MACHINE LEARNING
PROJECTS
PEOPLE
PUBLICATIONS
PATENTS
Machine Learning
Christopher Malon is a Senior Researcher in the Machine Learning Department at NEC Laboratories America. He received his Bachelor’s degree in Mathematics from the University of Chicago and holds a PhD in Mathematics from the Massachusetts Institute of Technology. He focuses on building more trustworthy AI systems, leading projects in fact verification, opinion summarization, and hallucination avoidance in generative AI.
In AI for healthcare, he contributed to NEC’s research and development of the world’s first automatic cancer screening system based on digital biopsy images, and now leads efforts at NEC on trustworthy medical report generation for multiple modalities. A key theme in his fact verification and medical report generation research is recognizing missing or incomplete information, to pinpoint which details need additional consideration by an AI agent or a human. With a strong track record in interdisciplinary collaboration, Dr. Malon’s research is helping NEC translate machine learning innovations into trustworthy AI partners to improve productivity in healthcare and other domains.
Overview: We are focused on developing advanced LMMs that integrate explainable reasoning and safe generation to optimize healthcare workflows. These models are designed to process and analyze multiple data modalities—such as text, images, and structured data—enabling them to assist in complex tasks like diagnostics, treatment planning, and patient management with exceptional accuracy.
Overview: This research project is at the forefront of integrating advanced Large Language Models (LLMs) into the process of deriving actionable insights from vast and complex document repositories. This initiative focuses on creating a system that allows users to interact with the LLM, guiding its analysis and refining the results based on the user’s expertise and evolving needs.
Overview: Our research spans many areas of text understanding and text generation, with a particular emphasis on factuality checking and factually guided text generation. NEC’s products help humans safely draw conclusions from large quantities of text that they don’t have the time to read. As a leader in the FEVER fact extraction and verification competitions, we have developed systems that achieved higher evidence precision and higher robustness to adversarial attack, and pioneered the ability to pursue missing evidence through multiple retrieval steps.
Overview: The Trustworthy Generative AI Project is focused on developing advanced multimodal generative models that can create and reason with content across text, images, reports, and 3D videos. These models are designed for applications in advertisement, entertainment, law enforcement, and healthcare.
Radiology report generation requires advanced medical image analysis, effective temporal reasoning, and accurate text generation. Although recent innovations, particularly multimodal large language models, have shown improved performance, their supervised fine-tuning (SFT) objective is not explicitly
Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating human-like text, yet they largely operate as reactive agents, responding only when directly prompted. This passivity creates an “awareness gap,” limiting their potential as truly collaborative partners
Question Answering (QA) accounts for a significantportion of LLM usage “in the wild”.However, LLMs sometimes produce false ormisleading responses, also known as hallucinations.Therefore, grounding the generatedanswers in contextually provided information—i.e., providing evidence for the generated
Our NEC Lab America team attended the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025. The purpose of the AAAI conference series was to promote research in AI and foster scientific
Multimodal Large Language Models (MLLMs) have shown impressive performance in vision and text tasks. However, hallucination remains a major challenge, especially in fields like healthcare where details are critical. In this work, we show how MLLMs may be enhanced to support Visual RAG (V-RAG), a retrieval-augmented
Separating disinformation from fact on the web has long challenged both the search and the reasoning powers of humans. We show that the reasoning power of large language models (LLMs) and the retrieval power of modern search engines can be combined to automate this process and explainably verify claims.
When performing complex multi-step reasoning tasks, the ability of Large Language Models (LLMs) to derive structured intermediate proof steps is important for ensuring that the models truly perform the desired reasoning and for improving models’ explainability. This paper is centered around a focused
We are thrilled to announce the launch of our latest research initiative, the Trustworthy Generative AI Project. This ambitious project is set to revolutionize how we interact with multimodal content by developing cutting-edge generative models capable of compositional generation and reasoning across
Self-consistency has emerged as a powerful method for improving the accuracy of short answers generated by large language models. As previously defined, it only concerns the accuracy of a final answer parsed from generated text. In this work, we extend the idea to open response generation, by integrating
When faced with a large number of product reviews, it is not clear that a human can remember all of them and weight opinions representatively to write a good reference summary. Wepropose an automatic metric to test the prevalence of the opinions that a summary expresses, based on counting the number
Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) tasks such as link prediction, i.e., answering (h, p, ?) or (?, p, t) queries. Such models are usually evaluated with averaged
Product reviews may have complex discourse including coreference and bridging relations to a main product, competing products, and interacting products. Current approaches to aspect-based sentiment analysis (ABSA) and opinion summarization largely ignore this complexity. On the other hand, existing systems
We study few-shot debugging of transformer based natural language understanding models, using recently popularized test suites to not just diagnose but correct a problem. Given a few debugging examples of a certain phenomenon, and a held-out test set of the same phenomenon, we aim to maximize accuracy
We develop a system for the FEVEROUS fact extraction and verification task that ranks an initial set of potential evidence and then pursues missing evidence in subsequent hops by trying to generate it, with a “next hop prediction module” whose output is matched against page elements in a predicted
Image captioning systems are expected to have the ability to combine individual concepts when describing scenes with concept combinations that are not observed during training. In spite of significant progress in image captioning with the help of the autoregressive generation framework, current approaches
Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretraining. We show that examples that depend critically on a rarer word are more challenging for natural language inference models.
One major source of vulnerability of neural nets in classification tasks is from overparameterized fully connected layers near the end of the network. In this paper, we propose a new neighborhood preserving layer which can replace these fully connected layers to improve the network robustness. Networks
Learning disentangled representations of natural language is essential for many NLP tasks, e.g., conditional text generation, style transfer, personalized dialogue systems, etc. Similar problems have been studied extensively for other forms of data, such as images and videos. However, the discrete nature
We propose a framework for answering open domain multi hop questions in which partial information is read and used to generate followup questions, to finally be answered by a pretrained single hop answer extractor. This framework makes each hop interpretable, and makes the retrieval associated with later
Existing entailment datasets mainly pose problems which can be answered without attention to grammar or word order. Learning syntax requires comparing examples where different grammar and word order change the desired classification. We introduce several datasets based on synthetic transformations of
We develop a system for the FEVER fact extraction and verification challenge that uses a high precision entailment classifier based on transformer networks pretrained with language modeling, to classify a broad set of potential evidence. The precision of the entailment classifier allows us to enhance