Professor Tim Oates
Tim Oates, Ph.D. is Cofounder and Chief Data Scientist at Synaptiq and the Oros Family Professor of Computer Science at the University of Maryland Baltimore County (UMBC).
Tim is known for his expertise in artificial intelligence and machine learning and his successful application of these fields in both academic and industry settings. His work often aims to translate theoretical concepts into practical applications, particularly focusing on developing AI tools that work with and for humans to improve their lives.
His recent work has explored deep neural networks for weakly supervised EEG denoising for brain-machine interfaces, human-in-the-loop deep reinforcement learning to train robots using video demonstrations, and learning declarative representations of the functionality of “found” hardware using black box methods. Read MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin Lesion Diagnosis.
Ongoing efforts include novel methods for learning semantically rich compositional sentence embeddings, learning policies for monitoring and updating deployed deep learning models to maintain performance in the face of domain shifts, and unsupervised methods for learning grounded, relational policies for understanding and control of real and simulated environments.
A key real-world implication of his research is in healthcare, interpreting medical time series to predict brain damage severity and utilizing AI to enhance data-driven decision-making in healthcare challenges. Read Detecting Epileptic Seizures from EEG Data using Neural Networks.
Tim cofounded Synaptiq in 2017. He is the Chief Data Scientist, leading innovative projects that leverage advanced algorithms and data science techniques to deliver data-driven solutions. He is instrumental in shaping the company’s direction in data science applications and is referred to as the “sage of Synaptiq” for providing insights to client partners.
Synaptiq broadly offers data science, machine learning, and artificial intelligence services. Watch About Us & The Humankind of AI and Synaptiq Chief Data Scientist Tim Oates on Legal Technology Panel at Nextpoint’s 2021 On Point User Conference.
Their core activities involve helping businesses build AI-driven products, focusing on solutions that utilize computer vision. The company initially focused on training AI models but expanded into building data platforms, data strategy services, and product development.
Synaptiq aims to raise humanity’s collective Artificial Intelligence Quotient (AIQ), which is defined as a person’s ability to use AI to perform a wide variety of tasks and views client projects as opportunities to solve for impact. They work across over 20 industries, including Healthcare, Legal, and Government.
Specific projects include developing an AI-powered tool with Microsoft to prevent central line infections and applying computer vision and OCR for immigration document classification. The company emphasizes applying AI for the “greater good” and considering its impact on humanity.
Read Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions, Immigration Document Classification and Automated Response Generation, How to Know When You Need an AI Expert vs. DIY, Ask Tim: Using Machine Learning to Detect Objects with No Data, and How Much Data Do We Need?
As the Oros Family Professor of Computer Science at UMBC since 2001, Tim directs the university’s Cognition, Robotics, and Learning (CORAL) Laboratory. He teaches computer science courses, including artificial intelligence and robotics. Through the CORAL lab, he pursues research in machine learning, artificial intelligence, natural language processing, mobile healthcare, and robotics. The lab’s core activity is understanding how artificial systems can acquire “grounded knowledge” from interacting with their environment to enable cognitive functions like language communication and planning.
His research delves into the sensorimotor origins of knowledge, language learning, grammar induction, and automated representation development. A significant area of his academic research involves collaborations with medical organizations such as Intelomed, Shock Trauma, and the U.S. Air Force to interpret medical time series, such as predicting the severity of traumatic brain injuries using vital signs to avoid invasive procedures.
Specifically, Tim and his collaborators have been working with traumatic brain injury patients who are at risk of dangerous brain swelling as a result of increased intracranial pressure. Their goal is to be able to gauge the severity of the patient’s condition using vital signs in order to avoid unnecessarily drilling holes into the skulls of patients.
He is also deeply interested in understanding human brain development and applying sensory-motor learning concepts to artificial systems, aiming to create robots capable of learning like human children. His recent research explores topics like deep neural networks for EEG denoising, human-in-the-loop reinforcement learning for robots, and learning declarative representations for hardware.
Read Time series classification from scratch with deep neural networks: A strong baseline, Using dynamic time warping to bootstrap HMM-based clustering of time series, PERUSE: An unsupervised algorithm for finding recurring patterns in time series, Neo: learning conceptual knowledge by sensorimotor interaction with an environment, and Learning from Observations Using a Single Video Demonstration and Human Feedback.
Between 2019 and 2020, Tim served on the Board of Advisors at Karotene, which was focused on using artificial intelligence to enhance data-driven decision-making, particularly in healthcare-related challenges. His role involved providing valuable insights to bridge the gap between theoretical AI research and practical applications in this domain.
Previously, Tim was Chief Scientist at CircleBack between 2013 and 2015. This technology company is focused on solving a major productivity problem of keeping contact data up-to-date. Using their proprietary data engine, CircleBack discovers when important connection details change and surfaces new contact info to help users keep connections alive and get stuff done.
Throughout his career, Tim has made numerous contributions to the fields of computer science and artificial intelligence. His research has been published in high-impact journals and has gained recognition within academic and professional circles for its relevance and applicability.
His achievements include winning an NSF CAREER award in 2004 and being named an Oros Family Professor of Computer Science at UMBC in 2012. He has published over 150 peer-reviewed papers and mentored many graduate students, including nine Ph.D. students and over 40 Masters students. He has also been an invited speaker at various conferences and workshops. Read Blood Transfusions and Vampire Bats: Finding Patterns in Time Series.
Tim earned his Bachelor’s Degree of Science in Electrical Engineering and his Bachelor’s Degree of Science in Computer Science from North Carolina State University in 1989. He earned his Master’s Degree of Science in Computer Science from University of Massachusetts, Amherst in 1997 and his Ph.D. in Computer Science at the University of Massachusetts, Amherst in 2002.
After completing his Ph.D., Tim further refined his expertise by undertaking postdoctoral studies at the Artificial Intelligence Lab at the Massachusetts Institute of Technology (MIT). Here, he was exposed to cutting-edge research and innovations in AI, collaborating with some of the brightest minds in the field. This experience enriched his knowledge and significantly advanced his research capabilities, positioning him as a leading figure in artificial intelligence and data science. He did his Post-Doc work at the MIT AI laboratory.
On a more personal note, Tim’s career was initially inspired by a childhood fascination with robots and science fiction. His interest in understanding human brain development was sparked by watching his three daughters grow up.
He holds the philosophical view that humans are fundamentally machines, believing that the processes in his head are not significantly different from those in a laptop. He finds the collaborative problem-solving sessions in AI engagements to be particularly interesting. He is coauthor of the book Mushrooms, Goats, and Machine Learning and has a side project at Synaptiq that uses computer vision to identify aquatic insects, connecting back to his graduate studies in freshwater ecology.
Visit his LinkedIn profile, Papers With Code page, his Old Homepage, ResearchGate profile, and Google Scholar page. Follow him on Facebook, Crunchbase, Instagram, and X.