Key Challenges for Theoretical Computer Science

1. Key Challenges for Theoretical Computer Science Richard M. Karp NSF, August 31, 2005

2. What is Theoretical Computer Science ? TCS applies abstract models and mathematical reasoning to problems related to computation. • Provides a set of tools, and ways of thinking applicable to a wide variety of applied problems. • Contributes to national security through cryptographic protocols and to computational science through fundamental algorithms. Its core: fundamental and interrelatedquestions about the nature of computation.

3. Topics at the Core of TCS • Algorithms and complexity of computation • Computational limits of proof methods • Logic and program verification • The power of randomization • Cryptography • Quantum computation • Distributed computation and communication • Computational learning theory

4. The Revolutionary Impact of Algorithms • Optimization • Scientific computing • Cryptography • Genome sequencing • Compiler construction • Algebraic computation • Data structures

5. Fundamental Questions:Complexity and Algorithms • (P = NP) Combinatorial search easy but cryptography impossible. • Two possible worlds: • (P  NP) Combinatorial search hard but unbreakable cryptography possible • Find best algorithms for multiplying numbers, Discrete Fourier transform and matrix multiplication. • Which tautologies in propositional logic have short proofs?

6. Fundamental Questions:Nature & Limits of Proofs • Find limits of computationally sound interactive proofs, which prove a statement by performing a computation that would be infeasible if the statement were false. • Can we prove that statement is true without revealing any additional information? (Prove you earned <$100K without revealing salary.) • Can we design proofs that can be verified by spot checking rather than checking every step? (Traditional proofs are only strong as weakest link.)

7. Fundamental Questions:Randomness, Quantum, Crypto • Does access to random numbers give more computing power? • Are hypothetical computers based on principles of quantum mechanics more powerful? • Is there cryptosystem where everyone can send encrypted message to Alice, but only she can read it?

8. Fundamental Questions: Mechanism Design, Distributed Comp., Learning • Can we cause self-interested agents to co-operateover the Internet? • Can we reach agreement in the face of asynchronicity and faulty parties? • What are the inherent limits on the ability of computers to infer patterns from examples?

9. Automata Complexity PL NP-Complete PK Crypto Random Approx Learning Parallel, Dist Derand Interactive Proofs Approx Hardness Boosting Network Sec Mechanisms Sublinear Quantum Comp. Comp. Bio Web Search Model Checking Stat. Physics Con. Large Datasets Evolution of TCS 70s: 60s: 80s: 90s–

10. Automata Complexity PL NP-Complete PK Crypto Random Approx Persistent Themes: efficient algorithms, complexity, nature of proofs, cryptography, randomness, verification Learning Parallel, Dist Derand Interactive Proofs Approx Hardness Boosting Network Sec Mechanisms Sublinear Quantum Comp. Comp. Bio Web Search Model Checking Stat. Physics Con. Large Datasets Evolution of TCS 70s: 60s: 80s: 90s–

11. TCS’s Greatest Strength: Unexpected Pay-offs NP-Completeness PK Crypto,Blum-Micali-Yao etc. Boosting Interactive proofs, PCP Loss-Resilient Coding List Decoding AdaBoost Zero Knowledge Machine learningHardness amp.

12. Emerging Challenges • Theory of networked computation • Security and privacy • Incentives, pricing and sharing • Reliable communication • Massive distributed data sets • Ties to physical and biological sciences: • Statistical physics. • Quantum computing • Computational biology • Formal methods for reliable systems.

13. Theory of Networked Computation • Emergence of large networks (e.g. the Web) is profound shift in focus of CS. • Networks built, operated and used by parties w/ diverse interests and varying degrees of cooperation and competition. • Challenges: build and manage large systems consisting of autonomous parties. • Ensure rights of individuals and full and fair exploitation of shared resources.

14. Internet Algorithmics • Emerged with the spread of the Web. • Produced significant results on • Search and information retrieval • Network protocols • Error correction • Peer-to-peer networks • E-commerce • Internet-based auctions • Mechanism design • Massive distributed data sets.

15. Theory of Networked Computation: Agenda • Theoretical complement to GENI Initiative and Cyber-infrastructure program. • Close in spirit to Patterson’s SPUR manifesto:Security, Privacy, Usability, Reliability.

16. Security and Privacy • Users today invoke complex financial interactions with as single click. • Current design of the Internet based on trust. Inadequate protection against worms, viruses, spam and identity theft. • Must ensure appropriate use of information by dynamic and potentially large set of authorized users.

17. Formal Models of Security • Security can not be tested by experimentation or simulation. • We need quantitative measures of security with respect to realistic models of user behavior. • Past TCS work: cryptographic primitives (RSA, Diffie-Hellman, DES), protocols (signatures, e-commerce, secure interactions), study of protocol composition.

18. Security: Ongoing TCS Work • Expand protocol design to address scale, complexity and interactivity of modern environment. • Use economic theory to obtain security through positioning incentives • New techniques for sanitizing public data, traceback, intrusion detection, etc..

19. Incentives, Pricing and Sharing • Networks are built, operated and used by multiple parties with diverse goals and interests. • Algorithmic distributed mechanism design studies economic mechanisms that induce globally efficient behavior in self-interested agents. • Builds on algorithms, economic theory and game theory. • Areas of study: auctions, routing, congestion control, caching, border gateway protocol, pricing of multicast, network design, price of anarchy.

20. Massive Distributed Data Sets • Robust trends in IT: ever-decreasing cost of datastorage, ever-increasing ubiquity of computers and networks, accelerating deployment of sensornetworks and surveillance systems. • New computational models: data streaming, external memory and cache oblivious models, sampling, property testing, sublinear time algorithms. • Randomization and approximation are essential.

21. Massive Data Sets: Challenges • Data replication, placement, access and persistence. • Security and privacy, strategic and adversarial behavior, complex data formats (images, video, audio) • Personalized search, complex queries, full-text search, defenses against adversarial behavior by web page owners.

22. Reliable Storage and Communication • Maintaining integrity of data is a classical challenge to computing. • Modern issues: • Explosion in amount of data • Radical differences in nature of communication and storage media • Communication medium = Internet • Storage medium = Worldwide Web

23. Reliable Storage & Communications: TCS Achievements & Challenges • Achievements: ability to correct more errors, faster error-correction algorithms, rateless codes, checkable codes, list decoding, computationally bounded channels. • Challenges: more powerful error-correction techniques, ultra-fast decoding, malicious errors, integration with network protocols such as multicast. • Connections with probabilistically checkable proofs, cryptographic protocols, pseudorandom number generation.

24. Complexity Theory of Networked Computation • Needed: A theory of the fundamental limits of networked computation. • How is a networked computational problem involving multiple agents specified? • What is meant by a correct solution? • Require formal models capturing massive scale, user self-interest, subnetwork autonomy, distributed control, network failures. • Must define computational resources and cost, reductions between problems, complexity classes, complete problems, intractable problems.

25. Formal Models of Reliable Systems • Classical approach to reliability is simulation and testing. • Detects errors only in late stages of development; coverage is only partial. • More principled approach: rigorous mathematical specification and formal verification of system behavior. • Need certified software with precise and well understood specifications. • Particularly critical in embedded systems and autonomous medical applications.

26. Role of Logic Logic provides languages for formalizing requirements: • Floyd-Hoare logic for sequential programs • Temporal and fixpoint logics for reactive programs • Logics tailored for authentication and security properties of crypto protocols. • Led to standardized industrial-strength formal specification languages.

27. Role of Automata Theory • Model checker SPIN uses linear temporal logic as requirement language and an automata-theoretic model checking algorithm. • Research on timed and hybrid automata provides foundation for the emerging area of embedded systems.

28. Role of Decision Procedures • Modern solvers for propositional satisfiability used routinely on industrial-scale problems with hundreds of thousands of variables. • Symbolic fixpoint evaluation research led to industrial interest in model checking. • Decision procedures are continually being refined and improved for use in verification tools.

29. TCS Connections with Biology and the Physical Sciences • Statistical physics • Quantum computation • Computational Biology

30. Statistical Physics • Studies macroscopic properties of large systems involving simple microscopic components undergoing local interactions. Examples: freezing of water, ferromagnetism. • CS analogy: global properties of WWW emerge from local interactions; structure of complex combinatorial problems derives from local constraints. • Statistical physics studies random interactions; TCS studies algorithms on random structures.

31. Phase Transitions and Sharp Thresholds • Infinitesimal change in the parameters governing local interactions causes a drastic change in macroscopic behavior: • Physics: transformation from water to steam. • CS: random satisfiability instances switch from easy to hard when ratio of clauses to variables passes a critical value.

32. Cross-Fertilization • Spin glasses are fluid at high temperatures but at lower temperatures have many clusters of stable configurations. Similarly, constraint satisfaction problems with sparse constraints are fluid, but with dense constraints get stuck in suboptimal solutions. • Algorithmic paradigm based on this analogy has been spectacularly successful. • Markov Chain Monte Carlo used in physics as model of evolution of a physical system, and in CS as technique for approximation algorithms.

33. Quantum Computation • Quantum mechanics holds the promise of exponentially faster computers and perfectly secure communication channels. • Large numbers can be factored rapidly, allowing RSA to be broken. • To realize quantum computers, must guard against decoherence, the dissipation of quantum information into the environment.

34. Challenges for Theory of Quantum Computation • Defeat decoherence using quantum error-correcting codes. • Understand structure of problems that can be solved exponentially faster on quantum computers than on classical ones. • Use quantum computation as a test of the validity of quantum mechanics.

35. Revolution in Biology • Sequencing of human genome is a landmark event in history of science. • Biology is becoming a quantitative, information-based science. • Goals: • Detailed, predictive model of how cells work at molecular level. • Understand mechanisms of cancer, global organization of physiological systems, processes of development from embryo to complex organism • Tailor therapy to genetic makeup of individuals.

36. Role of Algorithms in Computational Biology • Understand the information hidden in the genome. • Construct mathematical models of complex cellular processes. • Extract patterns from large biological data sets. • Determine associations between genetic variation and disease.