BMI 702, Spring 24

_config.yml

@@ -29,7 +29,7 @@ heading_anchors: true
 permalink: pretty
 aux_links:
   Canvas:
-    - 'https://canvas.harvard.edu/courses/117878'
+    - 'https://canvas.harvard.edu/courses/134015'
   Harvard DBMI:
     - 'https://dbmi.hms.harvard.edu'
   Zitnik Lab:

_modules/week-01.md

@@ -4,10 +4,10 @@ title: Week 1
 
 Course overview and introduction to biomedical AI
 
-Jan 23
+Jan 25
 : **Lecture**{: .label .label-purple }[What makes biomedical problems unique](/BMI702/lectures/week01)
-  : [Slides](/BMI702/assets/zitnik-BMI702-L1.pdf), [Reading List](/BMI702/lectures/week01)
+  : [Slides](#), [Reading List](/BMI702/lectures/week01)
 
-Jan 24
-: **Quiz**{: .label .label-green }[Week 2 pre-class quiz](#) (due Jan 29)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Jan 26
+: **Quiz**{: .label .label-green }[Week 2 pre-class quiz](#) (due Feb 1)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-02.md

@@ -4,10 +4,10 @@ title: Week 2
 
 Clinical research using EHR data, subtype discovery, disease diagnosis and prognosis prediction
 
-Jan 30
+Feb 1
 : **Module 1**{: .label .label-blue }**Lecture**{: .label .label-purple }[Clinical AI Part I](/BMI702/lectures/module1/week02)
-  : [Slides](/BMI702/assets/zitnik-BMI702-L2.pdf), [Reading List](/BMI702/lectures/module1/week02)
+  : [Slides](#), [Reading List](/BMI702/lectures/module1/week02)
 
-Jan 31
-: **Quiz**{: .label .label-green }[Week 3 pre-class quiz](#) (due Feb 5)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Feb 2
+: **Quiz**{: .label .label-green }[Week 3 pre-class quiz](#) (due Feb 8)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-03.md

@@ -4,10 +4,10 @@ title: Week 3
 
 Multi-institutional EHR systems, transfer learning, federated learning, clinical workflows
 
-Feb 6
+Feb 8
 : **Module 1**{: .label .label-blue }**Lecture**{: .label .label-purple }[Clinical AI Part II](/BMI702/lectures/module1/week03)
-  : [Slides](/BMI702/assets/zitnik-BMI702-L3.pdf), [Reading List](/BMI702/lectures/module1/week03)
+  : [Slides](#), [Reading List](/BMI702/lectures/module1/week03)
 
-Feb 7
-: **Quiz**{: .label .label-green }[Week 4 pre-class quiz](#) (due Feb 12)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Feb 9
+: **Quiz**{: .label .label-green }[Week 4 pre-class quiz](#) (due Feb 15)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-04.md

@@ -4,10 +4,10 @@ title: Week 4
 
 Interpretability and explainability in biomedical AI
 
-Feb 13
+Feb 15
 : **Module 2**{: .label .label-blue }**Lecture**{: .label .label-purple }[Trustworthy AI Part I](/BMI702/lectures/module2/week04)
-  : [Slides](/BMI702/assets/zitnik-BMI702-L4.pdf), [Reading List](/BMI702/lectures/module2/week04)
+  : [Slides](#), [Reading List](/BMI702/lectures/module2/week04)
 
-Feb 13
-: **Quiz**{: .label .label-green }[Week 5 pre-class quiz](#) (due Feb 26)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Feb 16
+: **Quiz**{: .label .label-green }[Week 5 pre-class quiz](#) (due Feb 22)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-05.md

@@ -4,14 +4,14 @@ title: Week 5
 
 Bias and fairness in biomedical AI
 
-Feb 27
+Feb 22
 : **Module 2**{: .label .label-blue }**Lecture**{: .label .label-purple }[Trustworthy AI Part II](/BMI702/lectures/module2/week05)
-  : [Slides](/BMI702/assets/zitnik-BMI702-L5.pdf), [Reading List](/BMI702/lectures/module2/week05)
+  : [Slides](#), [Reading List](/BMI702/lectures/module2/week05)
 
-Feb 28
-: **Quiz**{: .label .label-green }[Week 6 pre-class quiz](#) (due Mar 5)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Feb 23
+: **Quiz**{: .label .label-green }[Week 6 pre-class quiz](#) (due Feb 29)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)
 
-Mar 1
-: **PSet released**{: .label .label-yellow }[PSet 1: Bias, explainability, and fairness](#)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Feb 23
+: **PSet released**{: .label .label-yellow }[PSet 1: Bias, trustworthiness, and fairness](#)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-06.md

@@ -4,10 +4,10 @@ title: Week 6
 
 Foundations of geometric deep learning, graph representation learning, link prediction, node classification, graph clustering, graph classification, semi-supervised learning, label propagation, network medicine, disease modules and endotypes
 
-Mar 6
+Feb 29
 : **Module 3**{: .label .label-blue }**Lecture**{: .label .label-purple }[Biomedical graph learning Part I](/BMI702/lectures/module3/week06)
-  : [Slides](/BMI702/assets/zitnik-BMI702-L6.pdf), [Reading List](/BMI702/lectures/module3/week06)
+  : [Slides](#), [Reading List](/BMI702/lectures/module3/week06)
 
-Mar 7
-: **Quiz**{: .label .label-green }[Week 7 pre-class quiz](#) (due Mar 12)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Mar 1
+: **Quiz**{: .label .label-green }[Week 7 pre-class quiz](#) (due Mar 7)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-07.md

@@ -4,14 +4,14 @@ title: Week 7
 
 Machine learning with heterogeneous graphs, multimodal learning, graph neural networks, knowledge graph embeddings, reasoning over knowledge graphs
 
-Mar 20
+Mar 7
 : **Module 3**{: .label .label-blue }**Lecture**{: .label .label-purple }[Biomedical graph learning Part II](/BMI702/lectures/module3/week07)
-  : [Slides - Part 1](/BMI702/assets/zitnik-BMI702-L7-Part-1.pdf), [Slides - Part 2](/BMI702/assets/li-BMI702-L7-Part-2.pdf), [Reading List](/BMI702/lectures/module3/week07)
+  : [Slides - Part 1](#), [Slides - Part 2](#), [Reading List](/BMI702/lectures/module3/week07)
 
-Mar 21
-: **Quiz**{: .label .label-green }[Week 8 pre-class quiz](#) (due Mar 26)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Mar 8
+: **Quiz**{: .label .label-green }[Week 8 pre-class quiz](#) (due Mar 21)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)
 
-Mar 22
-: **PSet due**{: .label .label-yellow }[PSet 1: Bias, explainability, and fairness](#)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Mar 8
+: **PSet due**{: .label .label-yellow }[PSet 1: Bias, trustworthiness, and fairness](#)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-08.md

@@ -4,14 +4,14 @@ title: Week 8
 
 Foundations of natural language processing and understanding
 
-Mar 27
+Mar 21
 : **Module 4**{: .label .label-blue }**Lecture**{: .label .label-purple }[Medical language modeling Part I](/BMI702/lectures/module4/week08)
-  : [Slides](/BMI702/assets/li-BMI702-L8.pdf), [Reading List](/BMI702/lectures/module4/week08)
+  : [Slides](#), [Reading List](/BMI702/lectures/module4/week08)
 
-Mar 28
-: **Quiz**{: .label .label-green }[Week 9 pre-class quiz](#) (due Apr 2)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Mar 22
+: **Quiz**{: .label .label-green }[Week 9 pre-class quiz](#) (due Mar 28)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)
 
-Mar 29
-: **PSet released**{: .label .label-yellow }[PSet 2: Biomedical networks and graph embeddings](#)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Mar 22
+: **PSet released**{: .label .label-yellow }[PSet 2: Knowledge graphs and geometric deep learning](#)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-09.md

@@ -4,10 +4,10 @@ title: Week 9
 
 Clinical trial site identification, patient trial matching, clinical trial recruitment
 
-Apr 3
+Mar 28
 : **Module 4**{: .label .label-blue }**Lecture**{: .label .label-purple }[Medical language modeling Part II](/BMI702/lectures/module4/week09)
-  : [Slides](/BMI702/assets/zitnik-BMI702-L9.pdf), [Reading List](/BMI702/lectures/module4/week09)
+  : [Slides](#), [Reading List](/BMI702/lectures/module4/week09)
 
-Apr 4
-: **Quiz**{: .label .label-green }[Week 10 pre-class quiz](#) (due Apr 9)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Apr 29
+: **Quiz**{: .label .label-green }[Week 10 pre-class quiz](#) (due Apr 4)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-10.md

@@ -4,14 +4,14 @@ title: Week 10
 
 Foundations of biomedical imaging, self-supervised learning, analysis of radiology images
 
-Apr 10
+Apr 4
 : **Module 5**{: .label .label-blue }**Lecture**{: .label .label-purple }[Biomedical imaging Part I](/BMI702/lectures/module5/week10)
-  : [Slides](/BMI702/assets/rajpurkar-BMI702-L10.pdf), [Reading List](/BMI702/lectures/module5/week10)
+  : [Slides](#), [Reading List](/BMI702/lectures/module5/week10)
 
-Apr 11
-: **Quiz**{: .label .label-green }[Week 11 pre-class quiz](#) (due Apr 16)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Apr 5
+: **Quiz**{: .label .label-green }[Week 11 pre-class quiz](#) (due Apr 11)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)
 
-Apr 12
-: **PSet due**{: .label .label-yellow }[PSet 2: Biomedical networks and graph embeddings](#)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Apr 5
+: **PSet due**{: .label .label-yellow }[PSet 2: Knowledge graphs and geometric deep learning](#)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-11.md

@@ -4,14 +4,14 @@ title: Week 11
 
 Multimodal learning, analysis of histopathology slides, quantitative pathology in cancer diagnosis and prognosis
 
-Apr 17
+Apr 11
 : **Module 5**{: .label .label-blue }**Lecture**{: .label .label-purple }[Biomedical imaging Part II](/BMI702/lectures/module5/week11)
-  : [Slides](https://drive.google.com/file/d/1w6ED8dOF8DKFgFdFbmwPNYZRT9z2HwCY/view?usp=share_link), [Reading List](/BMI702/lectures/module5/week11)
+  : [Slides](#), [Reading List](/BMI702/lectures/module5/week11)
 
-Apr 18
-: **Quiz**{: .label .label-green }[Week 12 pre-class quiz](#) (due Apr 23)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Apr 12
+: **Quiz**{: .label .label-green }[Week 12 pre-class quiz](#) (due Apr 18)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)
 
-Apr 19
+Apr 12
 : **PSet released**{: .label .label-yellow }[PSet 3: Biomedical imaging methods and applications](#)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-12.md

@@ -4,10 +4,10 @@ title: Week 12
 
 Overview of drug discovery and development, AI-guided drug design, small-molecule generation, molecule optimization, identification and characterization of therapeutic targets, high-throughput chemical and genetic perturbations
 
-Apr 24
+Apr 18
 : **Module 6**{: .label .label-blue }**Lecture**{: .label .label-purple }[Therapeutic science and drug discovery Part I](/BMI702/lectures/module6/week12)
-  : [Slides](/BMI702/assets/zitnik-BMI702-L12.pdf), [Reading List](/BMI702/lectures/module6/week12)
+  : [Slides](#), [Reading List](/BMI702/lectures/module6/week12)
 
-Apr 25
-: **Quiz**{: .label .label-green }[Week 13 pre-class quiz](#) (due Apr 30)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Apr 19
+: **Quiz**{: .label .label-green }[Week 13 pre-class quiz](#) (due Apr 25)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-13.md

@@ -4,14 +4,14 @@ title: Week 13
 
 Label-efficient learning, few-shot learning, biomarker discovery, indication inference, drug repurposing, adverse event prediction
 
-May 1
+Apr 25
 : **Module 6**{: .label .label-blue }**Lecture**{: .label .label-purple }[Therapeutic science and drug discovery Part II](/BMI702/lectures/module6/week13)
-  : [Slides](/BMI702/assets/zitnik-BMI702-L13.pdf), [Reading List](/BMI702/lectures/module6/week13)
+  : [Slides](#), [Reading List](/BMI702/lectures/module6/week13)
 
-May 2
-: **Quiz**{: .label .label-green }[Week 14 pre-class quiz](#) (due May 7)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+Apr 26
+: **Quiz**{: .label .label-green }[Week 14 pre-class quiz](#) (due May 2)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)
 
-May 3
+Apr 26
 : **PSet due**{: .label .label-yellow }[PSet 3: Biomedical imaging methods and applications](#)
-  : [Canvas](https://canvas.harvard.edu/courses/117878)
+  : [Canvas](https://canvas.harvard.edu/courses/134015)

_modules/week-14.md

@@ -4,7 +4,7 @@ title: Week 14
 
 Introduction to ethical frameworks, data privacy, regulation and liability aspects of AI
 
-May 8
+May 2
 : **Lecture**{: .label .label-purple }[Ethical and legal considerations for biomedical AI ](/BMI702/lectures/week14)
-  : [Slides](/BMI702/assets/gerke-BMI702-L14.pdf), [Reading List](/BMI702/lectures/week14)
+  : [Slides](#), [Reading List](/BMI702/lectures/week14)
 

_schedules/weekly.md

@@ -23,32 +23,27 @@ timeline:
 schedule:
   - name: 
   - name: Monday
-    events:
-      - name: Lecture
-        start: 1:00 PM
-        end: 3:00 PM
-        location: Armenise Modell 100A, 200 Longwood Ave
-      - name: OH Prof Zitnik
-        start: 3:00 PM
-        end: 4:00 PM
-        location: Countway 309, 10 Shattuck St
   - name: Tuesday
     events:
-      - name: OH Chen
-        start: 5:00 PM
-        end: 6:00 PM
-        location: Countway 423/424 Open area
+    - name: OH Ullanat
+      start: 5:00 PM
+      end: 6:00 PM
+      location: Countway 423/424
   - name: Wednesday
   - name: Thursday
     events:
-      - name: OH Huang
-        start: 3:00 PM
+      - name: Lecture
+        start: 2:00 PM
         end: 4:00 PM
-        location: Countway 423/424 Open area
+        location: Countway 403
+      - name: OH Prof Zitnik
+        start: 4:00 PM
+        end: 5:00 PM
+        location: Countway 309
   - name: Friday
     events:
       - name: OH Ektefaie
         start: 10:00 AM
         end: 11:00 AM
-        location: Countway 423/424 Open area
+        location: Countway 423/424
 ---

_staffers/marinka.md

@@ -4,5 +4,5 @@ role: Instructor
 email: [email protected]
 website: https://zitniklab.hms.harvard.edu
 photo: marinka.png
-office-hours: Mo, 3pm - 4pm
+office-hours: Thu, 4pm - 5pm
 ---

_staffers/varun.md

@@ -0,0 +1,8 @@
+---
+name: Varun Ullanat
+role: Staff
+email: [email protected]
+website: https://dbmi.hms.harvard.edu/people/varun-ullanat
+photo: varun.png
+office-hours: TBD
+---

_staffers/yasha.md

@@ -4,5 +4,5 @@ role: Staff
 email: [email protected]
 website: https://www.yashaektefaie.com
 photo: yasha.png
-office-hours: Fri, 10am - 11am
+office-hours: TBD
 ---

index.md

@@ -9,15 +9,15 @@ description: BMI 702 - Biomedical Artificial Intelligence
 # [BMI 702](https://dbmi.hms.harvard.edu/education/courses/bmi-702) | Biomedical Artificial Intelligence
 
 {: .mb-2 }
-Harvard - Foundations of Biomedical Informatics II, Spring 2023
+Harvard - Foundations of Biomedical Informatics II, Spring 2024
 {: .mb-0 .fs-6 .text-grey-dk-000 }
 
 <div>
 	<p>
-		Artificial intelligence is poised to enable breakthroughs in science and reshape medicine. This introductory course provides a survey of artificial intelligence for biomedical informatics, covering methods for key data modalities: clinical data, networks, language, and images. It introduces machine learning problems from a practical perspective, focusing on tasks that drive the adoption of machine learning in biology and medicine. 
+		Artificial intelligence is poised to enable breakthroughs in science and reshape medicine. This course provides a survey of artificial intelligence for biomedical informatics, covering methods for key data modalities: clinical data, networks, language, and images. It introduces machine learning problems from a practical perspective, focusing on tasks that drive the adoption of machine learning in biology and medicine. 
 	</p>
 	<p>
-		It outlines foundational algorithms and emphasizes the subtleties of working with biomedical data and ways to evaluate and transition machine learning methods into biomedical and clinical implementation. An important consideration in this course is the broader impact of artificial intelligence, particularly topics of bias and fairness, interpretability, and ethical and legal considerations when dealing with artificial intelligence.
+		The curriculum delves into foundational algorithms and highlights the nuances of handling biomedical data. It places a strong emphasis on strategies for evaluating and seamlessly integrating machine learning methods into biomedical research and clinical practice. A key aspect of this course is its focus on the broader implications of artificial intelligence. This includes critical discussions on topics such as trustworthiness, interpretability, evaluation, and the ethical and legal challenges associated with the implementation of artificial intelligence in healthcare.
 	</p>
 </div>