Essays on Executive Search

My dissertation focuses on how the market values different attributes of top managers and the role of market intermediaries in shaping firm and individual outcomes. I draw on a unique dataset from an executive search firm, which allows me to track the progress of nearly 1,700 top managers who emerged as candidates for around 400 executive roles spanning multiple industries. In Chapter 2, I explore the mixed signals sent by the frequency of moves between employers and tenure with their current employer at the time a candidate is being considered for a senior management job with a new employer. I argue that while frequently changing employer may decrease a candidate’s attractiveness to potential employers by signaling that the candidate is a serial job hopper, it may also increase attractiveness by signaling the accumulation of a breadth of experience typically valued in top managers. Also, while potential employers may view longer tenure with a current employer as a negative signal regarding a candidate’s level of cultural flexibility and adaptability, it may also provide opportunities for upward mobility with the firm, which is a visible signal of competence. I find empirical support for these relationships. In Chapter 3, I use the topic modeling technique to parse job descriptions to understand how clients’ preferences affect candidate selection and explore gender prejudice. I find evidence that is line with role congruity theory, as firms are more likely to select male candidates when agentic qualities are emphasized. In Chapter 4, I investigate how employee mobility affects the broker (the executive search firm) who has facilitated the process, specifically in its ability to work for the firm that lost its employee to the broker’s client. The effect I find is positive and is stronger for poached firms that are located outside the city where the search firm is located, suggesting that the poaching event may help the search firm increase its saliency towards its potential clients. These studies together provide a better understanding of the executive labor market and the role of market intermediary

Weakly supervised pre-training for brain tumor segmentation using principal axis measurements of tumor burden

Peer reviewe

Theoretical results on bet-and-run as an initialisation strategy

Bet-and-run initialisation strategies have been experimentally shown to be beneficial on classical NP-complete problems such as the travelling salesperson problem and minimum vertex cover. We analyse the performance of a bet-and-run restart strategy, where k independent islands run in parallel for t1 iterations, after which the optimisation process continues on only the best-performing island. We define a family of pseudo-Boolean functions, consisting of a plateau and a slope, as an abstraction of real fitness landscapes with promising and deceptive regions. The plateau shows a high fitness, but does not allow for further progression, whereas the slope has a low fitness initially, but does lead to the global optimum. We show that bet-and-run strategies with non-trivial k and t1 are necessary to find the global optimum efficiently. We show that the choice of t1 is linked to properties of the function. Finally, we provide a fixed budget analysis to guide selection of the bet-and-run parameters to maximise expected fitness after t = k · t1 + t2 fitness evaluations

CryptOpt: Verified Compilation with Random Program Search for Cryptographic Primitives

Most software domains rely on compilers to translate high-level code to multiple different machine languages, with performance not too much worse than what developers would have the patience to write directly in assembly language. However, cryptography has been an exception, where many performance-critical routines have been written directly in assembly (sometimes through metaprogramming layers). Some past work has shown how to do formal verification of that assembly, and other work has shown how to generate C code automatically along with formal proof, but with consequent performance penalties vs. the best-known assembly. We present CryptOpt, the first compilation pipeline that specializes high-level cryptographic functional programs into assembly code significantly faster than what GCC or Clang produce, with mechanized proof (in Coq) whose final theorem statement mentions little beyond the input functional program and the operational semantics of x86-64 assembly. On the optimization side, we apply randomized search through the space of assembly programs, with repeated automatic benchmarking on target CPUs. On the formal-verification side, we connect to the Fiat Cryptography framework (which translates functional programs into C-like IR code) and extend it with a new formally verified program-equivalence checker, incorporating a modest subset of known features of SMT solvers and symbolic-execution engines. The overall prototype is quite practical, e.g. producing new fastest-known implementations for the relatively new Intel i9 12G, of finite-field arithmetic for both Curve25519 (part of the TLS standard) and the Bitcoin elliptic curve secp256k1

Learning to Sparsify Travelling Salesman Problem Instances

CPAIOR 2021: 18th International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, Vienna, Austria, 5 - 8 July 2021In order to deal with the high development time of exact and approximation algorithms for NP-hard combinatorial optimisation problems and the high running time of exact solvers, deep learning techniques have been used in recent years as an end-to-end approach to find solutions. However, there are issues of representation, generalisation, complex architectures, interpretability of models for mathematical analysis etc. using deep learning techniques. As a compromise, machine learning can be used to improve the run time performance of exact algorithms in a matheuristics framework. In this paper, we use a pruning heuristic leveraging machine learning as a pre-processing step followed by an exact Integer Programming approach. We apply this approach to sparsify instances of the classical travelling salesman problem. Our approach learns which edges in the underlying graph are unlikely to belong to an optimal solution and removes them, thus sparsifying the graph and significantly reducing the number of decision variables. We use carefully selected features derived from linear programming relaxation, cutting planes exploration, minimum-weight spanning tree heuristics and various other local and statistical analysis of the graph. Our learning approach requires very little training data and is amenable to mathematical analysis. We demonstrate that our approach can reliably prune a large fraction of the variables in TSP instances from TSPLIB/MATILDA (>85%) while preserving most of the optimal tour edges. Our approach can successfully prune problem instances even if they lie outside the training distribution, resulting in small optimality gaps between the pruned and original problems in most cases. Using our learning technique, we discover novel heuristics for sparsifying TSP instances, that may be of independent interest for variants of the vehicle routing problem.Science Foundation IrelandOpen access funding provided by SF