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Optimization for Radiology and Social Media. Ken Goldberg IEOR (EECS, School of Information, BCNM). UC Berkeley College of Engineering Research Council, May 2010. Outline. IEOR Dept, BCNM Radiology Social Media. UC Berkeley IEOR Department. The only IEOR department in the UC system

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optimization for radiology and social media

Optimization for Radiology and Social Media

Ken Goldberg

IEOR (EECS, School of Information, BCNM)

UC Berkeley College of Engineering Research Council, May 2010

  • IEOR Dept, BCNM
  • Radiology
  • Social Media

UC Berkeley IEOR Department

  • The only IEOR department in the UC system
  • Ranked #3 in USA
  • 55 BS, 10 BA, 30 MS, 5-8 PhD degrees per year

IEOR Faculty:Ilan AdlerAlper AtamturkJon BurgstoneYing-Ju ChenLaurent El Ghaoui Ken GoldbergXin GuoDorit S. HochbaumRichard Karp Philip M. KaminskyRobert C. LeachmanAndrew LimShmuel S. OrenChristos PapadimitriouRhonda L. Righter (Chair)Lee W. SchrubenZuo-Jun "Max" ShenIkhlaq SidhuCandace Yano



To critically analyze and shape developments in new media from trans-disciplinary and global perspectives that emphasize humanities and the public interest.







Art History




Public Health

Film Studies







Art Practice





New Media Initiative


Ken Goldberg, AlperAtamturk, Laurent El Ghaoui (IEOR)

James O’Brien, Jonathan Shewchuck (EECS)

I.-C. Hsu, MD, J. Pouliot, PhD (UCSF)

prostate cancer
Prostate Cancer

1 in 6 men will be diagnosed with prostate cancer

over 230,000 cases each year in the US

one death every 16 minutes

high dose rate brachytherapy
High Dose Rate Brachytherapy



robot motion planning
Robot Motion Planning
  • Theorem (Completeness): A sensorless plan exists for any polygonal part.
  • Theorem (Complexity): For a polygon of n sides, the algorithm runs in time O(n2) and finds plans of length O(n).
  • Extensions:
  • Stochastically Optimal Plans
  • Extension to Non-Zero Friction
  • Geometric Eccentricity / constant time complexity
  • Part Fixturing and Holding
dosimetry inverse planning
Dosimetry: Inverse Planning

Dose Distribution

Dosimetric Criteria

inverse planning with simulated annealing ipsa
Inverse Planning with Simulated Annealing (IPSA)
  • Inverse planning software developed at UCSF by Pouliot group
  • FDA-approved: used clinically worldwide
  • Simulated annealing dose point penalty method
inverse planning with linear programming iplp
Inverse Planning with Linear Programming (IPLP)
  • LP formulation (UC Berkeley)
  • Guarantees global optima
  • Optimization of HDR Brachytherapy Dose Distributions using Linear Programming with Penalty Costs.

Ron Alterovitz, Etienne Lessard, Jean Pouliot, I-Chow Joe Hsu, James F. O\'Brien, and Ken Goldberg. Medical Physics, vol. 33, no. 11, pp. 4012-4019, Nov. 2006.

limitations of penalty model
Limitations of Penalty Model
  • Only specifies dosimetry at dose points, not to organs
  • Not equivalent to dosimetric indices
    • Not intuitive for Physicians
    • Results not always clinically viable.
    • Results difficult to customize for special cases
  • Dosimetric index: if dose at x > R, then x = 1, x = 0 otherwise
  • Discrete Variables

Inverse Planning with Integer Programming (IP2)

  • Indices
    • s: organ
    • i: point in organ
    • j: dwell position
  • Variables
    • tj: dwell time at j
    • xsi: counting variable for s,i
  • Parameters:
    • Dsij: dose rate from j to s,i
    • Rs: Dose threshold for s
    • Ms: Max dose for points in s
    • Ls: Lower bound for dosimetric s
    • Us: Upper bound for dosimetric s
  • Model
    • Maximize Σ x0i
    • Subject to:
      • Σ Dsij tj ≥ Rs xsi
      • Σ Dsij tj ≤ Rs + (Ms – Rs) xsi
      • Ls ≤ Σ xsi ≤ Us
      • tj ≥ 0
      • xsiє {0,1}
initial results comparing ipsa with ip 2
Initial Results: Comparing IPSA with IP2
  • Average Runtime (sec):
    • IPSA: 5
    • IP2 (heuristic 1): 23
    • IP2 (heuristic 2): 900
  • Compliance with all clinical criteria
    • IPSA: 0% of patients
    • IP2 (heuristic 1): 95% of patients
    • IP2 (heuristic 2): 100% of patients
ip 2 for needle reduction
IP2 for Needle Reduction
  • Minimize number of needles
  • Minimize trauma
  • Speed Recovery

Possible Needles

Optimal Needle Selection (example)

future work
Conic Optimization

Robust Optimization

Model uncertainties in:

Organ location, motion


Catheter displacement

Future Work
tissue simulation
Tissue Simulation


Nuttapong Chentanez, Ron Alterovitz, Daniel Ritchie, Lita Cho, Kris K. Hauser, Ken Goldberg, Jonathan R. Shewchuk, and James F. O\'Brien. "Interactive Simulation of Surgical Needle Insertion and Steering". In Proceedings of ACM SIGGRAPH 2009, pages 88:1–10, Aug 2009.


Superhuman Performance of Surgical Tasks by Robots using Iterative Learning from Human-Guided Demonstrations

Jur van den Berg, Stephen Miller, Daniel Duckworth, Humphrey Hu, Andrew Wan, Xiao-Yu Fu, Ken Goldberg, Pieter Abbeel

University of California, Berkeley

  • 1. Robot learns surgical task from human demonstrations
    • Knot tying
    • Suturing
  • 2. Robot learns to execute tasks with superhuman performance
    • Increase smoothness
    • Increase speed
social media
Social Media

Ken Goldberg, Gail de Kosnik, Kimiko Ryokai

Alec Ross, Katie Dowd (US State Dept)


collaborative robot control:





Goals of Organization

  • Engage community
  • Understand community
    • Solicit input
    • Understand the distribution of viewpoints
    • Discover insightful comments

Goals of Community

  • Understand relationships to other community members
  • Consider a diversity of viewpoints
  • Express ideas, and be heard

Classical approaches: surveys, polls

Drawbacks: limited samples, slow, doesn’t increase engagement

Current approaches: online forums, comment lists

Drawbacks: data deluge, cyberpolarization, hard to discover insights

related work visualization
Related Work: Visualization

Clockwise, starting from top left:

Morningside Analytics, MusicBox, Starry Night

related work info filtering
Related Work: Info Filtering
  • K. Goldberg et al, 2001: Eigentaste
  • E. Bitton, 2009: spatial model
  • Polikar, 2006: ensemble learning

Six 50-minute Learning Object Modules, preparation materials, slides for in-class lectures, discussion ideas, hand-on activities, and homework assignments.

canonical correlation analysis cca
Canonical Correlation Analysis (CCA)


  • Observed variables: x, y
  • Latent variable: z
  • Learn MLEs for low-rank projections A and B
  • Equivalently, find inverse mapping that maximizes correlation between A, B



Graphical model for CCA

x = Az + ε

y = Bz + ε

z = A-1x = B-1y





canonical correlation analysis cca1
Canonical Correlation Analysis (CCA)
  • CCA gives three posterior expectations
      • E(z|x)
      • E(z|y)
      • E(z|x,y)
      • E(z|x,y) is used to visualize the opinion space

Opinion Vector






canonical correlation analysis cca2
Canonical Correlation Analysis (CCA)

Each point in the Canonical representation has an expected list of words associated to it.

A visualization of this list of words can be used to give users more information about their location


Opinion Space: Crowdsourcing Insights

Scalability: n Participants, n Viewpoints

n2 Peer to Peer Reviews

Viewpoints are k-Dimensional

Dim. Reduction: 2D Map of Affinity/Similarity

Insight vs. Agreement: Nonlinear Scoring

Ken Goldberg, UC Berkeley

Alec Ross, U.S. State Dept

optimization for radiology and social media1

Optimization for Radiology and Social Media

Ken Goldberg

IEOR (EECS, School of Information, BCNM)

UC Berkeley College of Engineering Research Council, May 2010

ip 2 heuristics

Allocate dose budget to dose points that are likely to need it.


Solve LP relaxation

Analyze solution and impose new constraints on hottest dose points.

Resolve to feasible solution.

Hard Cuts

Apply custom cuts so that IP2 emphasizes dosimetric indices.


Solve LP relaxation.

Add cuts to incorrectly counted dose points.

Repeat until feasible for IP2

IP2 Heuristics
hard cuts
Hard Cuts


Hard cut


Fractional Optimal Solution (cut off by Hard cut)




dimensionality reduction
Dimensionality Reduction

Principal Component Analysis (PCA)

  • Assumes independence and linearity
  • Minimizes squared error
  • Scalable: compute position of new user in constant time

Berkeley Center for New Media (BCNM):

David Wong: EECS Undergraduate Student

Tavi Nathanson: EECS Graduate Student

Ephrat Bitton: IEOR Graduate Student

Siamak Faridani: IEOR Graduate Student

Elizabeth Goodman: School of Information Graduate Student

Alex Sydell: EECS Undergraduate Student

Meghan Laslocky: Outside Consultant on Content

Ari Wallach: Outside Consultant on Content and Strategy

Steve Weber: Outside Consultant on Content

Peter Feaver: Outside Consultant on Content

U.S. State Department:

Alec Ross: Senior Advisor for Innovation

Katie Dowd: New Media Director

Daniel Schaub: Director for Digital Communications

multidimensional scaling
Multidimensional Scaling
  • Goal: rearrange objects in low dim space so as to reproduce distances in higher dim
  • Strategy: Rearrange & compare solns, maximizing goodness of fit:
  • Can use any kind of similarity function
  • Pros
    • Data need not be normal, relationships need not be linear
    • Tends to yield fewer factors than FA
  • Con: slow, not scalable







kernel based nonlinear pca
Kernel-based Nonlinear PCA
  • Intuition: in general, can’t linearly separate n points in d < n dim, but can almost always do so in d ≥ n dim
  • Method: compute covariance matrix after transforming data into higher dim space
  • Kernel trick used to improve complexity
  • If Φ is the identity, Kernel PCA = PCA
kernel based nonlinear pca1
Kernel-based Nonlinear PCA

Input data

KPCA output with Gaussian kernel

  • Pro: Good for finding clusters with arbitrary shape
  • Cons: Need to choose appropriate kernel (no unique solution); does not preserve distance relationships
stochastic neighbor embedding
Stochastic Neighbor Embedding
  • Converts Euclidean dists to conditional probabilities
  • pj|i = Pr(xi would pick xj as its neighbor | neighbors picked according to their density under a Gaussian centered at xi)
  • Compute similar prob qj|i in lower dim space
  • Goal: minimize mismatch between pj|i and qj|i:
  • Cons: tends to crowd points in center of map; difficult to optimize
canonical correlation analysis cca3
Canonical Correlation Analysis (CCA)

CCA visualization with tag cloud for that location in the space. The tag cloud uses stemmed keywords.


Six 50-minute Learning Object Modules, preparation materials, slides for in-class lectures, discussion ideas, hand-on activities, and homework assignments.


Opinion Space

Wisdom of Crowds: Insights are Rare

Scalable, Self-Organizing, Spatial Interface Visualize Diversity of Viewpoints

Incorporate Position into Scoring Metrics

Ken Goldberg

UC Berkeley

optimization for radiology treatment and visualizing public opinion

Optimization for Radiology Treatment and Visualizing Public Opinion

Ken Goldberg

Alec Ross, Director of Innovation, U.S. State Dept


Opinion Space: Crowdsourcing Insights

Scalability: N Participants, N Viewpoints

Each Viewpoint is n-Dimensional

Dim. Reduction: 2D Map of Affinity/Similarity

Insight vs. Agreement: Nonlinear Scoring

N2 Peer to Peer Reviews

Ken Goldberg, UC Berkeley

Alec Ross, U.S. State Dept

objective function improvement over ipsa
Objective Function Improvement over IPSA

Statistically significant improvement (P = 1.5410-7)

standard dosimetric indices
Standard Dosimetric Indices

No significant improvement in any dosimetric index (P > 0.01)