Alina Oprea

Professor, Khoury College of Computer Sciences

I am a Professor at Northeastern University in the Khoury College of Computer Sciences. I obtained a BS in Mathematics and Computer Science from University of Bucharest, Romania in 2000, and MS and PhD in Computer Science from Carnegie Mellon University in 2003 and 2007, respectively.

I joined Northeastern University in Fall 2016 after spending 9 years as a Research Scientist at RSA Laboratories. I am the recipient of the Technology Review TR35 award for research in cloud security in 2011, the Google Security and Privacy Award in 2019, and the CMU Cylab Distinguished Alumni Award in 2024. I was on sabbatical at Google Research during the 2022-2023 academic year.

I co-direct the Network and Distributed Systems Security (NDS2) Lab together with Cristina Nita-Rotaru.

Office: 177 Huntington Ave. Office 516, Boston, MA, 02115

Email: a.oprea@northeastern.edu

Research

My research interests are broadly at the intersection of AI and cyber security, with a focus on:

Publications

New Papers and Manuscripts

R1dacted: Investigating Local Censorship in DeepSeek's R1 Language Model
Ali Naseh, Harsh Chaudhari, Jaechul Roh, Mingshi Wu, Alina Oprea, Amir Houmansadr
arXiv 2025
arXiv
SAGA: A Security Architecture for Governing AI Agentic Systems
Georgios Syros, Anshuman Suri, Cristina Nita-Rotaru, Alina Oprea
arXiv 2025
arXiv
ACE: A Security Architecture for LLM-Integrated App Systems
Evan Li, Tushin Mallick, Evan Rose, William Robertson, Alina Oprea, Cristina Nita-Rotaru
arXiv 2025
arXiv
DROP: Poison Dilution via Knowledge Distillation for Federated Learning
Georgios Syros, Anshuman Suri, Farinaz Koushanfar, Cristina Nita-Rotaru, Alina Oprea
arXiv 2025
arXiv
UTrace: Poisoning Forensics for Private Collaborative Learning
Evan Rose, Hidde Lycklama, Harsh Chaudhari, Anwar Hithnawi, Alina Oprea
arXiv 2024
arXiv
Model-agnostic clean-label backdoor mitigation in cybersecurity environments
Giorgio Severi, Simona Boboila, John Holodnak, Kendra Kratkiewicz, Rauf Izmailov, Alina Oprea
arXiv 2024
arXiv
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation
Harsh Chaudhari, Giorgio Severi, John Abascal, Matthew Jagielski, Christopher A. Choquette-Choo, Milad Nasr, Cristina Nita-Rotaru, Alina Oprea
arXiv 2024
arXiv
CELEST: Federated Learning for Globally Coordinated Threat Detection
Talha Ongun, Simona Boboila, Alina Oprea, Tina Eliassi-Rad, Jason Hiser, Jack W. Davidson
arXiv 2022
arXiv

Conference and Journal Publications

Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation
Ali Naseh, Yuefeng Peng, Anshuman Suri, Harsh Chaudhari, Alina Oprea, Amir Houmansadr
ACM CCS 2025
arXiv
Quantitative Resilience Modeling for Autonomous Cyber Defense
Xavier Cadet, Simona Boboila, Edward Koh, Peter Chin, Alina Oprea
Reinforcement Learning Conference (RLC) 2025
arXiv
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense
Aditya Vikram Singh, Ethan Rathbun, Emma Graham, Lisa Oakley, Simona Boboila, Peter Chin, Alina Oprea
Reinforcement Learning Conference (RLC) 2025
arXiv
Adversarial Inception for Bounded Backdoor Poisoning in Deep Reinforcement Learning
Ethan Rathbun, Alina Oprea, Christopher Amato
ICML 2025
arXiv
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents
Ethan Rathbun, Christopher Amato, Alina Oprea
NeurIPS 2024
arXiv Paper Code
User Inference Attacks on Large Language Models
Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu
EMNLP 2024
arXiv Paper
Synthesizing Tight Privacy and Accuracy Bounds via Weighted Model Counting.
Lisa Oakley, Steven Holzen, Alina Oprea
IEEE Computer Security Foundations Symposium (CSF) 2024
arXiv Paper Code
Backdoor Attacks in Peer-to-Peer Federated Learning
Georgios Syros, Gokberk Yar, Simona Boboila, Cristina Nita-Rotaru, Alina Oprea.
ACM TOPS 2024
arXiv Paper
TMI! Finetuned Models Leak Private Information from their Pretraining Data
John Abascal, Stanley Wu, Alina Oprea, Jonathan Ullman. In Privacy Enhancing
PETS 2024
arXiv Paper Code
One-shot Empirical Privacy Estimation for Federated Learning
Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, H. Brendan McMahan, Vinith Suriyakumar.
ICLR 2024, Oral
arXiv Paper
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning
Harsh Chaudhari, Giorgio Severi, Alina Oprea, Jonathan R. Ullman
ICLR 2024
arXiv Paper Code
Dropout Attacks
Andrew Yuan, Alina Oprea, Cheng Tan.
IEEE Symposium on Security and Privacy 2024
arXiv Paper Code
Unleashing the Power of Randomization in Auditing Differentially Private ML
Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh
NeurIPS 2023
arXiv Paper Code
Poisoning Network Flow Classifiers
Giorgio Severi, Simona Boboila, Alina Oprea, John Holodnak, Kendra Kratkiewicz, Jason Matterer
ACSAC 2023
arXiv Paper Code
Modeling Self-Propagating Malware with Epidemiological Models
Alesia Chernikova, Nicolo Gozzi, Nicola Perra, Simona Boboila, Tina Eliassi-Rad, Alina Oprea.
Applied Network Science 2023
arXiv Paper
Attacking Neural Binary Function Detection
Joshua Bundt, Michael Davinroy, Ioannis Agadakos, Alina Oprea, William Robertson
RAID 2023
arXiv Paper
FL4IoT: IoT Device Fingerprinting and Identification Using Federated Learning.
Han Wang, David Eklund, Alina Oprea, and Shahid Raza
ACM Transactions on Internet of Things (TOIT) 2023
arXiv
How to Combine Membership-Inference Attacks on Multiple Updated Models
Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan Ullman, Roxana Geambasu
PETS 2023
arXiv Paper
SNAP: Efficient Extraction of Private Properties with Poisoning
Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman
IEEE Symposium on Security and Privacy 2023
arXiv Paper Code
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning
Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis, Sebastiano Vascon, Werner Zellinger, Bernhard A Moser, Alina Oprea, Battista Biggio, Marcello Pelillo, Fabio Roli
ACM Computing Surveys 2023
arXiv Paper
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning
Harsh Chaudhari, Matthew Jagielski, Alina Oprea.
IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) 2023
arXiv Paper
A Recent Year On the Internet: Measuring and Understanding the Threats to Everyday Internet Devices
Afsah Anwar, Yi Hui Chen, Roy Hodgman, Tom Sellers, Engin Kirda, Alina Oprea
ACSAC 2022
Paper
Poisoning Attacks Against Machine Learning: Can Machine Learning Be Trustworthy?
Alina Oprea, Anoop Singhal, and Apostol Vassilev
IEEE Computer 2022
Paper
Network-Level Adversaries in Federated Learning
Giorgio Severi, Matthew Jagielski, Gokberk Yar, Yuxuan Wang, Alina Oprea, Cristina Nita-Rotaru
IEEE Conference on Communications and Network Security (CNS) 2022
arXiv Paper Code
Cyber Network Resilience against Self-Propagating Malware Attacks
Alesia Chernikova, Nicolò Gozzi, Simona Boboila, Priyanka Angadi, John Loughner, Matthew Wilden, Nicola Perra, Tina Eliassi-Rad, Alina Oprea
ESORICS 2022
arXiv Paper Code
FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments
Alesia Chernikova, Alina Oprea
ACM Transactions of Privacy and Security (TOPS) 2022
arXiv Paper
Adversarial Robustness Verification and Attack Synthesis in Stochastic Systems
Lisa Oakley, Alina Oprea, Stavros Tripakis
IEEE Computer Security Foundations Symposium (CSF) 2022
arXiv Paper Code
Subpopulation Data Poisoning Attacks
Matthew Jagielski, Giorgio Severi, Niklas Pousette-Harger, Alina Oprea
ACM CCS 2021
arXiv Paper Code
Poisoning Attacks and Data Sanitization Mitigations for Machine Learning Models in Network Intrusion Detection Systems
Sridhar Venkatesan, Harshvardhan Sikka, Rauf Izmailov, Ritu Chadha, Alina Oprea, Michael J. De Lucia
MILCOM 2021
Paper
PORTFILER: Port-Level Network Profiling for Self-Propagating Malware Detection
Talha Ongun, Oliver Spohngellert, Benjamin Miller, Simona Boboila, Alina Oprea, Tina Eliassi-Rad, Jason Hiser, Alastair Nottingham, Jack Davidson, Malathi Veeraraghavan
IEEE Conference on Communications and Network Security (CNS) 2021
arXiv Paper
Living-Off-The-Land Command Detection Using Active Learning
Talha Ongun, Jay Stokes, Jonathan Bar Or, Ke Tian, Farid Tajaddodianfar, Joshua Neil, Christian Seifert, Alina Oprea, John Platt
RAID 2021
Paper
Extracting Training Data from Large Language Models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel
USENIX Security Symposium 2021
arXiv Paper
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers
Giorgio Severi, Jim Meyer, Scott Coull, Alina Oprea
USENIX Security Symposium 2021
arXiv Paper Code
With Great Dispersion Comes Greater Resilience: Efficient Poisoning Attacks and Defenses for Linear Regression Models
Jialin Wen, Benjamin Zi Hao Zhao, Minhui Xue, Alina Oprea, Haifeng Qian
ACM Transactions on Information Forensics and Security (TIFS) 2021
arXiv Paper
Auditing Differentially Private Machine Learning: How Private is Private SGD?
Matthew Jagielski, Jonathan Ullman, Alina Oprea
NeurIPS 2020
arXiv Paper Code
What’s in an Exploit? An Empirical Analysis of Reflected Server XSS Exploitation Techniques
Ahmet Salih Buyukkayhan, Can Gemicioglu, Tobias Lauinger, Alina Oprea, William Robertson, and Engin Kirda
RAID 2020
Paper
QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security Game
Lisa Oakley, Alina Oprea
Conference on Decision and Game Theory for Security (GameSec) 2019. Received Outstanding Student Paper Award.
arXiv Paper
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, Fabio Roli.
USENIX Security Symposium 2019
arXiv Paper
Differentially Private Fair Learning
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman
ICML 2019
arXiv Paper
Automated Generation and Selection of Interpretable Features for Enterprise Security
Jiayi Duan, Ziheng Zeng, Alina Oprea, Shobha Vasudevan
IEEE International Conference on Big Data 2018
Paper
MADE: Security Analytics for Enterprise Threat Detection
Alina Oprea, Zhou Li, Robin Norris, Kevin Bowers
ACSAC 2018
Paper
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, Bo Li
IEEE Symposium on Security and Privacy 2018
arXiv Paper Code
Lens on the endpoint: Hunting for malicious software through endpoint data analysis.
Ahmet Buyukkayhan, Alina Oprea, Zhou Li, William Robertson
RAID 2017
Paper
Catching Predators at Watering Holes: Finding and Understanding Strategically Compromised Websites
Sumayah Alrwais, Kan Yuan, Eihal Alowaisheq, Xiaojing Liao, Alina Oprea, Xiaofeng Wang, Zhou Li
ACSAC 2016
Paper
Operational security log analytics for enterprise breach detection
Zhou Li and Alina Oprea
IEEE SecDev 2016
Paper
Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data
Alina Oprea, Zhou Li, Ting-Fang Yen, Sang H. Chin, Sumyah Alrwais
IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2015
arXiv Paper
An Epidemiological Study of Malware Encounters in a Large Enterprise
Ting-Fang Yen, Victor Heorhiadi, Alina Oprea, Michael K. Reiter, Ari Juels
ACM CCS 2014
Paper
Beehive: Large-Scale Log Analysis for Detecting Suspicious Activity in Enterprise Networks
Ting-Fang Yen, Alina Oprea, Kaan Onarlioglu, Todd Leetham, William Robertson, Ari Juels, Engin Kirda
ACSAC 2013
Paper
FlipIt: The Game of Stealthy Takeover
Marten van Dijk, Ari Juels, Alina Oprea, Ronald L. Rivest
Journal of Cryptology 2013
ePrint Paper
New Approaches to Security and Availability for Cloud Data
Ari Juels and Alina Oprea
Communications of the ACM (CACM) 2013
Paper
Iris: A Scalable Cloud File System with Efficient Integrity Checks
Emil Stefanov, Marten van Dijk, Alina Oprea, Ari Juels
ACSAC 2012
ePrint Paper
Hourglass Schemes: How to Prove that Cloud Files Are Encrypted
Marten van Dijk, Ari Juels, Alina Oprea, Ronald L. Rivest, Emil Stefanov, Nikos Triandopoulos
ACM CCS 2012
Paper
Defending Against the Unknown Enemy: Applying FlipIt to System Security.
Kevin D. Bowers, Marten van Dijk, Robert Griffin, Ari Juels, Alina Oprea, Ronald L. Rivest, Nikos Triandopoulos
Conference on Decision and Game Theory for Security (GameSec) 2012
ePrint Paper
Practical Scrubbing: Getting to the Bad Sector at the Right Time
George Amvrosiadis, Bianca Schroeder, Alina Oprea
IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2012
Paper
Efficient Implementation of Large Finite Fields GF(2^n) for Secure Storage Applications
Jianqiang Luo, Kevin D. Bowers, Alina Oprea, Lihao Xu
ACM Transactions on Storage 2012
Paper
How to Tell if Your Cloud Files Are Vulnerable to Drive Crashes
Kevin D. Bowers, Marten van Dijk, Ari Juels, Alina Oprea, Ronald L. Rivest
ACM CCS 2011
Paper
HomeAlone: Co-Residency Detection in the Cloud via Side-Channel Analysis
Yinqian Zhang, Ari Juels, Alina Oprea, Michael K. Reiter
IEEE Symposium on Security and Privacy 2011
Paper
A Clean-Slate Look at Disk Scrubbing
Alina Oprea and Ari Juels
USENIX Conference on File and Storage Technologies (FAST) 2010
Paper
HAIL: A High-Availability and Integrity Layer for Cloud Storage
Kevin D. Bowers, Ari Juels, Alina Oprea
ACM CCS 2009
ePrint Paper
Authentic Time-Stamps for Archival Storage
Alina Oprea and Kevin Bowers
ESORICS 2009
ePrint Paper
Integrity Checking in Cryptographic File Systems with Constant Trusted Storage
Alina Oprea and Michael K. Reiter
USENIX Security 2007
Paper
On Consistency of Encrypted Files
Alina Oprea and Michael K. Reiter
Symposium on Distributed Computing (DISC) 2006
Paper
Secure Key-Updating for Lazy Revocation
Michael Backes, Christian Cachin, Alina Oprea
ESORICS 2006
ePrint Paper
Space-Efficient Block Storage Integrity
Alina Oprea, Michael K. Reiter, Ke Yang
NDSS 2005. Received Best Paper Award.
Paper
Securing a Remote Terminal Application with a Mobile Trusted Device
Alina Oprea, Dirk Balfanz, Glenn Durfee, Diana K. Smetters
ACSAC 2004
Paper
Private Keyword-Based Push and Pull with Applications to Anonymous Communication
Lea Kissner, Alina Oprea, Michael K. Reiter, Dawn Song, Ke Yang
Applied Cryptography and Network Security Conference (ACNS) 2004
Paper
Automatic Generation of Two-Party Computations
Philip MacKenzie, Alina Oprea, Michael K. Reiter
ACM CCS 2003
Paper

Workshop Publications

Private Hierarchical Clustering and Efficient Approximation
Xianrui Meng, Dimitrios Papadopoulos, Alina Oprea, and Nikos Triandopoulos
ACM Cloud Computing Security Workshop (CCSW) 2021
arXiv Paper
Does Differential Privacy Defeat Data Poisoning?
Matthew Jagielski, Alina Oprea.
Distributed and Private Machine Learning (DPML) Workshop 2021
Paper
Collaborative Information Sharing for ML-Based Threat Detection
Talha Ongun, Simona Boboila, Alina Oprea, Tina Eliassi-Rad, Alastair Nottingham, Jason Hiser, Jack Davidson
AI/ML for Cybersecurity Workshop at SIAM International Conference on Data Mining (SDM), 2021
arXiv
On Generating and Labeling Network Traffic with Realistic, Self-Propagating Malware
Molly Buchanan, Jeffrey W. Collyer, Jack W. Davidson, Saikat Dey, Mark Gardner, Jason D. Hiser, Jeffry Lang, Alastair Nottingham, Alina Oprea
AI/ML for Cybersecurity Workshop at SIAM International Conference on Data Mining (SDM), 2021
arXiv
AppMine: Behavioral Analytics for Web Application Vulnerability Detection
Indranil Jana, Alina Oprea
ACM Cloud Computing Security Workshop (CCSW) 2019.
arXiv Paper
Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural Networks for Self-Driving Cars
Alesia Chernikova, Alina Oprea, Cristina Nita-Rotaru, BaekGyu Kim
IEEE Workshop on the Internet of Safe Things 2019
arXiv
User-Profile-Based Analytics for Detecting Cloud Security Breaches
Trishita Tiwari, Ata Turk, Alina Oprea, Katzalin Olcoz, Ayse K. Coskun
International Workshop on Privacy and Security of Big Data 2017
Paper
Robust Linear Regression Against Training Data Poisoning
Chang Liu, Bo Li, Yevgeniy Vorobeychik, Alina Oprea
ACM Workshop on Artificial Intelligence and Security (AISEC) 2017. Received Best Paper Award.
Paper
Proofs of Retrievability: Theory and Implementation
Kevin Bowers, Ari Juels, Alina Oprea
ACM Cloud Computing Security Workshop (CCSW) 2009
ePrint Paper
Lazy Revocation in Cryptographic File Systems
Michael Backes, Christian Cachin, Alina Oprea
IEEE Security in Storage Workshop (SISW) 2005
Paper

Other Publications

The House That Knows You: User Authentication Based on IoT Data.
Talha Ongun, Oliver Spohngellert, Alina Oprea, Cristina Nita-Rotaru, Mihai Christodorescu, Negin Salajegheh
arXiv 2019
arXiv
On Designing Machine Learning Models for Malicious Network Traffic Classification
Talha Ongun, Timothy Sakharaov, Simona Boboila, Alina Oprea, Tina Eliassi-Rad
arXiv 2019
arXiv
Detecting Self-Propagating Attacks in Cyber Networks
Timothy A Sakharov, Benjamin Miller, Talha Ongun, Alina Oprea, Tina Eliassi-Rad
The International Conference on Network Science (NetSci), oral presentation, 2019
Mobilizing Intelligent Security Operations for Advanced Persistent Threats
Sam Curry, Bret Hartman, David P. Hunter, David Martin, Dennis R. Moreau, Alina Oprea, Uri Rivner, Dana E. Wolf
RSA Security Brief, February 2011

Thesis

Efficient Cryptographic Techniques for Securing Storage Systems
Alina Oprea, PhD Thesis
Carnegie Mellon University, Technical Report CMU-CS-07-119, May 2007
Thesis

Teaching

CY 4100: Machine Learning Security and Privacy
Fall 2025
Explores security and privacy challenges in machine learning and generative artificial intelligence.
Course Webpage →
CS 4973 / CS 6983: Trustworthy Generative AI
Fall 2024
Explores security and privacy challenges in machine learning and artificial intelligence systems.
Course Webpage →
CS 7775 Seminar in Computer Security: Machine Learning Security and Privacy
Fall 2023
Explores security and privacy challenges in machine learning and artificial intelligence systems.
Course Webpage →
DS 4400 Machine Learning and Data Mining I
Spring 2022
Introduction to machine learning and fundamental algorithms for supervised learning.
Course Webpage →
CY 7790 Special Topics in Security and Privacy: Machine Learning Security and Privacy
Fall 2021
Explores security and privacy challenges in machine learning and artificial intelligence systems.
Course Webpage →
DS 4400 Machine Learning and Data Mining I
Spring 2021
Introduction to machine learning and fundamental algorithms for supervised learning.
Course Webpage →
DS 4400 Machine Learning and Data Mining I
Fall 2020
Introduction to machine learning and fundamental algorithms for supervised learning.
Course Webpage →
CY 2550 Foundations of Cybersecurity
Spring 2020
Overview of basic cybersecurity principles and concepts, including systems and communications security.
Course Webpage →
DS 4400 Machine Learning and Data Mining I
Fall 2019
Introduction to machine learning and fundamental algorithms for supervised learning.
Course Webpage →
DS 5220 Supervised Machine Learning and Learning Theory
Fall 2019
Introduction to machine learning and fundamental algorithms for supervised learning.
Course Webpage →
DS 4400 Machine Learning and Data Mining I
Spring 2019
Introduction to machine learning and fundamental algorithms for supervised learning.
Course Webpage →
DS 4400 Machine Learning and Data Mining I
Fall 2018
Introduction to machine learning and fundamental algorithms for supervised learning.
Course Webpage →
CS 4770 Cryptography / CS-6750 Cryptography and Communication Security
Spring 2017
Introduction to modern cryptography with an emphasis on formal definitions and provably secure constructions.
Course Webpage →
CS 4770 Cryptography / CS-6750 Cryptography and Communication Security
Spring 2018
Introduction to modern cryptography with an emphasis on formal definitions and provably secure constructions.
Course Webpage →
CS 7775 Seminar in Computer Security: Security Analytics
Fall 2016
Explores applications of machine learning and artificial intelligence in cybersecurity.
Course Webpage →

Students

I am fortunate to advise and work with the following students, postdoctoral researchers, and research scientists:

PhD Students

Ethan Rathbun, Fall 2023 - present, co-advised with Chris Amato

Evan Rose, Fall 2023 - present

Georgios Syros, Fall 2023 - present, co-advised with Cristina Nita-Rotaru

John Abascal, Fall 2021 - present, co-advised with Jonathan Ullman

Harsh Chaudhari, Fall 2021 - present

Lisa Oakley, Fall 2020 - present

Postdocs and Research Scientists

Simona Boboila, Research Scientist

Anshuman Suri, Postdoc

Alumni

Giorgio Severi, Graduated 2024, First position: Microsoft

Alesia Chernikova, Graduated 2024, First position: Network Science Institute at Northeastern University

Talha Ongun, PhD in Computer Science, Graduated 2023, First position: Crowdstrike

Matthew Jagielski, PhD in Computer Science, Graduated 2021, co-advised with Cristina Nita-Rotaru, First position: Google Research

Aditya Vikram Singh, Graduated Spring 2025

Gokberk Yar, MS in Computer Science, Graduated May 2023

Alexander Gomez, MS in Computer Science, Graduated May 2020

Indranil Jana, MS in Computer Science, Graduated May 2019

Muazzam Asani, MS in Computer Science, Graduated May 2019

Nico Berrios, Catholic University of Chile, Visited Summer 2024

Pramodh Gopalan, IIT Kanpur, Graduated 2023

Andrew Yuan, BS in Computer Science, Graduated 2023

Stanley Wu, BS in Computer Science, Graduated 2022

Hava Kantrowitz, BS in Cybersecurity, Graduated 2022

Niklas Pousette-Harger, BS in Computer Science, Graduated 2021

Yuxuan Wang, BS in Computer Science, Graduated 2020

Oliver Spohngellert, BS in Computer Science, Graduated in 2019

Diego Delgado, BS in ECE, Graduated 2019

Gen Ohta, BS in ECE, Graduated May 2017

Afsah Anwar, Postdoc 2021 - 2022