Erik Hemberg, Ph.D

Research Scientist

Anyscale Learning For All (ALFA) Group
MIT Computer Science and Artificial Intelligence Lab


Email: hembergerik (@)
Office: 32-D540 Stata Center


Erik Hemberg is a Research Scientist with ALFA Group at MIT-CSAIL. His work focuses on developing autonomous, proactive cyber defenses that are anticipatory and adapt to counter attacks. Novel methods for: cyber-hunting, automated methods of cyber-attack and defense scenarios in Software Defined and Peer-to-Peer Networks, models of networks and a proof-of-concept for how adversarial modeling can inform adaptive cyber security defenses and pro-actively design better network protocols. Additional work includes: automated semantic parsing of law, predicting stop-out in Massive Open Online Courses (MOOCs) and analyzing neuronal development of autism. Research sponsorships include: cybersecurity research with DARPA XD3, DARPA CHASE program, Lincoln Labs and CyberSecurity @CSAIL. Data science for MOOC with MIT-HKUST alliance. Erik also supervises Ph.Ds, SM, M.Eng students and UROPs.

Most recently, Erik won: Best Student Paper Award at IEEE Learning With MOOCs 2019, Outstanding Poster IBM Research AI Horizons Colloquium 2018; HUMIES Bronze award at GECCO 2017; Co-author of Best Paper at the NIPS 2016 Machine Learning for Health workshop; is a member of the founding team of the YCombinator Fellowship, 2016. Erik's work was featured in the NY Times article, Computer Scientists Wield Artificial Intelligence to Battle Tax Evasion, in 2015.

Erik completed his M.Sc in Industrial Engineering and Management at Chalmers University of Technology, Gothenburg, Sweden in 2003. He went on to get his Ph.D. in Computer Science from the University College Dublin, Computer Science and Informatics Department, Dublin, Ireland in 2010 as a member of the Natural Computing Research & Applications (NCRA) in UCD's Complex and Adaptive Systems Lab (CASL). Erik continued on as a Postdoc with NCRA group in CASL, University College Dublin, where he lead industrial collaboration with Bell Labs Dublin, researched Evolutionary Computation as a method for creating pilot power control algorithms for Femtocells. He worked on developing Grammatical Evolutions in Matlab in addition to hiring and managing a research assistant for 12 weeks, supervising Ph.D. students and final year undergraduate projects.

Erik joined ALFA Group as a Postdoc in 2012 where he lead research in Simulating Tax Evasion and Law with Heuristics with MITRE Corp, collaborated with Sentient Ltd researching Evolutionary Computation on Volunteer Compute for Machine Learning. Erik developed and delivered a "blended" online course in Evolutionary Processes and Systems for Shantou University in China and material to US kids. He assisted grant writing for cybersecurity research projects and supervised S.M., M.Eng. students and UROPs. Erik is the author of a multitude of academic papers, journal articles, and scholarly book chapters.


Awards & Honors

2019 Best Student Paper, IEEE Learning With MOOCs 2019, Milwaukee WI
2018 Outstanding Poster IBM Research AI 2018 AI Horizons Colloquium, Cambridge MA
2017 HUMIES Bronze award at the GECCO Conference, Berlin Germany

Co-author of Best Paper at the NIPS 2016 Machine Learning for Health workshop

YCombinator Fellowship 2016, member of founding team


MIT Translational Fellow Award Fall 2015, 20% of salary funded for pursuing translation of research to technology

Peter Jackson Award for Best Innovative Application Paper at 15th ICAIL for STEALTH paper

STEALTH won finalist highlight award at the MIT CSAIL Amazing Research Highlights Competition

Supervised best thesis for Computer Aided Tax Avoidance Policy Analysis from the Technology and Policy Program


Top 3 Publications

2021 E Hemberg, J Toutouh, A Al-Dujaili, T Schmiedlechner, U-M O'Reilly, Spatial Coevolution Generative Adversarial Network Training, ACM Transactions On Evolutionary Learning and Optimization, 1, 2, Article 6, 2021
2020 UM O'Reilly, J Toutouh, M Pertierra, D Prado-Sanchez, A Lugo, D Garcia, J Kelly, E Hemberg, Adversarial genetic programming for cyber security: a rising application domain where GP matters, Genetic Programming and Evolvable Machines 21, 219-250, 2020
2015 E. Hemberg, J Rosen, G Warner, S Wijesinghe, UM O'Reilly, Tax non-Compliance Detection Using Co-Evolution of Tax Evasion Risk and Audit Likelihood, International Conference on AI and Law, 2015, San Diego, (Peter Jackson Award for Best Innovative Application Paper. Invited and extended as a journal article in the AI and Law Journal.)

For additional publications please see the ALFA Publications page


Invited Talks

Introduction to Genetic Programming Tutorial on Genetic Programming, GECCO Tutorial, Virtual July 10 2021

Evolutionary Computation Application: Cybersecurity SIGEVO Summer School 2021, Virtual, 8 2021 

How to make a conference presentation SIGEVO Summer School 2021, Virtual, https://gecco-2021. 6 2021

Adversarial AI Cybersecurity for Robotics 2020 Conference, Virtual, https://cybersecurityforrobotics. com/conference-csfr2020/December 18 2020

Scalable Machine Learning, Evolutionary Algorithms, and Frameworks The 2nd Workshop on Deep Models and Artifcial Intelligence for Defense Applications: Potentials, Theories, Prac- tices, Tools and Risks, AAAI Fall Symposium, Virtual, November 11 2020

Introduction to Genetic Programming Tutorial on Genetic Programming, GECCO Tutorial, July 2020

Learning Analytics for Computational Thinking (STEM) in Higher Education and K-12 CITE, Hong Kong University, January 15 2020

Artifcial Adversarial Intelligence for Cyber Systems Boston ROTC Joint Service Conference, October 5 2019

Spatial Coevolutionary Generative Adversarial Networks Oak Ridge National Laboratory, August 2019

Introduction to Genetic Programming Tutorial on Genetic Programming, GECCO Tutorial, July 2019

Autonomous Adaptive Cyber Systems Talk on AI and Cyber Security, MIT Lincoln Labs, May 2019

Practical Data Science Seminar on practical Data Science, CoLAB-Workshop-2019, CoLAB Workshop, Uruguay, May 2019

Adversarial AI with RIVALS Army Research Office Invitational Workshop on Foundations of Autonomous Adaptive Cyber Systems, George Mason University, April 2019

STEALTH - a New Tool for Catching Tax Cheats Talk on AI and tax for MIT Club of Cape Cod, March 29, 2019

Analysing Educational Data With MOOC-Learner-Project LearnLab Summer School, Carnegie Mellon University, July 2018 ; CITE, Hong Kong University, July 2018 ; for MIT-ILP at Kawasaki Heavy Industries, ASICS, Toshiba, Japan, July 2018

Proactive Cyber Security for MIT-ILP at Hitatchi, Konica Minolta, Japan, July 2018

Algorithmic Simulation of Tax Avoidance and Automated Representation of Law Talk on AI and tax at American Tax Policy institute conference on improving tax system at Northwestern Law, Law & Technology seminar series, March 28 2017

Simulating Tax Evasion And Law Through Heuristics Talk on AI and tax at American Tax Policy institute conference on improving tax system in Washington DC, November 18, 2016


Computing in High-Energy AstroParticle Research Talk on Genetic Programming at work- shop at Ohio State University, August 24 2016

Simulating Tax Evasion And Law Through Heuristics Talk on Simulating Tax Avoidance heuristics at CodeX seminar at Stanford University, September 10 2015