Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.
Sudha Ram is the Anheuser-Busch Endowed Professor of MIS, Entrepreneurship & Innovation at the Eller College of Management, University of Arizona. She holds joint faculty appointments as Professor of Computer Science and is a member of the BIO5 Institute and the Institute for the Environment. She is also the Director of INSITE: Center for Business Intelligence and Analytics, a leading research center in data-driven decision-making. Her research focuses on Big Data Analytics , Business Intelligence , Large Scale Network Science , and Machine Learning , with applications in healthcare, smart cities, environmental policy, and social media. She has pioneered methods in explainable AI, conceptual modeling, and multimodal data fusion, integrating statistical, ontological, and machine learning approaches. Recent publications demonstrate a strong trend in healthcare analytics (e.g., asthma, diabetes, fracture prediction), explainable AI (ROLEX, argumentation-based models), and urban/smart systems (mobility, wearables, environmental impact). Her work consistently appears in top-tier journals and conferences, reflecting sustained scholarly impact. AIS Fellow (2018) INFORMS ISS Distinguished Fellow IBM Faculty Award Peter Chen Award Best Paper Award, IEEE Smart Cities (2016) Best Paper Award, ACM Digital Health (2016) Woman of Impact Award, University of Arizona (2023) Dr. Ram has secured over $70 million in research funding from agencies like NSF, NASA, CIA, and corporations including IBM, Intel, and SAP. She has mentored numerous students and leads a multidisciplinary research team at INSITE. She has held editorial leadership roles in Information Systems Research , Journal of AIS , and is founding co-editor of the Journal of Business Analytics . She directs the INSITE Center, which fosters collaboration across business, computer science, and health domains, enabling large-scale data synthesis and knowledge discovery. The center supports projects in healthcare innovation, smart cities, and environmental policy analytics.
Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.
Mohammed Aledhari is an Assistant Professor at the University of North Texas, specializing in cybersecurity, machine learning, and data science. His research focuses on applications in computational medicine, bioinformatics, and autonomous systems. He holds a Ph.D. from Western Michigan University and degrees from the University of Basrah and the University of Anbar. His research interests include social cybersecurity techniques, federated learning in IoT, and AI-driven solutions for healthcare and transportation. Recent work explores blockchain-enabled digital twins, DDoS attack detection, and equitable ASD diagnostics using machine learning. His publications span cybersecurity frameworks, autonomous vehicle communication protocols, and biomedical IoT innovations. Notable contributions include optimizing intrusion detection in IoMT networks and developing interpretable machine learning models for healthcare. While no formal awards or grants are listed, his work emphasizes interdisciplinary applications of AI in healthcare, transportation, and energy markets. His email is Mohammed.Aledhari@unt.edu .
Shungeng Zhang serves as an Assistant Professor in the Department of Computer & Cyber Sciences at Augusta University's School of Computer and Cyber Sciences. He holds a Ph.D. in Computer Science from Louisiana State University (2021) and a B.E. in Computer Engineering from Huazhong University of Science and Technology (2014). His educational background includes: Ph.D. in Computer Science, Louisiana State University, 2021 B.E. in Computer Engineering, Huazhong University of Science and Technology, 2014 Dr. Zhang's research spans distributed systems, cloud computing, and cybersecurity with a focus on enhancing performance and scalability of web applications and IoT stream processing in cloud environments. He employs sophisticated timeline analysis and fine-grained monitoring to identify transient bottlenecks causing long-tail latency problems, addressing propagation effects in complex dependency chains among application components. His publication record (2017-2022) reveals consistent contributions to cloud systems performance, particularly in n-tier architectures, concurrency control, and latency optimization. Key themes include stream processing synchronization, fanout query performance, adaptive concurrency for SLO compliance, and mitigation of transient resource contention attacks across distributed systems. No scientific awards were mentioned in the provided information. Dr. Zhang actively seeks graduate students with strong computer systems backgrounds for research collaboration. His departmental service includes Faculty Assembly participation (2021-2022) and faculty interviewing duties. Professionally, he serves as a reviewer for ACM SoCC'23, ACM TOIT, SmartCom 2023, The Journal of Supercomputing, and ACM SoCC'22. Teaching responsibilities include AIST 4720 (Enterprise System Architectures), CSCI 1200 (Introduction to Computers and Programming), and graduate courses such as CSCI 8940 (Dissertation Research) and AIST 3310 (Advanced Networking). No dedicated research labs or teams were specified in the available documentation.
Emmett Witchel is a Professor of Computer Science at the University of Texas at Austin , with research spanning computer architecture, systems, networking, security, and privacy . His work focuses on low-level systems optimization and secure concurrent execution. Research Interests: Concurrent systems, secure execution environments, GPU integration, distributed systems. Teaching: CS 380L (Advanced Operating Systems), CS 371M (Mobile Computing). Scientific Awards: Runner-up Best Paper, ASPLOS 2013 Runner-up Award for Outstanding Research, USENIX Symposium 2012 IEEE Micro Top Pick Award 2007 ACM Honorable Mention 2004 George M. Sprowls Award, MIT EECS 2004 Recent Publications demonstrate leadership in CXL pod databases ( Tigon ), stateful serverless computing ( Boki ), SmartNIC-accelerated file systems ( LineFS ), and GPU security ( Telekine ). His work bridges hardware-software co-design and practical systems implementation.
Saurabh Amin is a Professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), where he also serves as the Edmund K. Turner Professor and Undergraduate Officer. He is a Principal Investigator at the Laboratory of Information and Decision Systems and holds affiliations with the Operations Research Center and the Center for Computational Science and Engineering. His educational background includes: B.Tech. 2002, Indian Institute of Technology (IIT) Roorkee M.S. 2004, University of Texas (UT) Austin Ph.D. 2011, University of California (UC) Berkeley Saurabh Amin's research focuses on the design and control of infrastructure systems using game theory and optimization in networks. His work spans three main areas: resilient network control, information systems and incentive design, and optimal resource allocation in large-scale infrastructure systems. By concentrating on critical infrastructure domains including highway transportation, electric power distribution, and urban water networks, his research develops innovative theory and tools to enhance system performance against both stochastic and adversarial disruptions. His approach involves modeling cyber-physical interactions in infrastructures to assess vulnerabilities, developing detection and response tools for failures at various scales, and designing economic incentive schemes that improve aggregate public good while accounting for dependencies and private information among strategic entities. Amin's work bridges mathematical systems theory with practical civil engineering applications, creating a rigorous theoretical foundation for infrastructure resilience that addresses diverse failure mechanisms from natural disasters to deliberate malicious actions. His recent publications demonstrate a strong focus on decarbonization of energy systems, resilient infrastructure planning under climate uncertainty, optimization methods for complex networked systems, and game-theoretic approaches to sustainable infrastructure management. His work increasingly integrates artificial intelligence and machine learning techniques with traditional control theory to address contemporary challenges in infrastructure resilience and sustainability. The research shows a clear trajectory toward addressing climate change impacts on infrastructure systems while maintaining economic efficiency and operational reliability. Professor Amin has received numerous prestigious awards and honors: Common Ground Excellence in Teaching Award, 2025 HSCC Test-of-Time Award, 2024 MIT CEE, Distinguished Service and Leadership Award, 2023 Samuel M. Seegal Prize (SoE) – inspiring students in pursuing and achieving excellence, 2022 Earll M. Murman for Excellence in Undergraduate Advising, 2022 C3.ai Digital Transformation Institute Research Award, 2020 MIT, Ole Madsen Mentoring Award, 2020 MIT, Energy Initiative Research Award, 2020 National Academy of Engineering, China-America Frontiers of Engineering Symposium speaker, 2019 MIT, Robert N. Noyce Career Development Professor, 2015-2018 Google Faculty Research Award, 2015 National Science Foundation CAREER Award, 2015 Siebel Energy Institute Research Award, 2015 MIT, Solomon Buchsbaum AT&T Research Fund Award, 2012 Professor Amin has been actively involved in significant research projects including the C3.ai DTI project on Causal Reasoning for Real-Time Attack Identification in Cyber-Physical Systems and another on Learning in Routing Games for Sustainable Electromobility. He serves as the chief scientist on multi-institutional NSF grants, including the $9 million Foundations of Resilient Cyber-Physical Systems (CPS) project. His teaching portfolio includes courses such as 1.008 Engineering for a Sustainable World, 1.104 Sensing and Intelligent Systems, 1.020 Engineering Sustainability: Analysis and Design, and 1.208 Resilient Networks. As Undergraduate Officer, he plays a key role in shaping the educational experience for civil and environmental engineering students at MIT. Professor Amin leads the Resilient Infrastructure Networks Lab at MIT, where his team develops theoretical foundations and practical tools for infrastructure resilience. The lab focuses on the intersection of control theory, game theory, and optimization applied to cyber-physical infrastructure systems. Current research directions include pandemic-resilient urban mobility and hurricane-resilient smart grid operations, reflecting the lab's commitment to addressing pressing societal challenges through rigorous systems engineering approaches.
Alan Zaoxing Liu is an Assistant Professor of Computer Science at the University of Maryland, College Park , with appointments at the University of Maryland Institute for Advanced Computer Studies (UMIACS) and Maryland Cybersecurity Center (MC2) . His research bridges systems, networking, and cybersecurity to design scalable, trustworthy approximate computing systems. Ph.D. in Computer Science from Johns Hopkins University (2018) Postdoctoral research at Carnegie Mellon CyLab (2018–2020) Research Interests : Networked and data-intensive systems Telemetry/analytics for heterogeneous networks Machine learning for network optimization Security in programmable networks Recent Publications include work on future-proof telemetry (PromSketch, VLDB’25), scalable caching (OctoCache, ASPLOS’25), and secure network analytics (TrustSketch, NDSS’24). His NSF-funded projects focus on optics-enabled DDoS defense and data-driven network management. Scientific Awards : USENIX FAST Best Paper (2019) USENIX ATC 'Best of Rest' (2021) Red Hat Collaboratory Research Awards (2022, 2023) Teaching : Leads Cloud Computing at Boston University , emphasizing agile development, open-source collaboration, and cloud infrastructure.
Thomas Peyrin is a Full Professor at Nanyang Technological University (NTU) in Singapore, leading the Symmetric Key and Lightweight Cryptography Lab (SyLLab) since 2012. He co-founded TT-logic.ai, focusing on cryptographic innovations. His academic journey includes a Ph.D. in Cryptology from Orange Labs and University of Versailles (2008), a Master in Computer Science from MPRI (2005), and engineering training at CPE Lyon (2004). Education : Ph.D. (2008), MPRI Master (2005), EPFL Exchange (2003-2004), CPE Lyon (1999-2004) Career : Full Professor at NTU (2022-present), Associate Professor (2017-2022), Nanyang Assistant Professor and NRF Fellow (2012-2017), Research Fellow at NTU (2010-2012), Cryptography Expert at Ingenico (2008-2010) Thomas specializes in symmetric cryptography, cryptanalysis of hash functions and block ciphers, and machine learning applications to security. His work includes groundbreaking research on SHA-1 collisions, lightweight cryptographic primitives (e.g., GIFT, PHOTON), and automated cryptanalysis frameworks. Recent projects explore integrating AI with cryptographic verification and fault attack resistance. His publications span top conferences like CRYPTO, EUROCRYPT, and CHES, with awards including the FSE Test of Time Award (2025), FSE Best Paper (2023), and NRF Investigatorship (2022). He has advised numerous students in cryptographic research and co-founded SyLLab, a leading lab for symmetric cryptography innovation. Scientific Awards : Test of Time Award at FSE 2025 Best Paper Award at FSE 2023/ToSC NRF Investigatorship (2022) Lee Kuan Yew Postdoctoral Fellowship Best Paper Awards at FSE 2012 and ASIACRYPT 2007
Karl Henrik Johansson is a Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm, Sweden, where he also serves as the Founding Director of Digital Futures. He is a Fellow of both IEEE and the Royal Swedish Academy of Engineering Sciences, and has held leadership positions including Immediate Past President of the European Control Association and IEEE Control Systems Society Vice President Diversity, Outreach & Development. Dr. Johansson earned his MSc in Electrical Engineering and PhD in Automatic Control from Lund University. His academic journey includes visiting positions at prestigious institutions such as UC Berkeley, Caltech, and NTU. His research focuses on networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. His work investigates fundamental challenges in connecting physical world systems through communication networks, exploring how wireless communication and sensor technology can enhance system robustness, reliability, energy efficiency, and safety. Current research directions include security of cyber-physical systems, distributed optimization, multi-agent systems, and applications to intelligent transportation and energy networks. Analysis of his recent publications reveals a strong focus on distributed optimization algorithms, secure networked control, multi-agent systems, and applications to transportation and energy networks. His work increasingly integrates machine learning techniques with traditional control theory, addressing challenges in privacy-preserving distributed computation, resilient state estimation, and resource allocation in complex networked systems. IEEE Control Systems Society Hendrik W. Bode Lecture Prize (2024) Swedish Research Council Distinguished Professor (2018-2027) Wallenberg Scholar (2009-2026) IFAC Young Author Prize IEEE CSS Distinguished Lecturer (2017-2019) IFAC Outstanding Service Award IEEE Fellow Dr. Johansson has supervised over 100 postdocs and PhD students, with many now holding prominent positions at institutions worldwide. His research has been supported by significant grants including the Swedish Research Council Distinguished Professor Grant (2018-2027), multiple Wallenberg Foundation grants, and numerous EU and national research projects. He has directed major research centers including ACCESS Linnaeus Centre (2009-2016) and Strategic Research Area ICT TNG (2013-2020). His research group operates within the Digital Futures initiative and maintains strong connections with industry partners through projects like the Integrated Transport Research Lab (supported by Scania and Ericsson) and Smart Mobility Lab. The group actively collaborates with international institutions and participates in major EU-funded projects addressing challenges in cyber-physical systems, transportation, and energy networks.
Jiaxin Lin is an Assistant Professor in the Department of Electrical and Computer Engineering at Cornell University , affiliated with the Computer Systems Laboratory . She earned her Ph.D. in Computer Science from UT Austin (2025) , preceded by an M.S. from University of Wisconsin-Madison and a B.S. from ShenYuan Honors College at Beihang University. Her research focuses on co-designing software and hardware systems to enable high-performance data center communication, particularly through: Programmable network interface controllers (SmartNICs) Terabit network system stacks Cache/memory interconnects Compilers for in-network computing Chip-to-chip interconnects Her work addresses challenges in portability across heterogeneous SmartNICs, demonstrated through the development of the Alkali compiler framework (NSDI '25). Key themes include hardware abstraction, data center scalability, and network-compute co-design. Scientific Awards: Google Junior Faculty Award (2025) MIT EECS Rising Star (2024) Google Ph.D. Fellowship (2021) Meta Ph.D. Fellowship (2021)
Paul Barford serves as Department Chair and Carl de Boor Professor in the Computer Sciences Department at the University of Wisconsin-Madison, where he founded and directs the Wisconsin Advanced Internet Laboratory and participates in the networking research group. Education B.S. in Electrical Engineering, University of Illinois Ph.D. in Computer Science, Boston University Research Focus : His work centers on computer networking and communications , emphasizing Internet data measurement, protocol analysis, and topological structure. He extensively investigates Internet security domains including fraud detection, traffic anomaly identification, malicious attack analysis, and intrusion systems—bridging theoretical frameworks with real-world security challenges. Research Support : Funded by NSF, DHS, ARO, Cisco, and Intel, his projects drive innovation in network infrastructure security and measurement methodologies. Awards & Honors EPSRC Visiting Fellowship Professional Leadership : Served as Program Committee Chair for ACM SIGCOMM (2013), ACM SIGMETRICS (2010), and ACM IMC (2006). Held board positions at National Lambdarail, editorial roles at IEEE/ACM Transactions on Networking, and co-chaired the GENI Instrumentation and Measurement Working Group. Prior industry experience includes founding Nemean Networks (acquired by Qualys) and Mdotlabs (acquired by comScore), where he served as Chief Scientist.
Sankar Sivarajah is the Head of Kingston Business School and Professor of Technology Management and Circular Economy at Kingston University London. He joined the university in September 2024 after serving as Dean of the School of Management at the University of Bradford (2017-2024). His academic career began at Brunel University London in 2014 as a post-doctoral researcher. Qualifications: PhD in Management and Information Systems Studies, Brunel University London MSc in Management (Entrepreneurship), Bayes Business School BSc in Business and Management (Computing), Brunel University London His research focuses on leveraging digital technologies for societal benefit, particularly in Technology Management , Circular Economy , and Operations/Supply Chain Management . Recent work analyzes AI-driven decision-making in public sectors, blockchain integration for sustainable supply chains, and smart technology applications in green HRM. His publications span journals like Government Information Quarterly , Annals of Operations Research , and Information Systems Frontiers , covering topics from drone-based food security to ethical AI frameworks. Trends show increasing emphasis on Industry 5.0, 6G impact evaluation, and cross-country sustainability practices. Scientific Recognition: Included in World’s Top 2% Scientists (2023, 2024) Fellow of the UK Higher Education Academy (FHEA) He serves as Deputy Editor for the Journal of Enterprise Information Management , peer reviewer for EFMD and AACSB accreditations, and governing council member of Chartered Association of Business Schools (CABS). His £3M+ funded projects address 6G evaluation, AI strategy, and Smart Cities.
Amir-massoud Farahmand is an Associate Professor at the Polytechnique Montréal (Department of Computer and Software Engineering) and a Status-Only Associate Professor at the University of Toronto (Department of Computer Science). He is also a Core Academic Member at Mila (Quebec AI Institute). His research focuses on computational and statistical mechanisms for designing efficient reinforcement learning (RL) agents and adaptive algorithms. Dr. Farahmand's research spans reinforcement learning, optimal transport, adversarial robustness, and model-based methods. He has extensively studied regularization in RL, distributional approaches, and algorithm design for stability and convergence. His textbook Lecture Notes on Reinforcement Learning (2021) emphasizes mathematical intuition over algorithmic collections. Recent publications highlight trends in high-update-ratio RL, distributional equivalence, and self-prediction for task understanding. He is actively involved in teaching, having previously instructed courses on machine learning, neural networks, and RL at the University of Toronto. Scientific Awards : Ontario Early Researcher Award (2024) for Accelerated Reinforcement Learning Algorithms Dr. Farahmand has mentored numerous students, including his first PhD graduate Yangchen Pan (now at Oxford) and MSc students like Allen Bao (AMD) and Farnam Mansouri (University of Waterloo). He is currently recruiting graduate students at Polytechnique Montréal and Mila for 2025 admissions.
John Hale, Ph.D., is a Professor and Chair of Computer Science at The University of Tulsa's Tandy School of Computer Science, where he holds the Tandy Endowed Chair in Bioinformatics and Computational Biology. He is a founding member of the TU Institute of Bioinformatics and Computational Biology (IBCB) and a faculty research scholar in the Institute for Information Security (iSec). Education: Ph.D., Computer Science, The University of Tulsa (1997) M.S., Computer Science, The University of Tulsa (1992) B.S., Computer Science, The University of Tulsa (1990) Dr. Hale's research spans cybersecurity , bioinformatics , cyber-physical systems , and applied formal methods . His work focuses on neuroinformatics, cyber trust, attack modeling, secure software development, and information privacy. Recent publications highlight trends in large-scale graph analysis for cybersecurity, attack graph generation on high-performance computing clusters, and security frameworks for nuclear reactor control systems. His research also explores hybrid attack graph modeling, reflective deception strategies, and compliance methods for cyber-physical infrastructures. Scientific Awards: 2000 National Science Foundation CAREER Award Dr. Hale has advised numerous research projects and received funding from the U.S. Air Force, Army, NSF, NIH, DARPA, NSA, and NIJ. He has testified before Congress on cybersecurity and holds a patent for anti-piracy technology. His lab work includes developing cyber-physical testbeds and science DMZ security solutions.
