About
I am Professor of Informatics and Computing at Indiana University Luddy School, where I direct the Applied Intelligence Laboratory (AIL). My research concerns representation, quantitative reasoning and artificial intelligence (AI) for the long tail of complex, real-world problems that involve data that is messy, incomplete, and spread across sources. These methods are applied in drug discovery, healthcare, emergency response, economics, cybersecurity and defense. I have also launched several successful startups (staring age 12!) and am active in the Southern Indiana startup scene (yes, there is a startup scene here).
I trained as a computer scientist, completing a PhD in computer-aided drug discovery and parallel processing. I spent the first part of my career in the pharmaceutical industry, leading scientific computing teams in computational chemistry and molecular modeling. I came to Indiana University in 2005 from that industry background, which still shapes how I work: I am equally interested in whether something functions in the field and whether it is elegant in theory.
For over fifteen years I practised as a volunteer EMT, search and rescue technician, and emergency manager, which gave me a new lens on technology and data in critical decision making, as well as the human consequences of tech-enabled decisions. The gap between the data that exists and the decision that has to be made is what most of my work is about.
I love creating and exploring new ideas and possibilities. I am a persistent experimenter in all areas of my life, and I read widely outside my field. I write about AI and its wider consequences at Prior Shift, and consult in AI and data science. Originally from the UK; based in Bloomington, Indiana.
Affiliations
DIRECTOR
Foundational methods for hard, high-consequence problems
Develops and deploys foundational quantitative reasoning and AI approaches to high-consequence, problems in a framework that drastically reduces time to impact, in partnership with external research sponsors and student teams
DIRECTOR
Artificial intelligence for command, control and communications, and critical infrastructure technologies for first responders, cybersecurity and defense. Spanning basic research through to operational application.
Core faculty
Center of foundational and applied research in AI at Indiana University
Faculty fellow
Exploring applications in defense including international disaster response, safety, military medicine and situational awareness.
Faculty Fellow
Alongside faculty appointments in cybersecurity and data science at the Luddy School — the institutional home for the security applications of this work.
Lead Instructor
I am lead instructor or co-instructor for several applied project-based learning classes, including IU's Hacking for Defense program.
Research topics
For full publication list see my Google Scholar profile.
Integrating heterogeneous, often messy sources into a single object that permits inference: semantic frameworks, linked open data, ontological annotation, learned embeddings, and the topography of high-dimensional activity spaces. The representational choices made here determine what can subsequently be computed.
Vandeventer R, Imrem D, Wild DJ. Language Is an Insufficient Substrate for Quantitative Reasoning, and Consequential Domains Need Large Quantitative Models. arXiv:2609.12105 [cs.AI].
Gao Z, Fu G, Ouyang C, Tsutsui S, Liu X, Yang JJ, Gessner C, Foote B, Wild DJ, Ding Y, Yu Q. edge2vec: representation learning using edge semantics for biomedical knowledge discovery. BMC Bioinformatics 20:306.
Kulkarni VS, Wild DJ. An activity canyon characterization of the pharmacological topography. Journal of Cheminformatics 8:41.
Willighagen EL, Waagmeester A, Spjuth O, Ansell P, Williams AJ, Tkachenko V, Hastings J, Chen B, Wild DJ. The ChEMBL database as linked open data. Journal of Cheminformatics 5:23.
Kong X, Yu PS, Ding Y, Wild DJ. Meta path-based collective classification in heterogeneous information networks. Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM) 1567–1571.
Chen B, Dong X, Jiao D, Wang H, Zhu Q, Ding Y, Wild DJ. Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data. BMC Bioinformatics 11:255.
Inference across integrated biological, chemical and clinical evidence: estimating what is absent, treating the pattern of absence as evidence in its own right, and keeping provenance inspectable where ground truth is uncertain. Carried through, in the tuberculosis work, to wet-lab confirmed drug candidates.
Yang JJ, Goff A, Wild DJ, Ding Y, Annis A, Kerber R, Foote B, Passi A, Duerksen JL, London S, Puhl AC, Lane TR, Braunstein M, Waddell SJ, Ekins S. Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus. Tuberculosis 146:102500.
Yang JJ, Gessner CR, Duerksen JL, Biber D, Binder JL, Ozturk M, Foote B, McEntire R, Stirling K, Ding Y, Wild DJ. Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination. BMC Bioinformatics 23:37.
Yang JJ, Grissa D, Lambert CG, Bologa CG, Mathias SL, Waller A, Wild DJ, Jensen LJ, Oprea TI. TIGA: target illumination GWAS analytics. Bioinformatics 37(21):3865–3873.
Seal A, Wild DJ. NetPredictor: R and Shiny package to perform drug–target network analysis and prediction of missing links. BMC Bioinformatics 19:265.
Passi A, Rajput NK, Wild DJ, Bhardwaj A. RepTB: a gene ontology based drug repurposing approach for tuberculosis. Journal of Cheminformatics 10:24.
Seal A, Ahn Y-Y, Wild DJ. Optimizing drug–target interaction prediction based on random walk on heterogeneous networks. Journal of Cheminformatics 7:40.
Seal A, Yogeeswari P, Sriram D, OSDD Consortium, Wild DJ. Enhanced ranking of PknB inhibitors using data fusion methods. Journal of Cheminformatics 5:2.
Chen B, Ding Y, Wild DJ. Assessing drug target association using semantic linked data. PLoS Computational Biology 8(7):e1002574.
Wild DJ, Ding Y, Sheth AP, Harland L, Gifford EM, Lajiness MS. Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research. Drug Discovery Today 17:469–474.
Getting structure in front of a responder, commander or analyst in a form they can act on — and understanding the people on the other side of it: what information they actually need, how they experience vulnerability, and when such systems are and are not trusted.
Rolfson A, Rosen A, Jennings C, Wild DJ. Using semi-structured interviews to elicit information needs and flows for pre-incident planning and technology implementation in emergency management and response. Journal of Homeland Security and Emergency Management, submitted.
Obi I, Paul LJ, Liao W, Loukil M, Hayashi S, Comer M, Rogers CO, Wild DJ, Shih PC. Project APRED: a web-based data analytics platform for supporting community disaster resilience. Journal of Emergency Management 21(5):399–419.
Hill JJ, Wild DJ, Schmidt PM. Optimizing virtual health technologies through a performance-based readiness model: lessons from the field deployment of the National Emergency Tele-Critical Care Network. Military Medicine 188(Suppl 6):377–384.
Seberger JS, Obi I, Loukil M, Liao W, Wild DJ, Patil S. Speculative vulnerability: uncovering the temporalities of vulnerability in people's experiences of the pandemic. Proceedings of the ACM on Human-Computer Interaction 6(CSCW2):1–27.
Dubois E, Yuan X, Bennett Gayle D, Khurana P, Knight T, Laforce S, Turetsky D, Wild DJ. Socially vulnerable populations adoption of technology to address lifestyle changes amid COVID-19 in the US. Data and Information Management 6(2):100001.
The newest strand: applying the same graph and inference machinery where the adversary is active and the time budget is measured in seconds rather than months.
Vallabhaneni U, Cagwin CL, Wild DJ. SENTINEL-RL: offloading topological reasoning from LLM agents in the security operations center. arXiv:2609.04159 [cs.CR].
How organisations and teams actually adopt these methods: the process, the project management, and the education that has to accompany them.
Bentum S. Digital transformation strategies for applied science domains. Doctoral dissertation, Indiana University; supervised.
Saltz J, Hotz N, Wild DJ, Stirling K. Exploring project management methodologies used within data science teams.
Fox G, Maini S, Rosenbaum H, Wild DJ. Data science and online education. IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom).
Full list at Google Scholar.
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