• Network Economics: Analyzing the strategic interactions over the network among individuals
• Mechanism Design and Game Theory:  Theory and applications of Bayesian mechanism design, including optimal auctions in multidimensional settings, auction design for budget constrained agents, computationally efficient approximations of optimal auctions, and mechanism design for two sided matching markets.
• Algorithm Design: Designing computationally efficient algorithms (approximation and randomized) for problems in transportation,
  online/display advertising and energy monitoring applications.

I received my Ph.D. in Dec 2009 from the Computer Science Department at University of Maryland College Park. I was  advised by Professor Samir Khuller.  Before joining MIT,  I was a postdoctoral fellow in the Computer Science Department at Northwestern University from Jan 2010 until August 2011.
I received my B.Sc. from Sharif University of Technology in Tehran.

• Network Security and Contagion, Daron Acemoglu, Azarakhsh Malekian, Asu Ozdaglar, 2012 [Abstract]

  Abstract: This work develops a theoretical model of investments in security in a network of interconnected agents. The network connections introduce the possibility of cascading failures depending on exogenous or endogenous attacks and the profile of security investments by the agents. The general presumption in the literature, based on intuitive arguments or analysis of symmetric networks, is that because security investments create positive externalities on other agents, there will be underinvestment in security. We show that this reasoning is incomplete because of a first-order economic force: security investments are also strategic substitutes. In a general (non-symmetric) network, this implies that underinvestment by some agents will encourage overinvestment by others. We demonstrate by means of examples that not only there will be overinvestment by some agents but also aggregate probabilities of infection can be lower in equilibrium than in the social optimum. We then provide sufficient conditions for underinvestment. This requires both sufficiently convex cost functions (just convexity is not enough) and networks that are either symmetric or locally tree-like (i.e., either trees or in the case of stochastic networks, without local cycles with high probability). We also characterize the impact of network structure on equilibrium and optimal investments. Finally, we show that when the attack location is endogenized (by assuming that the attacker chooses a probability distribution over the location of the attack in order to maximize damage), there is another reason for overinvestment: greater investment by an agent shifts the attack to other parts of the network.

• Competitive Equilibrium in Two Sided Matching Markets with General Utility Functions, Saeed Alaei, Kamal Jain, Azarakhsh Malekian, 2011 [Abstract]

  Abstract: In this paper, we study the class of competitive equilibria in two sided matching markets with general (non-quasilinear) utility functions. Mechanism design in general non-quasilinear setting is one of the biggest challenges in mechanism design. Almost all of the existing work on general non-quasilinear utility function have resorted to non-constructive proofs based on fixed point theorems or discretization. In this paper, we give the first direct characterization of competitive equilibria in such markets. Our approach is constructive and solely based on induction. Our characterization reveals striking similarities between the payments at the lowest competitive equilibrium for general utilities and VCG payments for quasilinear utilities. We also show that the mechanism that outputs the lowest competitive equilibrium is group strategyproof. We also present a class of price discriminating truthful mechanisms for selling heterogeneous goods to unit-demand buyers with general utility functions and from that we derive a natural welfare maximizing mechanism for ad-auctions that combines pay per click and pay per impression advertisers with general utility functions.

•  Bayesian Optimal Auctions via Multi- to Single-agent Reduction, Saeed Alaei, Hu Fu, Nima Haghpanah, Jason Hartline, Azarakhsh Malekian, 2012 [Abstract]

  Abstract:

  We study an abstract optimal auction problem for a single good or service. This problem includes environments where agents have budgets, risk preferences, or multi-dimensional preferences over several possible configurations of the good (furthermore, it allows an agent's budget and risk preference to be known only privately to the agent). A single-agent problem is to optimize a given objective subject to a constraint on the maximum probability with which each type is allocated, a.k.a., an allocation rule. Our approach is a reduction from multi-agent mechanism design problem to collection of single-agent problems. We focus on maximizing revenue, but our results can be applied to other objectives (e.g., welfare).
  An optimal multi-agent mechanism can be computed by a linear/convex program on interim allocation rules by simultaneously optimizing several single-agent mechanisms subject to joint feasibility of the allocation rules. For single-unit auctions, [Border91]  showed that the space of all jointly feasible interim allocation rules for n agents is a D-dimensional convex polytope which can be specified by 2^D linear constraints, where D is the total number of all agents' types. Consequently, efficiently solving the mechanism design problem requires a separation oracle for the feasibility conditions and also an algorithm for ex-post implementation of the interim allocation rules. We show that the polytope of jointly feasible interim allocation rules is the projection of a higher dimensional polytope which can be specified by only O(D) linear constraints. Furthermore, our proof shows that finding a preimage of the interim allocation rules in the higher dimensional polytope immediately gives an ex-post implementation

•  Baysian Algorithmic Mechanism Design: Multi Dimensional Setting, Jason Hartine, Robert Kleinberg, Azarakhsh Malekian, 2011 [Abstract]

  Abstract: Even without strategizing on the part of participants, optimally allocating cellphone spectrum, advertisements on the Internet, and landing slots at airports is intractable. When the participants may strategize, not only must the optimizer deal with complex feasibility constraints but complex incentive constraints. The main approach for dealing with complex incentive constraints is the Vickrey-Clarke-Groves (VCG) mechanism. Unfortunately, finding the optimal allocation is the first step of the VCG mechanism, and moreover, this step cannot
  generally be replaced by a non-optimal heuristic or approximation algorithm without compromising the incentive properties of the mechanism.
  
  We give a very simple approach for constructing mechanisms from ad hoc algorithms. Our approach is based on calculating a maximum weighted matching in the type-space of each agent; such a calculation can be performed quickly when the type-space of each agent is reasonably sized. Furthermore, this calculation is performed independently for each agent and therefore scales well with the number of agents. Approximately: the mechanisms we give are Bayesian incentive compatible (meaning: truthful revelation is a Bayes-Nash equilibrium) and the expected welfare of the mechanism constructed is at least that of the original ad hoc algorithm.

• You can also see a more complete list of my publications here.