Maksym Andriushchenko is an incoming faculty member at the ELLIS Institute Tübingen and Max Planck Institute for Intelligent Systems , where he will lead the AI Safety and Alignment group as a principal investigator. Currently, he is a Doctoral Assistant at the Theory of Machine Learning Laboratory (TML) within the Department of Computer Science (IINFCOM) at Swiss Federal Institute of Technology in Lausanne (EPFL) . His work focuses on AI safety, adversarial robustness, and alignment of large language models (LLMs) with societal values.
Changxi Zheng is an Associate Professor in the Department of Computer Science at Columbia University's School of Engineering and Applied Science (SEAS). He directs Columbia's Computer Graphics Group (C2G2) within the Columbia Vision and Graphics Center (CVGC). After receiving his PhD from Cornell University, he joined the faculty of Computer Science Department at Columbia, where he has established himself as a leading researcher in computer graphics and scientific computing. Dr. Zheng's research spans multiple areas of applied computer science with a particular focus on computer graphics and scientific computing. His work centers around developing numerical models for simulating physical phenomena involving complex motions such as fluids, bubbles, and thin rods, along with their resulting acoustic waves. Leveraging computational insights from these models, he devises methods for improving tangible object creation, enabling novel human-computer interactions, and developing software tools for acoustic and photonic devices. His research has attracted significant public interest and media coverage, including projects like FontCode, AirCode, and Computational Metallophone Design. His recent publications reveal a strong interdisciplinary approach, bridging computer graphics, physics simulation, machine learning, and hardware design. His work demonstrates consistent innovation in computational methods for simulating physical phenomena and applying these techniques to practical problems in 3D printing, acoustic modeling, and interactive systems. The breadth of his research spans from fundamental physics-based simulations to practical applications in industry. Columbia SEAS Dean's Fellow (for advised students) NSF Graduate Research Fellow (for Ruilin Xu) Snap Research Fellow (for Rundi Wu) CKGSB Fellow (for Yun Fei) Adobe Research Fellow (for Gabriel Cirio) Marie Sklodowska-Curie Individual Fellow (for Rundi Wu) Best Paper Award at ACM International Conference on Multimedia (ACMMM), 2019 Dr. Zheng actively mentors a diverse group of students, including current PhD candidates and postdoctoral researchers. His research group has received support from various sources that enable their innovative work in computational graphics and physics-based simulation. He has supervised numerous successful students who have gone on to positions at leading technology companies including Adobe, Tencent, Facebook, and academic institutions. As director of Columbia's Computer Graphics Group (C2G2) within the Columbia Vision and Graphics Center (CVGC), Dr. Zheng leads a vibrant research team focused on advancing the state of the art in computer graphics, physics-based simulation, and their applications. The group maintains strong collaborations with industry partners and academic institutions worldwide, fostering an environment of innovation and practical application of theoretical concepts.
Daniel E. Ho holds multiple prestigious positions at Stanford University: William Benjamin Scott and Luna M. Scott Professor of Law Professor of Political Science Professor of Computer Science (by courtesy) Senior Fellow, Stanford Institute for Economic Policy Research Senior Fellow, Stanford Institute for Human-Centered Artificial Intelligence Faculty Fellow, Center for Advanced Study in the Behavioral Sciences He serves on the National Artificial Intelligence Advisory Commission (NAIAC), as Senior Advisor on Responsible AI at the U.S. Department of Labor, and as a Public Member of the Administrative Conference of the United States (ACUS). Ho earned his J.D. from Yale Law School and Ph.D. from Harvard University, completing a clerkship with Judge Stephen F. Williams on the U.S. Court of Appeals for the District of Columbia Circuit. His research bridges artificial intelligence, law, and public policy with emphasis on: Regulatory governance frameworks for AI systems Fairness and bias mitigation in algorithmic decision-making Environmental enforcement using satellite imagery and computer vision Methods for estimating racial disparities without direct demographic data Legal AI reliability and statutory research systems Analysis of his 2025 publications reveals a consistent focus on practical AI governance tools addressing real-world regulatory challenges. Key themes include developing benchmarks for legal applications, mitigating hallucination in legal AI tools, and creating systems for statutory research. His work demonstrates strong integration of technical AI methods with policy implementation, particularly in environmental enforcement and fairness assessment. As Director of the Regulation, Evaluation, and Governance Lab (RegLab), Ho leads interdisciplinary research partnerships with government agencies. While specific grant details aren't provided, RegLab's operational model indicates substantial research funding for policy-relevant AI projects. No student advisees are mentioned in available materials. Ho's leadership extends to national advisory roles where he shapes federal AI policy through evidence-based recommendations, particularly regarding environmental protection and civil rights enforcement mechanisms.
Mohsen Ghafouri is an Associate Professor at the Concordia Institute for Information Systems Engineering (Concordia University). His research focuses on cybersecurity, smart grids, and cyber-physical systems with emphasis on securing energy infrastructure against cyber-attacks. Key areas include detection and mitigation of false data injection attacks, grid resilience against load-altering threats, and secure transactive energy markets. Research interests include wide-area monitoring systems (WAMS), microgrid control, and integration of renewable energy sources. He has developed frameworks for real-time anomaly detection in power systems, blockchain-based security solutions, and machine learning approaches for cyber threat identification. His work addresses vulnerabilities in smart grid components like IEC 61850 substations and EV ecosystems. Recent publications (2024-2025) highlight advancements in securing FACTS controllers, EV charging systems, and distributed energy resources. He has proposed novel mitigation strategies using reinforcement learning, graph neural networks, and federated learning. No scientific awards or grant details are provided in the source text. No advising relationships or lab affiliations are explicitly stated.
Anthony Rollett is a Professor in the Department of Materials Science and Engineering at Carnegie Mellon University , where he has been a faculty member since 1995. He serves as the Principal Investigator and Co-Director of the NASA-supported Institute for Model-Based Qualification & Certification of Additive Manufacturing (IMQCAM) and co-director of the Next Manufacturing Center . Prior to CMU, he held leadership roles at Los Alamos National Laboratory (1991-1995). Education: Ph.D., Materials Engineering, Drexel University (1987) MA, Metallurgy and Materials Science, Cambridge University (1977) Research Interests: Rollett’s work focuses on microstructural evolution and microstructure-property relationships in 3D using experiments and simulations. His expertise spans additive manufacturing , metal 3D printing , materials for energy systems , grain growth , recrystallization , and stereology , with techniques like high-energy diffraction microscopy (HEDM) and dynamic x-ray radiography (DXR) . Scientific Contributions: He has over 320 peer-reviewed publications and an h-index >80 . His recent articles highlight machine learning for laser processing , fatigue analysis of additively manufactured alloys, and design optimization for heat exchangers in supercritical CO2 and solar thermal applications . Scientific Awards: Fellow of ASM International (1996) Fellow of the Institute of Physics (UK) (2004) Fellow of The Minerals, Metals & Materials Society (TMS) (2011) Cyril Stanley Smith Award (TMS, 2014) Member of Honor, French Metallurgical Society (2015) US Steel Professor (2017) Francqui International Professor (2020-2021) International FAME Award (2023) Leadership & Impact: Rollett co-led the development of a NASA Space Technology Research Institute for additive manufacturing and established a new master’s program in additive manufacturing (2018). His research group is funded by industry , federal agencies , and Pennsylvania state grants . He also serves on the Basic Energy Science Advisory Committee and Defense Programs Advisory Committee for the Department of Energy.
Massimo Piccardi is a Professor of Natural Language Processing (NLP), Computer Vision, and Machine Learning at the University of Technology Sydney (UTS) , where he has been since 2002. He currently serves as the Head of the School of Electrical and Data Engineering and leads the Big Data Analytics program at the Global Big Data Technologies Centre. His research focuses on advancing NLP, machine learning applications in healthcare, and cybersecurity in IoT systems. He has authored over 200 journal papers and conference proceedings, secured significant ARC and CRC grants, and holds the IEEE Computer Society Distinguished Contributor Award (2022). Education & Professional Roles: Joined UTS in 2002, progressing from Associate Professor (2002–2007) to Professor (2008–present). Serves as Associate Editor for IEEE Transactions on Big Data and Editor for Artificial Intelligence in Medicine. Active in professional societies including IEEE, ACL, and ALTA (President, 2023–2024). Research Interests: Core areas include NLP (translation, summarization, adversarial attacks), healthcare informatics (clinical NLP, health service analysis), and cybersecurity (IoT security, privacy-preserving systems). Cross-cutting themes include generative models, cross-lingual systems, and ethical AI. Grants & Projects: Principal Investigator on ARC Discovery/Linkage projects and CRC grants. Recent projects include controllable machine translation (Amazon), privacy-preserving digital agriculture, and STEM innovation (ASTRID project with NBN Co). Labs & Collaborations: Leads the UTS Global Big Data Technologies Centre, collaborating on projects like adversarial NLP attacks, medical machine translation, and secure IoT frameworks.
Ari Juels is the Weill Family Foundation and Joan and Sanford I. Weill Professor at Cornell Tech (Cornell University) and a faculty member in the Department of Computer Science. He co-founded and co-directs the Initiative for CryptoCurrencies and Contracts (IC³) and serves as Chief Scientist at Chainlink Labs. His research focuses on blockchain technology, cryptocurrency, smart contracts, applied cryptography, authentication, and privacy. Juels holds a Ph.D. in Computer Science from UC Berkeley and previously held roles as Chief Scientist and Director of the RSA Laboratories at EMC/Dell EMC until 2013. His work spans foundational contributions to decentralized systems, including Chainlink’s oracle networks and Ekiden’s privacy-preserving smart contracts. Juels has authored influential books like *The Oracle* (2024) and *Tetraktys* (2009), blending cryptography with thriller narratives. He advises numerous students and alumni, many of whom have joined prominent blockchain firms like Chainlink Labs, Mysten Labs, and Espresso Systems. Recent publications explore topics such as liquidity in blockchain assets, oracle network security, and economic analysis of smart contracts. His research emphasizes practical applications of cryptographic principles to real-world challenges in digital systems and decentralized finance. Juels’ lab collaborates on projects like HyDRA (automated bug bounty systems) and Lanturn (economic security metrics). He actively participates in academic and industry partnerships, bridging theoretical computer science with applied blockchain innovation.
Xiaoming Liu is the Anil K. and Nandita Jain Endowed Professor of Engineering and MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University . Holding a Ph.D. from Carnegie Mellon University (2004), he leads cutting-edge research in computer vision and machine learning. Research Interests : Computer Vision Pattern Recognition Image and Video Processing Machine Learning Medical Image Analysis Multimedia Retrieval Recent Research Trends : Focus on 3D object detection and depth estimation Development of robust biometric recognition systems Integration of radar-camera fusion for autonomous systems Advancements in self-supervised and multimodal learning Exploration of adversarial AI security Creation of interpretable forgery detection frameworks Teaching : Spring 2013: CSE891-006 Computer Vision Seminar Fall 2012-2015: CSE803 Computer Vision Spring 2014-2017: CSE 471 Media Processing and Multimedia Contact Information : Email: liuxm@cse.msu.edu Office: EB 3137, Michigan State University Phone: +1 (517) 355-2359
Elahe Soltanaghai is an Assistant Professor in the Department of Computer Science and a Faculty Affiliate in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. She is also a 2022 NCSA Fellow and received her PhD in Computer Science from the University of Virginia (2019), MS in Computer Engineering from Sharif University of Technology (2014), and dual BS degrees in Computer and Information Technology Engineering from Amirkabir University of Technology (2011, 2013). PhD: University of Virginia, Computer Science, 2019 MS: Sharif University of Technology, Computer Engineering, 2014 BS (Computer Engineering): Amirkabir University of Technology, 2011 BS (Information Technology Engineering): Amirkabir University of Technology, 2013 Her research spans wireless sensing and communication, focusing on Millimeter-wave Radar Sensing (for automotive, mixed reality, structural monitoring), Machine Learning for Wireless Systems (adaptive sensing/communication), Forest IoT (through-canopy biomass and soil sensing), Metaverse Technologies (gaze-based VR/AR), and Low-Power Backscatter Communication (WiFi/power-line tags). She directs the Wireless, Sensing & Embedded Networked Systems (iSENS) Lab and co-directs the Illinois Center for IoT. Her work bridges wireless networking with cyber-physical sensing , emphasizing environmental monitoring (e.g., wildfire fuel detection via radar tags) and human-computer interaction (e.g., gaze-tracking in VR). Recent articles include innovations in passive radar profiling , through-canopy biomass characterization , and integrated communication-sensing protocols . Scientific Awards: Google Research Scholar Award (2022) N2Women Rising Star (2021) ACM SIGMOBILE Dissertation Award (2020) EECS Rising Stars (2019) NCSA Faculty Fellowship (2023) Best Demo Runner-up, IPSN (2023) Teaching Excellence Award (2023) Grants: NASA FireTech Program Grant (2025) NSF Grant for Radar-based Perception (2024) Insper-Illinois Grant for VR Research (2024) Keysight Research Gifts (2022, 2023) T-Mobile Research Gift (2022)
Professor Hong Hao is a John Curtin Distinguished Professor at Curtin University, affiliated with the School of Civil and Mechanical Engineering and the Curtin Research Centre for Infrastructural Monitoring & Protection. His expertise spans Structural Dynamics, Earthquake Engineering, Blast and Impact Engineering, and Structural Health Monitoring. He holds prestigious roles like Fellow of ATSE, ISEAM, and ASCE, and has led organizations such as the International Association of Protective Structures and the Australian Earthquake Engineering Society. Education: BE (Tianjin University, 1982), MSc (UC Berkeley, 1985), PhD (UC Berkeley, 1989). Awards include the Tan Chin Tuan Fellowship and multiple Ko Medals. He has authored over 200 journal articles, with recent work focusing on blast-resistant materials, seismic fragility, and AI-driven structural health monitoring. His research emphasizes resilient infrastructure, including metaconcrete structures, corrosion-resistant materials, and sensor-based damage detection. Ongoing projects involve smart tunnel safety under BLEVE explosions and modular building systems.
Dr. Euijin (Alley) Choo is an Assistant Professor in the Department of Computer Science at the University of Alberta, specializing in data-driven cybersecurity and big data analytics. Her research focuses on AI-based cybersecurity solutions, anomaly detection in network traffic, and adversarial attacks on federated learning systems. She holds a Ph.D. from North Carolina State University and has held roles at Qatar Computing Research Institute, Korea University, and the University of Missouri-Rolla. Education: Ph.D., Computer Science, North Carolina State University (2015) M.S., Computer Science & Engineering, Korea University (2008) B.S. Dual Degree in Computer Science & Mathematics, Korea University (2006) Research Interests: Security and big data analysis intersections Federated learning security and privacy Anomaly detection in network logs and enterprise systems Malware/phishing detection using graph inference AI-driven threat intelligence aggregation Recent Grants: Mitacs Accelerate Program Grant: Fraud Detection in Financial Graphs ($60,000, 2025) National CyberSecurity Consortium Grant: IntruderInsight ($2M, 2025-2027) NSERC Discovery Grant: Threat Detection Framework ($180,000, 2025-2031) Awards: Best Paper Award at DBSEC 2015 NSERC Early Career Researcher Award (2025) Provost Fellowship (NC State, 2009-2010) Labs/Teams: Leads the Data-driven Network and Cyber Security (DANS) Lab, focusing on federated learning defenses, compromised entity detection, and malicious domain analysis.
Sushmita Roy is a Professor at the University of Wisconsin–Madison, affiliated with the Department of Computer Sciences and the College of Letters and Science. Her research focuses on developing computational methods in statistical machine learning to understand gene regulatory networks in living cells, particularly under environmental, developmental, disease, and evolutionary contexts. She explores bulk and single-cell genomic data integration to study processes like cell fate specification, host-microbe interactions, and diseases such as cancer and neurodevelopmental disorders. Her work emphasizes three key areas: inference of genome-scale transcriptional networks, evolutionary analysis of regulatory networks, and 3D genome organization dynamics. Roy’s lab collaborates across disciplines, leveraging genomic data from plant and mammalian systems. She has contributed to methodologies for analyzing chromatin accessibility, single-cell profiling, and network-based models of pathogen systems. Her affiliations include Wisconsin Institutes for Discovery, and she is a leader in computational biology and systems genomics research.
Dr. Aditya Joshi is a Senior Lecturer in the School of Computer Science & Engineering at the University of New South Wales (UNSW). He specializes in Natural Language Processing (NLP), with a focus on sarcasm detection, dialectal NLP, and ethical AI applications in public health and cybersecurity. He joined UNSW in 2023 following industry roles at SEEK, Notiv, and Fractal Analytics, where he developed NLP systems for recommendation engines and meeting analytics. His research has garnered over 3,000 citations (h-index 26) and secured $3.1M in grants, including Defence Trailblazer and Google exploreCSR awards. Education: Joint PhD (2018) from IIT Bombay (India) and Monash University (Australia); MTech in CSE (2011) from IIT Bombay. Research Interests: Making NLP models robust for non-native English speakers and the LGBTI+ community, algorithmic enhancements to transformers, and applications in public health, cybersecurity, and societal issues. His work spans epidemic intelligence (collaborations with EPIWATCH and IFCYBER), cybersecurity tools like AuditNet, and inclusive AI initiatives such as queer-inclusive workshops funded by Google. He designed UNSW's new NLP course (COMP6713) and co-authored a Wiley textbook on NLP. Notable grants include the A$1.4M 'Comprehensive Defence Data Platform' (Lead CI) and A$92K Google exploreCSR grant for benchmarking dialectal sentiment. His awards include the Best PhD Thesis from IITB-Monash and Best Paper accolades at FAccT 2023 and MoMM 2020. He supervises projects on kernel-based attention reformulation, prompt-based sarcasm detection, and multilingual small-scale LLMs. His service roles include Executive Committee Member at ALTA and arXiv moderator for computational linguistics.
Rida Khatoun is a Professor in Cybersecurity at the Computer Sciences and Networks (Infres) Department at Télécom Paris. He holds a M.Sc. in Computer Engineering and a Ph.D. from the University of Technology of Troyes (UTT), France, awarded in 2004 and 2008, respectively. His research focuses on cybersecurity in networks, including cloud computing security, IoT security, vehicular networks security, intrusion detection systems, and blockchain technology. He has taught at Telecom Paris since 2014, Shanghai Jiao Tong University (SJTU) since 2016, and other institutions in China and France since 2005. His work spans theoretical frameworks and practical solutions for network security challenges. Key research areas include DDoS attack detection, vehicular communication security, and cryptographic protocols. He leads the Cybersecurity and Cryptography (C²) research team and is affiliated with the Laboratoire Traitement et Communication de l'Information (LTCI). His contributions include developing secure routing protocols (e.g., ASROP), statistical trust systems in wireless networks, and blockchain-based authentication for IoT. He has authored over 115 publications, including peer-reviewed articles and conference proceedings, with recent work addressing vehicular platooning security, TLS handshake optimization for C-ITS, and machine learning for cyberbullying detection. Rida’s academic activities include teaching courses on network security protocols, wireless network fundamentals, and QoS mechanisms. He collaborates internationally, contributing to smart city cybersecurity architectures and 6G programmable communications. His research emphasizes practical cybersecurity solutions for emerging technologies, ensuring robust protection against evolving threats in vehicular, IoT, and cloud environments.
Dr. Md Manjurul Ahsan serves as a Research Assistant Professor in the Department of Industrial & Systems Engineering at the University of Oklahoma, where he develops AI-driven solutions for healthcare diagnostics and advanced manufacturing optimization. His work bridges theoretical AI advancements with practical industrial and medical applications. Education: Ph.D. in Industrial and Systems Engineering, University of Oklahoma M.S. in Industrial Engineering, Lamar University B.S. in Industrial and Production Engineering, Shahjalal University of Science and Technology Research Focus: Dr. Ahsan specializes in Artificial Intelligence with technical depth in Machine Learning , Deep Learning , and Computer Vision to solve critical challenges in healthcare diagnostics and additive manufacturing . His research emphasizes Explainable AI to enhance model trustworthiness and deployment efficiency across Cyber-Physical-Social Systems, with significant contributions to Aerospace and Defense applications. Publication Trends: Recent work (2023-2025) reveals a strategic expansion from core manufacturing applications into medical AI (diffusion models for diagnostics), cultural preservation (NLP for Dravidian languages), and geopolitical AI analysis. His publications consistently address data imbalance challenges while advancing digital twin integration in quality control systems. Scientific Recognition: GCOE Dissertation Excellence Award (2023) International Student Scholarship (2022) Outstanding Academic Achievement in Engineering (2022) IEEE IEMCON Best Paper Award (2020) Netti Vincent Boggs Engineering Excellence Award (2020) Research Leadership: As director of the Sooner Additive Manufacturing Laboratory , Dr. Ahsan leads cross-disciplinary teams developing real-time monitoring systems using FARO arms and CMM metrology. His postdoctoral work at Northwestern University (2023-2024) advanced AI deployment frameworks, resulting in 60+ peer-reviewed publications with multiple papers ranking in engineering's top 1% for citations.
