{"id": "urn:uuid:d4bca784-f267-4bcf-8e54-d1e7353a70fe", "type": ["VerifiableCredential", "OpenBadgeCredential"], "proof": {"type": "Ed25519Signature2020", "created": "2026-06-16T12:39:58Z", "proofValue": "z5rZYTcxd8PRBPDpdmiRFacEJh4yAAZXz3Eh5VKAr5vDp4toJj8iBbWiMcXvZWJA4tQw5rUAeQguD2udsQWiwB5rA", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkfwJEckNVbaLwbCETHMYkpfb7g2Nnvqn78ASyrYJfqvow#z6MkfwJEckNVbaLwbCETHMYkpfb7g2Nnvqn78ASyrYJfqvow"}, "issuer": {"id": "did:key:z6MkfwJEckNVbaLwbCETHMYkpfb7g2Nnvqn78ASyrYJfqvow", "name": "MIT Learn", "type": ["Profile"], "image": {"id": "https://learn.mit.edu/images/mit-red.png", "type": "Image", "caption": "MIT Learn logo"}}, "@context": ["https://www.w3.org/ns/credentials/v2", "https://purl.imsglobal.org/spec/ob/v3p0/context-3.0.3.json", "https://w3id.org/security/suites/ed25519-2020/v1"], "validFrom": "2026-06-16T12:39:58Z", "credentialSubject": {"type": ["AchievementSubject"], "identifier": [{"salt": "not-used", "type": "IdentityObject", "hashed": false, "identityHash": "Samuel Oladipupo ", "identityType": "name"}], "achievement": {"id": "https://learn.mit.edu/courses/course-v1:UAI_SOURCE+UAI.9", "name": "Model-Driven Prescriptive AI, Part 1", "type": ["Achievement"], "criteria": {"narrative": "- Describe the core components of optimization models:\u00a0decision variables,\u00a0objectives,\u00a0constraints, feasible regions, and optimal solutions.\r\n- Explain the differences between\u00a0linear,\u00a0integer, and\u00a0nonlinear optimization, and note when each class is most useful.\r\n- Formulate optimization models systematically to address\u00a0practical decision-making challenges.\r\n- Implement and solve optimization models\u00a0computationally, and analyze solutions to: quantify the\u00a0edge of optimization in practice, derive\u00a0insights for decision-making, test\u00a0what-if scenarios.\r\n- Outline how linear optimization is applied in\u00a0revenue management, particularly in\u00a0airline networks\u00a0with multiple flights and fare classes.\r\n- Summarize how\u00a0network optimization models\u00a0(shortest path, assignment, flows) capture\u00a0costs,\u00a0capacities, and\u00a0interdependencies\u00a0in complex systems.\r\n- Indicate how\u00a0multi-commodity flows\u00a0and\u00a0nutritional constraints\u00a0extend network models to\u00a0humanitarian challenges.\r\n- Recognize trade-offs such as\u00a0model complexity vs. tractability\u00a0and\u00a0procurement vs. transportation costs\u00a0in applied optimization."}, "description": "Samuel Oladipupo  has successfully completed all modules and earned a Course Certificate in Model-Driven Prescriptive AI, Part 1.", "achievementType": "Course"}, "activityEndDate": "2026-06-16T12:39:58Z", "activityStartDate": "2026-06-12T12:13:39Z"}}