Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Our Staff
AITG Staff:
| lukas.diduch [at] nist.gov (Lukas Diduch) (Fed) | Lukas Diduch is a senior software and systems engineer at the AI Technologies Group. He develops datasets, scoring and analysis software, as well as automated scoring and evaluation systems that facilitate advanced ML evaluation programs in the NLP, Speech, and Video domains. He has contributed to multiple NIST AI/ML evaluation programs, including LORELEI, BOLT, SRE, ActEV, TrecVID, OpenSAT, OpenFAD, and OpenMFC, and currently supports the GenAI evaluation program. He is currently the engineering lead for GenAI infrastructure development and program lead for the GenAI Audio Evaluation Challenge. |
| lowen.dipaula [at] nist.gov (Lowen DiPaula) (Fed) | Lowen DiPaula is a software engineer in the AI Technology Group at NIST. His bachelor’s degree is in computer science and his master’s degree is in software engineering. He works on developing and maintaining the testing and evaluation web-infrastructure and associated pipelines for the GenAI team. |
| brittany.falcone [at] nist.gov (Brittany Falcone) (Fed) | Brittany Falcone is the administrative support assistant in the Artificial Intelligence Technologies Group and Human-Centered Technologies Group at the National Institute of Standards and Technology. Brittany provides reliable and effective support to ensure the daily operation run smoothly. |
| peter.fontana [at] nist.gov (Peter Fontana) (Fed) | Peter C. Fontana, Ph.D. is a Computer Scientist in the AI Technologies Group within ITL. In ITL, he currently leads the NIST Generative AI Code Challenge and is a technical contributor in the NIST Generative AI Program. Past projects include serving as a technical contributor to the NIST efforts in the DARPA Knowledge-directed AI Reasoning Over Schemas (KAIROS) program and the NIST Data Science Evaluation Series. Peter holds a Doctor of Philosophy and a Master of Science in Computer Science from the University of Maryland at College Park, whose dissertation research was in model checking (formal methods). He holds a Bachelor of Science and Engineering in Computer Science Engineering from the University of Pennsylvania. |
| yooyoung.lee [at] nist.gov (Yooyoung Lee) (Fed) | Yooyoung Lee serves as the group leader of the AI Technologies Group and leads the NIST GenAI program, which supports testing and evaluation of generative AI technologies across multiple modalities. She holds a Ph.D. in Computer Science and Engineering from Chung-Ang University in South Korea. She has completed the Essentials of Forensic Interpretation training at the University of Lausanne in Switzerland, improving her understanding of probabilistic reasoning, evidence evaluation, and interpretation in forensic contexts. |
| kay.peterson [at] nist.gov (Kay Peterson) (Fed) | Kay Peterson is a social science analyst and linguist. She has experience in human language technology (HLT) development and evaluation in academic, industry, and government settings, with a focus on HLT and more recently AI metrology and evaluation at NIST since 2007. |
| sonika.sharma [at] nist.gov (Sonika Sharma) (Fed) | Sonika Sharma is a Computer Scientist in the Artificial Intelligence Technologies Group (AITG) within the Information Technology Laboratory (ITL). She is a technical contributor to the NIST Generative Artificial Intelligence (GenAI) Program, where she worked on the GenAI Code Pilot Challenge and is currently working on the GenAI Web Platform. Additionally, she serves as the lead presenter and contributor to the GenAI Demo. Sonika holds a bachelor's degree in Computer Science from the University of Delaware and is currently pursuing her master's degree in Computer Science from the University of Illinois. |
| rishie.raj [at] nist.gov (Rishie Raj) (IntlAssoc) | Rishie is an AI researcher, currently working at the National Institute of Standards and Technology (NIST) on AI forensics and deepfake detection. He builds evaluation benchmarks for detecting synthetic text, images, audio, and deepfake media. Rishie graduated with a master's degree in AI and Robotics from the University of Maryland, College Park, where he worked on audio-visual reasoning and benchmarking, and agentic frameworks for multi-turn editing. |
| bikesh.regmi [at] nist.gov (Bikesh Regmi) (IntlAssoc) | Bikesh is a Computer Scientist (PREP) in the AI Technology Group at NIST, where he works on building the infrastructure for the NIST Generative AI challenge, with a primary focus on backend systems and machine learning workflows. Bikesh also contributes to research and development for the Generative AI code Evaluation Challenge, focusing on developing evaluation methods, datasets, scoring metrics and supporting infrastructure for assessing AI-generated code. |
|
| nour.riman [at] nist.gov (Nour Riman) (IntlCtr) | Coming Soon! |
|
| seungmin.seo [at] nist.gov (Seungmin Seo) (IntlAssoc) | Seungmin Seo is a federal contractor in the AI Technologies Group at NIST and an information and research scientist at Chakra Consulting Inc., focusing on the testing and evaluation of AI technologies across multimodal domains. He holds a B.S. and Ph.D. in Computer Science from Yonsei University and was awarded the 2026 ITL Outstanding Associate of the Year Award for advancing measurement science in trustworthy AI through novel privacy and generative AI metrics. His extensive project work includes technical contributions to NIST's roles in DARPA's KAIROS, IARPA's ARTS, and NIST's GenAI program, with research published in NIST Trustworthy and Responsible AI reports as well as peer-reviewed venues such as AAAI, EMNLP, IJCB, and IEEE TKDE. |
Collaborators:
| george.awad [at] nist.gov (George Awad) (Fed from MLTG) | George Awad is a Computer Scientist and Project Leader at the National Institute of Standards and Technology (NIST), where his research focuses on AI measurement and evaluation, multimodal AI, video understanding, information retrieval, and generative AI. He leads the TRECVID evaluation program and develops benchmarks, datasets, metrics, and evaluation methodologies for assessing the capabilities and limitations of advanced AI systems. His current work includes the evaluation of multimodal and vision-language models for video retrieval, question answering, and reasoning, as well as NIST’s Generative AI evaluation efforts for measuring AI-generated content and detection systems. His research also addresses digital content authenticity and provenance, including the evaluation of digital watermarking technologies for generative AI content. George has contributed to TRECVID research and evaluation at NIST since 2007. He received his Ph.D. in Computer Science from Dublin City University, Ireland, and holds M.Sc. and B.Sc. degrees in Computer Engineering. His research has been recognized with the 57th Niwa-Takayanagi Best Paper Prize and the 2018 IEEE Computer Society PAMI Mark Everingham Prize. |
| kary.cabrera [at] nist.gov (Angelly Cabrera) (Associate from AIRMSD) | Angelly is an Associate Researcher from the AI Standards and Guidelines Group, joining NIST through the Professional Research Experience Program (PREP). She is currently developing an evaluation program for digital watermarking technologies to assess their robustness and imperceptibility. Prior to joining NIST, she completed multiple software engineering internships at Microsoft and, through a technology policy fellowship, worked with municipal governments to develop AI literacy and risk assessment frameworks. She earned her B.S. in Electrical and Computer Engineering from the University of Southern California, where she received accolades from the National Center for Women & Information Technology (NCWIT), the National Academy of Engineering, and the Ming Hsieh Institute. |
| abraham.donaty [at] nist.gov (Abraham Donaty ) (Associate from MLTG) | Avi Donaty is a Computer Scientist in the Multimedia Language Technologies Group at the National Institute of Standards and Technology. He conducts research on watermark robustness and imperceptibility, and deepfake methods and detection accuracy. He received his Master’s Degree from the University of Maryland, Baltimore County, where his research focused on photogrammetry. |
|
| hariharan.iyer [at] nist.gov (Hari Iyer) (Fed from SED) | Coming Soon! |
Created September 4, 2026, Updated September 22, 2026