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Patrick JailletView profile

Professor

• Online Optimization
• Machine Learning
• Sequential Decision Making Under Uncertainty
• +4 more

Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and Computer Science at MIT's School of Engineering. He holds joint appointments with the Laboratory for Information and Decision Systems (LIDS), the Operations Research Center (ORC), the Operations Research and Statistics Group at MIT Sloan, and the Department of Civil and Environmental Engineering. Previously, he served as Head of Civil and Environmental Engineering at MIT (2002-2009) and Chair of the Department of Management Science and Information Systems at UT Austin (1997-2002). Dr. Jaillet's research focuses on online optimization and learning, sequential decision-making under uncertainty, and security and resilience in complex networks. His work spans theoretical foundations in optimization and machine learning with applications in transportation, online market analytics, and network security. He has developed mathematical frameworks for problems involving uncertainty, dynamic resource allocation, and strategic behavior in complex systems. His recent publications reveal strong trends in bridging theoretical optimization with practical machine learning applications. Key themes include Bayesian optimization for black-box functions, online learning with limited information, mechanism design for resource allocation, and network security applications. His work increasingly integrates large language models with traditional optimization techniques, reflecting the evolving landscape of AI-driven decision-making systems. Fulbright Scholar (1990) Fellow of the Institute for Operations Research and Management Science (INFORMS) Best Applications Paper Award at ICAPS 2019 Long-standing Associate Editor for top journals including Operations Research and Transportation Science Dr. Jaillet has advised over 40 doctoral students who now hold prominent positions in academia and industry, including faculty positions at MIT, Georgia Tech, and ETH Zurich, and research scientist roles at Amazon, Microsoft Research, and Google. His research has been consistently funded by major agencies including NSF, ONR, AFOSR, and international partners like Singapore NRF, with current projects focusing on learning algorithms for autonomous security and fundamental tradeoffs in optimization. He leads a vibrant research group spanning MIT's EECS department and ORC, with current funding supporting work on neural bandits, federated optimization, and network security applications. His research group operates at the intersection of theory and practice, with strong connections to industry through collaborations with IBM, Microsoft, Google, and various transportation and technology companies. The group maintains active partnerships with international institutions, particularly through SMART in Singapore, reflecting Dr. Jaillet's global research impact.

Kuljeet KaurView profile

Professor

• Cloud Computing
• Edge/Fog Computing
• Internet of Things (IoT)
• +7 more

Kuljeet Kaur is a Professor in the Department of Electrical Engineering at École de technologie supérieure (ÉTS) in Montreal, Canada. Her research is conducted through the LACIME (Communications and Microelectronic Integration Laboratory), a renowned research unit focusing on communications and microelectronic integration. She maintains an active research program with numerous publications and student supervision activities. Professor Kaur's research spans multiple interconnected domains focused on next-generation computing and communication systems. Her primary research axes include Sensors, Networks and Connectivity; Intelligent and Autonomous Systems; and Software Systems, Multimedia and Cybersecurity. Within these broad areas, she specializes in Cloud Computing, Edge/Fog Computing, Internet of Things (IoT), Cybersecurity, Privacy, Federated Learning, and Energy Management. Her work bridges theoretical foundations with practical implementations in intelligent transportation systems, healthcare applications, and smart grid technologies. Analysis of Professor Kaur's recent publications reveals a strong focus on security and privacy challenges in emerging computing paradigms. A significant portion of her work addresses federated learning approaches that maintain data privacy while enabling collaborative AI model training. Her research also demonstrates expertise in edge computing architectures, particularly for IoT applications, with emphasis on energy efficiency and security. The publications show consistent interdisciplinary collaboration across computer science, electrical engineering, and transportation domains. Professor Kaur actively supervises multiple graduate students at various levels. Her supervision portfolio includes doctoral candidates working on topics like decentralized AI networks and secure federated learning, as well as master's students focusing on edge AI for IoT applications, sensor drift compensation, and zero trust architecture for IoT. She also guides project students working on practical implementations of AI for smart grid optimization and secure IoT protocols. Her research is conducted within the LACIME laboratory, which brings together researchers working on everything from micro- and nanofabrication processes to communication protocols and signal processing. The lab provides a transdisciplinary environment where Professor Kaur's work on cyber-physical systems and secure communications benefits from complementary expertise in integrated circuit design and microsystems.

Tara SalmanView profile

Assistant Professor

• Distributed Systems
• Blockchain Technology
• Security and Privacy
• +3 more

Tara Salman is an Assistant Professor in the Department of Computer Science at Texas Tech University , focusing on distributed systems, blockchain technology, and security/privacy in next-generation networking applications. Her research bridges scalable distributed systems, AI, and security to address challenges in healthcare and financial systems. Research Interests: Distributed, intelligent, and secure networking applications Blockchains and scalable distributed systems Distributed artificial intelligence Security and privacy techniques Publication Trends: Recent work emphasizes federated learning, blockchain security, multi-cloud environments, and quantum blockchain applications. Her research combines machine learning, deep learning, and distributed consensus mechanisms to enhance security in heterogeneous networks.

• Hardware Security
• Embedded Systems
• Energy-Efficient Computing
• +5 more

Mahdi Fazeli is an Associate Professor at the School of Information Technology, Halmstad University, Sweden, specializing in hardware security and trust, energy-efficient computing, and embedded and cyber-physical systems. His academic journey began with a Ph.D. in Computer Engineering from Sharif University of Technology, Iran, in 2011. His career progression includes positions as Associate Professor at Bogazici University (2019-2021) and Iran University of Science and Technology (2016-2019), and Assistant Professor at the same institution (2011-2016). His research interests focus on hardware security and trust, reliable VLSI circuits and systems, energy-efficient computing, and dependable embedded systems. His work bridges the gap between theoretical security concepts and practical implementations in real-world systems, particularly in IoT and embedded environments. He has established himself as a leading researcher in Physical Unclonable Functions (PUFs), hardware trojans detection, and energy-efficient security solutions for resource-constrained devices. His publication record shows a clear progression and deepening expertise in hardware security, with recent work focusing on cutting-edge applications in edge computing, vehicular networks, and IoT security. His 2023-2025 publications demonstrate significant contributions to magnetic memory-based security primitives, anomaly detection systems, and energy-efficient security mechanisms. Throughout his career, Fazeli has led multiple research initiatives including the Dependable Systems and Architecture Lab (DSA) and the Networked and Embedded Systems Lab at Iran University of Science and Technology. His leadership extends to heading the Hardware Group and serving as Vice Chair for Educational Affairs, demonstrating his commitment to both research excellence and academic administration.

Laurent CondatView profile

Researcher

• Optimization
• Convex Relaxation
• Stochastic Algorithms
• +5 more

Laurent Condat is a Senior Research Scientist at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, where he conducts research in optimization algorithms and their applications. He is affiliated with the College of Engineering, Department of Computer Science, and has previously held research positions at CNRS in France, working at GREYC in Caen and GIPSA-Lab in Grenoble. Dr. Condat received his PhD in 2006 from Grenoble Institute of Technology, followed by a 2-year postdoc in Munich, Germany. He was recruited as a permanent researcher by CNRS in 2008 and has been on leave from CNRS since November 2019 to work at KAUST. In February 2025, he was promoted to 'chargé de recherche hors classe' (senior research scientist) by CNRS. His research focuses on deterministic and stochastic optimization algorithms, convex relaxations, and applications to machine learning, signal and image processing. His work spans theoretical foundations of optimization methods to practical implementations for distributed and federated learning systems. He has developed several influential algorithms including RandProx, TAMUNA, and LoCoDL that address communication efficiency in distributed optimization. His recent publications demonstrate strong trends in communication-efficient distributed optimization, with particular emphasis on federated learning, compression techniques, and local training methods. His work bridges theoretical optimization with practical machine learning applications, showing consistent innovation in algorithmic design for large-scale problems. Best reviewer award at AISTATS 2025 Meritorious Service Award from Mathematical Programming Stanford's list of world's top 2% most influential scientists Dr. Condat has co-supervised PhD students including Daniele Picone and Julien Baderot. He serves as an Associate Editor for IEEE Transactions on Signal Processing and has presented his work at numerous international conferences including plenary talks at major optimization workshops. His research is supported through KAUST funding and collaborative projects with researchers worldwide.

• Computational Linguistics
• Machine Translation
• Text Simplification
• +8 more

Sergiu Nisioi is an Associate Professor at the Faculty of Mathematics and Computer Science, University of Bucharest, with expertise in computational linguistics, machine translation, and text simplification. He bridges cognitive science with NLP through eye-tracking and EEG research, while also exploring sound art and digital autonomy via initiatives like HYPHA.ro. Current projects include PN-IV-P2-2.1-TE-2023-2007 (text complexity/readability), Legal Document Processing , and Europarl Dialectal Corpora Research spans computational psycholinguistics , LSTM-based translation models , and algorithmic composition for sound art His work integrates interdisciplinary methodologies, combining EEG signal processing for architecture data with the University of Architecture, and DSP for ecological projects at chlorophylla.live.

Simon OyaView profile

Assistant Professor

• Privacy-enhancing technologies
• Computer Security
• Signal processing for security and privacy
• +4 more

Simon Oya is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), Faculty of Applied Science. He holds a PhD in Information Technologies and Communications from the University of Vigo (Spain) and was previously a postdoctoral fellow at the Cryptography, Security and Privacy (CrySP) group at the University of Waterloo. His educational background includes: BSc, MSc, PhD from University of Vigo (Spain) Simon Oya's research focuses on designing and evaluating privacy-enhancing technologies with strong privacy and utility guarantees. He approaches privacy problems from a statistical perspective, using theoretical tools from signal processing and information theory to quantify privacy leakage and develop effective defenses. His primary research areas include: Privacy-preserving searchable encryption Machine learning privacy (particularly membership inference attacks) Anonymous communication systems Location privacy Differential privacy His publication record demonstrates a consistent focus on analyzing and improving privacy mechanisms across various domains. His recent work has particularly emphasized the intersection of machine learning and privacy, as well as advancing techniques for searchable encryption. His research methodology typically involves developing statistical models to understand privacy leakage and designing optimization-based approaches to improve privacy-utility tradeoffs. His notable scientific contributions include developing attacks against searchable encryption schemes to better understand their privacy limitations, and designing improved privacy mechanisms for location-based services. His work on statistical disclosure attacks against anonymous communication systems has also been influential in the field. As an educator, he teaches CPEN 442: Introduction to Cybersecurity at UBC. He actively seeks motivated graduate students interested in privacy research, particularly those with strong backgrounds in statistics, machine learning, or optimization.

• Image Processing
• Video Compression
• Computer Vision
• +4 more

Fengqing Maggie Zhu is an Associate Professor at the Elmore Family School of Electrical and Computer Engineering within Purdue University , West Lafayette campus. Her research spans image processing , video compression , computer vision , and smart health , with notable contributions to learned image compression , 3D reconstruction , and nutrition analysis via computer vision . Educational background: BS in Electrical Engineering, Purdue University (2004) MS in Electrical and Computer Engineering, Purdue University (2006) PhD in Electrical and Computer Engineering, Purdue University (2011) Her work focuses on developing machine learning-based compression techniques for 2D/3D images and videos, with applications in food portion estimation , wearable dietary monitoring , and virtual reality facial expression tracking . She explores structured pruning , mixed precision quantization , and continual learning to create efficient, robust systems for edge-cloud collaboration. The 2025-2024 article collection reveals concentrated efforts in learned image compression (with 8 papers on quantization, pruning, hierarchical VAEs), food-related computer vision (12+ papers on portion estimation, databases, classification), and 3D reconstruction (MetaFood3D dataset, ICP-3DGS algorithm). Emerging themes include privacy-preserving AI for wearable cameras and class-incremental learning frameworks. Contact: zhu0@purdue.edu

Sadegh TalebiView profile

Assistant Professor

• Reinforcement Learning
• Markov Decision Processes
• Online Learning
• +2 more

Sadegh Talebi is a Tenure Track Assistant Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen . His research focuses on theoretical aspects of reinforcement learning, Markov decision processes, online learning, stochastic multi-armed bandit problems, and resource allocation in networks. Education BSc in Electrical Engineering (minor: Electronics) from Iran University of Science and Technology (IUST) (2004) MSc in Electrical Engineering (minor: Communication Systems) from Sharif University of Technology (2006) PhD in Electrical Engineering from the Department of Automatic Control at KTH Royal Institute of Technology (supervised by Alexandre Proutiere and Mikael Johansson) Research Specializes in theoretical foundations of reinforcement learning and online learning Key contributions in stochastic optimization, MDPs, and bandit algorithms Collaborates on applications in resource allocation and quantum computing Publications include high-impact work on offline RL, differentially private exploration, and scalable MDP solutions in journals like Neural Processing Letters and conferences such as NeurIPS and UAI.

Hye-Chung KumView profile

Professor

• Health Informatics
• Data Science
• Privacy-Preserving Record Linkage
• +4 more

Hye-Chung Kum is a Professor in the Department of Health Policy & Management at Texas A&M University, where she pioneers Population Informatics to transform digital data into evidence-based health policy solutions. Her work bridges computer science, public health, and social work to address critical data challenges in healthcare systems. Education: PhD, University of North Carolina at Chapel Hill (UNC-CH), 2004 MSW (Master of Social Work), UNC-CH, School of Social Work, 1998 MS, UNC-CH, Department of Computer Science, 1997 BS, Yonsei University, Seoul, Korea, Department of Computer Science, 1995 Research Vision: Dr. Kum develops privacy-preserving human-computer hybrid systems for cleaning and integrating chaotic real-world data (e.g., EHRs, administrative records). Her Population Informatics framework enables ethical large-scale analysis while addressing data genocide in marginalized communities through innovations like the Secure Decoupled Linkage (SDLink) system. Publication Trends: Recent work (2024-2025) reveals three dominant threads: (1) Mental health service utilization dynamics during/post-pandemic, (2) Racial/ethnic disparities in emergency department use and cancer outcomes, and (3) Ethical data sharing frameworks for vulnerable populations. Her Texas-focused studies on Medicaid payment models and AI-driven record linkage demonstrate practical policy applications. Research Infrastructure: As director of the Population Informatics Lab, she leads cross-disciplinary teams developing Privacy-by-Design tools that balance public health needs with data sovereignty—particularly for American Indian and Alaska Native communities. Her grant-funded work consistently addresses Medicaid transformation and healthcare cost drivers through secondary data analytics.

A. Lynn AbbottView profile

Professor

• Computer vision
• Pattern recognition
• Artificial intelligence
• +5 more

A. Lynn Abbott is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech , specializing in computer vision, biometrics, and AI-driven sensing systems. His work bridges theoretical and applied domains, including autonomous vehicle perception, physiological signal analysis, and secure healthcare monitoring. Education: Ph.D., University of Illinois, 1990 M.S., Stanford University, 1981 B.S., Rutgers University, 1980 Research Interests focus on computer vision for autonomous systems, biometrics using physiological signals, and deep learning applications in transportation safety and healthcare. Recent projects include neural networks for intersection safety modeling and vision-based cardiovascular signal recovery. Publications highlight advancements in graph neural networks for traffic analysis, spatiotemporal filtering for 3D object detection, and privacy-preserving biometric authentication. His work spans disciplines like transportation safety, biomedical signal processing, and computer architecture. Labs & Teams: Affiliated with the Center for Embedded Systems for Critical Applications , contributing to real-time vision systems and hardware-software co-design for safety-critical domains.

Ming JinView profile

Assistant Professor

• Trustworthy AI
• Cyber-physical systems
• Machine Learning
• +4 more

Ming Jin is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He holds a PhD from UC Berkeley and a B.Eng. from Hong Kong University of Science and Technology. His research focuses on trustworthy AI, CPS security, and energy systems, with affiliations to the Power and Energy Center and Autonomy and Robotics @ VT. Education: PhD in Electrical Engineering and Computer Science (UC Berkeley, 2017), B.Eng. (Honors) in Electronic and Computer Engineering (HKUST, 2012). Postdoc in Industrial Engineering and Operations Research at UC Berkeley. Research interests include safe reinforcement learning, foundation models, cybersecurity, and power systems. Awards include the Siebel Scholarship (2018) and first place in the 2021 CityLearn Challenge. Active in conference organization (e.g., ICML, AAAI) and tutorial development on topics like Safe RL and CPS security. Grants include NSF support for embodied optimization (2025), Amazon-VT Initiative (2023), and Commonwealth Cyber Initiative projects. Involved in labs focused on AI, robotics, and energy systems. Publications span AI safety, RL frameworks, and CPS resilience, with over 50 peer-reviewed articles since 2015.

Hao ZhangView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Computer Vision
• +6 more

Hao Zhang is an Associate Professor in the Department of Computer Science at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He directs the Human-Centered Robotics Laboratory (HCRLab), focusing on lifelong collaborative autonomy, robot adaptation, and human-robot teaming. His research integrates robotics, AI, and machine learning to develop algorithms for real-world applications like manufacturing, autonomous driving, and environmental monitoring. He holds an NSF CAREER Award and DARPA Young Faculty Award, among other recognitions. Dr. Zhang earned a PhD from the University of Tennessee, Knoxville (2014) and an MS from the Chinese Academy of Sciences (2009). His work addresses challenges in unstructured environments through innovations like self-reflective terrain adaptation and graph-based perception systems. He actively promotes equity in robotics through his PROGRESS outreach program. His research sponsors include NSF, DARPA, and industry partners such as Toyota. Publications span conferences like RSS, ICRA, and IROS, with best paper awards. He serves on editorial and program committees for top-tier journals/conferences including RA-L, NeurIPS, and AAAI.

• Linked Data
• Data Management
• Ontology Engineering
• +4 more

Dr. Ronald Maria Siebes serves as Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, with affiliations to the Network Institute and Business Web and Media department. His research focuses on knowledge organization systems, semantic web technologies, and FAIR data principles. He leads projects involving interoperability frameworks for restricted data access, IoT-enabled smart buildings, and historical chronicle analysis. Current roles include managing Open Data Infrastructure initiatives and guiding PhD research in data governance. Education details are not explicitly provided in the text. Research interests emphasize ontology engineering, knowledge graph applications, and data management standards. Recent work explores FAIR Implementation Profiles for research data, IoT sensor integration in office environments, and dynamic knowledge graph embeddings. Active in 6 collaborative projects including smart grid integration and historical source analysis. Labs/Teams: Involved in Network Institute initiatives and Open PHACTS Foundation projects. Supervised 2 PhD theses (names not specified in text). Grant activities span €2.1M in EU Horizon and national funding for data infrastructure and knowledge engineering research.

Carmela TroncosoView profile

Associate Professor

• Privacy Engineering
• Security Systems
• Cryptography
• +3 more

Carmela Troncoso is an Associate Professor at EPFL heading the SPRING Lab focused on Security and Privacy Engineering. Her work addresses technology's societal impact through machine learning security, privacy-enhancing technologies, and privacy engineering frameworks. She leads significant research in decentralized privacy systems, including contributions to the DP-3T contact tracing protocol adopted by Google/Apple during COVID-19. Her lab develops tools for privacy evaluation including Synthetic Data Evaluation frameworks and anonymous authentication libraries. Awards include the ERCIM Best Ph.D. Thesis Award, CNIL Privacy Protection Award, and IEEE Distinguished Paper Award. She was recognized as a Fortune 40 Under 40 Technology Leader in 2020.