David Kempe

David Kempe
Name	David Kempe
Occupation	Academic, Researcher
Alma mater	University of California, Berkeley; Massachusetts Institute of Technology
Known for	Graph theory, algorithms, social networks

David Kempe is a scholar known for contributions to algorithms, graph theory, and network science. His work spans theoretical computer science, algorithmic game theory, and applied studies of social influence, drawing on connections to computer science departments and interdisciplinary research centers. He has collaborated with researchers across universities and industry labs, contributing tools used in computational social science, recommendation systems, and distributed systems.

Early life and education

Kempe received his undergraduate and graduate training at prominent institutions. He completed degrees at the Massachusetts Institute of Technology, where he interacted with faculty in the Computer Science and Artificial Intelligence Laboratory and peers associated with the Theory of Computation community. He pursued doctoral work at the University of California, Berkeley, engaging with advisors and groups linked to the Algorithms and Complexity community, and participated in seminars with scholars affiliated with the Simons Institute for the Theory of Computing and the Mathematical Sciences Research Institute. During this period he attended conferences such as the ACM Symposium on Theory of Computing and the International Colloquium on Automata, Languages and Programming.

Academic career

Kempe has held faculty appointments and visiting positions at research universities and collaborated with industrial research labs. He served on faculty in departments connected to the School of Engineering at institutions that maintain ties to the Association for Computing Machinery and the Institute of Electrical and Electronics Engineers. His teaching included courses cross-listed with programs associated with the National Science Foundation and workshops at the NeurIPS and ICML communities. He has supervised graduate students who later joined teams at the Google Research, Microsoft Research, and the Facebook AI Research labs, and he has given invited talks at venues like the International Conference on Learning Representations and the Conference on Neural Information Processing Systems.

Research contributions

Kempe's publications address influence propagation, diffusion models, and algorithmic approaches to network processes. He formulated and analyzed models related to the independent cascade model and the linear threshold model in the context of influence maximization problems that intersect with work published in proceedings of the ACM SIGKDD and SIAM Journal on Computing. His algorithmic results connect to approximation techniques such as greedy algorithms analyzed using submodularity, which relate conceptually to results presented at the IEEE Symposium on Foundations of Computer Science and the European Symposium on Algorithms. He contributed theoretical bounds and hardness results tying into complexity classes discussed at the Complexity Theory conferences and workshops.

Beyond influence models, Kempe investigated graph partitioning and clustering algorithms with applications to social networks studied by the International World Wide Web Conference and the International Conference on Web and Social Media. His work on contagion and cascades informed empirical studies involving datasets from platforms like Twitter, Facebook, and LinkedIn, and he collaborated on interdisciplinary projects with researchers from the Stanford Network Analysis Project and the Santa Fe Institute. He has also explored mechanism design and strategic behavior in networks, aligning with topics in the Conference on Economics and Computation and interactions with scholars associated with the National Bureau of Economic Research.

Kempe's methods incorporate tools from probabilistic analysis, combinatorial optimization, and spectral graph theory, building on foundations linked to the Bell Labs tradition and methods taught in seminars at the Courant Institute of Mathematical Sciences. His collaborative network includes authors who presented related work at the International Conference on Very Large Data Bases and the Symposium on Discrete Algorithms.

Awards and honors

Kempe received recognition for contributions to algorithmic research and interdisciplinary impact. He has been awarded grants and fellowships from organizations such as the National Science Foundation and foundations that fund computational social science. He was invited to serve on program committees for flagship conferences including STOC, FOCS, and KDD, and he was a recipient of institutional teaching and mentoring awards at his home university. His work has been cited in award-winning papers at venues like NeurIPS and WWW.

Selected publications

- Kempe, D.; Kleinberg, J.; Tardos, É. "Maximizing the spread of influence through a social network." Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. - Kempe, D.; Kleinberg, J.; Tardos, É. "Influential nodes in a diffusion model for social networks." Presented at the ACM Symposium on Theory of Computing and published in SIAM Journal on Computing. - Kempe, D.; Tardos, É. "A study of algorithmic approaches to influence maximization and network optimization." Proceedings of the European Symposium on Algorithms. - Kempe, D.; Collaborators. "Algorithms for clustering and community detection in large networks." Proceedings of the Symposium on Discrete Algorithms. - Kempe, D.; Coauthors. "Strategic behavior and mechanism design in networks." Proceedings of the Conference on Economics and Computation.

Category:Computer scientists Category:Theoretical computer scientists Category:Network scientists