boaz Barak

20. Introduction to Algorithms by Coreman Rivest et al.

21. Algorithm Design by Kleinberg and Tardos

22. Convex Optimization by Boyed et al.

23. Probability Models by Sheldon Ross

24. Probability and Statistics for Scientists and Engineers by Sheldon Ross

25. Probability and Computing Mitzenmacher and Upfal

26. Randomized Algorithms by Motwani and Raghvan

27. Cryptography: Theory and Practices  by Douglas Stinson

28. Modern Cryptography by Katz and Lindell

29. Complexity of Lattices Problems by D. Micciancio and S. Goldwasser,

30. Machine Learning by Tom M Mitchell

31. Elements of Statistical Learning: Data Mining, Inference and Prediction by Hastie, Tibshirani and Friedman.

32. Compiler Design by Aho, Ullman, Sethi, and Lam

33. Database Management Systems by Navathe et al.

34. Software Engineering by Pressman

35. C++ by Balaguruswamy

36. Java by Herbert Schmidt

37. Java by Deitel and Deitel

38. Operating Systems by Galvin et al.

39. Computer Network by Forouzen 

40. etc.

Attended Offline Lectures:

1. Modern Algebra Prof. P.A.S. Krishna (Attended)

2. Modern Algebra Prof. Shyamsree Upadhyay (Attended)

3. Randomized Algorithm Prof. Benny George Kenkireth (Attended)

4. Probabilistic Methods Prof. Benny George Kenkireth (Attended)

5. Parallel Algorithms Prof Sushant Karmakar (Credited)

6. Algebraic Number Theory Prof. Rupam Berman (Attended)

7. Real Analysis by Prof Bhabha Kumar Sharma (Attended)

8. Advanced Theory of Computation (Complexity Theory) by Prof. Deepanjan Kesh (Attended)

9. Mathematica Foundation of CS by Prof. Purandar Bahduri (Credited)

10. Cryptography and Network Security by Prof. Sukumar Nandi (Credited)

11. Graph Theory Prof. Bikash Bhattacharya (Attended)

Listened Online Videos and Made Notes:

1. Linear Algebra by Prof. Vittal Rao

2. Mathematical Logic by Prof. Arindama Singh

3. Machine Learning by Prof. Balram Ravindran

4. Deep Learning by Prof. Mithesh Khapra

5. Algorithms by Prof. Tim Roughgarden

6. Galois Theory by Prof. Dilip Patil

7. Introduction to Commutative Algebra and Algebraic Geometry by Prof. Dilip Patil

8. Probability MIT OCW by Prof. John Tsitsiklis

9. Cryptography Prof. Ashish Choudhary

10. Randomized Methods in Complexity by Prof. Nitin Saxena (NPTEL Certificate)

11. Quantum Algorithms and Cryptography by Prof. Shweta Agrawal (NPTEL Certificate)

12. Enumerative Combinatorics by Prof. Fedrico Ardila