István Miklós is a Hungarian mathematician and bioinformatician at the Rényi Institute in Budapest. He holds a Ph.D. from Eotvos University in Budapest. His research interests lie in theoretical and applied computer science and combinatorics, particularly in the study of Markov chain, Monte Carlo methods and in sampling and counting combinatorial objects appearing in applied mathematics. He has more than 50 peer-reviewed scientific papers.

• 1. Background on computational complexity2. Algebraic dynamic programming and monotone computations3. Linear algebraic algorithms. The power of subtracting4. #P-complete counting problems5. Holographic algorithms6. Methods of random generations7. Mixing of Markov chains and their applications in the theory ofcounting and sampling8. Approximable counting and sampling problems

István Miklós is a Hungarian mathematician and bioinformatician at the Rényi Institute in Budapest. He holds a Ph.D. from Eotvos University in Budapest. His research interests lie in theoretical and applied computer science and combinatorics, particularly in the study of Markov chain, Monte Carlo methods and in sampling and counting combinatorial objects appearing in applied mathematics. He has more than 50 peer-reviewed scientific papers.

• 1. Background on computational complexity2. Algebraic dynamic programming and monotone computations3. Linear algebraic algorithms. The power of subtracting4. #P-complete counting problems5. Holographic algorithms6. Methods of random generations7. Mixing of Markov chains and their applications in the theory ofcounting and sampling8. Approximable counting and sampling problems

István Miklós is a Hungarian mathematician and bioinformatician at the Rényi Institute in Budapest. He holds a Ph.D. from Eotvos University in Budapest. His research interests lie in theoretical and applied computer science and combinatorics, particularly in the study of Markov chain, Monte Carlo methods and in sampling and counting combinatorial objects appearing in applied mathematics. He has more than 50 peer-reviewed scientific papers.

• 1. Background on computational complexity2. Algebraic dynamic programming and monotone computations3. Linear algebraic algorithms. The power of subtracting4. #P-complete counting problems5. Holographic algorithms6. Methods of random generations7. Mixing of Markov chains and their applications in the theory ofcounting and sampling8. Approximable counting and sampling problems