Derek Riley, Ph.D.

Professor, Program Director Milwaukee School of Engineering

  • Milwaukee WI

Dr. Derek Riley is an expert in machine learning, deep learning, artificial intelligence, simulation, and high-performance computing.

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Spotlight

3 min

Key topics at RNC 2024: Artificial Intelligence, Machine Learning and Cybersecurity

As the Republican National Convention 2024 begins, journalists from across the nation and the world will converge on Milwaukee, not only to cover the political spectacle but also to cover how the next potential administration will tackled issues that weren't likely on the radar or at least front and center last election: Artificial Intelligence, Machine Learning and Cybersecurity With technology and the threats that come with it moving at near exponential speeds the next four years will see challenges that no president or administration has seen before. Plans and polices will be required that impact not just America but one a global scale. To help visiting journalists navigate and understand these issues and how and where the Republican policies are taking on these topics our MSOE experts are available to offer insights. Dr. Jeremy Kedziora, Dr. Derek Riley and Dr. Walter Schilling are leading voices nationally on these important subjects and are ready to assist with any stories during the convention. . .    . Dr. Jeremy Kedziora Associate Professor, PieperPower Endowed Chair in Artificial Intelligence Expertise: AI, machine learning, ChatGPT, ethics of AI, global technology revolution, using these tools to solve business problems or advance business objectives, political science. View Profile “Artificial intelligence and machine learning are part of everyday life at home and work. Businesses and industries—from manufacturing to health care and everything in between—are using them to solve problems, improve efficiencies and invent new products,” said Dr. John Walz, MSOE president. “We are excited to welcome Dr. Jeremy Kedziora as MSOE’s first PieperPower Endowed Chair in Artificial Intelligence. With MSOE as an educational leader in this space, it is imperative that our students are prepared to develop and advance AI and machine learning technologies while at the same time implementing them in a responsible and ethical manner.” MSOE names Dr. Jeremy Kedziora as Endowed Chair in Artificial Intelligence MSOE online March 22, 2023 . .     . Dr. Derek Riley Professor, B.S. in Computer Science Program Director Expertise: AI, machine learning, facial recognition, deep learning, high performance computing, mobile computing, artificial intelligence View Profile “At this point, it's fairly hard to avoid being impacted by AI," said Derek Riley, the computer science program director at Milwaukee School of Engineering. “Generative AI can really make major changes to what we perceive in the media, what we hear, what we read.” Fake explicit pictures of Taylor Swift cause concern over lack of AI regulation CBS News January 26, 2024 . .    . Dr. Walter Schilling Professor Expertise: Cybersecurity and the latest technological advancements in automobiles and home automation systems; how individuals can protect their business operations and personal networks. View Profile Milwaukee School of Engineering cybersecurity professor Walter Schilling said it's a great opportunity for his students. "Just to see what the real world is like that they're going to be entering into," said Schilling. Schilling said cybersecurity is something all local organizations, from small business to government, need to pay attention to. "It's something that Milwaukee has to be concerned about as well because of the large companies that we have headquartered here, as well as the companies we're trying to attract in the future," said Schilling. Could the future of cybersecurity be in Milwaukee?: SysLogic holds 3rd annual summit at MSOE CBS News April 26, 2022 . .     . For further information and to arrange interviews with our experts, please contact: Media Relations Contact To schedule an interview or for more information, please contact: JoEllen Burdue Senior Director of Communications and Media Relations Phone: (414) 839-0906 Email: burdue@msoe.edu . .     . About Milwaukee School of Engineering (MSOE) Milwaukee School of Engineering is the university of choice for those seeking an inclusive community of experiential learners driven to solve the complex challenges of today and tomorrow. The independent, non-profit university has about 2,800 students and was founded in 1903. MSOE offers bachelor's and master's degrees in engineering, business and nursing. Faculty are student-focused experts who bring real-world experience into the classroom. This approach to learning makes students ready now as well as prepared for the future. Longstanding partnerships with business and industry leaders enable students to learn alongside professional mentors, and challenge them to go beyond what's possible. MSOE graduates are leaders of character, responsible professionals, passionate learners and value creators.

Derek Riley, Ph.D.Walter Schilling, Jr., Ph.D.Jeremy Kedziora, Ph.D.

1 min

From Facial Recognition to Deepfakes: What Could Be Done With Your Image?

Facial Recognition: Convenience and Controversy Facial recognition technology is everywhere, making our day-to-day tasks faster and more convenient. It offers substantial benefits, from enhanced security measures to streamlined user experiences. Airports utilize it for faster check-ins, smartphones use it for secure authentication, and law enforcement agencies employ it for identifying suspects. However, the technology also raises considerable privacy concerns. The pervasive deployment of facial recognition without adequate oversight can lead to unwarranted surveillance, potential biases in profiling, and the erosion of personal privacy. The Rise of Deepfake Technology Meanwhile, deepfake technology has advanced rapidly, leveraging AI to create highly realistic synthetic, or "fake", media. These hyper-realistic videos, showing individuals doing or saying things they never actually did, have become a significant concern. The potential misuse of deepfakes ranges from spreading misinformation and manipulating elections to causing personal distress by enabling crimes like fraud and defamation. Dr. Derek Riley, a seasoned media expert, professor and program director of the B.S. in Computer Science program at Milwaukee School of Engineering, is available to discuss how these technologies work, how they're regulated, how they can be used in a positive manner, and how individuals can protect themselves.

Derek Riley, Ph.D.

2 min

Tracking down those who tried to capture the Capitol buildings – our expert can explain how they’re doing it

On January 06, America watched with shock as a mob of protesters stormed the gates in Washington, D.C. and invaded the Capitol buildings. For hours, the rioters looted and occupied America’s halls of power and though some were apprehended, many found a way to get out and get back home avoiding arrest. However, media coverage was substantial and some of the protesters were even bold enough to be caught posing for social media. Slowly, authorities are tracking them down, and Dr. Derek Riley, an expert at Milwaukee School of Engineering (MSOE) in the areas of computer science and deep learning, has been explaining how artificial intelligence (AI) technology that’s taught at MSOE is capable of enabling law enforcement's efforts to identify individuals from pictures. "With these AI systems, we’ll show it example photos and we’ll say, 'OK, this is a nose, this is an ear, this is Billy, this is Susie,'" Riley said. "And over lots and lots of examples and a kind of understanding if they guess right or wrong, the algorithm actually tunes itself to get better and better at recognizing certain things." Dr. Riley says this takes huge amounts of data and often needs a supercomputer—like MSOE's "Rosie"— to process it. To get a computer or software to recognize a specific person takes more fine-tuning, Riley says. He says your smartphone may already do this. "If you have a fingerprint scan or facial recognition to open up your phone, that’s exactly what’s happening," Riley said. "So, they’ve already trained a really large model to do all the basic recognition, and then you provide a device with a fingerprint scanning or pictures of your face at the end to be able to fine-tune that model to recognize exactly who you are." Riley says this technology isn't foolproof—he says human intelligence is needed at every step. He added we might be contributing to the data sources some of the technology needs by posting our pictures to social media. "Folks are uploading their own images constantly and that often is the source of the data that is used to train these really, really large systems," Riley said. January 14 – WTMJ, Ch. 4, NBC News The concept of facial recognition and the use of this technology in law enforcement (and several other applications) is an emerging topic – and if you are a reporter looking to cover this topic or speak with an expert, then let us help. Dr. Derek Riley is an expert in big data, artificial intelligence, computer modeling and simulation, and mobile computing/programming. He’s available to speak with media about facial recognition technology and its many uses. Simply click on his icon now to arrange an interview today.

Derek Riley, Ph.D.

Education, Licensure and Certification

Ph.D.

Computer Science

Vanderbilt University

2009

M.S.

Computer Science

Vanderbilt University

2006

B.S.

Computer Science

Wartburg College

2004

Biography

Dr. Derek Riley joined the MSOE faculty in 2016 and is a professor in the Computer Science and Software Engineering Department. He is also program director of MSOE’s Bachelor of Science in Computer Science program, which has a focus in artificial intelligence. In addition to teaching at MSOE, Riley provides consulting services and expert witness services related to machine learning, deep learning, facial recognition, computational modeling, high-performance computing, and other related fields. His areas of expertise include deep learning, machine learning, computer vision, algorithms, process modeling and simulation, Scrum, and mobile computing/programming. He is an NVIDIA DLI Certified Instructor.

Areas of Expertise

Large Language Models, Generative AI
Machine Learning
Deep Learning
Computational Science
Computer Science
Algorithms
High-Performance Computing
Scrum
Software Engineering
RAG LLM

Affiliations

  • Association for Computing Machinery (ACM) : Member

Social

Media Appearances

Fake explicit pictures of Taylor Swift cause concern over lack of AI regulation

WDJT - Ch. 58 - CBS  tv

2024-01-27

Dr. Derek Riley weighs in on fake explicit pictures of Taylor Swift and cause for concern over lack of AI regulation

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Milwaukee tech leaders discuss deepfakes and advancing Artificial Intelligence technology

WDJT - Ch. 58 - CBS  tv

2023-12-14

Dr. Derek Riley discusses the latest addition to the world of artificial intelligence known as deepfake models, an extension of deep learning, which creates false videos or images which have been altered to misrepresent a person or situation which has never happened.

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Detroit Today: How facial recognition software in criminal investigations can harm communities of color

Detroit Public Radio - WDET 101.9FM  radio

2023-08-11

Dr. Derek Riley explains how biases get embedded in AI systems and facial recognition technologies.

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Event and Speaking Appearances

Invited Talk

Wisconsin Technology Association Conference  

2019-05-08

AI Education

Wisconsin Technology Council Early Stage Symposium  

2019-06-11

Invited Talk

Wisconsin Society of Professional Engineers Discovery Conference  

2019-04-30

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Selected Publications

An Investigation on Machine Learning Models for the Prediction of Cyanobacteria Growth

Journal of Fundamental and Applied Limnology

Giere, Johannes; Riley, Derek; Nowling, R. J.; McComack, Joshua; Sander, Hedda

2020

Harmful algal blooms, which are a danger to the lives of humans and animals, are caused by a sudden increase in the concentration of cyanobacteria in freshwater lakes. Cyanobacteria concentrations can be reliably measured using chemical and biological indicators, but the measurement process of the indicators is either labor-intensive or very costly. These limitations do not allow the general public to measure concentrations, so local health organizations or departments regularly assume the responsibility of measuring water quality. While computational models exist to predict algal concentrations, the accuracy of these models and need for customization due to varied lake conditions make them generally not yet reliable. We find that common regression-error functions cannot sufficiently evaluate the performance of cyanobacteria prediction models because the occurrence of harmful algal blooms is rare. Therefore, we present a method of forecasting cyanobacteria concentrations in freshwater lakes based on a machine-learning model trained on a dataset from Lake Utah with automatically-measured indicators from lake buoys. We compare several models and find that a support vector machine with a radial basis function kernel for regression reliably forecasts harmful algal blooms using comparatively few and easy-to-obtain input parameters. The special feature of the model is that it exclusively uses variables that can be measured by the general public without great effort and costs, and the amount of data necessary to train such a model is relatively minimal, allowing different models to be trained to accommodate for the nuances of different lakes.

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Diurnal vertical migration of cyanobacteria and chlorophyta in eutrophied shallow freshwater lakes

Fundamental and Applied Limnology / Archiv für Hydrobiologie,

von Orgies-Rutenberg, M., Rolfes, C., Eckel, T., Quiroz, A., Skalbeck, J., Riley, D., Sander, H.

2017

Circadian rhythms are thought of as means for adaptation helping survival fitness of a species. For algal species associated with harmful algal blooms (HAB) in eutrophied freshwater lakes usually light and nutrient availability, especially phosphate, seem to drive patterns of the vertical migration within the water column. The vertical migration patterns of species associated with HAB in freshwater lakes (Cyanobacteria) should be taken as input parameters for modelling algae. As HAB present a health risk to the public they should be monitored and predicted via simulation models, and the results of the predictions should be shared with the public using familiar tools such as smartphone apps or websites. To gather the data on which the model will be formulated, two shallow freshwater lakes (eutrophic condition: Lake Stadtgraben, Northern Germany, oligotrophic condition: Lake Russo, Wisconsin, USA in temperate climates were selected to serve as models for investigating the vertical migration in different seasonal times under natural conditions. Phosphate concentrations, as well as light and temperature over time in hourly increments at the lake surface and bottom were monitored. In addition the vertical migration pattern of Cyanobacteria and Chlorophyta populations was followed over 24 hrs in spring (May) and fall (August) in order to derive a behavior assumption as input for a model predicting HAB. In Lake Stadtgraben the vertical migration pattern was strongly influenced by light rather than by phosphate availability in spring, as phosphate was readily available at that time in all depths, while temperature was significantly different between the top and -bottom. The vertical migration pattern was dampened in fall season in both, the oligotrophic and the eutrophic lake, while temperature was not significantly different from the top to the bottom. Thus, vertical migration patterns observed may change slightly with season, which will impact on the outcome of simulation models dependent on the time of day and lake depth, at which input parameters such as Chlorophyll-a are measured.

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Using Data Mining in Combination with Machine Learning to Enhance Crowdsourcing of a Formal Model of Biodiesel Production

Midwest Instructional Computing Symposium

Fischer, M., Riley, D.

2016

Formal modeling, simulation, and analysis of complex systems is valuable because it can provide insights into complex systems that are too expensive or difficult to analyze otherwise. In this work, we present an approach for improving simulation trajectory choices in a Monte Carlo framework using a combination of crowdsourcing, machine learning, and data mining. We apply machine learning to analysis of a formal model of biodiesel production as a method of improving the efficiency of the crowd sourced mobile simulation analysis of the model. Data is collected and data mined in a central server where machine learning is applied and recommendations from the machine learning algorithm are fed back to crowd workers via suggestions on the mobile app. Ultimately, we show that this approach can improve efficiency of optimal safe state identification in the biodiesel model analysis.

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