Home

Dr. Phan is an Associate Professor at NJIT. Dr. Phan's topic of interest mainly focuses on privacy and security, social network analysis, machine learning, and spatio-temporal data mining. In particular, Dr. Phan is interested in understanding the nature of human behavior and human expression on health social networks and social media. At NJIT, Phan has established the required expertise in the field, i.e., privacy and security, ML, and health informatics, with over 65 publications. Many of them were published at leading venues, including IEEE S&P, ICML, ECML, AAAI, IJCAI, IEEE ICDM, IEEE PerCom, AISTATS, IEEE BigData, ACM SigSpatial GIS, ACM Multimedia, etc., with an AAAI 2023 Distinguished Paper Award and several selected as best papers, i.e., IEEE SDS'22, IEEE ICDM’17, Springer CSoNet’19, Springer CSoNet’18, ACM BCB’15, IEEE/ACM ASONAM’15. Phan’s research has been generously supported by NSF and (long-term) industry partners, including Qualcomm Technology Inc., Adobe System Inc., and Wells Fargo.

We are hiring Post-doc and PhD students in Spring/Fall 2024 with a strong math and machine learning background! Please send your CV to phan at njit.edu.

Students:

Apurv Verma (PhD) 2024 - present

Long Vu (PhD) 2024 - present

Khiem Ton (PhD) 2024 - present

Khoa Nguyen (PhD) 2023 - present

Khang Tran (PhD) 2020 - present

Andrew Dennis (PhD) 2020 - present (Bloomberg)

Hang Nguyen (Honor Undergraduate) 2019 - present

Simran Kaur (Honor Undergraduate) 2021 - present

Huong Ly Ngo (Master in Data Science) 2021 - present

Alumni:

Phung Lai (PhD) 2018 - 2023, Tenure-track Assistant Professor, University at Albany, SUNY

Pelin Ayranci (PhD) 2020 - 2022, Technical Product Manager, Paypal

Han Hu (PhD) 2016 - 2020

Pradnya Desai (Honor Undergraduate) 2019 - 2022, Microsoft

Anuja Badeti (Honor Undergraduate) 2018 - 2022, Bloomberg

Media Cover: YWCC News, YWCC News (2), NJIT Weekly, Vox.com, NJSpotlight, NJ101.5, newstrotteur.fr, modrogen.com, actualite-sante.fr, scienmag.com,  Medicalxpress, Healthitanalytics.com, Princeton Magazine, Daily Emerald

News | Slideshare | Dataset Released

April 2024: Our project "XCopilot: Private Code Generating with Large Language Models" in collaboration with Microsoft has been partially funded by an NSF Accelerating Research Translation (ART) grant and NJIT Collaborative Research and Innovation Strategic Partnership (CRISP) investment plan through the NJIT Center for Translational Research (CTR).

March 2024: Our Handbook of Trustworthy Federated Learning is out. The first-of-its-kind book focuses on providing insights into trustworthy federated learning. Part of the book series: Springer Optimization and Its Applications (SOIA, volume 213).

Feb 2024: Our patent application Secure Prompt, in which we can fix vulnerabilities in software codes generated by LLMs in a cost-effective and optimized manner, has been accepted as a US provisional patent, and we are working on a US patent filing.

Nov 2023: Exciting news! We will work with Qualcomm Inc. to bring the next generation of Generative AI to mobile devices in a secure, private, and scalable manner. 

Nov 2023: Invited talk "Effects of Generative AI and Opportunities" at the Newark Regional Business Partnership - Regional Economic Outlook, Nov 8, 2023. 

Nov 2023: Our team won a QRDI Award of ~$740k no overhead ~ $1m+ with overhead to develop Trustworthy Federated Learning for Deployable Distributed Generative AI (award rate: 18 / 500+, 3.6%).

Nov 2023: Invited to serve on an NSF panel.

August 2023: Our paper "Differential Privacy in HyperNetworks for Personalized Federated Learning" has been accepted to the 32nd ACM International Conference on Information and Knowledge Management (ACM CIKM 2023). 

July 2023: Our paper "Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection" has been accepted to the 45th IEEE Symposium on Security and Privacy (IEEE S&P 2024).

Jun 2023: Invited to serve on an NSF panel.

May 2023: Our paper "Exploring COVID-19’s Impact on Mental Health: A Longitudinal and Thematic Analysis of Reddit Users’ Discourse" has been accepted in the Journal of Medical Internet Research (JMIR). IF: 7.08.

Feb 2023: Our paper "XRand: Differentially Private Defense against Explanation-Guided Attacks" was selected as one of the twelve AAAI 2023 Distinguished Paper Awards. AAAI Conference Paper Awards and Recognition honors papers that exemplify the highest standards in technical contribution and exposition.

Jan 2023: Our paper "Active Membership Inference Attack under Local Differential Privacy in Federated Learning" has been accepted to the 26th AISTATS 2023. This paper shows that passively applying Local Differential Privacy (LDP) is vulnerable to Active Membership Inference Attacks.

Jan 2023: Our paper "Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness" has won the Best Paper Award at the 9th IEEE International Conference on Software Defined Systems (IEEE SDS-2022). In this paper, we develop Un-Fair Trojan to severely damage model fairness while remaining stealthy in Federated Learning.

Dec 2022: Our paper "Zone-based Federated Learning for Mobile Sensing Data" has been accepted at the IEEE PerCom 2023, a leading venue in mobile computing. We propose Zone-based Federated Learning (ZoneFL) to simultaneously achieve good model accuracy while adapting to user mobility behavior, scale well as the number of users increases, and protect user data privacy.

Dec 2022: Serve as a Senior PC member - IJCAI 2023, PC member of KDD 2023, Reviewer of ICML 2023.

Dec 2022: Gave an invited lecture on  "Privacy in Machine Learning: Policies, Societal Concerns, and Challenging Gaps" at the University of Alberta.

Nov 2022: Our paper "XRand: Differentially Private Defense against Explanation-Guided Attacks" has been accepted in the AAAI 2023. In this paper, we introduce a new concept of achieving local differential privacy (LDP) in the explanations, and from that, we establish a defense, called XRand, against explanation-guided attacks.

Nov 2022: Our paper "FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps" has been accepted in IEEE Transactions on Mobile Computing. We developed a scalable system to open an ecosystem of FL on mobile apps. This is a joint work among NJIT, Kent State University, Qualcomm, and other industrial partners.

Oct 2022: Invited to serve on an NSF Panel.

Oct 2022: Two papers accepted in the IEEE BigData'22.

August 2022: I gave an invited talk on "Privacy in Machine Learning: Policies, Societal Concerns, and Challenging Gaps" at Qualcomm, San Diego.

July 2022: Our paper "Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning" has been accepted to the Conference on Lifelong Learning Agents - CoLLAs 2022 and will be published in the Proceedings of Machine Learning Research (PMLR).

March 2022: Our paper "Social and Motivational Factors for the Spread of Physical Activities in a Health Social Network" has been invited to a fast-track of the IEEE Transactions on Network Science and Engineering (TNSE), IF: 5.213.

March 2022:  We have launched our field trial for our FL system on mobile devices. If you are interested in this opportunity, please sign up here: https://docs.google.com/forms/d/1oAkQT8apIdHC2Cbo2804UZu2PdKu-knlUpxytpxn6gE/edit. 

Jan 2022: Our Pradyna Desai (an undergrad student from CS Department in my research team) has been selected to the 80 Finalist for the 2022 National Center for Women & Information Technology (NCWIT) Collegiate Award. NCWIT is a non-profit community of over 1,500 universities, companies, non-profits, and government organizations national wide working to increase the influential and meaningful participation of girls and women in the field of computing. This is the inspiration of our research work on "Continual Learning with Differential Privacy,” in which Pradyna Desai is the lead author. 

Dec 2021: Our article "Ontology-based Interpretable Machine Learning: A Comprehensive Study" has been accepted with minor revision by the Journal of Combinatorial Optimization - Springer.

Nov 2021: Exciting to serve on an NSF Panel.

Nov 2021: We are happy to announce our Ecosystem for Federated Learning on Mobile Applications. Check out our paper "FLSys: Toward an Open Ecosystem for FederatedLearning Mobile Apps." This is a joint work among NJIT, Kent State University, Qualcomm, and associated industrial partners. If there are any interests in collaborating with us on this journey, please feel free to drop me an email.

Oct 2021: Two regular papers about Trustworthy and Secure AI, including a synergic poisoning attack on deep neural networks and a novel metric c-eval to evaluate Explanable AI models, were accepted at IEEE International Conference on Big Data (IEEE BigData'21).  Acceptance Rate: 97 / 486.

Oct 2021: Our great Han Hu has successfully defended his Ph.D. dissertation, titled "Private and Federated Deep Learning: System, Theory, and Applications for Social Good." Big Congratulation to Dr. Hu!!!

Sept 2021: Our Honor Undergraduate Student, Pradnya Desai, has a paper, titled "Continual Learning with Differential Privacy," accepted at the 28th International Conference on Neural Information Processing (ICONIP2021) (Rank A, CORE2020) with an oral presentation. This paper establishes the first formal connection between Differential Privacy and Continual Learning.  

Sept 2021: We are preparing to launch the first Data Science Challenge for all NJIT students! An official announcement is coming soon.

Sept 2021: Invited paper to Springer CSoNet'21. NhatHai Phan, David Kil, Brigitte Piniewski, and Dejing Dou. "Social and Motivational Factors for the Spread of Physical Activities in a Health Social Network."

May 2021: Serve on PC of ICLR'22, AAAI'22, WSDM'22.

May 2021: Our project about Federated Learning in the Wild has been funded by NJIT Seed Grant! This is a joint project with Cristian Borcea.

Apr 2021: Invited to serve on two NSF Panels. Joyful to serve!

Apr 2021: Phung's provisional pattern in preserving privacy in natural language modeling has been approved. Congrats Phung! This is joint work with Adobe Inc.

Dec 2020: Serve on PC of ICML 2021, KDD 2021, NeurIPS'21, ICDM'21, MedInfo'21.

Dec 2020: Exciting to serve on an NSF Panel.

Nov 2020: Grateful to continue receiving funding support from Adobe System Inc.

Aug 2020: Exciting to serve on an NSF Panel.

Aug 2020: Our Secure and Scalable Federated Learning in the Wild during the COVID-19 Pandemic project has been funded by NSF: IIS: EAGER grant. This is a collaborative project with Prof. Ruoming Jin from KSU.

Aug 2020: Serve on PC of AAAI 2021, and WSDM 2021.

June 2020: Grateful to receive an award from the NSF: SaTC: Core for our project, "When Adversarial Learning Meets Differential Privacy: Theoretical Foundation and Applications," collaborating with Prof. My T. Thai from the University of Florida. Great thanks to the community for continuing to support our research!

May 2020: Our paper "Scalable Differential Privacy with Certified Robustness in Adversarial Learning" has been accepted to ICML'20. The paper established 1) the first connection between differential privacy for adversarial training with certified robustness in both input and latent spaces, and 2) scalable training to bypass the iterative training process in the leading DP mechanisms. [Code has been released on Github]

May 2020: Greatful to continue receiving great support from Adobe System Inc. to push the boundary of "Differential Privacy in Natural Language Processing." Stay tuned for significant results to come out soon!

May 2020: Invited to serve on the PC of NeurIPS'20, IEEE BigData'20, and IEEE ICMLA'20.

May 2020: Our research on Human Behavior during and after the COVID-19 pandemic using federated machine learning is covered on NJIT Weekly.

May 2020: Gave an invited talk at Adobe System Inc. on "Deep Learning in the Wild with Certified Defenses."

March 2020: Our paper about "Ontology-based Interpretable Machine Learning for Textual Data" has been accepted for an Oral Presentation at the IEEE International Joint Conference on Neural Networks (IJCNN'2020). [Github] See you in Glasgow, Scotland, UK! 

Feb 2020: Exciting to announce a long-term partnership with Qualcomm Technologies Inc. to build a Federated Machine Learning Framework in the Wild.

Feb 2020: Exciting to serve on an NSF Panel.

Jan 2020: Our paper "Limiting the Neighborhood: De-Small-World Network for Outbreak Prevention" has been selected as "Best Papers" at CSoNet 2019. 

Dec 2019: Invited to serve in the PC of IJCAI-PRICAI'20, ICDM'20, PAKDD'20.

Nov 2019: Honor to receive the "Best Service Award" at CSoNet 2019. 

Nov 2019: Our class "IS 698: Emerging Topics in Deep Learning - Artificial Intelligence" will be available from Spring 2020. Please consider registering for the course, which will cover significant research and practice directions in DL - AI.

Sept 2019: Our paper about "Differentially Private Lifelong Learning" has been accepted to present at the Privacy in Machine Learning (PriML) Workshop at NeurIPS'19. See you at NeurIPS'19!

Sept 2019: Our paper about "Ontology-based Interpretable Machine Learning" has been accepted for an Oral Presentation at the Knowledge Representation & Reasoning Meets Machine Learning (KR2ML) Workshop at NeurIPS'19. See you at NeurIPS'19!

Sept 2019: Gave an Invited Talk about "Deep Learning in the Wild with Certified Defenses" at the Computer Science Department, Kent State University.

Sept 2019: Two papers accepted at Springer CSoNet 2019!

Aug 2019: Grateful to receive gift fundings from Adobe Systems!

Aug 2019: Attend MedInfo 2019, present our work on DrugTracker, and grateful for exciting questions! Went back to Montpellier and regroup with Prof. Pascal Poncelet. 

Aug 2019: Our paper "DrugTracker: A Community-focused Drug Abuse Monitoring and Supporting System using Social Media and Geospatial Data" has been accepted at the International Conference on Advances in Geographic Information Systems 2019 (ACM SIGSPATIAL 2019), Nov 5-8, 2019, Chicago.

May 2019: Invited to serve in the PC of AAAI 2020.

June 2019: We are organizing the "AI with Geospatial Information System for Social Good" Special Track at CSoNet 2019. Please submit your good works. 

June 2019: Grateful to receive the IJCAI-AIJ Travel Grant 2019, invited to serve as a Session Chair IJCAI'19.

May 2019: Invited to serve in the PC of CIKM 2019, ICDM 2019, ICTAI 2019, FLAIRS 2020.

May 2019:  Our paper "Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness" has been accepted at the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), August 10-16, 2019, Macao, China. (acceptance rate = 850/4752, 17.9%) [GitHub]

March 2019: Our paper "An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere," has been accepted at The 17th MedInfo 2019, Lyon, France (A leading venue in Medical and Health Informatics). We addressed the extreme sparse classification problem in this work to detect drug abuse risk behaviors in Twitter-Sphere. 

March 2019: We are looking for a Ph.D. student, fully funded, to work on Privacy and Security in Deep Learning...

Feb 2019: NJIT has been named a "Very High Research Activity (R1)" institution by the Carnegie Classification.

Feb 2019: Our paper "Deep Self-Taught Learning for Detecting Drug Abuse Risk Behavior in Tweets," has been selected as the best papers at CSoNet 2018, and invited to a Special Issue of COSN (Computational Social Networks, Springer).

Dec 2018: Exciting to serve on the PC of IJCAI 2019 and MedInfo 2019.

Oct 2018: Exciting to serve on the PC of ICDCS 2019, PAKDD 2019, and ICDS 2019. 

August 2018: Our paper, titled "Deep Self-Taught Learning for Detecting Drug Abuse Risk Behavior in Tweets," has been invited to The 7th International Conference on Computational Data & Social Networks (CSoNet 2018).

August 2018: Our paper, titled "Recursive Structure Similarity: A Novel Algorithm for Graph Clustering," has been accepted at The 30th IEEE ICTAI 2018.