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Spring 2022 MW 9:45am 11:15am, from March 28th to June 1st

Location: Thornt 211 (Charles B. Thornton Center for Engineering and Management)


Edward Chang (

Adjunct Professor, Computer Science, Stanford University


Visiting Professor, EECS, UC Berkeley (2017-2021)

President, HTC DeepQ Healthcare (2012-2021)

Director of Research, Google (2006-2012)

Professor, ECE, UC Santa Barbara (1999-2006)


CA: Ruishan Liu (


Announcements (last update 5/25)


§     (5/25) All lectures are online. Project presentations have been scheduled to be on June 1st.

§     (5/18) Lectures #15 and #16 are both online. Assignment #3 due on 5/25.

§     (5/12) Lectures #13, and #14 are both online.

§     (5/7) We kicked off the final segment of the course today and began our 6-lecture series on Consciousness.

§     (4/27) Lecture #12 slides online, Saahil Jain’s short talk on Search Engine on Canvas.

§     (4/21) Lectures #8 and #10 recorded in 2021 are online.

§     (4/18) Lecture #7 guest speaker Alexey Bochkovskiy’s slides and video are posted.

§     (4/11) Lecture #6 slides is on Canvas together with last year’s recorded lecture.

§     (4/06) Lecture #5 slides is online. Please formulate your project proposal by 4/13.

§     (4/03) Lecture #4 slides is online. The recorded lecture in 2021 is posted on Canvas. Assignment #1 due on 4/11.

§     (4/02) Lecture #3 slides on protein folding and DNA sequencing will be posted on Canvas later this quarter.

§     (3/30) Lecture #2 slides is online. The link to the recorded lecture in 2021 is announced on Canvas.

§     (3/28) Lecture #1 slides and audio recording is online.

§     (3/26) The University requires in-person attendance in Spring. The first lecture is essential to attend to decide enroll/drop. I will record only the first two lectures for course shoppers.

§     (2/22) Received top review in 2021.

§     (2/1) First syllabus draft posted.




Artificial intelligence, specifically deep learning, stands out as one of the most transformative technologies of the past decade. AI can already outperform humans in several computer vision and natural language processing tasks.  However, we still face some of the same limitations and obstacles that led to the demise of the first AI boom phase five decades ago.  This research-oriented course will first review and reveal the limitations (e.g., iid assumption on training and testing data, voluminous training data requirement, and lacking interpretability) of some widely used AI algorithms, including convolutional neural networks (CNNs), transformers, reinforcement learning, and generative adversarial networks (GANs). To address these limitations, we will then explore topics including transfer learning for remedying data scarcity, knowledge-guided multimodal learning for improving data diversity, out of distribution generalization, subspace learning for enabling interpretability, privacy-preserving data management, and other topics. (The 2022 edition plans to cover consciousness modeling.)  


The course will be taught through a combination of lecture and project sessions. Lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment) will feature guest speakers from academia and industry. Students will be assigned to work on a term project that is relevant to their fields of study (e.g., CS, Medicine, and Data Science). Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Students are welcome to formulate a project that leverages their own graduate research.


Level: Senior/graduate of EE/CS, Medicine, Medical Informatics, Engineering; proficient in programming

Perquisite: Introductory course in AI, Statistics, or Machine Learning




·   No exams.

·   Three written assignments in the first half of the quarter.

·   Reading: A list of papers/articles/podcasts grouped by topics. Each student selects one topic of interest to conduct in-depth surveys and presentation.

·   Term project: A group project of two to three involving algorithm design and coding. Some past example projects are:

§     COVID-19 demographic analysis

§     COVID-19 outbreak tracking and prediction

§     Thoracic disease diagnosis with limited X-ray images

§     News article summarization (extractive and abstractive)

§     Methods for fusing knowledge and perception (text and image)

§     Symptom checker




Probabilistic Machine Learning, Kevin P. Murphy, March 2022

Projections: A Story of Human Emotion, Karl Deisseroth, 2021




·   Assignments 20%

·   Class participation 20%

·   Literature survey and project proposal 20%

·   Project implementation and demo 40%






Course Materials


Week #1



#1 Course Aims and Syllabus


Overview the resurgence of AI since 2012, including some promises, constraints, and future directions. Go over course logistics.

Prof. Edward Chang


#2 P4 Medicine, Part 1 of 4:

Healthcare Paradigm Shift

[slides][video-2021 Canvas]

[1] Edward Y. Chang, et al., "Artificial Intelligence in XPRIZE DeepQ Tricorder. " ACM MM Workshop for Personal Health and Health Care, 2017.

[2] FC Chang, JJ Chang, CN Chou, EY Chang, “Toward Fusing Domain Knowledge with Generative Adversarial Networks to Improve Supervised Learning for Medical Diagnoses”, FC Chang, JJ Chang, CN Chou, EY Chang, IEEE MIPR Conference, 2019.

[3] Edward Y. Chang, et al., "Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning." AAAI, 2018.

[4] Transfer representation learning for medical image analysis, Chuen-Kai Shie, Chung-Hisang Chuang, Chun-Nan Chou, Meng-Hsi Wu, Edward Y Chang, IEEE EMBC, 2015.


Prof. Edward Chang




#3 P4 Medicine, Part 2 of 4

Protein Folding &

DNA Sequencing

(& project discussion)

[slides, video on Canvas]


[1] Highly accurate protein structure prediction with AlphaFold, DeepMind, Nature, May 2021.

[2] The sequence of the sequencers, The history of sequencing DNA, J. M. Heather and B. Chain, Genomics 107(1), pp. 1-8, January 2016. [link]


Prof. Edward Chang

Week #2


#4 P4 Medicine, Part 3 of 4:

ML with Data and Inductive Biases [slides] [video-2021 Canvas]

[1] Schwartz, W. B., R. S. Patil, and P. Szolovits. "AI in Medicine: where do we stand." New England Journal of Medicine 316 (1987): 685-688. [link].

[2] Wu, T. D. "Efficient Diagnosis of Multiple Disorders Based on a Symptom Clustering Approach." Proceedings of AAAI, 1990, pp. 357-364.

[3] Universal Equivariant Multilayer Perceptrons, Siamak Ravanbakhsh, ICML, 2020.

[4] The problem of Protein Folding, Wikipedia.

[5] On Bottleneck of Graph Neural Networks and its Practical Implications, Uri Alon, Eran Yahav, ICRL 2021.

[6] SIGN: Scalable Inception Graph Neural Networks, Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, Federico Monti, GraphSaint, 2020.

[7] Panel: VR/AR for Surgery and Medical Education, April 2018 [link].

[8] Stanford SNI Talk: Advancing Healthcare w/ AI and VR, Edward Y. Chang [link] (Stanford ID required).

[9] Daphne Koller: Biomedicine and Machine Learning, AI Podcast #93 with Lex Fridman, May 2020 [link]


Prof. Edward Chang


Assignment #1:

Deadline 4/11


#5 Finish last lecture;

Start NLP, Part 1 of 3:

Hand-engineering, Similarity, Collaborative Filtering, Latent Dirichlet Allocation [slides] [video-2021 Canvas]


Brainstorming on term projects, datasets, and resources


[1] Attention is all you need, Ashish Vaswani, et al., [link].

[2] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [link].

[3] Reformer: The Efficient Transformer [link].

[4] Perceiver: General Perception with Iterative Attention [link].

Prof. Edward Chang



Week #3


#6 NLP, Part 2 of 3:

Attention, Transformers, BERT, and GPT [slides Canvas] [ video-2021 Canvas]


[1] Attention is all you need, Ashish Vaswani, et al., [link].

[2] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [link].

[3] Reformer: The Efficient Transformer [link].

[4] Perceiver: General Perception with Iterative Attention [link].


Prof. Edward Chang




CA led project discussion

Four to five candidate projects will be presented in class for selection.

[1] Nodule detection from DeepQ Health,

[2] Document summarization from,

[3] News recommendation from, and

[3] Two healthcare projects from Stanford Medical School.


Prof. Edward Chang

CA: Ruishan


Project Proposal

Deadline 4/20

Week #4


#7 Object Detection, YOLOv4/YOLOR guest lecture [slides][video]

YOLOv4: Optimal Speed and Accuracy of Object Detection, A. Bochkovskiy, C-Y Wang, H-Y M. Liao [link], 2020.

Other links are provided on the last page of the slides by Dr. Bochkovskiy.


Dr. Alexey Bochkovskiy



#8 NLP Part 3 of 3:

CLI: Conversational Language Interface, Dialogue & Virtual Assistant (Alexa, Google Home, & Oval), and Conversational Language Interface [slides][video]


Project signed up, project group formed, and project abstract due


[1] GPT3 and multimodal pre-trained models

[2] OVAL

[3] Chirpy Cardinal

[4] Genie

[5] Tricorder revisit, Edward Y. Chang, et al., "Artificial Intelligence in XPRIZE DeepQ Tricorder. " ACM MM Workshop for Personal Health and Health Care, 2017.




Prof. Edward Chang


Project Proposal due

@ 11:59pm


Week #5


#9 Cancer, Part 1 od 2: Cancer Causes and Diagnosis (recorded in 2021)


[1] Principles and methods of integrative genomic analyses in cancer, Vessela N. Kristensen, Ole Christian Lingjærde, Hege G. Russnes, Hans Kristian M. Vollan, et al., Nature Reviews, 2014.

[2] Biomarker development in the precision medicine era: lung cancer as a case study, Ashley J. Vargas, and Curtis C. Harris, Natural Reviews, 2016.

[optional] Artificial intelligence in radiology, Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H. Schwartz,and Hugo J. W. L. Aerts, Nature Reviews, 2018.


Dr. Melissa Ko,

PhD, Cancer Biology,





#10 P4 Medicine, Part 4 of 4:

History of AI in Diagnosis, Principles of Precision Diagnosis, Precision Surgery, and Optogenetics.




[1] Edward Y. Chang et al., "Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning." AAAI (2018).

[2] Edward Y. Chang et al., "REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis." NIPS (2018).

[3] Szolovits, P., and S. G. Pauker. "Categorical and Probabilistic Reasoning in Medical Diagnosis.” Artificial Intelligence 11(1-2), 1978: 115-144.

[4] Wu, T. D. "Efficient Diagnosis of Multiple Disorders Based on a Symptom Clustering Approach." Proceedings of AAAI, 1990, pp. 357-364.

[5] Patil, R. S., P. Szolovits, and W. B. Schwartz. "Causal Understanding of Patient Illness in Medical Diagnosis." In Proceedings of the Seventh International Joint Conference on Artificial Intelligence.


Prof. Edward Chang

4/27 (bonus)

#11 Title: An Intro to Search: Past, Present, & Future [video][slides]



Abstract: In this talk, we will cover a range of topics related to the past, present, and future of search. Topics will introduce the following concepts: search, relevance, search system considerations, search architectures, classical information retrieval, neural information retrieval, reranking, search system evaluation, benchmarks, and future research directions.

Biography: Saahil is currently an engineer at, a next generation search engine. Previously, Saahil was a graduate researcher within the Stanford Machine Learning Group under Professor Andrew Ng, where he focused on the intersection of artificial intelligence and healthcare. Prior to Stanford, he worked at Microsoft on Office 365. Saahil received his MS in Computer Science from Stanford University and his BS in Computer Science from Columbia University


Saahil Jain,

Week #6


#12 Cancer, Part 2 of 2: Cancer Treatment and How AI May Help (recorded in 2021) [video]

[1] Mass cytometry: blessed with the curse of dimensionality, Evan W Newell & Yang Cheng, Nature Immunology, June 2016.

[2] Next-Generation Machine Learning for Biological Networks, Diogo M. Camacho, Katherine M. Collins, Rani K. Powers, James C. Costello, and James J. Collins, Leading Edge Review, June 2018.

[optional] Personalized Cancer Models for Target, Discovery and Precision Medicine, Carla Grandori1 and Christopher J. Kemp, Trends in Cancer, CellPress Reviews, September 2018.


Dr. Melissa Ko,

PhD, Cancer Biology,





Project Check-in: methods, datasets, experiments, and preliminary results



CA: Ruishan Liu

Consciousness Modeling, the Next Frontier of Artificial Intelligence

Week #7


#13 Consciousness & Mind, Part 1 of 6, AI for Psychiatry: Beyond Brainless and Mindless? [slides][video]

[1] Students Face Mental Health Challenges --- barriers to care (during COVID-19)”, Stanford Daily (4/30/2020) [link].

[2] Meditation Music: Sangchhen Dorji Lhuendrup Nunnery, Bhutan, Himalayas, Edward Y. Chang [link].


Dr. Vikas Duvvuri,

MD/PhD Stanford




#14 Consciousness & Mind, Part 2 of 6: What is Consciousness?

Rule of Nature in Physics and Biology [slides][video]


[1] What is Life, Schrodinger, Cambridge University Press, 1992

[2] Discovery of Discovery of a Perceptual Distance Function for Measuring Image Similarity Beitao Li, Edward Chang, and Yi Wu, Multim. Syst. [link].

[3] What is Life, Erwin Schrodinger, online book [link].


Prof. Edward Chang

Week #8


#15 Consciousness & Mind, Part 3 of 6: Where is Consciousness, Optogenetics for Neuron/Brain Visualization, Joy and Pain, Memory, and Evolution [slides][video]


[1] Projections, by Karl Deisseroth, Random House, 2021.

[2] Tutorials to Optogenetics and Evolution (pointers in lecture video].

[3] Podcast links on Evolution (distributed on Slack)


Prof. Edward Chang


#16 Consciousness & Mind, Part 4 of 6: An Uncertain Future, Coping with COVID-19 anxiety with facts and right perspectives [slides][video]


This pandemic is not going away soon.  It has impact health, economy, and living tremendously, perhaps the most drastic one in our lifetime this far.  How should we deal with it mentally?  Dr. Duvvuri lectures on COVID-19, its current statistics, and future concerns.


Dr. Vikas Duvvuri,

MD/PhD Stanford

Week #9


#17 Consciousness & Mind, Part 5 of 6: Free Will, Ethics, and Mind [slides][video]


[1] The Emperor’s New Mind, Roger Penrose, 2016 edition

[2] Divine Comedy, Dante Alighieri, 1320

[3] Podcast links


Prof. Edward Chang


#18 Provable Privacy-Preserving Machine Learning with Multi-level Blockchains [slides][video]



[2] Differential Privacy.

[3] Soteria: A Provably Compliant User Right Manager Using a Novel Two-Layer Blockchain Technology, Stanford University, DeepQ, NTU, Edward Y. Chang el al., 2020 [link].


Prof. Edward Chang

5/25 (bonus)

#19 Consciousness & Mind, Part 6 of 6: Putting All Together: The Design of a Conscious Agent [slides, abridged]


Registered students only. Some students will continue working on projects in the summer.

Prof. Edward Chang

Week #10



Memorial Holiday



6/1: 9:45am – 11:20am

Project: final presentations

Session #1


Final report due on 6/8

6/3: 9:45am – 11:20pm

Project: final presentations

Session #2

Final report due on 6/8