Ancestral maximum likelihood (AML) is a method that simultaneously reconstructs a phylogenetic tree and ancestral sequences from extant data (sequences at the leaves). The tree and ancestral sequences maximize the probability of observing the given data under a Markov model of sequence evolution, in which branch lengths are also optimized but constrained to take the same value on any edge across all sequence sites. AML differs from the more usual form of maximum likelihood (ML) in phylogenetics because ML averages over all possible ancestral sequences. ML has long been known to be statistically consistent -- that is, it converges on the correct tree with probability approaching 1 as the sequence length grows. However, the statistical consistency of AML has not been formally determined, despite informal remarks in a literature that dates back 20 years. In this short note we prove a general result that implies that AML is statistically inconsistent. In particular we show that AML can `shrink' short edges in a tree, resulting in a tree that has no internal resolution as the sequence length grows. Our results apply to any number of taxa.

We introduce a new phylogenetic reconstruction algorithm which, unlike most previous rigorous inference techniques, does not rely on assumptions regarding the branch lengths or the depth of the tree. The algorithm returns a forest which is guaranteed to contain all edges that are: 1) sufficiently long and 2) sufficiently close to the leaves. How much of the true tree is recovered depends on the sequence length provided. The algorithm is distance-based and runs in polynomial time.

We introduce a simple algorithm for reconstructing phylogenies from multiple gene trees in the presence of incomplete lineage sorting, that is, when the topology of the gene trees may differ from that of the species tree. We show that our technique is statistically consistent under standard stochastic assumptions, that is, it returns the correct tree given sufficiently many unlinked loci. We also show that it can tolerate moderate estimation errors.

We demonstrate the use of computational phylogenetic techniques to solve a central problem in inferential network monitoring. More precisely, we design a novel algorithm for multicast-based delay inference, i.e. the problem of reconstructing the topology and delay characteristics of a network from end-to-end delay measurements on network paths. Our inference algorithm is based on additive metric techniques widely used in phylogenetics. It runs in polynomial time and requires a sample of size only $\poly(\log n)$.

We prove and extend a conjecture of Kempe, Kleinberg, and Tardos (KKT) on the spread of influence in social networks.

A social network can be represented by a directed graph where the nodes are individuals and the edges indicate a form of social relationship. A simple way to model the diffusion of ideas, innovative behavior, or ``word-of-mouth'' effects on such a graph is to consider an increasing process of ``infected'' (or active) nodes: each node becomes infected once an activation function of the set of its infected neighbors crosses a certain threshold value. Such a model was introduced by KKT in~\cite{KeKlTa:03,KeKlTa:05} where the authors also impose several natural assumptions: the threshold values are (uniformly) random to account for our lack of knowledge of the true values; and the activation functions are monotone and submodular, i.e.~have ``diminishing returns.'' The monotonicity condition indicates that a node is more likely to become active if more of its neighbors are active, while the submodularity condition, indicates that the marginal effect of each neighbor is decreasing when the set of active neighbors increases.

For an initial set of active nodes $S$, let $\sigma(S)$ denote the expected number of active nodes at termination. Here we prove a conjecture of KKT: we show that the function $\sigma(S)$ is submodular under the assumptions above. We prove the same result for the expected value of any monotone, submodular function of the set of active nodes at termination.

In other words, our results demonstrate that ``local'' submodularity is preserved ``globally'' under diffusion processes. This is of natural computational interest, as many optimization problems have good approximation algorithms for submodular functions. In particular, our results coupled with an argument in~\cite{KeKlTa:03} imply that a greedy algorithm gives an $(1-1/e-\eps)$-approximation algorithm for maximizing $\sigma(S)$ among all sets $S$ of a given size. This result has important practical implications for many social network analysis problems, notably viral marketing.

The design of algorithms on complex networks, such as routing, ranking or recommendation algorithms, requires a detailed understanding of the growth characteristics of the networks of interest, such as the Internet, the web graph, social networks or online communities. To this end, preferential attachment, in which the popularity (or relevance) of a node is determined by its degree, is a well-known and appealing random graph model, whose predictions are in accordance with experiments on the web graph and several social networks. However, its central assumption, that the popularity of the nodes depends only on their degree, is not a realistic one, since every node has potentially some intrinsic quality which can differentiate its attractiveness from other nodes with similar degrees.

In this paper, we provide a rigorous analysis of preferential attachment with fitness, suggested by Bianconi and Barabasi and studied by Motwani and Xu, in which the degree of a vertex is scaled by its quality to determine its attractiveness. Including quality considerations in the classical preferential attachment model provides a much more realistic description of many complex networks, such as the web graph, and allows to observe a much richer behavior in the growth dynamics of these networks. Specifically, depending on the shape of the distribution from which the qualities of the vertices are drawn, we observe three distinct phases, namely a first-mover-advantage phase, a fit-get-richer phase and an innovation-pays-off phase. We precisely characterize the properties of the quality distribution that result in each of these phases and we compute the exact growth dynamics for each phase. The dynamics provide rich information about the quality of the vertices, which can be very useful in many practical contexts, including ranking algorithms for the web, recommendation algorithms, as well as the study of social networks. Furthermore, the mathematical techniques we introduce to establish these dynamics could be applicable to a wide variety of problems.

We establish the exact threshold for the reconstruction problem for a binary asymmetric channel on the b-ary tree, provided that the asymmetry is sufficiently small. This is the first exact reconstruction threshold obtained in roughly a decade. We discuss the implications of our result for Glauber dynamics, phylogenetic reconstruction, and so-called ``replica symmetry breaking'' in spin glasses and random satisfiability problems.

It is well known that in order to reconstruct a tree on $n$ leaves, sequences of length $\Omega(\log n)$ are needed. It was conjectured by M. Steel that for the CFN evolutionary model, if the mutation probability on all edges of the tree is less than $p^{\ast} = (\sqrt{2}-1)/2^{3/2}$ than the tree can be recovered from sequences of length $O(\log n)$. This was proven by the second author in the special case where the tree is ``balanced''. The second author also proved that if all edges have mutation probability larger than $p^{\ast}$ then the length needed is $n^{\Omega(1)}$. This ``phase-transition'' in the number of samples needed is closely related to the phase transition for the reconstruction problem (or extremality of free measure) studied extensively in statistical physics and probability.

Here we complete the proof of Steel's conjecture and give a reconstruction algorithm using optimal (up to a multiplicative constant) sequence length. Our results further extend to obtain optimal reconstruction algorithm for the Jukes-Cantor model with short edges. All reconstruction algorithms run in time polynomial in the sequence length.

The algorithm and the proofs are based on a novel combination of combinatorial, metric and probabilistic arguments.

In this paper, we study the problem of learning phylogenies and hidden Markov models. We call the Markov model nonsingular if all transtion matrices have determinants bounded away from $0$ (and $1$). We highlight the role of the nonsingularity condition for the learning problem. Learning hidden Markov models without the nonsingularity condition is at least as hard as learning parity with noise. On the other hand, we give a polynomial-time algorithm for learning nonsingular phylogenies and hidden Markov models.

Transient growth due to non-normality is investigated for the Taylor-Couette problem with counter-rotating cylinders as a function of aspect ratio $\eta$ and Reynolds number $Re$. For all $Re \leq 500$, transient growth is enhanced by curvature, i.e. is greater for $\eta < 1$ than for $\eta = 1$, the plane Couette limit. For fixed $Re < 130$ it is found that the greatest transient growth is achieved for $\eta$ between the Taylor-Couette linear stability boundary, if it exists, and one, while for $Re > 130$ the greatest transient growth is achieved for $\eta$ on the linear stability boundary. Transient growth is shown to be approximately 20% higher near the linear stability boundary at $Re = 310$, $\eta = 0.986$ than at $Re = 310$, $\eta = 1$, near the threshold observed for transition in plane Couette. The energy in the optimal inputs is primarily meridional; that in the optimal outputs is primarily azimuthal. Pseudospectra are calculated for two contrasting cases. For large curvature, $\eta = 0.5,$ the pseudospectra adhere more closely to the spectrum than in a narrow gap case, $\eta = 0.99$.

We consider a group of agents on a graph who repeatedly play the prisoner's dilemma game against their neighbors. The players adapt their actions to the past behavior of their opponents by applying the win-stay lose-shift strategy. On a finite connected graph, it is easy to see that the system learns to cooperate by converging to the all-cooperate state in a finite time. We analyze the rate of convergence in terms of the size and structure of the graph. [Dyer et al., 2002] showed that the system converges rapidly on the cycle, but that it takes a time exponential in the size of the graph to converge to cooperation on the complete graph. We show that the emergence of cooperation is exponentially slow in some expander graphs. More surprisingly, we show that it is also exponentially slow in bounded-degree trees, where many other dynamics are known to converge rapidly.

If someone is nice to you, you feel good and may be inclined to be nice to somebody else. This every day experience is borne out by experimental games: the recipients of an act of kindness are more inclined to help in turn, even if the person who benefits from their generosity is somebody else. This behavior, which has been called `upstream reciprocity', appears to be a misdirected act of gratitude: you help somebody because somebody else has helped you. Does this make any sense from an evolutionary or game theoretic perspective? In this paper, we show that upstream reciprocity alone does not lead to the evolution of cooperation. But upstream reciprocity can evolve and increase the level of cooperation if it is linked to either direct reciprocity or network reciprocity. We calculate the random chains of altruistic acts that are induced by upstream reciprocity. Our analysis shows that gratitude and other positive emotions, which increase the willingness to help others, can evolve in the competitive world of natural selection.

In this paper, we study the problem of learning phylogenies and hidden Markov models. We call the Markov model nonsingular if all transtion matrices have determinants bounded away from $0$ (and $1$). We highlight the role of the nonsingularity condition for the learning problem. Learning hidden Markov models without the nonsingularity condition is at least as hard as learning parity with noise. On the other hand, we give a polynomial-time algorithm for learning nonsingular phylogenies and hidden Markov models.

We provide a global heuristic for a class of bilevel programs with bilinear objectives and linear contraints. The algorithm relies on an interior point approach that can be seen as a combination of the smoothing and implicit programming techniques well-known to the MPEC litterature. But contrary to what is usually done there, here the focus is on global optimality. We tested the algorithm on large-scale instances of a network pricing problem, one of the main applications of this model. The results show that, on hard instances, the heuristic is a good alternative to exact solvers based on mixed 0-1 programming formulations.

Maximum likelihood is one of the most widely used techniques to infer evolutionary histories. Although it is thought to be intractable, a proof of its hardness has been lacking. Here, we give a short proof that computing the maximum likelihood tree is NP-hard by exploiting a connection between likelihood and parsimony observed by Tuffley and Steel.

In a series of recent works, Boyd, Diaconis, and their co-authors have introduced a semidefinite programming approach for computing the fastest mixing Markov chain on a graph of allowed transitions, given a target stationary distribution. In this paper, we show that standard mixing-time analysis techniques---variational characterizations, conductance, canonical paths---can be used to give simple, nontrivial lower and upper bounds on the fastest mixing time. To test the applicability of this idea, we consider several detailed examples including the Glauber dynamics of the Ising model---and get sharp bounds.

We consider the problem of maximizing the revenue raised from tolls set on the arcs of a transportation network, under the constraint that users are assigned to toll-compatible shortest paths. We first provide a polynomial algorithm with a worst-case guarantee of $O(\log m_T)$, where $m_T$ denotes the number of toll arcs. Next we show that the approximation is tight with respect to the linear programming relaxation by constructing examples for which the integrality gap is reached.

Transient growth due to non-normality is investigated for the Taylor-Couette problem with counter-rotating cylinders as a function of aspect ratio $\eta$ and Reynolds number $Re$. For all $Re \leq 500$, transient growth is enhanced by curvature, i.e. is greater for $\eta < 1$ than for $\eta = 1$, the plane Couette limit. For fixed $Re < 130$ it is found that the greatest transient growth is achieved for $\eta$ between the Taylor-Couette linear stability boundary, if it exists, and one, while for $Re > 130$ the greatest transient growth is achieved for $\eta$ on the linear stability boundary. Transient growth is shown to be approximately 20% higher near the linear stability boundary at $Re = 310$, $\eta = 0.986$ than at $Re = 310$, $\eta = 1$, near the threshold observed for transition in plane Couette. The energy in the optimal inputs is primarily meridional; that in the optimal outputs is primarily azimuthal. Pseudospectra are calculated for two contrasting cases. For large curvature, $\eta = 0.5,$ the pseudospectra adhere more closely to the spectrum than in a narrow gap case, $\eta = 0.99$.

We show that the function $h(x)=\prod_{i<j}(x_j-x_i)$ is harmonic for any random walk in $\R^k$ with exchangeable increments, provided the required moments exist. For the subclass of random walks which can only exit the Weyl chamber $W=\{x\colon x_1<x_2<\cdots<x_k\}$ onto a point where $h$ vanishes, we define the corresponding Doob $h$-transform. For certain special cases, we show that the marginal distribution of the conditioned process at a fixed time is given by a familiar discrete orthogonal polynomial ensemble. These include the Krawtchouk and Charlier ensembles, where the underlying walks are binomial and Poisson, respectively. We refer to the corresponding conditioned processes in these cases as the Krawtchouk and Charlier processes. In [O'Connell and Yor (2001b)], a representation was obtained for the Charlier process by considering a sequence of $M/M/1$ queues in tandem. We present the analogue of this representation theorem for the Krawtchouk process, by considering a sequence of discrete-time $M/M/1$ queues in tandem. We also present related results for random walks on the circle, and relate a system of non-colliding walks in this case to the discrete analogue of the circular unitary ensemble (CUE).

In this dissertation, we consider a number of inference problems related to Markov models on trees---mostly motivated by applications in evolutionary biology.

We begin with the ``ancestral reconstruction'' problem---also known simply as the reconstruction problem: Given the state at the leaves of the tree, how accurately can one guess the state at the root? Our main result in this area is a new condition for non-reconstructibility. More precisely, we extend a known bound, the Kesten-Stigum bound, to a more general class of Markov transition matrices than previously known, more specifically, roughly symmetric binary channels.

The second problem we consider is the ``phylogenetic reconstruction'' problem: Given samples of the leaf states, when can one infer efficiently the tree structure from which the data was generated? This is a central problem in biology where it corresponds to reconstructing the evolutionary history of a group of species from molecular sequences. Here we prove two main results. First, we show that a standard inference technique used in biology, maximum likelihood, is NP-hard, that is, computationally intractable on worst-case instances. Then, using a connection to the ancestral reconstruction problem, we show that, when the data is generated from a Markov model with sufficiently low mutation rates, the sample requirement for an accurate reconstruction drops significantly. Moreover, we show that this transition, conjectured by Mike Steel, is sharp.

The third problem we consider is the ``full reconstruction'' problem: Given samples of the leaf states, when can one reconstruct efficiently the full Markov model, that is, the tree structure as well as the edge transition matrices? Using a standard framework from computational learning theory, we give conditions on the transition matrices for the feasibility of an efficient reconstruction of the full model.

Finally, we consider a related model in networking, multicast delay inference. We give an efficient algorithm for reconstructing the routing tree and its delay parameters.