Alexander Herzog, Ph.D.Lecturer, School of Computing
Office: 227 McAdams Hall
I am a computational social scientist who specializes in using machine learning and natural language processing to study political and social problems. My work develops and applies scalable solutions to process and extract knowledge from large volumes of unstructured data, such as social media, speeches, scientific publications, and newspaper articles.
My current position is Lecturer in Computer Science in the School of Computing at Clemson University. I have extensive experience in teaching undergraduate computer science topics, including core introductory courses for CS majors and non-majors in C++ and Python, data science and machine learning, software development, agile project management, and professional development.
From 2017-2021, I was the founder and director of the School of Computing capstone program, an industry-sponsored software development experience for upper-level CS students for which I developed the curriculum and built a network of industry partners. During my time as the capstone director, I have secured more than $120,000 in industry funding and supervised more than 30 software development projects with clients such as Amazon, BMW, Boeing, IBM, NVIDIA, and SYNNEX/Microsoft.
Before joining the School of Computing, I received my Ph.D. in Political Science from New York University, taught at the London School of Economics, and conducted research in Clemson's Social Analytics Institute as a postdoc in Clemson's management information systems (MIS) group.
Hannah Béchara, Alexander Herzog, Slava Jankin Mikhaylov, and Peter John, “Transfer learning for topic labeling: Analysis of the UK House of Commons speeches 1935--2014”, Research & Politics, vol. 8, 2021. [ bib | pdf | replication files ]
Farah Alshanik, Amy Apon, Alexander Herzog, Ilya Safro, and Justin Sybrandt, “Accelerating text mining using domain-specific stop word lists”, in Proceedings of the International Workshop on Big Data Reduction, December 2020, Workshop held in conjunction with the 2020 IEEE International Conference on Big Data. [ bib | pdf | code ]
Alexander Herzog and Slava Jankin Mikhaylov, “Intra-cabinet politics and fiscal governance in times of austerity”, Political Science Research and Methods, vol. 8, July 2020. [ bib | pdf | replication files ]
Chris Gropp, Alexander Herzog, Ilya Safro, Paul Wilson, and Amy Apon, “Clustered latent Dirichlet allocation (CLDA) for scientific discovery”, in Proceedings of the 1st International Workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD), December 2019, Workshop held in conjunction with the 2019 IEEE International Conference on Big Data. [ bib | pdf | code ]
Sara Hagemann, Stefanie Bailer, and Alexander Herzog, “Signals to their parliaments: Governments' strategic use of votes and policy statements in the Council of the European Union”, Journal of Common Market Studies, vol. 57, no. 3, May 2019. [ bib | pdf ]
Justin Sybrandt, Angelo Carrabba, Alexander Herzog, and Ilya Safro, “Are abstracts enough for hypothesis generation?”, in Proceedings of the 2018 IEEE International Conference on Big Data, December 2018. [ bib | pdf | replication files ]
Brandon Posey, Christopher Gropp, Boyd Wilson, Boyd McGeachie, Sanjay Padhi, Alexander Herzog, and Amy Apon, “Addressing the challenges of executing a massive computational cluster in the cloud”, in Proceedings of the 18th Annual IEEE/ACM International Symposium in Cluster, Cloud, and Grid Computing (CCGrid 2018), May 2018. [ bib | pdf ]
Alexander Herzog and Slava Jankin Mikhaylov, “Database of parliamentary speeches in Ireland, 1919-2013”, in Proceedings of the 1st IEEE International Conference on the Frontiers and Advances in Data Science, 2017. [ bib | pdf | data ]
Brandon Posey, Christopher Gropp, Alexander Herzog, and Amy Apon, “Automated cluster provisioning and workflow management for parallel scientific applications in the cloud”, in Proceedings of the 10th Workshop on Many-Task Computing on Clouds, Grids, and Supercomputers (MTAGS), 2017, Workshop held in conjunction with the 2017 International Conference for High Performance Computing, Networking, Storage and Analysis. [ bib | pdf ]
Neela Avudaiappan, Alexander Herzog, Sneha Kadam, Yuheng Du, Jason Thatcher, and Ilya Safro, “Detecting and summarizing emergent events in microblogs and social media streams by dynamic centralities”, in Proceedings of the 2017 IEEE International Conference on Big Data, 2017. [ bib | pdf | code ]
Yuheng Du, Alexander Herzog, Andre Luckow, Ramu Nerella, Christopher Gropp, and Amy Apon, “Representativeness of latent Dirichlet allocation topics estimated from data samples with application to Common Crawl”, in Proceedings of the 2017 IEEE International Conference on Big Data, 2017. [ bib | pdf ]
Benjamin E. Lauderdale and Alexander Herzog, “Measuring political positions from legislative speech”, Political Analysis, vol. 24, no. 3, pp. 374--394, 2016. [ bib | pdf | online appendix | replication files ]
Alexander Herzog and Kenneth Benoit, “The most unkindest cuts: Speaker selection and expressed government dissent during economic crisis”, Journal of Politics, vol. 77, no. 4, pp. 1157--1175, 2015. [ bib | pdf | online appendix | replication files ]
Alexander Herzog and Joshua A. Tucker, “The dynamics of support: The winners-losers gap in attitudes towards EU membership in post-communist countries”, European Political Science Review, vol. 2, no. 2, pp. 235--267, 2010. [ bib | pdf | replication files ]
Franz Urban Pappi, Alexander Herzog, and Ralf Schmitt, “Koalitionssignale und die Kombination von Erst- und Zweitstimme bei den Bundestagswahlen 1953 bis 2005 [Coalition signals and the combination of first and second vote in Bundestag elections 1953 to 2005]”, Zeitschrift für Parlamentsfragen, vol. 37, no. 3, pp. 493--513, 2006. [ bib | pdf ]
Franz Urban Pappi, Axel Becker, and Alexander Herzog, “Regierungsbildung in Mehrebenensystemen: zur Erklärung der Koalitionsbildung in den deutschen Bundesländern [Government formation in multilevel systems: Explaining coalition building in the German Länder]”, Politische Vierteljahresschrift, vol. 46, no. 3, pp. 432--458, 2005. [ bib | pdf ]
Alexander Herzog, Slava Jankin Mikhaylov, and Liam Weeks, “Ireland: The paradox of a weak legislature in a candidate-centered system”, in Politics of Legislative Debate, Hanna Back, Marc Debus, and Jorge M. Fernandes, Eds. Oxford University Press, 2021. [ bib | link to book ]
Liam Weeks, Mícheál Ó Fathartaigh, Slava Jankin Mikhaylov, and Alexander Herzog, “'More than words': A quantitative text analysis of the treaty debates”, in The Treaty: Debating and Establishing the Irish State, Liam Weeks and Mícheál Ó Fathartaigh, Eds., pp. 206--224. Irish Academic Press, 2018, (Winner of the Irish Political Studies Best Paper Prize 2016). [ bib | link to book ]
Kenneth Benoit and Alexander Herzog, “Text analysis: Estimating policy preferences from written and spoken words”, in Analytics, Policy and Governance, Jennifer Bachner, Kathryn Wagner Hill, and Benjamin Ginsberg, Eds., pp. 137--159. Yale University Press, 2017. [ bib | link to book ]
Continuation of CPSC 1010. Continued emphasis on problem solving and program development techniques. Examines typical numerical, nonnumerical, and data processing problems. Introduces basic data structures. Introduces object-oriented programming in C++.
Introduces the central idea of computer science, instill ideas and practices of computational thinking. Students engage in creative activities to learn how computing can change the world.
Covers applied methods and techniques in Data Science, including data scraping, cleaning, and storage; technical issues when working with different types of data; basic topics in machine learning; parallel and distributed computing; cloud computing; data visualization; and ethical issues in Data Science.
Considers the impact of computing system development on society. Discusses ethical issues in the design and development of computer software. Students discuss standards for professional behavior, the professional's responsibility to the profession, and techniques for maintaining currency in a dynamic field.
Provides upper division computer science students a value-added capstone learning experience. Students work in teams of three to four individuals on a semester-long, team-based software development project with a real-world sponsoring client, using agile project management.
Office: 227 McAdams Hall