CHAN Mun Choon is a Professor at the School of Computing, National University of Singapore (NUS) , where he directs the NUS-NCS Joint Laboratory for Cyber Security . He previously worked at Bell Labs (1997-2003) and holds a PhD from Columbia University (1997). His research spans systems and networking with specific interests in mobile computing, software-defined networking, and cyber-physical systems . PhD, Electrical Engineering (1997), Columbia University M.Phil., Electrical Engineering (1993), Columbia University MS, Electrical Engineering (1993), Columbia University BS, Computer & Electrical Engineering (1990), Purdue University His recent work focuses on 5G network architecture , data center fault debugging , and energy-efficient mobile sensing . He has published over 100 papers and holds 7 US patents , including cache-based compaction techniques with 210+ citations. His projects include fronthaul slicing for 5G, network-wide packet history frameworks, and participatory indoor localization. Scientific recognition includes: Best Paper Awards: IEEE ICNP 2019, ACM SOSR 2019, ICDCN 2016 Best Demo: IPSN 2016 Distinguished Member, INFOCOM TPC (2016, 2020, 2021) He serves as Vice-Dean, Graduate Studies and Vice-Dean, Academic Affairs at NUS Computing, and has graduated 21 PhD students . His lab develops solutions for network security , latency-sensitive applications , and mobile sensing .
Elisa Ricci is a Full Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento and serves as Head of the Research Unit Deep Visual Learning at Fondazione Bruno Kessler. She coordinates the Doctoral Program in Information Engineering and Computer Science at the University of Trento and holds prestigious fellowships from ELLIS and IAPR. Her research focuses on advancing computer vision and deep learning systems capable of operating in open-world environments. Key interests include domain adaptation, continual learning, and self-supervised learning for visual and multi-modal data processing. Her work addresses critical challenges in enabling machines to adapt to new domains without forgetting prior knowledge, with applications spanning robotics perception, medical imaging, and privacy-preserving AI systems. Recent publications reveal a dominant trend toward leveraging vision-language models for open-vocabulary tasks, training-free adaptation methods, and federated learning architectures. Significant research thrusts include machine unlearning for privacy, robustness against bias in visual classifiers, and novel class discovery using foundation models—particularly evident in 2025 publications addressing medical imaging, 3D segmentation, and collaborative generative systems. Her major recognitions include: ELLIS Fellow IAPR Fellow As Doctoral Program Coordinator at the University of Trento, she oversees PhD training while leading the Deep Visual Learning unit at Fondazione Bruno Kessler. Her research group secures competitive grants in AI-driven perception systems, though specific funding sources aren't detailed in the source material. The Deep Visual Learning Research Unit specializes in open-world computer vision challenges, developing frameworks for domain adaptation, continual learning, and multi-modal perception. Current projects integrate generative models with robotics applications while addressing privacy concerns in vision-language systems through unlearning techniques.
Nate Foster is a Professor of Computer Science at Cornell University and currently serves as the Associate Dean for Research in the Ann S. Bowers College of Computing and Information Science. He is also a Visiting Researcher at Jane Street and served as a Visiting Professor at École Polytechnique Fédérale de Lausanne during the 2023-24 academic year. His research uses ideas from programming languages to solve problems in networking, databases, and security. BA in Computer Science, Williams College (2001) MPhil in History and Philosophy of Science, University of Cambridge (2008, all work completed in 2003) PhD in Computer and Information Science, University of Pennsylvania (2009) Foster's research focuses on developing languages and tools that make it easy for programmers to build secure and reliable systems. His current work centers on the design and implementation of languages and tools for programmable networks, particularly using the P4 language. His past work includes bidirectional languages (also known as 'lenses'), database query languages, data provenance, type systems, mechanized proof, and formal semantics. His research group at Cornell has made significant contributions to network verification, software-defined networking, and formal foundations for programmable data planes. Analysis of Foster's recent publications reveals a strong focus on network verification and programming language foundations for networking. His work consistently applies formal methods to practical networking problems, with a particular emphasis on the NetKAT and P4 languages. Over the past five years, his research has evolved toward more complex network verification techniques, including infinite state verification, active learning of network models, and dependently-typed approaches to network programming. His work bridges theoretical computer science with practical networking systems, making formal methods accessible to network engineers. ACM Fellow (2025) ACM SIGPLAN Robin Milner Award (2023) ACM SIGCOMM Rising Star Award (2018) NSF CAREER Award (2013) Alfred P. Sloan Fellowship (2012) Multiple distinguished paper awards across top conferences including POPL, PLDI, and SIGCOMM Foster has advised numerous PhD and Master's students who have gone on to prominent positions in both industry and academia, with many continuing work in programming languages and networking. He has led multiple significant research grants including an NSF CAREER Award and has been involved in the P4 Language Consortium, serving as Chair of the P4 Language Governing Board. His work has been supported by various organizations including NSF, DARPA, and industry partners like Intel and Jane Street. Foster is also active in the programming languages research community, serving on numerous program committees and as Vice Chair of DARPA's Information Science and Technology (ISAT) study group. Foster leads a vibrant research group at Cornell focused on programming languages for networks, with collaborators from academia and industry. His group has developed several influential tools and frameworks including NetKAT, Petr4, and KATch. They maintain strong connections with the P4 community and work closely with industry partners to ensure their research has practical impact on real-world networking systems.
Karthik R. Narasimhan is a Professor at Princeton University's School of Engineering and Applied Science in the Department of Computer Science. Previously, he earned his PhD from MIT under Regina Barzilay and served as a visiting research scientist at OpenAI during 2017-18. His research focuses on the intersection of language and decision-making, building autonomous agents that learn from both experience and human knowledge. His research spans multiple high-impact areas including language agents (Text-DQN, CALM, ReAct, Tree of Thoughts), reinforcement learning (h-DQN, Multi-Objective RL), and AI safety (Toxicity in ChatGPT, DataMUX). He has developed critical datasets and benchmarks such as WebShop, InterCode, SWE-bench, and SILG that have become standard evaluation tools in the field. Current work emphasizes agent capabilities, software engineering automation, and multimodal interaction. His publication trends show strong focus on practical agent deployment (SWE-agent, Tree of Thoughts), safety evaluation (Probing AI Safety), and efficiency improvements (DataMUX). Recent work increasingly addresses real-world challenges in software engineering, security, and human-AI collaboration through rigorous benchmarking. Co-author of foundational GPT (2018) paper Key developer of Text-DQN (2015), CALM (2020), ReAct (2022), Tree of Thoughts (2023) Creator of influential benchmarks: WebShop (2022), SWE-bench (2023), InterCode (2023) He actively advises students through Princeton's computer science program, with research supported by multiple grants focused on autonomous agent development and language-based decision systems. His GitHub repositories (nlp-datasets, text-world-player) demonstrate strong community engagement in open-source research tools. Current projects include advancing language agent capabilities through Reflexion (2023) and Tree of Thoughts (2023) frameworks while addressing critical safety and efficiency challenges.
Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk
Gerard A. Ateshian is the Andrew Walz Professor of Mechanical Engineering and Professor of Biomedical Engineering at Columbia University, where he has been a faculty member since 1991. He also served as Chair of the Department of Mechanical Engineering from 2011 to 2014 and is the founding director of the Musculoskeletal Biomechanics Laboratory (MBL), established in 1996. Education: BS (1986), MS (1987), MPhil (1990), and PhD (1991) in Mechanical Engineering from Columbia University. Research Interests: Professor Ateshian's research focuses on the mechanics of soft biological tissues , particularly articular cartilage . His work integrates theoretical, experimental, and computational approaches to understand cartilage lubrication, tissue engineering, and growth and remodeling in living tissues. He has extended continuum mechanics frameworks to model complex biological phenomena such as mass transport, osmotic effects, and reactive mechanics in tissue mixtures. His recent efforts include developing open-source computational tools (FEBio) and translating research into clinical treatments for osteoarthritis . Scientific Awards: H.R. Lissner Medal, ASME (2017) OARSI Basic Science Award (2013) Columbia Engineering Alumni Association Distinguished Faculty Teaching Award (2012) Great Teacher Award, Society of Columbia Graduates (2002) YC Fung Young Investigator Award, ASME (1997) Fellow: ASME, BMES, AIMBE Advising and Mentorship: Professor Ateshian has advised numerous PhD students, including recent graduates Vince Sise and Katherine Spack , who completed their dissertations on cartilage fatigue and osteochondral allografts, respectively. His lab actively mentors students from both Mechanical and Biomedical Engineering programs. Laboratory and Collaborations: He directs the Musculoskeletal Biomechanics Laboratory (MBL) , which collaborates closely with the Cellular Engineering Laboratory of Prof. Clark Hung and with Dr. Jeffrey Weiss at the University of Utah on computational modeling (FEBio). He also collaborates with Columbia University Medical Center faculty on clinical translation of cartilage research.
Gert Cauwenberghs is a Professor of Bioengineering at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He co-directs the Institute for Neural Computation and holds a visiting professorship at MIT. His research focuses on neuromorphic engineering, energy-efficient neural interfaces, and wearable biosensors. Key contributions include silicon-based adaptive neural circuits, implantable neural recording systems, and in-ear biosensing devices. Education: M.Eng. in Applied Physics (University of Brussels, 1988), M.S. and Ph.D. in Electrical Engineering (Caltech, 1989–1994). Prior roles include Professorships at Johns Hopkins University and Visiting Professor at MIT. Research Interests: Biomedical integrated circuits, neuromorphic computing, brain-machine interfaces, and energy-efficient neural systems. His work bridges neuroengineering and clinical applications, emphasizing adaptive intelligence and low-power designs. Recent Work: Development of femtojoule-efficient neural chips, high-density neural interfaces, and closed-loop wearable systems. Projects include neurobench benchmarking frameworks and RRAM-based neuromorphic hardware. Awards: NSF Career Award (1997), ONR Young Investigator (1999), PECASE (2000), IEEE Distinguished Lecturer (2003–2004). Grants & Labs: Active in NIH and DoD-funded projects, co-directs the UCSD Institute for Neural Computation. Collaborates with industry on neural interface technologies. Labs/Teams: Cauwenberghs Lab at UCSD focuses on integrated neuroengineering systems, including neural recording systems and neuromorphic computing architectures.
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.
Gustavo Alonso is Full Professor at the Department of Computer Science (D-INFK) of ETH Zurich and Head of the Institute for Computing Platforms . He has been at ETH since 1995, first as a post-doc, then as Assistant Professor from April 1998, and promoted to Full Professor in October 2001. Within the Systems Group he leads the Information and Communication Systems Research Group . Education: 1989 – Telecommunications Engineering (undergraduate), Madrid Technical University (UPM-ETSIT), Spain 1992 – M.S. Computer Science, University of California, Santa Barbara (UCSB) 1994 – Ph.D. Computer Science, University of California, Santa Barbara (UCSB) Research Interests: His work spans databases, distributed systems, cloud-computing architecture, FPGAs, hardware acceleration for data science, parallel and reconfigurable computing . The group investigates how modern heterogeneous hardware—from GPUs to SmartNICs—can be integrated into data-processing systems to achieve orders-of-magnitude performance gains, energy savings, and new functionality such as in-network computation and serverless acceleration. Scientific Awards & Honors: Fellow of the ACM (Association for Computing Machinery) Fellow of the IEEE (Institute of Electrical and Electronics Engineers) Distinguished Alumnus, Department of Computer Science, UC Santa Barbara Four Test-of-Time / Most Influential Paper Awards across databases, programming languages, cloud computing, and software engineering Labs & Projects: He directs the Information and Communication Systems Research Group within the Systems Group ( systems.ethz.ch ). The lab develops open-source platforms such as Coyote v2 for FPGA abstractions, Shuhai for HBM benchmarking, and MicroRec for micro-second recommendation serving, while collaborating with industry on SmartNICs, serverless analytics, and cloud-scale data analytics.
Andy Pavlo is an Associate Professor with Indefinite Tenure in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He is an active member of the CMU Database Group and the Parallel Data Laboratory, where he leads research in database management systems with a focus on self-driving architectures, transaction processing, and large-scale analytics. His work bridges academic research and industry applications through projects like NoisePage, OtterTune (which he co-founded and served as CEO before it ceased operations), and Peloton. Dr. Pavlo's research interests span database management systems with particular emphasis on autonomous database architectures that can self-tune and optimize without human intervention. His work explores transaction processing systems that can handle high-throughput workloads while maintaining consistency, and large-scale data analytics techniques that efficiently process massive datasets. He has made significant contributions to query optimization, database extensibility, and automatic database tuning using machine learning techniques. His recent work on database extensibility revealed critical issues in PostgreSQL's extension ecosystem, showing that approximately 16% of extensions are incompatible with at least one other extension due to API violations and memory errors. His research output demonstrates a consistent focus on practical database systems challenges, with recent publications examining database extensibility, user-defined function optimization, and the cyclical nature of database research. The articles show a strong trend toward making database systems more autonomous, with increasing integration of machine learning techniques for automatic tuning and optimization. His work often combines deep theoretical analysis with practical implementation in open-source systems. Dijkstra Award 2024 for contributions to database systems research Dr. Pavlo actively mentors graduate students, with current advisees including Wan Shen Lim, William Zhang, and Sam Arch (co-advised with Todd Mowry). His former students have gone on to successful careers in both industry and academia. He has secured significant research funding through CMU's affiliate program with major database companies including ClickHouse, DataStax, dbt, Firebolt, MotherDuck, RelationalAI, SingleStore, Spiral, PingCAP/TiDB, Yellowbrick, and Yugabyte. His research is supported by these industry partnerships and likely includes NSF funding given his active participation in the database research community. At CMU, Dr. Pavlo leads the Database Group and organizes several seminar series including "SQL or Death," "Database Building Blocks," and "ML⇄DB Technical Talks." These seminars bring together researchers and practitioners to discuss cutting-edge developments in database systems. He also runs a summer research internship program that has attracted students for multiple consecutive years, indicating a strong research group with ongoing projects and funding.
Prof. Dr. Timothy Roscoe is a Full Professor in the Department of Computer Science at ETH Zurich. His research focuses on operating systems, networking, and distributed systems. He was inducted as an ACM Fellow in 2014 for contributions to these fields. Previously, he worked at Intel Research Berkeley, UC Berkeley (Adjunct Professor), and Sprint Labs. His work includes foundational contributions to systems like PlanetLab, P2 Declarative Networking, and the Nemesis OS. Roscoe holds a PhD from the University of Cambridge and has led projects in cloud computing, wide-area network measurement, and secure systems design. His current research emphasizes hardware-software co-design, formal verification of systems, and scalable memory architectures. Education: PhD in Computer Science, University of Cambridge (1995) Key Roles: Principal Researcher at Intel Berkeley Lab, Visiting Researcher at National ICT Australia Research Highlights: Contributions to distributed systems (PlanetLab), declarative networking (P2), and secure microkernels (seL4 integration). Current projects address challenges in disaggregated memory systems, coherent interconnects, and trustworthy embedded systems. Awards: ACM Fellow (2014), numerous grants and industry collaborations. Labs/Teams: Systems Group at ETH Zurich, focusing on OS design, hardware architectures, and formal methods.
Michael J. Aziz is the Gene and Tracy Sykes Professor of Materials and Energy Technologies at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He serves as Area Chair for Materials Science and Mechanical Engineering and is a Faculty Associate at the Harvard University Center for the Environment. His research focuses on electrochemical engineering for energy and environmental applications, including redox flow batteries, carbon capture, and sustainable energy technologies. Aziz leads the Aziz Group, which develops grid-scale energy storage solutions and innovative methods for CO₂ removal. He holds equity in Quino Energy, a startup commercializing his battery research, and serves as Chief Scientist and Board Member. His work bridges fundamental materials science with practical engineering, emphasizing ClimateTech solutions. Key contributions include aqueous organic redox flow batteries, quinone-based carbon capture systems, and wearable energy storage devices. Education & Affiliations: Affiliated with SEAS since joining Harvard, his academic roles include coordinating the Graduate Consortium for Energy and Environment (2009–2018). His lab (Materials Science Group) is located at McKay 504, with administrative support from Sabrina Azinheira. Research Interests: Aziz's group investigates electrochemical energy storage, CO₂ capture via electrochemical systems, and novel materials for sustainable technologies. They employ advanced techniques like operando electrochemical fluorescence microscopy to study porous electrode dynamics and battery degradation mechanisms. Their work emphasizes scalability and real-world applicability, such as grid-scale battery infrastructure and decarbonization strategies. Recent Trends in Publications: Aziz's recent work emphasizes carbon capture innovations (e.g., acid-base concentration swing methods), hydrogen storage under ambient conditions, and electrochemical synthesis of industrial chemicals like hydrogen peroxide. His group also develops open-source tools like RFBzero for battery modeling and explores bioinspired materials (e.g., self-gelling hydrogel batteries). Awards & Recognition: While no personal awards are explicitly listed in the text, his team members (e.g., Dawei Xi) have received accolades such as the 2025 Carbon Future Young Investigator Award. Aziz's contributions have been recognized through industry partnerships and startup ventures. Advising & Industry Impact: Aziz advises PhD students focusing on electrochemical systems (e.g., Jordan Sosa, Tommy George). His industry engagement includes licensing intellectual property to Quino Energy, which achieved a manufacturing milestone in 2024 for grid-scale battery systems. His research bridges academia and industry, addressing climate challenges through technological innovation. Labs & Teams: The Aziz Group includes interdisciplinary researchers from electrochemistry, chemical engineering, and materials science. Collaborators include institutions like MIT and industry partners. Current projects target next-gen batteries, CO₂ removal systems, and scalable energy storage solutions.
Alexey Gorshkov is an Adjunct Professor at the University of Maryland (UMD) affiliated with the Joint Quantum Institute (JQI) and the Quantum Information and Computer Science Laboratory (QuICS). His primary academic role is in theoretical physics, focusing on quantum optics, quantum information science, and condensed matter physics. He leads a research group exploring quantum magnetism with alkaline-earth atoms, driven-dissipative systems, topological matter, and strongly interacting photons. His work bridges AMO (atomic, molecular, and optical) systems with high-energy and condensed matter physics, emphasizing quantum simulation and novel quantum technologies like precise clocks and quantum computers. Education details are not explicitly listed, but his research collaborations with institutions like JQI and UMD suggest advanced academic training in theoretical physics. His research interests revolve around understanding and controlling quantum many-body systems, particularly in far-from-equilibrium scenarios, entanglement dynamics, and dissipation effects. He has contributed to studies on Rydberg atoms, quantum routing protocols, and error mitigation in quantum simulators. Recent articles highlight his work on quantum protocols for verifying speedups, time-independent information flow, and entanglement dynamics. His group's achievements include demonstrating one-dimensional anyons and developing methods for correlated noise estimation with quantum sensors. Awards and grants are not explicitly mentioned in the provided text, but his prolific publication record indicates sustained research impact. Labs and teams associated with him include the JQI and QuICS, where he collaborates on experimental and theoretical projects. Graduate student and postdoc positions are available in his group, focusing on areas like quantum magnetism and topological systems. His work often involves close ties with experimental groups, emphasizing practical applications of theoretical breakthroughs.
Colin Jones is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the Automatic Control Laboratory, School of Engineering. He earned his BASc and MASc in Electrical Engineering and Mathematics from the University of British Columbia (1994-2002) and a PhD in Control Theory from the University of Cambridge (2002-2005). Prior to EPFL, he was an assistant professor there and a senior researcher at ETH Zürich. Current role: Director of the Robotics, Control, and Intelligent Systems Doctoral Program at EPFL Research focus: Optimization-based and model predictive control (MPC) for renewable energy systems, green energy management, and data-driven control methods His recent work (2023-2025) spans high-speed predictive control , smart grid optimization , and physically consistent neural networks , with applications to buildings, hovercrafts, and power systems. He has secured an ERC Starting Grant for his research on optimal control of building networks. Publications include over 200 papers in journals like Automatica , IEEE Transactions , and Energy and Buildings . Notable article trends include distributed optimization , data privacy in energy systems , and nonlinear MPC for autonomous vehicles . Scientific Awards : ERC Starting Grant for optimal control of building networks Advising : Supervises 10 current PhD students and has advised 19 past PhD students, including Alessandretti Andrea and Diwale Sanket Sanjay. Grants and projects emphasize smart energy systems , predictive demand response , and nonlinear control .
Dr. Mo Adda is a Principal Lecturer at the University of Portsmouth's School of Computing, part of the Faculty of Technology. He holds a PhD in Distributed Systems and Parallel Processing from the University of Surrey. His research focuses on network security, distributed systems, wireless networks, and cybercrime prevention. He leads projects in the Centre for Cybercrime and Economic Crime, exploring fault management in networks, blockchain applications, and IoT forensics. With 16 supervised theses, he advises on topics like energy-efficient cloud systems and machine learning for environmental modeling. His work bridges academia and industry, addressing challenges in software-defined networks, traffic control, and secure data sharing in social networks. Education: PhD in Distributed Systems (University of Surrey) Affiliations: Centre of Excellence in Defence, Risk & Resilience; Portsmouth Centre for Advanced Materials and Manufacturing Research Interests Dr. Adda's research spans: Network security and fault detection mechanisms Blockchain applications in IoT and forensics Energy-efficient cloud data center optimization Machine learning for climate modeling Self-organizing network protocols Grants & Collaborations His projects include collaborations with industry partners on secure data leakage detection in cloud systems and resilient wireless protocols for harsh environments. He has pioneered fault classification systems using clustering algorithms and fuzzy logic. Labs & Teams He contributes to the Centre for Cybercrime and Economic Crime, focusing on digital forensics and network intrusion analysis. His team develops frameworks for privacy management in social networks and proactive routing in software-defined networks.
