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It's an AI/ML world. But is that software being used, as it should?
Is it optimized? Fair? Unbiased? Explainable?
Shouldn't more people be checking that? Improving that?

So I seek talented grad students & industrial partners to find + fix the problems in real-world AI/ML.
Is that you? Maybe "yes" if you want to be a leader in AI (and not just another follower).

videos |        

News:   Tim Menzies (IEEE Fellow, Ph.D., UNSW, 1995) is a full Professor in Computer Science at North Carolina State where he explores how SE can improve optimization, ethics, and explainable AI. He is the director of the RAISE lab (real world AI for SE) and the author of over 280 publications (refereed) with 20,000+ citations and an h-index of 69. He has graduated 18 Ph.D. students, and has been a lead researcher on projects for NSF, NIJ, DoD, NASA, USDA (total funding of $13+ million) as well as joint research work with private companies. Prof. Menzies is the editor-in-chief of the Automated Software Engineering journal and associate editor of TSE (IEEE Transactions on Software Engineering) and other leading SE journals. For more, see his web site


For Students

My graduated Ph.D. students:
Scott Chen David Owen Ashutosh Nandeshwar Ekrem Kocaguneli Abdel Salem Sayyad Fayola Peters Joe Krall Greg Gay Wei Fu Vivek Nair Amritanshu Agrawal Jianfeng Chen Rahul Krishna Zhe Yu Joymallya Chakraborty Rui Shu Shrikanth Chandrasekaran Tianpei Xia Huy Tu

My current Ph.D. students:
Xueqi(Sherry) Yang Xiao Ling Kewen Peng Suvodeep Najumder Andre Motta Andre Motta

Ask me how to accelerate your SE career (in research and/or industry). For example, by exploring:

  • Optimizing AL/ML systems.
  • Dramatically reducing ML's cost.
  • Making AI explainable.
  • Measuring and mitigating ML discrimination.
See also my work on learning significant patterns from software project data:
  • Defect prediction
  • Effort estimation
  • Configuration (including cloud config)
  • Static code analysis false alarms
  • Technical debt
  • Reduce cost of testing

Thinking about graduate studies at NC State?

Applying: American universities accept new graduate students for start-of-study in mid-August and mid-January. The application process for those dates starts months in advance. You should be planning your application at least one year before your desired start date. I have no authority to accept students but, once they are accepted, I can supervise graduate students. So please review our admissions procedures at our departmental Web site. That said, during the admissions review process, all faculty are sent a sheet listing the candidate students and I am allowed to "nudge" a handful of names. So if you elect to enroll here then please ping me so I can watch for your application in the system.

Tips for writing to Professors: When writing to a CS prof in the USA, here is a big tip on how to attract their attention. Do not send a form letter-- we get enough of those. To prove that you are not sending a form letter, best to make some reference to their current research, perhaps even to one of their recent papers (and how your prior work or interests match up). Demonstrate that you have done a little homework before sending and email about the department. E.g.

  • Read over the department's recruitment policies and say things like "I am targeting an application for your next round of applications for (say) January 2016 which I will submit to ".

Working with me: As to me supervising you, I do not take on students until they have completed one of my grad subjects. This lets us check each other out before we commit too much to each other. As to funding, I do not guarantee that my supervised students get funding from me. Funding comes and goes depending on the whims of the funding agencies and my policy is to fund my long-term Ph.D. students before anyone else.

  • That said, for your information, over the last decade, 75% of my students were fully funded.

Fall 2023 Indepdent Study Topics

For fall 2023, I will be running teams of ugrad, masters, PhDs working on the following topics.

Please note that:

  • Indepdent study is an unpaid position where you work for the ideas (and for the grade), not cash.
  • Depedending on your dedication and success on these topics, this work would be suitable for publciation and a top-tiered refereed event.
  • Also, for Ph.D. Students I go on to say
    • This work would be suitable for a Ph.D. topic.
    • In 2024, this work might be able to fund a few of you as a GRA (depending on the random number generator called "funding bodies").

The best data is fake data?


Is (Human and AI) better than (Human or AI)?

Why wait for data? Why not synthesis it? Many fields have been fumblding and bumbling around with synthetic data generation, for decades. But a new generation of synthesis algirithms suggest that all that prior work could be blown away by much better and much faster algoriths (e.g. GAN is really slow and new methods are far faster). By exploring those new algorithms we could:
  • making the data needed to stress test a system;
  • changing existing biases in data, thereby (e.g.) removing data discrimination;
  • create all the data needed to satisfy the needs of data hungry machine learning algorithms.
  • impute missing information in existing data;
  • enable data sharing, without incurring the wrath of legislative bodies (in this way, organizations can share insights, thereby assisting in scientific reasoning).
  • When most people work with the synthetic , not the real data, then this increases data security;
  • Synthetic data can exploit current trends in data, thereby supporting forecasting.

Interested? Then please read more and some more.

To forge an effective partnership, humans and artificial intelligence (AI) need to understand each another’s strengths and limitations. For example, AI tools work better when taking advice from one person but have issues dealing with advice from large teams. Therefore, we propose a system to extend current AI tools with particle-swarm optimization and generative transformer models to handle teams; specifically:
  • debates and disagreements between team members;
  • team members with an established track record of offering good/bad advice; and
  • team members that (consciously or unconsciously) offer advice that leads to discriminatory models.

Interested? Then please read more and some more.