Current Events

Date: Tuesday, November 4, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: R. James Cotton, MD, PhD
                Assistant Professor, Physical Medicine and Rehabilitation
                Assistant Professor, McCormick School of Engineering
                Northwestern University
 
Abstract: This talk will review recent advances in AI-enabled biomechanical human pose estimation and analysis. It will first cover differentiable biomechanics for end-to-end recovery of kinematics with confidence intervals and kinetics. It will show how this approach can be extended to track high quality arm and hand biomechanics, and to monocular whole-body gait analysis. It will also demonstrate new opportunities that emerge with large rehabilitation data such as imitation learning and training multimodal language models that are fluent in human movement.

Date: Tuesday, October 21, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Carl Yang, PhD
                Assistant Professor, Department of Computer Science
                Emory University
 
Abstract: Large language models (LLM) have brought disruptive progress to information technology from accessing data to performing analytical tasks. While demonstrating unprecedented capabilities, LLMs have been found unreliable in tasks requiring factual knowledge and rigorous reasoning, posing critical challenges in domains such as healthcare. Knowledge graphs (KG) have been widely used for explicitly organizing and indexing biomedical knowledge, but the quality and coverage of KG are hard to scale up given the notoriously complex and noisy healthcare data with multiple modalities from multiple institutions. Existing approaches show promises in combining LLMs and KGs to enhance each other, but they do not study the techniques in real healthcare contexts and scenarios. In this talk, I will introduce our research vision and agenda towards KG-LLM co-learning for healthcare, followed by successful examples from our recent exploration on LLM-aided KG construction, KG-guided LLM enhancement, and federated multi-agent systems. I will conclude the talk with discussions on future directions that can benefit from further collaborations with researchers interested in data mining or biomedical informatics in general.

Date: Tuesday, October 7, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Ramakanth Kavuluru, PhD
                Professor, Division of Biomedical Informatics
                Department of Internal Medicine
                University of Kentucky
 
Abstract: While LLMs are dominating the field of NLP, biomedical information extraction (named entity recognition, relation extraction, slot filling) remains a relevant task for knowledge discovery. A recent example is the use of knowledge graphs extracted from scientific literature to facilitate grounding and mitigate hallucinations in LLMs. In this context, are tailored supervised models needed for information extraction or can we simply use zero-shot extraction from frontier LLMs? What about encoders (e.g., BERT) vs decoders (e.g., GPT-x)? Are we better off pretraining biomedical language models from scratch or can we simply continue pretraining on top of general LMs? Culling examples from my lab’s work in recent years, Dr. Kavuluru will try to answer these questions and present recent (and likely future) trends in biomedical information extraction.

Date: Tuesday, September 16, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Muhammed Idris, PhD
                Assistant Professor for Department of Medicine
                Morehouse School of Medicine
 
Abstract: This talk explores how biomedical informatics can be applied to investigate and address health disparities. I will introduce the Morehouse Model 3.0—a framework that combines community engagement, data science, and implementation research to develop culturally responsive, community-centered digital health interventions. The talk will feature two in-depth case studies: one on natural language processing (NLP) for clinical trial recruitment and another on EHR-based heart failure risk prediction, highlighting research and lessons learned in designing, developing, and evaluating AI/ML systems in resource-constrained settings. I will conclude with research areas and projects that I am excited about including LLM-based risk prediction, Small Language Models for health communication, and real-world performance evaluations of algorithms deployed in clinical and community health settings. Finally, I talk will also highlight key ethical considerations and how community partnerships are essential to implementing responsible AI and can help ensure equitable outcomes in health informatics.

Date: Tuesday, August 19, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Temiloluwa Prioleau 
                 Assistant Professor of Computer Science
                 Emory University 
Abstract: The prevalence of digital technology in daily living creates astounding amounts of user-generated data that can be helpful for understanding complex health conditions in a new way. These large data sources are also foundational for developing AI-driven solutions that are necessary to realize personalized healthcare. Yet, significant gaps exist around data analytic solutions capable of eliciting clinically meaningful and actionable insights from continuously generated user data. Additionally, challenges exist around the reproducibility of health-centered AI methods in literature.

To bridge these gaps, this talk will present our research efforts on opportunistic use of digital health data in the context of diabetes management. I will discuss how large volumes of user-generated data can improve our understanding of unique lifestyle factors that play a critical role in outcomes. The talk will be organized around three core topics including context-aware sensing for digital health interventions, wearable data analytics and AI for personalized care, and reproducibility in machine learning research for health. Finally, I will present concluding thoughts on how these research efforts can be extended to enable digital and computational health in other application domains.

Date: Tuesday, June 3, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: David L. Minkoff, MD 
                 Assistant Professor, Division of Hospital Medicine
                 Emory University School of Medicine
Abstract: Although Hospital Medicine is now one of the largest physician specialties in the country, research into hospitalist practice patterns and performance remains limited.  Significant variability exists in Hospital Medicine outcomes; for example, between two hospitalists in a given practice, readmission rates and length-of-stay can vary by as much as a factor of two. Data to explain such variability are extremely limited. Previously, research into clinical practice patterns was limited both by the data available in paper charts and the need for manual chart reviews. However, the widespread adoption of electronic health records (EHRs) in the past decade has allowed for the study of novel data types, such as audit logs, and recent advances in natural language processing will reduce the need for manual chart reviews, which will allow for superior studies in both detail and scale. Development of next-generation performance metrics, both for individual physicians and for the practice environment at large, will allow for improved patient care and bolster the financial sustainability of the healthcare system. Additional aims of the current work include:

• Development of novel clinical practice metrics incorporating EHR metadata to identify "practice phenotypes"
• Addition of data from clinical notes using natural language processing for clinical decision support
• Development of hospital state metrics to guide operations and quality improvement initiatives

Date: Tuesday, May 6, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Robert L. Ellis, PhD, MHA 
                 Postdoctoral Scholar, University of California, Davis

Abstract: In the U.S., 27% of adults consume alcohol at risky levels, and around 10% have Alcohol use disorder (AUD). The prevalence is increasing among vulnerable populations. Despite effective treatments, only 8% of adults with AUD receive alcohol-related care. Primary care, accessible to most adults, offers an ideal setting to improve AUD diagnosis and treatment systematically. However, AUD remains underdiagnosed and undertreated, partly due to the lack of reliable, standardized clinical assessments, leading to inconsistent and incomplete symptom documentation. Disparities in AUD diagnoses across race, ethnicity, and sex suggest potential biases in clinical practices. Additionally, stigmatizing language in clinical documentation may reflect and reinforce these biases. These challenges highlight the need for innovative, standardized approaches to support unbiased, accurate AUD diagnosis in primary care.

Dr. Ellis’ talk will cover the following:

• Foundational knowledge of alcohol use disorder in primary care settings;
• Intersectionality theory applied to health services research; and
• Implications of AUDIT-C (alcohol consumption screening tool), Alcohol Symptom Checklist (AUD symptom severity screening tool), and diagnosing description within EHRs for the equitable diagnosis of AUD, a stigmatized mental health condition, in clinical settings.

Date: Tuesday, April 29, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Ilanit Ronen Itzhaki, PhD 
                Assistant Professor of Pediatrics, School of Medicine, Emory University

Date: Tuesday, April 15, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Ehsan Hoque 
                 Professor of Computer Science
                 University of Rochester

Abstract: 
Parkinson’s disease (PD), a complex neurodegenerative disorder with a yearly economic burden projected to rise from $57 billion in 2017 to $80 billion by 2037 in the United States, demands innovative approaches to care. Digital health technologies—wearables, smartphones, and sensors—empower patients to monitor their health outside clinics, addressing the global shortage of clinicians.
Multimodal AI enhances this by integrating diverse data sources, such as speech and motor activity, to capture PD’s multifaceted symptoms. For instance, tremors may manifest in speech but not in physical movement. A smart multimodal fusion can enable early detection, personalized interventions, and equitable access to care. This talk will highlight cutting-edge multimodal AI research, showcasing its transformative potential for PD and beyond.

Date: Tuesday, March 18, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Shengpu Tang, PhD 
                  Assistant Professor of Computer Science
                  Emory University

Abstract: 
Decision making is at the core of healthcare: clinicians constantly make complex decisions that span diagnosis, treatment, care coordination, and resource allocation. Yet, human decisions are never perfect, leading to suboptimal patient care. My research aims to use AI to augment and improve decision-making in healthcare, following a synergistic approach that combines AI methods with practical, real-world implementation. In this talk, we will explore the two key themes of my research: (1) Application-Inspired AI Innovations, focused on novel AI methods grounded in practical healthcare problems; and (2) Path to Deployment and Impact, addressing AI integration into clinical workflows for real-world improvements. The talk will end with my future vision of human-AI teaming to enable better healthcare. 

Date: Thursday, March 13, 2025 | 2:00PM-3:00PM, BMI Classroom 4004 or on Zoom
Speakers: Ravi Parikh, M.D., MPP 
                 Associate Professor of Hematology and Medical Oncology
                 Emory University

Abstract: 
In this talk, I will describe 2 completed projects relating to using 1) digital twins – computational phenotypes of patients using real-world data – to understand real-world generalizability of clinical trials and improve future trial design, and 2) electronic health record-based language models to enhance trial matching and recruitment.

Date: Tuesday, March 4, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Zachary Danziger, Associate Professor, 
                 Department of Rehabilitation Medicine
                  W.H. Cluster Department of Biomedical Engineering, Emory University

Abstract: 
Simulating physiological systems is a powerful tool for generating hypotheses and for rapid prototyping of experimental treatments, but we often lack a full mathematical description of the system, which stimies our ability to simulate it. This talk explores a new approach to fill the gaps of missing ordinary differential equations (ODE) with small artificial neural networks (ANN). The goal is to train the entire hybrid ODE-ANN such that the embedded ANNs become approximations to the missing ODEs that can infer important but unmeasured physiological states of the system. The hybrid model (mostly) preserves interpretability and can be used to simulate the physiological system, thereby restoring our ability to study it computationally despite incomplete knowledge. We will explore the framework we are developing to build and train such hybrid ODE-ANN systems and deploy it for studying the lower urinary tract. 

Date: Tuesday, February 18, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Sang Won Bae, Ph.D., 
                 Assistant Professor, Department of Systems and Enterprises 
                 Schaefer School of Engineering and Science 
                 Stevens Institute of Technology

Abstract: 
This seminar examines the use of mobile sensors and machine learning to detect and intervene in acute substance intoxication in real-time, enabling just-in-time adaptive interventions. Dr. Bae will present her research on detecting binge drinking and marijuana intoxication through smartphones and wearable devices, emphasizing the role of explainable AI in providing transparency in decision-making. By leveraging data from smartphone sensors and wearables, her research explores how real-time predictions can empower individuals to make informed decisions, ultimately improving health outcomes and reducing substance-related harm. Dr. Bae will also discuss the technical and ethical challenges in implementing these technologies, including concerns around privacy, algorithmic transparency, and the need for personalized, adaptive systems that respect user autonomy. The talk will conclude with a forward-looking discussion on the future of digital health technologies, their potential to enhance public health, guide personalized interventions, and support clinical decision-making.

Date: Tuesday, January 21, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Benny Budiman Sc.D., Director of Research Data Analytics, Office of Research Informatics, Data, and AI, Emory University
Abstract: 
Data, Analytics, and AI-based solutions have potential to deliver enormous value to decision makers. Because of this astronomical potential, HBR reported that many organizations aspired to become data-driven so much so that they embarked on digital transformations by investing in big data and AI projects for enabling them to "compete on analytics" or to be "AI-first" in their business. Despite the promise of immense value, the percentage of failed data science projects has been alarmingly very high. Gartner Research estimated 80% of analytics insights failed to deliver value. A Gartner analyst also estimated that 85% of big data projects failed. A recent whitepaper from the Centre for Business Analytics at Melbourne Business School, while confirming the high failure rate of 80%, also reported the failure rate to be as high as 90% for analytically immature organizations but only as low as 40% for analytically mature organizations. In addition to the high failure rate, a high percentage of analytics projects failed to move beyond their pilot phase, 87% according to an estimate by VentureBeat AI. Even those that went into production faced low adoption rates. In a recent survey, DataIQ estimated only 23.9% of AI-driven solutions have been widely deployed into production.

Since 2004, the 7Cs framework for delivering values has been proven to identify and capture values. It has been used in the design, development, and deployment of data, analytics, and AI-based solutions in industrial settings where it captured and delivered over $1 billion in measurable outcomes from virtually every project in which it was used. The 7Cs functions like a checklist comprising seven elements all of which begins with the letter 'C' — hence, the name. The first is Context: understanding of objectives and goals. Next is Concept: requirement to be able to describe the flows of materials, resources, and information in the system / entity where challenges or problems need to be solved by using Physics, Chemistry, Biology, Business Dynamics, etc. to understand inputs, process, and outputs. After Concept, interdependency or interconnectedness — referred to in the framework as Connection — among elements of the flows need to be clearly articulated. The fourth C is Constraints that usually limit the flows or put restrictions on feasible actions. Because nothing is constant, the approach will evaluate impacts of Change involving variability and uncertainty by identifying the known unknown and the unknown known as well as anticipating the unknown unknown through scenarios so that risk can be assessed and planned to be mitigated accordingly. Communication, the sixth C, plays a crucial role in gathering and putting together a "picture" from the previous five Cs. It also plays an important role in creating data, analytics, and AI models in the seventh element of the framework which is Calculus.

Examples of how the 7Cs have been used in industrial settings will be presented next. In the end, ideas of how the 7Cs may be useful for Emory will be discussed.

Date: Tuesday, January 7, 2025 | 12:00PM-1:00PM, BMI Classroom 4004 or on Zoom
Speakers: Farzaneh Najafi, Assistant Professor, School of Biological Sciences, College of Sciences, Georgia Institute of Technology
Abstract: 
Precise motor timing underlies essential human behaviors such as speech, and motor coordination, with deficits profoundly impacting quality of life in conditions like ataxia. Two brain regions, cerebellum and parietal cortex, are both involved in motor behavior; however, their interaction for generating precisely timed movements remains poorly understood. My talk shows preliminary results from our lab addressing this question. We trained mice on visually and self-timed movements, while measuring and manipulating neural activity in the cerebellar and parietal cortex. We found temporal information in the parietal cortex, which requires intact cerebellar activity, hence suggesting potential causal interaction between these brain regions leading to temporally precise movements.