Talks and presentations

Overview of practical approaches to memory and personalization for health AI systems, including long-term context, retrieval, and longitudinal user experiences.

Perspective on why healthcare AI is at an inflection point, spanning regulation, market dynamics, and research directions for evaluation and agentic systems.

I gave an invited talk at the Machine Learning for Healthcare Roundtable during the Amazon Machine Learning Conference 2025, presenting our work on evaluating the trustworthiness of large language models (LLMs) in medical product question answering.

I delivered an invited talk for the Amazon North America Stores GenAI Learning Series, presenting a deep dive into the design, architecture, evaluation, and deployment of large-scale agentic systems across Amazon. The talk bridged my work across Shopping Conversations Foundations and Amazon Health AI / One Medical, highlighting the development of agentic LLM systems from consumer shopping experiences (specifically BuyForMe) to clinical and healthcare workflows.

I delivered a talk at Amazon’s Image and Video Generation Workshop 2025 in Seattle, presenting our work at Amazon Health on building AI-enabled virtual care experiences using LLM-powered digital avatar assistants.

I presented a poster at the Machine Learning for Health Symposium (ML4H) 2024 in Vancouver, corresponding to the paper Detecting sensitive medical responses in general purpose large language models. The work investigates how to identify sensitive or potentially harmful medical responses produced by general-purpose large language models.

I presented a poster at the KDD 2024 Workshop on GenAI Evaluation in Barcelona, corresponding to the paper “Trustworthiness in medical product question answering by large language models”. The work introduces a claim-level evaluation framework to assess whether large language models provide medically accurate and label-consistent answers when responding to questions about prescription drugs and medical products.

I presented a poster at the Machine Learning for Health (ML4H) Symposium 2022 in New Orleans, based on research conducted at Google Health. The work investigates how randomness in training deep learning models, despite identical data, architecture, and hyperparameters, can lead to meaningfully different patient-level predictions in clinical risk stratification tasks.

I was invited to participate as a panelist in the Careers in Academia and Industry session at MICCAI 2022. This flagship event brought together researchers, innovators, and industry leaders to discuss professional pathways, career development, and the evolving relationship between academic research and real world applications.

I presented our work at the Applications of Medical AI (AMAI) Workshop at MICCAI 2022, where our paper received the Best Paper Award. This work introduces a dynamic prediction model capable of identifying chronic kidney disease patients at high risk of requiring renal replacement therapy up to one year in advance.

I delivered an oral presentation at the International Conference on Affective Computing and Intelligent Interaction (ACII 2019) in Cambridge, UK, during the International Workshop on Social and Emotion AI for Industry (SEAIxI). The presentation summarized our work on detecting real world, driving induced affective states using physiological signals, based on our paper presented at the conference.

I delivered an oral presentation at the Engineering in Medicine and Biology Conference (EMBC 2019) in Berlin, summarizing our work on using deep reinforcement learning to support optimal pain management in the intensive care unit (ICU). The project introduced a sequential decision making framework that learns clinically interpretable morphine dosing strategies personalized to each patient’s evolving physiological and pain state, based on retrospective ICU data from the MIMIC-III database.

I presented a poster at the Harvard–MIT Health Sciences and Technology (HST) Forum at Harvard Medical School, describing my research on personalized machine learning approaches for estimating nociceptive pain. The work, conducted at the MIT Media Lab, explored how individual differences in physiological and behavioral responses to pain can be leveraged to improve continuous pain intensity estimation.

I delivered an oral presentation at the International Conference on Pattern Recognition (ICPR 2018) in Beijing, China, presenting our work on personalized pain detection using functional near-infrared spectroscopy (fNIRS) brain signals. The paper received the Best Student Paper Award.

I delivered an oral presentation at the Engineering in Medicine and Biology Conference (EMBC), describing our work on continuously estimating experimental heat pain intensity from autonomic physiological signals. The project sought to develop an objective pain monitoring method that provides high temporal resolution estimates using data that can be collected noninvasively from wearable sensors.

I presented a poster at the International Conference on Biomedical and Health Informatics (BHI 2018) in Las Vegas, describing our work on estimating pain intensity from skin conductance signals. The project, conducted at the MIT Media Lab, focused on leveraging noninvasive physiological sensing to quantify nociceptive responses when self-report is not feasible.

I presented a poster at the NeurIPS Machine Learning for Health (ML4H) Workshop 2017, describing our work on personalized pain estimation from multimodal data. The project introduced a method for building physiological and behavioral profiles based on individual responses to heat pain, and for using these profiles within a personalized multi-task neural network architecture.

I presented our startup concept, ZenAuto, at the Lee Kuan Yew Global Business Plan Competition (LKYGBPC) in Singapore, one of Asia’s leading deep-tech entrepreneurship challenges. The competition brings together next-generation founders from around the world to showcase innovations with the potential to reshape cities, industries, and society. Our work was selected for presentation on the competition stage alongside teams from top global universities.

I delivered an oral presentation at the Computer Vision and Pattern Recognition (CVPR 2017) Workshop on Deep Affective Learning and Context Modeling, where I presented our work on personalized estimation of self reported pain intensity from facial expressions. The project introduced a two stage machine learning framework that combines recurrent neural networks with a personalized Hidden Conditional Random Field model to estimate Visual Analog Scale (VAS) pain scores from facial landmarks.

I presented a poster at the 2017 Annual Meeting of the American Pain Society in Pittsburgh, describing collaborative work between the MIT Affective Computing group and MedImmune on automatic pain detection using computer vision and machine learning.

I delivered a talk at the MIT Media Lab during an industry symposium on how connectivity, wearable sensing, and data-driven approaches are transforming the drug development pipeline — from discovery through clinical trials and post-market surveillance.

I presented work at the White House Open Data Innovation Summit in Washington, D.C., where our team was invited to present our project developed during a Boston-based health hackathon. Our project, LymeDot, explored how mobile technology and open data could help patients with Lyme disease track symptoms over time, support clinical decision-making, and empower individuals managing complex, chronic conditions.

I presented our team’s work at Lyme Innovation, the first-ever Lyme-disease–focused hackathon, held at the Microsoft NERD Center in Cambridge and organized by Spaulding Rehabilitation’s Dean Center for Tick-Borne Illness, the Veterans Affairs Center for Innovation, MIT Hacking Medicine, UC Berkeley, and Harvard Medical School. The three-day event brought together clinicians, scientists, engineers, entrepreneurs, and patients to develop new solutions for Lyme disease. Our project was selected as one of the finalists and received a $5,000 award.

I delivered an oral presentation at the International Conference on Smart Portable, Wearable, Implantable and Disability Oriented Devices and Systems (SPWID 2016) in Valencia, Spain. The talk presented our work on wearable technologies for managing stress in individuals with multiple sclerosis (MS), based on research conducted at the MIT Media Lab.

I delivered an invited talk at the Building Bridges to Develop New Medical Technologies workshop hosted by the Real Colegio Complutense at Harvard University. The event brought together engineering and medical researchers from Boston and Spain to foster international collaboration and cross-disciplinary innovation in biomedical science and technology.

I delivered a demo presentation at the LEGO2NANO Summer School at the Shenzhen Open Innovation Lab (SZOIL), showcasing the atomic force microscope our team developed and, in particular, my work on imaging air pollution particles and creating a crowdsourcing-based air pollution measurement platform built around this technology.

I delivered an oral presentation at the Magnetoencephalography Laboratory of the McGovern Institute for Brain Research at MIT, summarizing the results of my research internship under the supervision of Dr Dimitrios Pantazis. The project focused on developing machine learning methods to process magnetoencephalography data and on understanding how the human brain binds visual and auditory information into a unified percept as part of a National Science Foundation supported effort.

I delivered an oral presentation in the Kreiman Laboratory at Harvard University, summarizing the results of my summer research internship under the supervision of Professor Gabriel Kreiman. The computational neuroscience project focused on understanding how spike timing dependent plasticity (STDP) shapes the architecture of recurrent cortical circuits and the conditions under which specific connectivity patterns emerge.

Gari Clifford (University of Oxford) and I gave an oral presentation at the Computing in Cardiology (CinC) Conference 2011 in Hangzhou, China, presenting our joint work conducted at the Oxford Institute of Biomedical Engineering (IBME). The talk covered our algorithm for assessing the diagnostic acceptability of electrocardiograms (ECGs) collected in noisy or low-resource ambulatory environments.

I delivered an oral presentation at the Cancer Research UK Cambridge Research Institute (CRI) summarizing the results of my summer research internship in the laboratory of Professor John Griffiths at the University of Cambridge. The project focused on evaluating advanced magnetic resonance imaging methods for the early detection of brain metastases in small cell lung cancer (SCLC).

I presented a poster at the Amgen Scholars European Symposium 2010 at the University of Cambridge, describing research conducted during my summer internship at the Wolfson Brain Imaging Centre under the supervision of Adrian Carpenter and Steve Sawiak. The symposium brought together undergraduate researchers from institutions across Europe to share summer projects and participate in a series of academic talks and poster sessions.