POSTDOCS IN DEEP LEARNING

Start date: Spring 2021

Baylor.AI is a forming research group at the Department of Computer Science at Baylor University.

We are interested in a variety of hot topics, such as graph-neural algorithms, recommendation systems, deep learning, and societal implications of deep learning. In our group we try to apply and unite the approaches and techniques of theory and implementation. Members of our group focus on algorithms and deep learning theory and some on systems and applications. All of us work together to build scalable, fault-tolerant, and efficient primitives and applications for the future of computing.

Job description -- The successful applicant will work together with a team of PhD students and faculty member in the area of deep learning with an emphasis on Graph Neural Networks, Natural Language Processing (NLP), or Learning Representations Theory. The role of the post-doctoral researcher will be to provide a strong theoretical support for our work and help in defining interesting directions for further research. Independence, curiosity and innovation are very much appreciated. The duration of the post-doctoral appointment is for a minimum of one year and a maximum of four years contingent to availability of funds. The salary is in accordance with Baylor standards and commensurate with experience.

Your profile -- We are looking for a researcher with a strong mathematical background, and an outstanding doctoral degree in Computer Science, Mathematics or a related field. A candidate should be proficient in machine learning, deep learning, statistics and learning theory. Although experience working on NLP and/or GNNs and/or LRs is appreciated, it is not required; however, the candidate must be keen on working on these topics.

Interested? -- We look forward to receiving your application with the following documents: letter of interest, and CV with a list of publications and two references. Please note that before formally applying to this position, you should send the above documents directly to Dr. Pablo Rivas to receive feedback and increase your chances of success when applying.

PhD Positions

Start date for all positions: Spring 2021

Baylor.AI is a forming research group at the Department of Computer Science at Baylor University. Current opportunities are under Dr. Pablo Rivas' supervision, preferably, but not limited to.

Adversarially Robust System-2 Algorithms

We are seeking doctoral students with a strong background and interest in Mathematics, Optimization, Machine Learning, and Deep Learning. Previous evident exposure to Optimization, either via courses or documented self study, is a must.

The PhD project will contribute to the development of the theoretical foundations of System-2 models and algorithms that are adversarially robust. Adversarial robustness will need to be certifiable, which justifies the required experience in numerical optimization methods.

Interested? -- We look forward to receiving your application! You may contact Dr. Pablo Rivas directly if you have questions about the opportunity; however, we recommend that you apply right away. To apply click here.

Unsupervised deep learning and cryptography

In the wide state-of-the-art concerning non-supervised attacks, machine-learning techniques appeared about ten years ago. In particular clustering methods attracted considerable interest. Today, the deep-learning research makes clustering algorithms evolve, in particular through "embedding" techniques. These techniques aim at represent data into a space that enhances certain “useful” relations among data. The principal application domain of these techniques today is the representation of words for the natural language analysis: a useful representation should embed words into a space where words belonging to the same semantic field are close to each other. The goal of this research is studying “deep embedding” techniques, evaluating their suitability for non-profiled attack scenarios, in particular in the context of both symmetric and public key cryptographic algorithms, formalizing an efficient deep-clustering-based attack strategy and deeply analyzing its properties.

Interested? -- We look forward to receiving your application! You may contact Dr. Pablo Rivas directly if you have questions about the opportunity; however, we recommend that you apply right away. To apply click here.

Data Representations and Deep Learning

We are seeking doctoral students with a strong background and interest in Mathematics, Optimization, Machine Learning, and Deep Learning. Previous evident exposure to Optimization, either via courses or documented self study, is a must.

The PhD project will contribute to the development of the theoretical foundations of Efficient Data Representation and Machine Learning over different applications. This can be done using self-supervised methods or other traditional approaches.

Interested? -- We look forward to receiving your application! You may contact Dr. Pablo Rivas directly if you have questions about the opportunity; however, we recommend that you apply right away. To apply click here.

About Baylor.AI

Baylor's AffinitY Lab for Orthopractic & Robust AI is a forming research group in the Department of Computer Science at Baylor University. The general aim of the group is to further the field of machine learning through rigorous research in deep learning and its applications while upholding ethical standards of fairness, diversity, and inclusivity whenever possible.

The group aims to be self-funded through research grants involving faculty/student collaborative work.

We believe that having a diverse and inclusive team will help us to advance AI, for the betterment of human life. We value different viewpoints. All backgrounds, ideas, and perspectives are welcome.

Volunteer with us

By working with our group, you will:

• Work on important problems in areas such as healthcare and climate change, using AI.
• Build and deploy machine learning / deep learning algorithms and applications.

Values

Here are some values that we would like to see in you:

• Hard work: We expect you to have a strong work ethic. Many of us work evenings and weekends because we love our work and are passionate about the AI mission. We also value velocity, and like people that get things done quickly.
• Flexibility: You should be willing to dive into different facets of a project. For example, besides developing machine learning algorithms, you may also need to work on data acquisition, conduct user interviews, or do frontend engineering. This may also require going outside your comfort zone, and learning to do new tasks in which you’re not an expert.
• Learning: You should have a strong growth mindset, and want to learn continuously. This can involve reading books, taking coursework, talking to experts, or re-implementing research papers. We will also prioritize your learning and help point you in the right direction; but you need to put in the work to take advantage of this.
• Teamwork: We will work together in small teams. You are expected to support and collaborate with others; in turn you will also receive support from your teammates.

Prerequisites

You should have a strong ML background, or a strong software engineering background.

• ML/AI background: You should have a solid background in probability and linear algebra, and have done well in AI/ML coursework. For example, before applying, Baylor undergraduate students should have taken CSI 4352 and graduate students CSI 5325. Previous ML/AI research experience would be a plus but is not required.
• Software engineering background: We also encourage engineers without much AI background who are interested in developing ML applications to apply. Applicants should have made significant contributions to software projects in the past, for example through developing software systems at a company or through significant open source contributions.

Applying

Please email us at rivas@baylor.ai with your resume (and your transcript if you're a student) and two paragraphs on why you’d like to get involved. We expect that volunteers will commit 25 hours a week as a minimum.

Baylor Students

• Outside of coursework, we expect this to be your primary academic activity. As it takes time to familiarize oneself with a research project and to make significant contributions, we expect that students will be involved for at least two quarters, with a strong preference for those who can potentially stay involved for the full school year.

Non-Baylor student Volunteers

• You must be authorized to work in the United States and able to work on the Baylor University campus. We are not able to sponsor visas nor take on volunteers that want to work remotely.
• Volunteers must be available for at least 12 weeks of research, with a strong preference for volunteers who can potentially stay involved for longer.

About Faculty

Pablo Rivas, Ph.D.

Dr. Rivas worked in industry for a decade as a software engineer before becoming an academic. He is a Senior Member of the IEEE, ACM, and SIAM. He was formerly at NASA Goddard Space Flight Center, and at Marist College in NY doing research and teaching. He considers himself a machine learning freak, deep learning evangelist, AI ethicist, and is a proponent of the democratization of machine learning and artificial intelligence in general. He is also an ally of women in computing and co-chairs the ACM New York Celebration of Women in Computing conference.

Dr. Rivas is a published author and presents his work in conferences such as ICML, NeurIPS, ACL, and AAAI; he is currently a machine learning consultant of Marist College's Cloud Computing and Analytics Center.

Prof. Rivas prefers Vim over Emacs and spaces over tabs.

• Postdoc at the Computer Science Department, Baylor University, 2015
• PhD, Electrical and Computer Engineering, The University of Texas at El Paso, 2011
• MS, Electrical Engineering, Chihuahua Institute of Technology, Mexico, 2007
• BS, Computer Science, Nogales Institute of Technology, Mexico, 2004

More faculty profiles will be addedd soon.

Contact

To inquire about Postdoc opportunities or PhD positions, please contact Dr. Pablo Rivas. The best way to contact Dr. Rivas is through email rivas@baylor.ai or LinkedIn.

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