Holonomic techniques have deep roots going back to Wallis, Euler, and Gauss, and have evolved in modern times as an important subfield of computer algebra, thanks in large part to the work of Zeilberger and others over the past three decades. In this talk, I will give an overview of the area, and in particular will present a select survey of known and original results on decision problems for holonomic sequences and functions. (Holonomic sequences satisfy linear recurrence relations with polynomial coefficients, and holonomic functions satisfy linear differential equations with polynomial coefficients.) I will also discuss some surprising connections to the theory of periods and exponential periods, which are classical objects of study in algebraic geometry and number theory; in particular, I will relate the decidability of certain decision problems for holonomic sequences to deep conjectures about periods and exponential periods, notably those due to Kontsevich and Zagier.

In the last three years, there are many breakthroughs in optimization such as nearly quadratic time algorithms for bipartite matching, linear programming algorithms that are as fast as Ax = b. All of these algorithms are based on a careful combination of optimization techniques and dynamic data structures. In this talk, we will explain the framework underlying all the recent breakthroughs.

Joint work with Jan van den Brand, Michael B. Cohen, Sally Dong, Haotian Jiang, Tarun Kathuria, Danupon Nanongkai, Swati Padmanabhan, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang, Sam Chiu-wai Wong, Guanghao Ye, Qiuyi Zhang.

Algebraic complexity is the field studying the intrinsic difficulty of algebraic problems in an algebraic model of computation, most notably arithmetic circuits. It is a very natural model of computation that attracted a large amount of research in the last few decades, partially due to its simplicity and elegance, but mostly because of its importance. Being a more structured model than Boolean circuits, one could hope that the fundamental problems of theoretical computer science, such as separating P from NP, deciding whether P = BPP and more, will be easier to solve for arithmetic circuits.

In this talk I will give the basic definitions, explain the main questions and how they relate to their Boolean counterparts, and discuss what I view as promising approaches to tackling the most fundamental problems in the field.

Let P be a sound proof system for propositional logic. For each CNF formula F, let SAT(F) be a CNF formula whose satisfying assignments are in 1-to-1 correspondence with those of F (e.g., F itself). For each integer s, let REF(F, s) be a CNF formula whose satisfying assignments are in 1-to-1 correspondence with the P-refutations of F of length s. Since P is sound, it is obvious that the conjunction formula SAT(F) & REF(F,s) is unsatisfiable for any choice of F and s. It has been long known that, for many natural proof systems P and for the most natural formalizations of the formulas SAT and REF, the unsatisfiability of SAT(F) & REF(F,s) can be established by a short refutation. In addition, for most P, these short refutations live in the proof system P itself. This is the case for all Frege proof systems. In contrast it was known since the early 2000’s that Resolution proofs of Resolution’s soundness statements must have superpolynomial length. In this talk I will explain how the soundness formulas for a proof system P relate to the computational complexity of the proof search problem for P. In particular, I will explain how such formulas are used in the recent proof that the problem of approximating the minimum proof-length for Resolution is NP-hard (Atserias-Müller 2019). Besides playing a key role in this hardness of approximation result, the renewed interest in the soundness formulas led to a complete answer to the question whether Resolution has subexponential-length proofs of its own soundness statements (Garlík 2019).

Deep learning has transformed Machine Learning and Artificial Intelligence in the past decade. It raises fundamental questions for mathematics and theory of computer science, since it relies upon solving large-scale nonconvex problems via gradient descent and its variants. This talk will be an introduction to mathematical questions raised by deep learning, and some partial understanding obtained in recent years.

Algorithmic Improvisation, also called control improvisation or controlled improvisation, is a new framework for automatically synthesizing systems with specified random but controllable behavior. In this talk, I will present the theory of algorithmic improvisation and show how it can be used in a wide variety of applications where randomness can provide variety, robustness, or unpredictability while guaranteeing safety or other properties. Applications demonstrated to date include robotic surveillance, software fuzz testing, music improvisation, human modeling, generating test cases for simulating cyber-physical systems, and generation of synthetic data sets to train and test machine learning algorithms. In this talk, I will particularly focus on applications to the design of intelligent autonomous systems, presenting work on randomized planning for robotics and a domain-specific probabilistic programming language for the design and analysis of learning-based autonomous systems.