I am an associate professor in the School of Information at the University of Michigan School.

I am interested in students looking to attack problems using both theoretical (formal mathematical) and other methods.

NSF CAREER Award Recipient

Google Faculty Award Recipient

Facebook Faculty Award Recipient

NSF Algorithms in the Field Grant Recipient

NSF CCF Small Recipient (3x)

Bo Li - former Post-Doc, now TT at UIUC

Yuqing Kong - former PhD student, now TT at Peking University

Fang-Yi Yu - former PhD Student/Post-Doc, now a TT at George Mason University

Biaoshuai Tao - former PhD Student, now TT at SJTU

Noah Burrell - current PhD Student

Yichi Zhang - current PhD Student

Md Sanzeed Anwar - current PhD Student

Shengwei Xu - current PhD Student

Christian David Gamba Contrera - current PhD Student

Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences

G. Schoenebeck, b. Tao

NeurIPs, 2021, Arxiv

Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach

G. Schoenebeck, F. Yu.

ITCS 2021, Arxiv.

An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling

Y. Kong, G. Schoenebeck.

TEAC '19,
Arxiv

Water from Two Rocks: Maximizing the Mutual Information

Y. Kong, G. Schoenebeck

EC '18, arXiv '18

Measurement Integrity in Peer Prediction: A Peer Assessment Case Study

N. Burrell, G. Schoenebeck

Arxiv

Escaping Saddle Points in Constant Dimensional Spaces: An Agent-based Modeling Perspective

G. Schoenebeck, F. Yu

EC' 2020, pdf

Two Strongly Truthful Mechanisms for Three Heterogeneous Agents Answering One Question

G. Schoenebeck, F. Yu

TEAC, 2022,
Wine 2020,
pdf.

False Consensus, Information Theory, and Prediction Markets.

Y. Kong, G. Schoenebeck

ITCS '23 (to appear), arXiv.

A System-Level Analysis of Conference Peer Review.

Y. Zhang, F. Yu, G. Schoenebeck, and D. Kempe

EC '22.

Optimal Local Bayesian Differential Privacy Over Markov Chains.

D. Chakrabarti, J. Gao, A. Saraf, G. Schoenebeck, and F. Yu

AAMAS '22 (extended abstract), arXiv.

Bayesian Persuasion in Sequential Trials.

S. Su, V. Subramanian, and G. Schoenebeck

WINE '21, arXiv.

Adaptive Greedy Versus Non-adaptive Greedy for Influence Maximization

W. Chen, B. Peng, G. Schoenebeck, B. Tao

JAIR '22, AAAI '20, arXiv

BONUS! Maximizing Surprise Labels

Z. Huang, Y. Kong, T. X. Liu, G. Schoenebeck, S. Xu

WWW '22, Arxiv

Measurement Integrity in Peer Prediction: A Peer Assessment Case Study

N. Burrell, G. Schoenebeck

Arxiv, 2021

Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences

G. Schoenebeck, b. Tao

NeurIPs, 2021, Arxiv, 2021

SURPRISE! and When to Schedule It

Z. Huang, S. Xu, Y. Shan, Y. Lu, Y. Kong, T. X. Liu, G. Schoenebeck

IJCAI '21, Arxiv

Survey Equivalence: A Procedure for Measuring Classifier Accuracy Against Human Labels

P. Resnick, Y. Kong, G. Schoenebeck, T. Weninger

Arxiv, 2021

Information Elicitation from Rowdy Crowds

G. Schoenebeck, F. Yu, Y. Zhang

WWW '21

Timely Information from Prediction Markets

G. Schoenebeck, C. Yu, F. Yu

Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach

G. Schoenebeck, F. Yu.

ITCS 2021, Arxiv

Relaxing Common Belief for Social Networks

N. Burrell, G. Schoenebeck

ITCS 2021, Arxiv

Escaping Saddle Points in Constant Dimensional Spaces: An Agent-based Modeling Perspective

G. Schoenebeck, F. Yu

EC' 2020, pdf

Limitations of greed: Influence maximization in undirected networks re-visited

G. Schoenebeck, B. Tao, F. Yu

AAMAS '20, arXiv

Information Elicitation Mechanisms for Statistical Estimation

Y. Kong, G. Schoenebeck, B. Tao, F. Yu

AAAI '20, pdf

Influence Maximization on Undirected Graphs: Towards Closing the (1-1/e) Gap

G. Schoenebeck, B. Tao

EC '19,
Video Presentation,
TEAC '20

Outsourcing computation: the minimal refereed mechanism.

Y. Kong, C. Peikert, G. Schoenebeck, B. Tao

Wine '19, arXiv

Think globally, act locally: On the optimal seeding for nonsubmodular influence maximization.

G. Schoenebeck, B. Tao, F. Yu

Approx/Random '19, arXiv

An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling

Y. Kong, G. Schoenebeck.

TEAC '19,
Arxiv

Complex Contagions in Charitable Donations

J. Gao, G. Ghsemisefeh and J. Jones, G. Schoenebeck.

SocArXiv '19.

Beyond Worst-Case (In)approximability of Nonsubmodular Influence Maximization

G. Schoenebeck, B. Tao

ToCT '19,
Wine '17,
arXiv '17

Think Globally, Act Locally: On the Optimal Seeding for Nonsubmodular Influence Maximization

G. Schoenebeck, B. Tao, F. Yu

Approx/Random '19

The Volatility of Weak Ties: Co-evolution of Selection and Influence
in Social Networks

J. Gao, G. Schoenebeck, F. Yu

AAMAS '19,
pdf

Outsourcing Computation: the Minimal Refereed Mechanism

Y. Kong, C. Peikert, G. Schoenebeck, B. Tao,

Wine'19, arXiv

Social learning with questions

S. Su, V. G. Subramanian, G. Schoenebeck

NetEcon '19, arXiv

Water from Two Rocks: Maximizing the Mutual Information

Y. Kong, G. Schoenebeck

Eliciting Expertise without Verification

Y. Kong, G. Schoenebeck

Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

X. Ma, B. Li, Y. Wang, S. M. Erfani, S. Wijewickrema, M. E. Houle, G. Schoenebeck, D. Song, J. Bailey

ICLR '18, arXiv '18

Consensus of Interacting Particle Systems on Erdos-Renyi Graphs

G. Schoenebeck, F. Yu

SODA '18, pdf

Optimizing Bayesian Information Revelation Strategy in Prediction Markets: the Alice Bob Alice Case

Y. Kong, G. Schoenebeck.

ITCS '18

Equilibrium Selection in Information Elicitation without Verification via Information Monotonicity

Y. Kong, G. Schoenebeck..

ITCS' 18, Arxiv '16

Contention-Aware Lock Scheduling for Transactional Databases

B. Tian, J. Huang, B. Mozafari, G. Schoenebeck

VLDB'18

Don't Be Greedy: Leveraging Community Structure to Find High Quality Seed Sets for Influence Maximization

R. Angell, G. Schoenebeck

WINE'17, arXiv '16

Cascades and Myopic Routing in Nonhomogeneous Kleinbergs Small World Model

J. Gao, G. Schoenebeck, F. Yu

WINE '17

A Top-Down Approach to Achieving Performance Predictability in Database Systems

J. Huang, B. Mozafari, G. Schoenebeck, T. Wenisch

SIGMOD '17

Engineering Agreement:The Naming Game with Asymmetric and Heterogeneous Agents

J. Gao, B. Li, G. Schoenebeck, F. Yu

AAAI '17

How Complex Contagions Spread Quickly in Preferential Attachment Models and Other Time-Evolving Networks

R.Ebrahimi, J. Gao, G. Ghasemiesfeh, G. Schoenebeck

IEEE Transactions on Network Science and Engineering '17, arXiv '14

Sybil Detection Using Latent Network Structure

A. Snook, G. Schoenebeck, F. Yu.

EC '16

General Threshold Model for Social Cascades: Analysis and Simulations

J. Gao, G. Ghasemiesfeh, G. Schoenebeck, F. Yu

EC '16

Complex Contagions on Configuration Model Graphs with a Power-Law Degree Distribution

G. Schoenebeck, F. Yu

WINE '16

Putting Peer Prediction Under the Micro(economic)scope and Making Truth-telling Focal

Y. Kong, K. Ligett, G. Schoenebeck.

WINE '16, Arxiv '15

Identifying the Major Sources of Variance in Transaction Latencies: Towards More Predictable Databases

J. Huang, B. Mozafari, G. Schoenebeck, T. Wenisch

arXiv'16

A Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling

Y. Kong, G. Schoenebeck..

Arxiv '15

Complex Contagions in Kleinberg's Small World Model

R. Ebrahimi, J. Gao, G. Ghasemiesfeh, G. Schoenebeck

ITCS '15

Buying Private Data without Verification

A. Ghosh, K. Ligett, A. Roth, G. Schoenebeck.

EC '14

Characterizing Strategic Cascades on Networks

T. Martin, G. Schoenebeck, M. Wellman

EC '14

Graph Isomorphism and the Lasserre Hierarchy

P. Codenotti, G. Schoenebeck, A. Snook

arXiv '14

Better Approximation Algorithms for the Graph Diameter.

S. Chechik, D. H. Larkin, L. Roditty, G. Schoenebeck, R. E. Tarjan, V. V. Williams

SODA '14

Potential Networks, Contagious Communities, and Social Network Structure.

G. Schoenebeck

WWW '13

Conducting Truthful Surveys, Cheaply

A. Roth, G. Schoenebeck.

EC '12

Finding Overlapping Communities in Social Networks: Toward a Rigorous Approach

S. Arora, R. Ge, S. Sachdeva, G. Schoenebeck

EC '12

Social Learning in a Changing World

R. Frongillo, G. Schoenebeck, O. Tamuz

Wine '11

General Hardness Amplification of Predicates and Puzzles

T. Hollenstein, G. Schoenebeck

TCC '11

Constrained Non-monotone Submodular Maximization: Offline and Secretary Algoritms.

A. Gupta, A. Roth. G. Schoenebeck, K. Talwar.

WINE '10

The Limitations of Linear and Semidefinite Programs

G. Schoenebeck

PhD Thesis, 2010

Optimal Testing of Reed-Muller Codes

A. Bhattacharyya, S. Kopparty, G. Schoenebeck, M. Sudan, D. Zuckerman

FOCS '10.

Detecting Spam in a Twitter Network.

S. Yardi, D. Romero, G. Schoenebeck. d. boyd.

First Monday '10

Reaching Consensus on Social Networks

E. Mossel, G. Schoenebeck

ICS '10.

On the Complexity of Nash Equilibria of Action-Graph Games

C. Daskalakis, G. Schoenebeck, G. Valiant, P. Valiant

Soda '09.

Linear Level Lasserre Lower Bounds for Certain *k*-CSPs

G. Schoenebeck.

FOCS '08

Tight Integrality Gaps for Lovasz-Schrijver LP Relaxations of Vertex Cover and Max Cut

G. Schoenebeck, L. Trevisan, M. Tulsiani.

STOC '07

A Linear Round Lower Bound for Lovasz-Schrijver SDP Relaxations of Vertex Cover

G. Schoenebeck, L. Trevisan, M. Tulsiani.

CCC '07

Chora: Expert-based Peer-to-peer web search

H. Gylfason, O. Khan, G. Schoenebeck

AP2PC workshop at AAMAS '06.

The computational Complexity of Concisely Represented Games

G. Schoenebeck, S. Vadhan.

EC '06. ACM Transactions on Computation Theory 2012.

GrowRange: Anytime VCG-Based Mechanisms

D. Parkes, G. Schoenebeck.

AAAI '04.

Winter 2020

SIADS 502: Math Methods for Data Science

SIADS 521: Visual Exploration of Data

Fall 2020

EECS 547 / SI: 652: Incentives and Strategic Behavior in Computational Systems

SI: 670: Applied Machine Learning

SIADS 502: Math Methods for Data Science

Winter 2020

SIADS 502: Math Methods for Data Science

SIADS 521: Visual Exploration of Data

Fall 2019

EECS 547 / SI: 652: Electronic Commerce (about algorithmic game theory)

SI: 670: Applied Machine Learning

Fall 2017

EECS 547 / SI: 652: Electronic Commerce (about algorithmic game theory) .

Winter 2017

EECS 376 Foundations of Computing

Fall 2015

EECS 598-06 Randomness and Computation

Winter 2015

EECS 376 Foundations of Computing

Fall 2014

EECS 574 Computational Complexity Theory

Fall 2013

EECS 574 Computational Complexity Theory

Winter 2013

EECS 203 Discrete Math

Fall 2012

EECS 598-06 Social Networks: Reasoning about Structure and Processes This class looked at social networks research and how a theoretical computer science prospective both brings new questions and gains additional insights into this growing body of research. Schedule and readings on the website.

New Jersey Governor's School: The Math Behind the Maching, Summer 2012

New Jersey Governor's School: The Math Behind the Maching, Summer 2011

3341 North Quad

501 State St.

University of Michigan

Ann Arbor, MI 48109-2121

Phone: (734)647-4712

Email:

I was born in Green Bay, WI and moved to Wichita, KS when I was nine. I attended Harvard University, graduating with highest honors in mathematics. Afterwards, I attended Oxford University as the von Clemm fellow and studied theology. I received my PhD from UC Berkeley in computer science where I was advised by Luca Trevisan. Subsequently I was the Simons Foundation Postdoctoral Research Fellow in Theoretical Computer Science at Princeton University.