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HTTP/2 301 content-length: 0 location: https://derek.ma/ date: Sun, 28 Dec 2025 10:39:01 GMT server: AmazonS3 x-cache: Hit from cloudfront via: 1.1 7d1975e97f05a3fc47c8f5eea10222bc.cloudfront.net (CloudFront) x-amz-cf-pop: BOM78-P5 x-amz-cf-id: Q3trJ8ygUsV6vvMMfjnpPGq1MSHksYqibcznuelSsRAvKlkX3PID-A== age: 82154 HTTP/1.1 301 Moved Permanently Connection: keep-alive Content-Length: 162 Server: GitHub.com Content-Type: text/html Location: https://derek.ma/ X-GitHub-Request-Id: EC13:328FD3:89C99D:9AB749:695249AF Accept-Ranges: bytes Age: 0 Date: Mon, 29 Dec 2025 09:28:15 GMT Via: 1.1 varnish X-Served-By: cache-bom-vanm7210033-BOM X-Cache: MISS X-Cache-Hits: 0 X-Timer: S1767000495.178913,VS0,VE197 Vary: Accept-Encoding X-Fastly-Request-ID: 08e3eefb0ab97795a48779c0f4f2d133be239f1b HTTP/2 200 server: GitHub.com content-type: text/html; charset=utf-8 last-modified: Tue, 02 Dec 2025 02:37:03 GMT access-control-allow-origin: * strict-transport-security: max-age=31556952 etag: W/"692e50cf-2a3cc" expires: Mon, 29 Dec 2025 09:38:15 GMT cache-control: max-age=600 content-encoding: gzip x-proxy-cache: MISS x-github-request-id: 1309:2685F2:882E6C:991DF0:695249AF accept-ranges: bytes age: 0 date: Mon, 29 Dec 2025 09:28:15 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210055-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1767000495.480994,VS0,VE216 vary: Accept-Encoding x-fastly-request-id: e08df640330efdf347c9ec4c5710bdbe700ec554 content-length: 26420 Mingyu Derek Ma

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Mingyu Derek Ma

Senior Machine Learning Scientist

Prescient Design, Genentech (Roche)

hi at derek.ma

Schedule a time to chat!

I am a Senior Machine Learning Scientist on the Foundation Models team at Prescient Design, Genentech (Roche).

My work involves leading the development of agentic automation and intelligent platforms for molecular drug discovery and contributing to the training of scientific large language models.

I hold a PhD in Computer Science from UCLA, advised by Prof. Wei Wang. I’m an award recipient of the J.P. Morgan Chase AI PhD Fellowship and the Amazon Fellowship. My prior research experience includes work at Amazon AGI, USC, CUHK, UC Santa Cruz, MIT and PolyU.

I develop machine learning (ML) systems inspired by scientific data and expert tasks, equipping large language models (LLMs) with the intuition and knowledge of domain experts. My research introduces machine learning innovations and insights to enable a comprehensive spectrum of expertise acquisition, from explicit to implicit knowledge and from individual decision-making to the automation of complex expert workflows. Specifically, I focus on:

Extracting explicit knowledge from unstructured data in low-resource scenarios: dataset (ACL'23), library (NAACL'21), data-efficient (ACL'23) and parameter-efficient (INTERSPEECH'23) methods, indirect supervision (ACL'23, EMNLP-F'22), cross-document (ACL-F'23), data synthesis/augmentation for zero-shot scenarios (AAAI'24, ACL'24)
Capturing implicit expert intuition: LLMs’ clinical decision-making benchmark (preprint 24), rich supervision to model decision sequences (AAAI'25), conveying intuition with a decoding-free paradigm (ACL'25)
Compositional, project-level reasoning and automation: KG-inspired reasoning (ICML'25), cross-modality (NeurIPS'24), drug discovery agents, scientific workflow agent platform (preprint 25), material design (NAACL'25)
Fairness and safety of generative LLMs: unsupervised bias mitigation (NAACL'24), attacking LLM with data poisoning (NAACL'24), ownership protection (NAACL'24), LLMs’ clinical bias analysis (preprint 24)
Empowered expert applications: clinical diagnosis (preprint 24, ACL'25, preprint 24), health outcome prediction (AAAI'25), clinical event extraction (ACL'23), biomedical and scientific QAs (ICML'25, NeurIPS'24, NAACL'24, ACL'23), computational social science (EMNLP'24, AAAI'24), political event forecasting (preprint 24), dialogue state tracking (INTERSPEECH'23), knowledge structure/graph construction (EMNLP-F'21, AKBC'22)

Obtained PhD and joined Prescient Design at Genentech/Roche

I’m excited to share that I defended my PhD in March and joined the Foundation Model team of Prescient Design at Genentech/Roche as a Senior Machine Learning Scientist!

Mar 21, 2025

Preprint

New preprint on new output paradigm for LLMs

Our new preprint explores a new paradigm for expressing LLMs’ decisions without decoding concrete words. We formally define the paradigm and introduce a comprehensive evaluation set covering a few to thousands of candidates. We found that this new output paradigm can outperform full decoding while being 40x faster.

Jan 29, 2025

Conference

Presenting at AAAI 2025 🇺🇸

At AAAI 2025, I will present my new paper on equipping LLMs with clinical decision-making capabilities with crafted learning objective design and clinical knowledge memorization. See you in Philadelphia!

Jan 28, 2025

Service

Call for papers for the AAAI Symposium on LLM Agents for Scientific Discovery

We are calling for papers and participation for the AAAI 2025 Spring Symposium on LLM Agents for Scientific Discovery.

Jan 20, 2025

Conference

Presenting at NeurIPS 2024 🇨🇦

Presenting papers on LLMs for health, diagnosis, and multimodal reasoning and graph reasoning at NeurIPS 2024 in Vancouver. Click for schedule and location details.

Dec 9, 2024

Preprint

New preprint using KG structure as inspiration for reasoning

Our new preprint simulates the thinking process of the knowledge graph constructors and utilizes the KG structure as inspiration for reasoning. This is especially helpful when expert-curated KG is sparse, enabling significantly better performance on biomedical KG-based QA.

Nov 20, 2024

More news

Publications

Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection

Mingyu Derek Ma^*, Yanna Ding^*, Zijie Huang, Jianxi Gao, Yizhou Sun, Wei Wang

ACL, 2025

Existing works have been using decoding-free candidate selection methods to obtain candidate probability from initial output logits over vocabulary. Though these estimation methods are widely used, they are not systematically evaluated, especially on end tasks. We introduce an evaluation of a comprehensive collection of decoding-free candidate selection approaches.

PDF Code

GIVE: Structured Reasoning with Knowledge Graph Inspired Veracity Extrapolation

Jiashu He, Mingyu Derek Ma, Jinxuan Fan, Dan Roth, Wei Wang, Alejandro Ribeiro

ICML, 2025

GIVE is a novel reasoning framework that integrates the parametric and non-parametric memories to enhance both knowledge retrieval and faithful reasoning processes on very sparse knowledge graphs. By leveraging the external structured knowledge to inspire LLM to model the interconnections among relevant concepts, our method facilitates a more logical and step-wise reasoning approach akin to human problem-solving, rather than gold answer retrieval.

PDF Code

Orchestrating Tool Ecosystem of Drug Discovery with Intention-Aware LLM Agents

Mingyu Derek Ma, Karina Zadorozhny, Jesse Swanson, Nathan C. Frey, Keunwoo Choi, Maksim Eremeev, Sabrina J Mielke, Wenmo Sun, Melody Liu, Jonathan Wickes, Vladimir Gligorijevic, Richard Bonneau, Henri Dwyer, Kyunghyun Cho, Stephen Ra

ICLR Workshop on Towards Agentic AI for Science, 2025

We introduce GenieAgent, a drug discovery agent that integrates a wide range of molecule design models and bridges the user intentions to concrete actions by navigating the large skill ecosystem. We also propose an evaluation framework simulating drug discovery conversations, based on real-world experiments. A large-scale assessment, validated by expert annotations, demonstrates that GenieAgent reliably meets the majority of molecular engineers' needs with high scientific accuracy and robustness.

PDF

SpatialAgent: An Autonomous AI Agent for Spatial Biology

Hanchen Wang^*, Yichun He^*, Paula P. Coelho^*, Matthew Bucci^*, Abbas Nazir, Bob Chen, Linh Trinh, Serena Zhang, Kexin Huang, Vineethkrishna Chandrasekar, Douglas C. Chung, Minsheng Hao, Ana Carolina Leote, Yongju Lee, Bo Li, Tianyu Liu, Jin Liu, Romain Lopez, Tawaun Lucas, Mingyu Derek Ma, Nikita Makarov, Lisa McGinnis, Linna Peng, Stephen Ra, Gabriele Scalia, Avtar Singh, Liming Tao, Masatoshi Uehara, Chenyu Wang, Runmin Wei, Ryan Copping, Orit Rozenblatt-Rosen, Jure Leskovec, Aviv Regev

bioRxiv, 2025

SpatialAgent integrates large language models with dynamic tool execution and adaptive reasoning. SpatialAgent spans the entire research pipeline, from experimental design to multimodal data analysis and hypothesis generation.

PDF Code

BIASINSPECTOR: Detecting Bias in Structured Data through LLM Agents

Haoxuan Li, Mingyu Derek Ma, Jen-Tse Huang, Zhaotian Weng, Wei Wang, Jieyu Zhao

arXiv, 2025

We introduce the first end-to-end, multi-agent synergy framework, BIASINSPECTOR, designed for automatic bias detection in structured data based on specific user requirements. It first develops a multi-stage plan to analyze user-specified bias detection tasks and then implements it with a diverse and well-suited set of tools. It delivers detailed results that include explanations and visualizations.

PDF

Entropy-Based Adaptive Weighting for Self-Training

Xiaoxuan Wang, Yihe Deng, Mingyu Derek Ma, Wei Wang

arXiv, 2025

We propose Entropy-Based Adaptive Weighting for Self-Training (EAST), an adaptive weighting strategy designed to prioritize uncertain data during self-training. Specifically, EAST employs a mapping function with a tunable parameter that controls the sharpness of the weighting, assigning higher weights to data where the model exhibits greater uncertainty.

PDF

Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction

Mingyu Derek Ma, Xiaoxuan Wang, Yijia Xiao, Anthony Cuturrufo, Vijay S Nori, Eran Halperin, Wei Wang

AAAI, 2025

We introduce MERA, a clinical diagnosis prediction model that bridges pertaining natural language knowledge with medical practice. We apply hierarchical contrastive learning on a disease candidate ranking list to alleviate the large decision space issue. With concept memorization through fine-tuning, we bridge the natural language clinical knowledge with medical codes.

PDF

How Californians Tweet about Extreme Heat Events on Social Media: A Health Equity Perspective

Gomathi B. Sriperumbudur, Yihang Fan, Xiaozhen Liu, Mingyu Derek Ma, Wei Wang, Chen Li, Suellen Hopfer

Weather, Climate, and Society, 2025

We examined Twitter heat discourse between 2016 and 2022 across California and by HPI. From keyword-filtered tweets, we inductively identified eight heat discourse categories, listed in order of prevalence: perceived heat risk, life impact, coping, venting, heat warnings, community action, expressing relief, and climate change concern.

PDF DOI

MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding

Fei Wang^*, Xingyu Fu^*, James Y. Huang^†, Zekun Li^†, Qin Liu^†, Xiaogeng Liu^†, Mingyu Derek Ma^†, Nan Xu^†, Wenxuan Zhou^†, Kai Zhang, Tianyi Lorena Yan, Wenjie Jacky Mo, Hsiang-Hui Liu, Pan Lu, Chunyuan Li, Chaowei Xiao, Kai-Wei Chang, Dan Roth, Sheng Zheng, Hoifung Poon, Muhao Chen

ICLR, 2025

We introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs consisting 12 diverse multi-image tasks (e.g., scene understanding, ordering) that involve 10 categories of multi-image relations (e.g., multiview, temporal relations).

PDF Dataset

MetaScientist: A Human-AI Synergistic Framework for Automated Mechanical Metamaterial Design

Jingyuan Qi, Zian Jia, Minqian Liu, Wangzhi Zhan, Junkai Zhang, Xiaofei Wen, Jingru Gan, Jianpeng Chen, Qin Liu, Mingyu Derek Ma, Bangzheng Li, Haohui Wang, Adithya Kulkarni, Muhao Chen, Dawei Zhou, Ling Li, Wei Wang, Lifu Huang

NAACL Demonstrations, 2024

We present a human-in-the-loop system that integrates advanced AI capabilities with expert oversight to accelerate the design of novel mechanical metamaterials. The system generate novel and scientifically sound hypotheses and synthesize 3D structures with high-quality.

PDF Video

CliBench: A Multifaceted and Multigranular Evaluation of Large Language Models for Clinical Decision Making

Mingyu Derek Ma, Chenchen Ye, Yu Yan, Xiaoxuan Wang, Peipei Ping, Timothy S Chang, Wei Wang

arXiv, 2024

We introduce CliBench, a novel benchmark offering a comprehensive and realistic assessment of LLMs' capabilities in clinical diagnosis. This benchmark not only covers diagnosis from a diverse range of medical cases across various specialties but also incorporates tasks of clinical significance: treatment procedure identification, lab test ordering and medication prescriptions.

Project PDF Code Dataset Leaderboard

GraphVis: Boosting LLMs with Visual Knowledge Graph Integration

Yihe Deng, Chenchen Ye, Zijie Huang, Mingyu Derek Ma, Yiwen Kou, Wei Wang

NeurIPS, 2024

GraphVis conserves the intricate graph structure through the visual modality to enhance the comprehension of KGs with the aid of Large Vision Language Models (LVLMs). Our approach incorporates a unique curriculum fine-tuning scheme which first instructs LVLMs to recognize basic graphical features from the images, and subsequently incorporates reasoning on QA tasks with the visual graphs.

PDF

Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach

Yanchen Lin, Mingyu Derek Ma, Wenna Qin, Azure Zhou, Jiaao Chen, Weiyan Shi, Wei Wang, Diyi Yang

EMNLP, 2024

We propose a computational model to infer users' susceptibility levels given their activities. Since user's susceptibility is a key indicator for their reposting behavior, we utilize the supervision from the observable sharing behavior to infer the underlying susceptibility tendency. Building upon such large-scale susceptibility labeling, we further conduct a comprehensive analysis of how different social factors relate to susceptibility.

PDF Cite DOI

Are Large-Language Models Graph Algorithmic Reasoners?

Alexander K. Taylor, Anthony Cuturrufo, Vishal Yathish, Mingyu Derek Ma, Wei Wang

arXiv, 2024

We introduce a novel benchmark designed to evaluate LLM performance on classical algorithmic reasoning tasks on explicit graphs. Our findings highlight the persistent challenges LLMs face in this domain and underscore the necessity for advanced prompting techniques and algorithmic instruction to enhance their graph reasoning abilities.

PDF

CLIMB: A Benchmark of Clinical Bias in Large Language Models

Yubo Zhang^*, Shudi Hou^*, Mingyu Derek Ma, Wei Wang, Muhao Chen, Jieyu Zhao

EMNLP Workshop on NLP for Positive Impact, 2024

We introduce a pioneering comprehensive benchmark to evaluate both intrinsic (within LLMs) and extrinsic (on downstream tasks) bias in LLMs for clinical decision tasks. Our experiments across popular and medically adapted LLMs, particularly from the Mistral and LLaMA families, unveil prevalent behaviors with both intrinsic and extrinsic bias. This work underscores the critical need to mitigate clinical bias and sets a new standard for future evaluations of LLMs' clinical bias.

PDF Code

MIRAI: Evaluating LLM Agents for Event Forecasting

Chenchen Ye^*, Ziniu Hu^*, Yihe Deng^*, Zijie Huang, Mingyu Derek Ma, Yanqiao Zhu, Wei Wang

arXiv, 2024

We introduce MIRAI, a novel benchmark designed to systematically evaluate LLM agents as temporal forecasters in the context of international events. Our benchmark features an agentic environment with tools for accessing an extensive database of historical, structured events and textual news articles.

Project PDF Code Dataset Demo Video Demo Notebook

Improving Event Definition Following For Zero-Shot Event Detection

Zefan Cai^*, Po-Nien Kung^*, Ashima Suvarna, Mingyu Derek Ma, Hritik Bansal, Baobao Chang, P. Jeffrey Brantingham, Wei Wang, Nanyun Peng

ACL, 2024

We aim to improve zero-shot event detection by training models to better follow event definitions. We hypothesize that a diverse set of event types and definitions are the key for models to learn to follow event definitions while existing event extraction datasets focus on annotating many high-quality examples for a few event types. Our experiments verify our hypothesis.

PDF Cite DOI

Mitigating Bias for Question Answering Models by Tracking Bias Influence

Mingyu Derek Ma, Jiun-Yu Kao, Arpit Gupta, Yu-Hsiang Lin, Wenbo Zhao, Tagyoung Chung, Wei Wang, Kai-Wei Chang, Nanyun Peng

NAACL, 2024

We propose BMBI, an approach to mitigate the bias of multiple-choice QA models. Based on the intuition that a model would lean to be more biased if it learns from a biased example, we measure the bias level of a query instance by observing its influence on another instance. We then use the bias level detected as an optimization objective to form a multi-task learning setting in addition to the original QA task.

PDF Cite Poster DOI

Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models

Jiashu Xu, Mingyu Derek Ma, Fei Wang, Chaowei Xiao, Muhao Chen

NAACL, 2024

Our studies demonstrate that an attacker can inject backdoors by issuing very few malicious instructions among thousands of gathered data and control model behavior through data poisoning. Through such instruction attacks, the attacker can achieve over 90% attack success rate across four commonly used NLP datasets, and cause persistent backdoors that are easily transferred to 15 diverse datasets zero-shot.

PDF Cite DOI

Instructional Fingerprinting of Large Language Models

Jiashu Xu, Fei Wang^*, Mingyu Derek Ma^*, Pang Wei Koh, Chaowei Xiao, Muhao Chen

NAACL, 2024

We present a pilot study on LLM fingerprinting as a form of very lightweight instruction tuning. Model publisher specifies a confidential private key and implants it as an instruction backdoor that causes the LLM to generate specific text when the key is present. Results on 11 popularly-used LLMs showed that this approach prevents publisher overclaim, maintains robustness against fingerprint guessing and parameter-efficient training, and supports multi-stage fingerprinting akin to MIT License.

Project PDF Cite Code DOI

STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models

Mingyu Derek Ma, Xiaoxuan Wang, Po-Nien Kung, P. Jeffrey Brantingham, Nanyun Peng, Wei Wang

AAAI, 2024

We propose STAR, a structure-to-text data generation method for complicated structure prediction tasks that first generates complicated event structures (Y) and then generates input passages (X), all with Large Language Models. We further reduce errors and improve data quality through self-reflection error identification and self-refinement with iterative revision. We show that the data generated by STAR significantly improves the performance of low-resource event extraction and relation extraction tasks, even surpassing the effectiveness of human-curated data.

PDF Cite Code Poster DOI

MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways

Mingyu Derek Ma, Alexander K. Taylor, Nuan Wen, Yanchen Lin, Po-Nien Kung, Wenna Qin, Shicheng Wen, Azure Zhou, Diyi Yang, Xuezhe Ma, Nanyun Peng, Wei Wang

AAAI Demonstrations, 2024

We present MIDDAG, an intuitive, interactive system that visualizes the information propagation paths on social media triggered by COVID-19-related news articles accompanied by comprehensive insights including user/community susceptibility level, as well as events and popular opinions raised by the crowd while propagating the information.

Project PDF Cite Code Poster Video DOI

DICE: Data-Efficient Clinical Event Extraction with Generative Models

Mingyu Derek Ma^*, Alexander K. Taylor^*, Wei Wang, Nanyun Peng

ACL, 2023

We introduce DICE, a robust and data-efficient generative model for clinical event extraction, which specializes in clinical mention identification, and MACCROBAT-EE, the first clinical event extraction dataset with event argument annotation.

PDF Cite Code Dataset Poster Slides Video DOI ACL Anthology

Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?

Jiashu Xu, Mingyu Derek Ma, Muhao Chen

ACL, 2023

We present NBR, which converts biomedical relation extraction as natural language inference formulation through indirect supervision.

PDF Cite Code DOI ACL Anthology

Multi-hop Evidence Retrieval for Cross-document Relation Extraction

Keming Lu, I-Hung Hsu, Wenxuan Zhou, Mingyu Derek Ma, Muhao Chen

ACL Findings, 2023

We propose Mr.CoD, a multi-hop evidence retrieval method based on evidence path mining and ranking with adapted dense retrievers.

PDF Cite Code DOI ACL Anthology

Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning

Mingyu Derek Ma, Jiun-Yu Kao, Shuyang Gao, Arpit Gupta, Di Jin, Tagyoung Chung, Nanyun Peng

INTERSPEECH, 2023 & ENLSP at NeurIPS, 2022

We use soft prompt tokens to learn task properties, incorporate segment information and reiterate the task before predicting value. Our method drastically reduces the number of parameters needed to less than 0.5% of prior works while achieving better low-resource dialogue state tracking performance.

PDF Cite Poster Slides Video DOI ISCA Archive ISCA PDF Amazon Science

Summarization as Indirect Supervision for Relation Extraction

Keming Lu, I-Hung Hsu, Wenxuan Zhou, Mingyu Derek Ma, Muhao Chen

EMNLP Findings, 2022

We present SuRE, which converts RE into a summarization formulation. SuRE leads to more precise and resource-efficient RE based on indirect supervision from summarization tasks.

PDF Cite Code DOI ACL Anthology

Bending the Future: Autoregressive Modeling of Temporal Knowledge Graphs in Curvature-Variable Hyperbolic Spaces

Jihoon Sohn, Mingyu Derek Ma, Muhao Chen

AKBC, 2022

We use more expressive hyperbolic spaces to tackle temporal knowledge graph reasoning with global representations to model chronological hierarchies between KGs, and local ones to model diverse hierarchical levels of KGs by variable curvatures of hyperbolic embeddings.

PDF Cite Code Poster AKBC Web

HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning

Mingyu Derek Ma, Muhao Chen^*, Te-Lin Wu^*, Nanyun Peng

EMNLP Findings, 2021

A taxonomy expansion algorithm that seeks to preserve the structure of a taxonomy in a more expressive hyperbolic embedding space and learn to represent concepts and their relations with a Hyperbolic Graph Neural Network.

PDF Cite Code Slides Video DOI ACL Anthology

EventPlus: A Temporal Event Understanding Pipeline

Mingyu Derek Ma^*, Jiao Sun^*, Mu Yang, Kung-Hsiang Huang, Nuan Wen, Shikhar Singh, Rujun Han, Nanyun Peng

NAACL Demonstrations, 2021

A temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction.

PDF Cite Code Poster Slides Video DOI Demo ACL Anthology

Dual Memory Network Model for Sentiment Analysis of Review Text

Jiaxing Shen^*, Mingyu Derek Ma^*, Rong Xiang, Qin Lu, Elvira Perez Vallejos, Ge Xu, Chu-Ren Huang, Yunfei Long

Knowledge-Based Systems, 2020

A dual user and product memory network (DUPMN) model to learn user profiles and product information for reviews classification using separate memory networks

PDF Cite Code DOI

Implicit Discourse Relation Identification for Open-domain Dialogues

Mingyu Derek Ma, Kevin K. Bowden, Jiaqi Wu, Wen Cui, Marilyn Walker

ACL, 2019

A novel dataset of implicit discourse relation argument pairs and labels for dialogic turns and a novel discourse relation identification pipeline specifically tuned for open-domain dialogue systems

PDF Cite Code Dataset Poster DOI ACL Anthology

Dual Memory Network Model for Biased Product Review Classification

Yunfei Long^*, Mingyu Derek Ma^*, Qin Lu, Rong Xiang, Chu-Ren Huang

EMNLP WASSA, 2018

Use of separate memory networks for user profile and product information helps sentiment analysis on Yelp and IMDB datasets

PDF Cite Code Poster DOI ACL Anthology

BlocHIE: a BLOCkchain-based platform for Healthcare Information Exchange

Shan Jiang, Jiannong Cao, Hanqing Wu, Yanni Yang, Mingyu Derek Ma, Jianfei He

SMARTCOMP, 2018

A Blockchain-based platform for healthcare information exchange consisting of two loosely-coupled Blockchains for different sources

PDF Cite DOI IEEE Xplore

Curriculum Vitae

Experience

Prescient Design, Genentech (Roche)
Senior Machine Learning Scientist
Since 2024; New York, NY
Amazon Alexa AI
Applied Scientist Intern
2021 & 2022; Sunnyvale, CA

Education

University of California, Los Angeles
Doctor of Philosophy in Computer Science
2020 - 2025; Los Angeles, CA
Advised by Prof. Wei Wang
The Hong Kong Polytechnic University
Bachelor of Science in Computing (First Class Honours)
2014 - 2018; Hong Kong
Best Thesis Award, commencement speaker, advised by Prof. Qin Lu and Prof. Jiannong Cao
University of Maryland, College Park
Exchange Student
2016; College Park, MD

Awards

J.P. Morgan PhD Fellowship , 2024

Amazon Fellowship , 2024

Top 15 Most Inﬂuential NAACL Paper (by Paper Digest) , 2024

Best Thesis Award, PolyU Dept. of Computing , 2018

Silver Award - Hong Kong ICT (Information and Communication Technologies) Awards (website | wiki) Student Innovation Award (Tertiary or Above), Hong Kong Government , 2018

HKSAR Government Scholarship Fund Talent Development Scholarship , 2018

CMA (The Chinese Manufacturers’ Association of Hong Kong) & Donors Scholarship , 2018

Champion and Most Innovative Award of the Region - Imagine Cup (website | wiki), Microsoft , 2017

Commercial Radio 50th Anniversary Scholarship, Hong Kong Commercial Broadcasting Company Limited & PolyU , 2017

Teaching

Teaching Associate, Bridge2AI ENABLE Scholar Program, a NIH-Funded program for health practitioner on AI and machine learning (Winter and Spring 2024)

Teaching Assistant, Data Science for ALL, an ICS NSF-Funded summer program for high-school students on data science and machine learning (Summer 2023), UC Irvine & UCLA

Teaching Associate, CS32 Introduction to Computer Science II (Summer 2023) with Prof. Edwin Ambrosio, UCLA

Teaching Assistant, CS35L Software Construction (Spring 2023) with Prof. Paul Eggert, UCLA

Teaching Assistant, CS188 Natural Language Processing (Winter 2022, later became CS162) with Prof. Nanyun (Violet) Peng, UCLA

Teaching Assistant, CS31 Introduction to Computer Science I (Fall 2021) with Prof. David Smallberg, UCLA

Services

Lead Organizer

AAAI 2025 Spring Symposium: Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation

Organizer:

TheWebConf 2025 Workshop: Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation

Area Chair:

ACL (2025), EMNLP (2025)

Reviewer:

ACL Rolling Review (2025, 2024, 2023, 2022, 2021), ICLR (2025), ACL (2024, 2023, 2022), EMNLP (2024, 2023, 2022, 2021), NAACL (2025, 2024, 2022), COLM (2024), KDD (2023), EACL (2023), NLPCC (2023, 2022)
IEEE/ACM Transactions on Audio, Speech, and Language Processing (since 2023)
NeurIPS workshop on Efﬁcient Natural Language and Speech Processing (2024)
AAAI Spring Symposium on Clinical Foundation Models (2024)
EMNLP workshop on Deep Learning for Low-resources NLP workshop (2019)
SoCal NLP Symposium (2023, 2022)

Handbook: ACL 2020 Volunteer: ACL 2023, SoCal NLP Symposium 2019

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Original Source | Taken Source

Senior Machine Learning Scientist

Experience

Prescient Design, Genentech (Roche)

Education

University of California, Los Angeles

The Hong Kong Polytechnic University

University of Maryland, College Park

Awards

Teaching

Services