Tokenomics 2023 - 5th International Conference on Blockchain Economics, Security and Protocols

Friday

,

October

27

-

Saturday

,

October

28

See the Agenda

Tokenomics is a premier international forum that focuses on the theory, design, analysis, implementation, and applications of platform economics, blockchains, and smart contracts. The conference aims to bring together leading economists, computer science researchers, and industry practitioners who are actively working on advancing the field of blockchain economics and technologies. The program offers a unique opportunity to engage with outstanding invited speakers and academic presentations.

This year's conference is hosted by the Columbia Center for Digital Finance and Technologies, and the Briger Family Digital Finance Lab, further amplifying its impact and relevance.

Organizing institutions: Columbia University and University of Pennsylvania

Register

IMPORTANT DATES

Submission deadline: August 18th, 2023, 23:59 AoE

Notification: September 22nd, 2023, 23:59 AoE

SUBMISSION GUIDELINES

The reviewers will carefully evaluate the initial 16 pages of the manuscript, including the cover page, figures, tables, and references. In case the paper exceeds 15 pages, there is no assurance that the referees will review the additional pages. Therefore, the final decision of acceptance or rejection will primarily depend on the content within the first 15 pages.

The cover page must contain the paper's title, the names and affiliations of the authors along with their email addresses, the contact author's information, a list of keywords, and an abstract. The abstract should consist of 1 to 2 paragraphs that provide a concise summary of the submission's contributions. Additionally, authors may include a clearly marked appendix.

All submissions are for presentation only. No conference proceedings will be published.

Submission Website

Call for Papers

GENERAL CO-CHAIRS

Agostino Capponi, Columbia Engineering
Ciamac Moallemi, Columbia Business School

PROGRAM COMMITTEES: COMPUTER SCIENCE TRACK AND ECONOMICS TRACK

PC: Computer Science Track

Brett Hemenway Falk, University of Pennsylvania (chair)

Elli Androulaki, IBM Research – Europe

Matheus Xavier Ferreira, Harvard

Chryssis Georgiou, University of Cyprus

Arthur Gervais, University College London

Maurice Herlihy, Brown University
Dimitris Karakostas, University of Edinburgh
Aggelos Kiayias, University of Edinburgh and IOG

William J. Knottenbelt, Imperial College

Philip Lazos, IOG
Andrew Lewis-Pye, London School of Economics

Francisco Marmolejo, Harvard
Jan Christoph Schlegel, City University of London

David Siska, University of Edinburgh
Qiang Tang, University of Sydney
Sara Tucci-Piergiovanni, CEA LIST

Catherine Tucker, MIT

Dimitrios Vasilopoulos, IMDEA Software Institute

PC Economics Track

Yeon-Koo Che, Columbia University (chair)
Arash Aloosh, Léonard de Vinci Business School
Bruno Biais, HEC
Christophe Bisiere, Toulouse School of Economics

Eric Budish, Chicago Booth

Catherine Casamatta, Toulouse School of Economics
Will Cong, Cornell University

Neil Gandal, Tel Aviv, Economics

Joshua Gans, Toronto, Rotman
Rod Garratt, BIS

Samuel Haefner, University of St. Gallen
Guillaume Haeringer, Baruch College

Wenqian Huang, BIS

Gur Huberman, Columbia University
Thorsten Koeppl, Queens University

Anthony Lee Zhang, Chicago Booth

Jacob Leshno, Chicago Booth

Emiliano Pagnotta, Singapore Management University

Julien Prat, Ecole Polytechnique
Mariana Rojas Breu, Panthéon-Assas University
Fahad Saleh, Wake Forest University
Linda Schilling, Washington University in St Louis

Philipp Strack, Yale, Economics

Alexander Teytelboym, University of Oxford
Katrin Tinn, McGill University

Gerry Tsoukalas, Boston University
Marteen Van Oord, Vrije University Amsterdam

Marianne Verdier, Panthéon-Assas University

Luana Zaccaria, EIEF

Marius Zoican, University of Toronto

PREVIOUS EDITIONS

Tokenomics 2022

LINK

Tokenomics 2021

LINK

Tokenomics 2020

LINK

Tokenomics 2019

LINK

Keynote

Philipp Strack, PhD

Professor of Economics, Yale University

Presentation: "Market Making and Decentralized Consensus"

We study the profitability of market makers like Uniswap, and explore the design decisions around market making.

Philipp Strack is a Professor of Economics at Yale. He is interested in behavioral economics, the design of institutions, and the economics of Web3.

Keynote

Scott Kominers, PhD

Professor of Business Administration, Harvard University

Presentation: "Token-Based Communities and Optimal Membership Design"

Scott Duke Kominers is a Professor of Business Administration in the Entrepreneurial Management Unit at Harvard Business School (HBS); as well as a Faculty Affiliate of the Harvard Department of Economics and the Harvard Center of Mathematical Sciences and Applications; Co-Principal Investigator of the Harvard Crypto, Fintech and Web3 Lab; and an a16z crypto Research Partner. He is an Editor of the Review of Economics and Statistics and serves on the Board of Editors of the Journal of Economic Literature.

After receiving his AB summa cum laude and Phi Beta Kappa in mathematics (with a minor in ethnomusicology) at Harvard University in 2009, Kominers earned his AM and PhD in Business Economics at Harvard, in 2010 and 2011, respectively. Prior to joining HBS, he was the inaugural Saieh Family Fellow in Economics at the Becker Friedman Institute at the University of Chicago, and then a Junior Fellow at the Harvard Society of Fellows.

Kominers's research focuses on market design, developing economic theory and analysis that provides practical solutions to real-world problems. In recent years, his work has particularly focused on blockchain-based platforms, crypto, and Web3. He also advises companies on marketplace and incentive design, and is involved in a number of NFT communities. His first book is The Everything Token: How NFTs and Web3 Will Transform the Way We Buy, Sell, and Create (co-authored with Steve Kaczynski and forthcoming from Portfolio in January, 2024).

Previous editions

PROGRAM - 2023

Friday, October 27, 2023

8:30 – 9:00 AM

Registration / Breakfast

Carleton Commons, Mudd Building 4th Floor Campus Level, 500 W 120th St

9:00 – 10:00 AM

Keynote:

Philipp Strack, Yale University

Davis Auditorium, Schapiro CEPSR

10:00 AM – 10:30 PM

Break

Carleton Commons, Mudd Building 4th Floor Campus Level, 500 W 120th St

10:30 AM – 12:00 PM

Session 1: DEXs

10:30AM - 11:00AM

"Augmenting Batch Exchanges with Constant Function Market Makers"

- Geoffrey Ramseyer (Stanford), Mohak Goyal (Stanford), Ashish Goel (Stanford), David Mazieres (Stanford)

11:00AM - 11:30AM

"An Economic Model of a Decentralized Exchange with Concentrated Liquidity"

- Joel Hasbrouck (NYU), Thomas Rivera (McGill), Fahad Saleh (Wake Forest)

11:30AM - 12:00PM

"The Geometry of Constant Function Market Makers"

- Guillermo Angeris (Bain Capital), Tarun Chitra (Gauntlet), Theo Diamandis (MIT & Bain Capital), Alex Evans (Bain Capital), Kshitij Kulkarni (Berkeley)

Davis Auditorium, Schapiro CEPSR

12:00 – 1:30 PM

Lunch Break (Lunch will be provided)

Carleton Commons, Mudd Building 4th Floor Campus Level, 500 W 120th St

1:30 – 3:00 PM

Session 2: Lending and Interest Rates

1:30PM - 2:00PM

"Stablecoin Devaluation Risk"

- My Nguyen (WashU), Barry Eichengreen (Berkeley), Ganesh Viswanath-Natraj

2:00PM - 2:30PM

"Phantom Liquidity in Decentralized Lending"

- Andreas Park (Toronto), Jona Stinner (Witten/Herdecke)

2:30 PM - 3:00PM
"Inflation Expectation and Cryptocurrency Investment"

- Qihong Ruan (Cornell), William Cong (Cornell), Jiasun Li (George Mason), Pulak Ghosh (IIM-Bangalore)

Davis Auditorium, Schapiro CEPSR

3:00 – 3:30 PM

Break

Carleton Commons, Mudd Building 4th Floor Campus Level, 500 W 120th St

3:30 – 5:00 PM

Session 3: Governance

3:30PM - 4:00PM

"Balancing Power in Decentralized Governance: Quadratic Voting under Imperfect Information"

- Alon Benhaim (Microsoft), Brett Falk (Penn), Gerry Tsoukalas (BU)

4:00PM - 4:30PM

"Will Blockchains Disintermediate Platforms? Limits to Decentralization in DAOs"

- Yannis Bakos (NYU), Hanna Halaburda (NYU)

4:30PM - 5:00PM
"Token-Based Platform Governance"

- Joseph Abadi (Philly FRB), Markus Brunnermeier (Princeton)

Davis Auditorium, Schapiro CEPSR

Saturday, October 28, 2023

8:30 – 9:00 AM

Registration / Breakfast

Carleton Commons, Mudd Building 4th Floor Campus Level, 500 W 120th St

9:00 – 10:00 AM

Keynote:

Scott Kominers, Harvard University

Davis Auditorium, Schapiro CEPSR

10:00 AM – 10:30 PM

Break

Carleton Commons, Mudd Building 4th Floor Campus Level, 500 W 120th St

10:30 AM – 12:00 PM

Session 4: Security

10:30AM - 11:00AM

"Speculative Denial-of-Service Attacks In Ethereum"

- Aviv Yaish (Hebrew), Kaihua Qin (Imperial College), Liyi Zhou (Imperial College), Aviv Zohar (Hebrew), Arthur Gervais (University College)

11:00AM - 11:30AM

"MEV Makes Everyone Happy under Greedy Sequencing Rule"

- Yuhao Li (Columbia), Mengqian Zhang (NYU) , Jichen Li (PKU), Elynn Chen (NYU), Xi Chen (NYU), Xiaotie Deng (PKU)

11:30AM - 12:00PM
"Undetectable Selfish Mining"

- Maryam Bahrani (a16z crypto), S. Matthew Weinberg (Princeton)

Davis Auditorium, Schapiro CEPSR

12:00 – 1:30 PM

Lunch Break (Lunch will be provided) - Voting Opens for Best Paper Award

Carleton Commons, Mudd Building 4th Floor Campus Level, 500 W 120th St

1:30 – 3:00 PM

Session 5: Empirics of Blockchain

1:30PM - 2:00PM

"Mempool: The Antechamber to the Blockchain"

- Paolo Guasoni (Dublin City), Gur Huberman (Columbia), Josiah Baker (Bitcoin), Clara Shikhelman (ChainCode)

2:00PM - 2:30PM

"Liquidity fragmentation on decentralized exchanges"

- Alfred Lehar (Calgary), Christine Parlour (Berkeley), Marius Zoican (Toronto)

2:30PM - 3:00PM
"Estimating Investor Preferences for Blockchain Security"

- Nir Chemaya (UCSB), Dingyue Liu (UCSB)

Best Paper Award Voting Closes at 3:10PM

Davis Auditorium, Schapiro CEPSR

3:00 - 4:00 PM

Networking & Best Paper Award Presentation

Carleton Commons, Mudd Building 4th Floor Campus Level, 500 W 120th St

Keynote Speakers

Gaurav Sukhatme, PhD

Fletcher Jones Foundation Endowed Chair in Computer Science and Professor of Computer Science and Electrical & Computer Engineering

University of Southern California

Big Data and Small Models: Lessons for Robotics

We have recently demonstrated the possibility of learning drone swarm controllers that are zero-shot transferable to real quadrotors via large-scale, multi-agent, end-to-end reinforcement learning. We train policies parameterized by neural networks that can control individual drones in a swarm in a fully decentralized manner. Our policies, trained in simulated environments with realistic quadrotor physics, demonstrate advanced flocking behaviors, perform aggressive maneuvers in tight formations while avoiding collisions with each other, break and re-establish formations to avoid collisions with moving obstacles, and efficiently coordinate in pursuit-evasion tasks. We will demonstrate the successful deployment of the model learned in simulation to highly resource-constrained physical quadrotors performing station-keeping and goal-swapping behaviors. Motivated by these results, and the observation that neural control of memory-constrained, agile robots requires small, yet highly performant models, we have begun a project that leverages graph hypernetworks to learn hyperpolicies trained with off-policy reinforcement learning. This results in networks that are two orders of magnitude smaller than commonly used networks yet encode policies comparable to those encoded by much larger networks trained on the same task. Our method can be appended to any off-policy reinforcement learning algorithm, without any change in hyperparameters, we illustrate this by showing early results across locomotion and manipulation tasks. The talk will conclude with some thoughts on the generality of such approaches for a more general class of devices with modest computational capabilities.

Gaurav S. Sukhatme holds the Fletcher Jones Foundation Endowed Chair in Computer Science at the University of Southern California (USC). He is Professor of Computer Science and Electrical & Computer Engineering and serves as the Executive Vice Dean at the USC Viterbi School of Engineering. He is an Amazon Scholar. He received his undergraduate education at IIT Bombay in Computer Science and Engineering, and M.S. and Ph.D. degrees in Computer Science from USC. He is the co-director of the USC Robotics Research Laboratory and the director of the USC Robotic Embedded Systems Laboratory, which he founded in 2000. His research interests are in networked robots, learning robots and field robotics. He has published extensively in these and related areas. Sukhatme has served as PI on numerous NSF, DARPA and NASA grants. He was a Co-PI on the Center for Embedded Networked Sensing (CENS), an NSF Science and Technology Center. He is a fellow of the AAAI, the IEEE and a recipient of the NSF CAREER award and the Okawa foundation research award. He is one of the founders of the Robotics: Science and Systems conference and the Editor-in-Chief of Autonomous Robots.

Jacob Devlin

Senior Staff Research Scientist

Google

Challenges and Opportunities in Large-Scale Language Modeling

In the last few years, neural network language models have been scaled to hundreds of billions of parameters, resulting in breakthrough capabilities as few-shot learners and conversational agents. In this talk, I will go over some of the challenges in training language models at this scale. Additionally, I will give an overview of key research areas in large-scale language modeling besides scale itself, such as meta-learning, optimization, sparsity, and efficient architectures.

Jacob Devlin is a Senior Staff Research Scientist at Google, where he works on deep learning models for natural language understanding. He is best known for developing the BERT model for language understanding. More recently, he co-led the PaLM project, which is Google's largest-scale language modeling effort to date.

Spark Talks

Sergul Aydore, PhD

Senior Applied Scientist, Amazon AI

Sergul has been a Senior Applied Scientist at Amazon AI, leading privacy in ML efforts. Her research interests are Differential Privacy and Robust Machine Learning. Before Amazon, Sergul was an Assistant Professor at the Stevens Institute of Technology, NJ, USA. She is also the General Chair for Women in ML Workshop at NeurIPS’22. Sergul received her PhD degree from the Signal and Image Processing Institute at University of Southern California in 2014. Her PhD work was on developing robust connectivity measures for neuroimaging data. Sergul’s scientific work has been published in AI and medical imaging venues.

Differentially Private Synthetic Data

In this talk, I will describe a formal solution for privacy-preserving data exchange. Imagine a hospital that wants to share its data with external researchers to accelerate scientific findings. Due to the sensitive nature of patient data, the data is anonymized to protect the privacy of individuals. However, anonymized data does not provide formal privacy guarantees and the utility of the data is likely to be destroyed. Instead, with help of machine learning tools, a synthetic version of the original data can be used for data exchange. The synthetic data represents the original data but is not the exact replica. To provide privacy guarantees, we rely on the notion of differential privacy. The differentially private synthetic data can then be shared between parties with formal privacy guarantees. I will present our approach for generating private synthetic data and demonstrate the usefulness for query release and machine learning tasks.

Suman Jana, PhD

Associate Professor of Computer Science, Columbia University

Suman Jana is an associate professor in the department of computer science and the data science institute at Columbia University. His primary research interest is at the intersections of computer security and machine learning. His research has received six best paper awards, a CACM research highlight, a Google faculty fellowship, a JPMorgan Chase Faculty Research Award, an NSF CAREER award, and an ARO young investigator award.

Efficient Neural Network Verification using Branch and Bound

In this talk, I will describe two recent Branch and Bound (BaB) verifiers developed by our group to ensure different safety properties of neural networks. The BaB verifiers involve two main steps: (1) recursively splitting the original verification problem into easier independent subproblems by splitting input or hidden neurons; and (2) for each split subproblem, using fast but incomplete bound propagation techniques to compute sound estimated bounds for the outputs of the target neural network. One of the key limitations of existing BaB verifiers is computing tight relaxations of activation functions' (i.e., ReLU) nonlinearities. Our recent works (α-CROWN and β-CROWN) introduce a primal-dual approach and jointly optimize the corresponding Lagrangian multipliers for each ReLU with gradient ascent. Such an approach is highly parallelizable and avoids calls to expensive LP solvers. Our verifiers not only provide tighter output estimations than existing bound propagation methods but also can fully leverage GPUs with massive parallelization. Our verifier, α, β-CROWN (alpha-beta-CROWN), won the second International Verification of Neural Networks Competition (VNN-COMP 2021) with the highest total score.

Govind Thattai, PhD

Principal Scientist, Amazon Alexa AI

Govind Thattai is a Principal Scientist at Alexa AI, and is leading the Multimodal science efforts for Embodied AI and Visual Question Answering. Prior to joining Amazon in 2018, Govind has worked in the areas of Speech Recognition, Computer Vision and NLU at BBN, KLA and eBay.

Embodied AI at Alexa
Embodied AI is the AI that enables a robotic agent to perceive, navigate, interact and learn in a 3D physical environment. This talk describes Alexa’s initiative to present Embodied AI as a University Challenge for Alexa Prize, and the new Embodied AI framework called Arena.

Ali Hirsa, PhD

(Moderator) Professor of Industrial Engineering and Operations Research, Columbia University

Ali Hirsa is a professor, director of financial engineering program, and director of Center for AI in business analytics & FinTech at Columbia University. Ali has worked at both sell-side and buy-side for more than 25 years. Ali’s research interests are AI/ML/DL applications in asset management and finance. Ali is author of two textbooks and is Editor-in-Chief of Journal of Investment Strategies. He is a frequent speaker at academic and practitioner conferences. Ali received his PhD in Applied Mathematics from University of Maryland at College Park under the supervision of Professors Howard C. Elman and Dilip B. Madan.

Panel: Autonomy & Trust

Eshan Bhatnagar, MBA

Head of Product, Alexa AI Natural Understanding

Eshan Bhatnagar is Head of Product at Alexa AI Natural Understanding team where he leads a multidisciplinary product, design and user research organization with a focus on applying AI across Natural Language Understanding, Computer Vision, Multimodal Learning & Generation, and Robotics to deliver delightful experiences to millions of Amazon customers worldwide. Eshan was an early product leader in the Amazon Alexa organization where he helped build the original Echo Show, and founded new product areas from scratch that are used by millions of customers worldwide. Eshan joined Amazon in 2013 where he led Commerce, Payments and Currency products at AWS, and helped launch AWS's local business in India. Prior to Amazon, Eshan led R&D efforts on multicore application processors at Freescale Semiconductors, and conducted technology and financial due-diligence for early-stage startups at Amiti Ventures. Eshan holds a bachelor’s degree in Electronics and Communications Engineering from Manipal Institute of Technology, India, and an MBA in Finance, Accounting, Entrepreneurship, and Marketing from the University of Chicago Booth School of Business.

David Boothe, PhD

Program Manager, Army Research Laboratory

David Boothe was born in Washington DC in 1967. He received a B.A. in Philosophy from the University of Maryland, College Park in 1989, and a Ph.D. in Computational Neuroscience from the Neuroscience and Cognitive Sciences program University of Maryland in 2007. He did Post-Doctoral research at the Rehabilitation Institute of Chicago, and the Feinberg School of Medicine at Northwestern University. He is currently the program manager of the Strengthening Teamwork for Novel Groups Collaborative Research Alliance at the Combat Capabilities Development Command, Army Research Laboratory. He specializes in human machine teaming, neuro-inspired intelligent system design, and brain modeling and simulation.

Kaleb McDowell, PhD

Branch Chief, Army Research Laboratory

Dr. Kaleb McDowell has a B.S in operations research and industrial engineering from Cornell University, and an M.S. in kinesiology and a Ph.D. in neuroscience and cognitive science both from the University of Maryland, College Park. He currently leads ARL’s Human-Guided System Adaptation Research Branch. Since joining ARL in 2003, Dr. McDowell has led several major research and development programs focused on neuroscience and human-intelligent technology integration; served as the chief scientist of the Humans in Complex Systems Directorate, and receiving Army Research and Development Achievement awards in 2007, 2009, and 2013; and ARL’s Director’s Awards in 2020.

Jeannette Wing, PhD

Executive VP for Research and Professor of Computer Science, Columbia University

Jeannette M. Wing is the Executive Vice President for Research and Professor of Computer Science at Columbia University. She joined Columbia in 2017 as the inaugural Avanessians Director of the Data Science Institute. From 2013 to 2017, she was a Corporate Vice President of Microsoft Research. She is Adjunct Professor of Computer Science at Carnegie Mellon where she twice served as the Head of the Computer Science Department and had been on the faculty since 1985. From 2007-2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. She received her S.B., S.M., and Ph.D. degrees in Computer Science, all from the Massachusetts Institute of Technology.

Garud Iyengar, PhD (Moderator)

Tang Professor of Operations and Senior Vice Dean for Research and Academic Programs, Columbia University

Garud Iyengar is the Tang Professor of Operations at Columbia Engineering. He received his B. Tech. in Electrical Engineering from IIT Kanpur, and an MS and PhD in Electrical Engineering from Stanford University. His research interests are broadly in control, machine learning and optimization. His published works span a diverse range of fields, including information theory, applied mathematics, operations research, economics and financing engineering. His current projects focus on the areas of large-scale power systems and supply chains, causal inference, and modeling of cellular processes. He was elected an INFORMS Fellow in 2018. He was the Chair of the Department of Industrial Engineering and Operations Research from 2013-19, and the Associate Director for Research at the Columbia Data Science Institute from 2017-19. He has been an Amazon Scholar since 2019. He is currently the Senior Vice Dean for Research and Academic Programs at Columbia Engineering.

Round Table Facilitators

Jack G. M. FitzGerald

Senior Scientist, Amazon Alexa

ROUND TABLE: Public Data Resources

Jack FitzGerald is a senior applied scientist with Amazon Alexa AI’s Natural Understanding group. His research interests include large language models, multilingual language understanding, efficient distributed training, and multitask modeling. Jack has been with Amazon since 2015 and Alexa since 2017. Before Amazon, he was an officer and nuclear physicist in the US Air Force.

Maryam Zolnoori, PhD, Research Scientist, Columbia University Irving Medical Center

ROUND TABLE: Public Data Resources

Maryam Zolnoori, PhD, is a research scientist at Columbia University Irving Medical Center. She received her PhD in Biomedical Informatics and her Master in both Information Technology and Health Informatics. Her research have focused on utilizing advanced data science methods and routinely generated data in clinical settings to build decision support tools for improving quality of healthcare. She is the author of more than 50 articles and recipient of several research awards from National Library of Medicine, National Institute on Aging, Federal Drug Administration, Mayo Clinic, and Columbia Center of AI Technology.

Sergul Aydore, PhD

Senior Applied Scientist, Amazon AI

ROUND TABLE: Responsible AI - Privacy

Sergul has been a Senior Applied Scientist at Amazon AI, leading privacy in ML efforts. Her research interests are Differential Privacy and Robust Machine Learning. Before Amazon, Sergul was an Assistant Professor at the Stevens Institute of Technology, NJ, USA. She is also the General Chair for Women in ML Workshop at NeurIPS’22. Sergul received her PhD degree from the Signal and Image Processing Institute at University of Southern California in 2014. Her PhD work was on developing robust connectivity measures for neuroimaging data. Sergul’s scientific work has been published in AI and medical imaging venues.

Roxana Geambasu, PhD

Associate Professor of Computer Science, Columbia University

ROUND TABLE: Responsible AI - Privacy

Roxana Geambasu is an Associate Professor of Computer Science at Columbia University and a member of Columbia's Data Sciences Institute. She joined Columbia in Fall 2011 after finishing her Ph.D. at the University of Washington. For her work in cloud and mobile data privacy, she received an Alfred P. Sloan Faculty Fellowship, a Microsoft Research Faculty Fellowship, an NSF CAREER award, a ``Brilliant 10'' Popular Science nomination, an Early Career Award in Cybersecurity from the University of Washington Center for Academic Excellence, the Honorable Mention for the 2013 inaugural Dennis M. Ritchie Doctoral Dissertation Award, a William Chan Dissertation Award, two best paper awards at top systems conferences, and the first Google Ph.D. Fellowship in Cloud Computing.

Rahul Gupta, PhD

Manager, Trustworthy Alexa AI

ROUND TABLE: Responsible AI - Fairness

Rahul Gupta is a Senior Applied Science manager at the Spoken Language Understanding Innovations (SLU-Innovations) team in Cambridge, Massachusetts. Since joining the Alexa organization, he has focused on designing NLU models for scalability and speed. Some of his more recent research focuses on Trustworthy Machine Learning with emphasis on privacy preserving techniques, fairness and federated learning. He received his PhD from the University of Southern California in 2016 on interpreting non-verbal communications in human interaction. He has published several papers at avenues such as EMNLP, ACL, NAACL, ACM Facct and IEEE-Transactions. He is also co-inventor on ten patent pending technologies at Amazon.

Shih-Fu Chang, PhD

Dean of Columbia Engineering and Morris A. and Alma Schapiro Professor

ROUND TABLE: Responsible AI - Fairness

Shih-Fu Chang is Dean of Columbia Engineering and Morris A. and Alma Schapiro Professor. He leads the education, research, and innovation mission of the School and has greatly contributed to its growth and advancement, propelling it to be one of the top engineering programs in the nation.

As one of the most influential experts in multimedia, computer vision and artificial intelligence, his research has led to development of innovative image search tools, which have been used by major media companies and law enforcement agencies in fighting online human trafficking crimes. He has also launched AI tools for online disinformation detection and attribution.

Dean Chang is a Fellow of the American Association for the Advancement of Science, the Association for Computing Machinery, and IEEE, and an elected member of Academia Sinica. He received the Great Teacher Award from the Society of Columbia Graduates and is director of the Columbia Center of AI Technology in collaboration with Amazon.

All presentations at Tokenomics 2023 will take place in Davis Auditorium in Schapiro CEPSR on Columbia University's Morningside Campus in NYC. Registration and all meals will be located next door in Carleton Commons (Mudd Building, 4th Floor Campus Level, 500 W 120th St, NYC). Please check in first at the registration desk in Carleton Commons before heading over to Davis Auditorium in Schapiro CEPSR.

Tokenomics is a premier international forum that focuses on the theory, design, analysis, implementation, and applications of platform economics, blockchains, and smart contracts. The conference aims to bring together leading economists, computer science researchers, and industry practitioners who are actively working on advancing the field of blockchain economics and technologies. The program offers a unique opportunity to engage with outstanding invited speakers and academic presentations.

This year's conference is hosted by the Columbia Center for Digital Finance and Technologies, and the Briger Family Digital Finance Lab, further amplifying its impact and relevance.

Organizing institutions: Columbia University and University of Pennsylvania

REGISTER

JOIN OUR MAILING LIST

ABOUT THE HOSTS

The Center for Digital Finance and Technologies within the School of Engineering and Applied Sciences at Columbia University aims to address the societal needs of Fintech companies, and understand the promises of emerging financial innovation. The Center leverages multi-disciplinary expertise at Columbia in diverse domains such as computer science, engineering, data science, finance, and economics, to answer these questions.

The Briger Family Digital Finance Lab seeks to bring together academics and practitioners to explore this space, and to understand challenges and opportunities such as the fundamental economics of blockchains, decentralized market microstructure, and mechanisms for decentralized organization and governance.

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