Vesna Terzic is a Professor in the Department of Civil Engineering and Construction Engineering Management at California State University Long Beach's College of Engineering. Her research specializes in seismic performance assessment, infrastructure resilience, and probabilistic risk analysis of structures. Education includes a PhD in Structural Engineering from UC Berkeley, MS in Earthquake Engineering from Ss. Cyril and Methodius University, and BS in Structural Engineering from University of Belgrade. Research focuses on developing advanced computational frameworks for evaluating post-earthquake functionality of buildings and bridges. Her work integrates performance-based engineering with resilience quantification, emphasizing practical applications for seismic design and retrofitting. Recent publications demonstrate consistent focus on functional recovery modeling, structural health monitoring, and optimization of protective systems. Research trends show progression from component-level analysis to regional recovery simulation frameworks. Major awards: ACI Chester Paul Siess Award for Excellence in Structural Research (2017) Popert Fellowship, UC Berkeley (2009) Professional service includes membership on FEMA P-58 project committees and contributions to OpenSees development. Current projects investigate soil-structure interaction effects on tall buildings and recovery-based design methodologies.
Dr. Khurram Aziz is a Senior Instructor in the Faculty of Computer Science at Dalhousie University , Halifax, Canada. He is actively engaged in teaching and research, with a focus on optical networks, data center interconnects, and network performance modeling. Education: PhD in Electrical Engineering, Vienna University of Technology, Austria (2008) MSc in Electrical Engineering, National University of Singapore (2003) BSc (Hons) in Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan (1998) His research interests include optical packet and burst switched networks , optical interconnects for data centers , analytical modeling and simulation , and network routing and switching . He has contributed extensively to the design and performance evaluation of scalable optical switches and hybrid switching systems. The recent publications reflect a strong trend in data center optical networks , focusing on performance, blocking probability, signal degradation, and architectural classification. His work bridges theoretical modeling with practical simulation frameworks, such as CloudNetSim++ in OMNeT++, contributing to cloud and high-capacity network research. Dr. Aziz has no listed scientific awards in the provided text. He teaches several core computer science courses including CSCI 2141: Intro to Database Systems , CSCI 3171: Network Computing , CSCI 3132: Object Orientation and Generic Programming , and CSCI 3120: Operating Systems . There is no mention of graduate student supervision or external research grants. He has co-authored book chapters in major handbooks on data centers and switched systems. Dr. Aziz has not listed any formal lab or research team affiliations in the provided content.
Petros Koumoutsakos is the Herbert S. Winokur, Jr. Professor of Computing in Science and Engineering at Harvard University's School of Engineering and Applied Sciences (SEAS), where he also serves as Area Chair for Applied Mathematics. His research integrates machine learning with computational science to advance understanding of complex systems, including fluid dynamics, turbulence modeling, and biomedical applications. He leads the CSE Lab, focusing on high-performance computing and interdisciplinary collaborations such as a recent study with Citadel Securities and Google Cloud to simulate heart disease in cloud environments. Key research interests include reinforcement learning for turbulence closures, generative models for PDE solutions, and physics-informed AI for biomedical imaging and wildfire prediction. He was awarded the PRACE HPC Excellence Award (2023) for contributions to high-performance computing. His work bridges computational methods with real-world applications, emphasizing interpretability and scalability in multiscale systems. Grants & Collaborations: Leadership in multi-institutional projects, including turbulence modeling via reinforcement learning and cloud-based HPC studies. Labs/Teams: Director of the CSE Lab, advancing AI, computational fluid dynamics, and biomedical simulations.
Prof. Dr. Nikolaus A. Adams is a full professor and Chair of Aerodynamics and Fluid Mechanics at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. Born in 1963, he holds a doctorate from TUM (1993) and habilitation from ETH Zurich (1999). His research focuses on numerical methods, turbulent flows, microfluidics, and multiphase systems. He has held leadership roles, including Dean of the Faculty of Mechanical Engineering since 2023 and Vice Dean (2015–2016). Education: PhD from TUM (1993), habilitation from ETH Zurich (1999) Research interests include aerodynamics, fluid-structure interaction, and numerical techniques for compressible flows. His work spans high-speed aerodynamics and computational fluid dynamics (CFD). Awards include ERC Advanced Grants (GENUFASD 2023, NANOSHOCK 2015), the Gordon Bell Prize (2013), and Fellow of the American Physical Society (2011). Grants and leadership: Spokesperson of DFG SFB/TRR 40 (2008–2020), co-author of 'Large-Eddy Simulation for Compressible Flows' (2009), and editorial roles in J. Comput. Phys.
Tim G. J. Rudner is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, a Faculty Member at the Vector Institute, and a Title A Fellow at Trinity College, University of Cambridge. He was previously an Assistant Professor and Faculty Fellow at New York University. University: University of Toronto School: Faculty of Arts and Science Department: Department of Statistical Sciences Affiliation: Vector Institute, Trinity College (Cambridge) He holds a PhD in Computer Science and an MSc in Statistics from the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal, and a BS in Applied Mathematics and Economics from Yale University. PhD: Computer Science, University of Oxford MSc: Statistics, University of Oxford BS: Applied Mathematics and Economics, Yale University His research focuses on building robust, transparent, and trustworthy machine learning systems, particularly for high-stakes applications. He develops probabilistic models that improve generalization under distribution shifts, provide reliable uncertainty estimates, and enable fair and interpretable predictions. His work spans generative models, large language models, healthcare, and biomedical discovery. The recent publications highlight a strong trend toward function-space modeling, Bayesian regularization, and AI safety. Tim's work emphasizes principled uncertainty quantification, robustness to subpopulation and semantic shifts, and the development of frameworks for AI governance and specification. His research bridges theoretical advances with real-world applications, especially in safety-critical domains like medicine and defense. Tim has received numerous accolades including being named a Rhodes Scholar, Qualcomm Innovation Fellow, and 2024 Rising Star in Generative AI. He was awarded a $700,000 Foundational Research Grant and a $30,000 Apple Seed Grant for improving LLM trustworthiness. Rhodes Scholar Qualcomm Innovation Fellow AISTATS Notable Paper Award (2024) Outstanding Paper Award, ICLR GenAI4DM Workshop (2024) Apple Seed Grant ($30,000) Foundational Research Grant ($700,000) NeurIPS Spotlight Talk 2024 Rising Star in Generative AI He actively mentors students, particularly first-generation and low-income scholars, and has contributed to major policy frameworks including the OECD AI Classification Framework and a series of CSET issue briefs on AI safety. His work demonstrates a strong commitment to responsible AI development, combining technical rigor with societal impact. Tim leads research efforts at the intersection of machine learning theory and practical deployment, with ongoing projects in generative modeling, reliable LLMs, and AI governance. His lab produces high-impact work regularly published at top-tier conferences such as NeurIPS, ICML, and AISTATS.
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 .
Professor Jiyuan Tu is a Professor in the Department of Mechanical and Automotive Engineering at RMIT University's School of Engineering. He specializes in computational fluid dynamics (CFD), multiphase flows, and their applications in renewable/nuclear energy, biomedical engineering, and built environment systems. His research has led to over 500 peer-reviewed articles, 9 books, and $10M+ in ARC grants. He has supervised over 50 postgraduate students and received prestigious awards such as the RMIT Research Excellence Award (2012) and Fulbright Senior Scholar Award (2008). Research interests include CFD modelling of bioaerosol transport, drug delivery systems, and thermal energy storage. He pioneered numerical models for multiphase flows, contributing to software implementations in industries. Notable works include books on CFD and multiphase flow analysis, and leadership in international conferences like COBEE 2018. He holds honorary professorships at Tsinghua University and is Editor-in-Chief of the Experimental and Computational of Multiphase Flow journal. Industry experience includes roles at ANSTO (1996-2001). Awards span fellowships from JSPS, KOSEF, and Fulbright programs. Grants include ARC Discovery, Linkage, and LIEF projects. His work ranks him among the world’s top researchers in pebble bed reactors and airborne infection studies (SciVal 2016-2025).
Amadeus Gebauer is a Researcher at the Chair of Computational Mechanics within the Institute for Computational Mechanics at the Technical University of Munich (TUM), serving as a Research Associate since 2019. His work specializes in computational biomechanics with emphasis on cardiac mechanics modeling, growth and remodeling processes, and multi-physics simulation frameworks. Education: Master of Science (M.Sc.) in Mechanical Engineering, Technical University of Munich, 2019 Research Interests: Gebauer's research centers on cardiac mechanics modeling, including growth and remodeling of cardiac tissue, cardiac active tissue mechanics, and medical image processing. He develops advanced computational methods for parallel and high performance computing, particularly through the 4C multi-physics simulation framework. His work integrates constrained mixture models to simulate organ-scale biological processes, bridging computational mechanics with clinical cardiology applications and focusing on mechanobiological stability in cardiac systems. Publication Trends: Gebauer's publications (2018-2025) demonstrate consistent innovation in computational cardiology, primarily using constrained mixture models to address cardiac growth and remodeling. His recent work introduces adaptive integration techniques for history variables and homogenized modeling approaches, while expanding into software benchmarking for cardiac elastodynamics and gastric motility simulations. These contributions highlight his expertise in developing robust numerical methods for multi-physics biomedical problems, with increasing focus on patient-specific applications and high-performance computing solutions. Teaching and Advising: Gebauer teaches core computational mechanics courses including Finite Elemente and Numerische Festkörpermechanik across multiple semesters. He has supervised diverse student projects ranging from term papers to Master's theses, with notable collaborations including Maximilian Grill's shoulder biomechanics research (2020) and Janina Datz's artery geometry framework development (2021). His advising consistently focuses on cardiac mechanics, computational modeling, and medical device simulation. Research Environment: As part of Professor Wolfgang A. Wall's Institute for Computational Mechanics (LNM) at TUM, Gebauer contributes to a leading research group in computational solid/fluid mechanics. The LNM develops the 4C simulation framework for complex engineering and biomedical challenges, with current emphasis on cardiac growth modeling, multi-physics integration, and high-performance computing applications in personalized medicine.
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.
Alexandre Bouchard-Côté is a Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on computational statistics, Bayesian methods, and Monte Carlo techniques, with applications in evolutionary biology, cancer genomics, and computational linguistics. Education : PhD in Computer Science (with Designated Emphasis in Statistics) from UC Berkeley (2010), BSc in Mathematics and Computer Science from McGill University (2005). Affiliations : Director of the Blang probabilistic programming project and leader of the Bouncy Particle Sampler research group. Research Interests : Bouchard-Côté develops scalable Bayesian computational methods, including non-reversible Monte Carlo algorithms like the Bouncy Particle Sampler, and applies these to problems in cancer phylogenetics, evolutionary dynamics, and historical linguistics. His work emphasizes bridging theoretical foundations with practical tools for data science. Publications Trends : Recent work spans distributed sampling frameworks (e.g., Pigeons.jl), variational phylogenetic inference, and cancer clonal evolution modeling. His articles often address algorithmic scalability and interdisciplinary applications in biology and astronomy. Awards : CRM-SSC Prize in Statistics (2024) PIMS-UBC Mathematical Sciences Young Faculty Award (2018) Tweedie New Researcher Award (2016) Advising & Grants : Supervises graduate students (e.g., Son Luu, Nikola Surjanovic) and leads funded projects on distributed MCMC and cancer genomics. Collaborates with institutions like the Simons Foundation and the Canadian Statistical Sciences Institute (CANSSI). Labs/Teams : Core member of the UBC Statistical Machine Learning group, contributing to open-source tools like Blang and the Bouncy Particle Sampler implementation.
Muhannad S. Bakir is the Dan Fielder Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology and serves as the Director of the 3D Systems Packaging Research Center. His research focuses on heterogeneous integration of microsystems, including 2.5D and 3D ICs and packaging technologies, with significant contributions to advanced cooling systems, electrical and photonic interconnects, and biosensor integration with CMOS. Dr. Bakir's research interests span heterogeneous microsystem design and integration, advanced cooling and power delivery for emerging architectures, electrical and photonic interconnect technologies, biosensor technologies, and nanofabrication. His work addresses critical challenges in next-generation electronics, enabling polylithic integration that concatenates heterogeneous ICs of various functionalities while mimicking monolithic-like densities. His research particularly focuses on co-design of thermal technologies, power delivery networks, and signaling networks for silicon nanoelectronic systems. His recent publications demonstrate strong trends in fused-silica stitch-chip technology for heterogeneous integration, with particular emphasis on RF and mm-wave applications, power delivery for AI accelerators, and thermal management solutions. His work bridges electrical engineering, materials science, and thermal management to solve critical bottlenecks in computing performance and efficiency. 2013 Intel Early Career Faculty Honor Award 2012 DARPA Young Faculty Award 2011 IEEE CPMT Society Outstanding Young Engineer Award 2012 National Academy of Engineering Frontiers of Engineering Symposium Invited Participant 2015 IEEE CPMT Society Distinguished Lecturer 2014 Best Paper of the IEEE Transactions on Components Packaging and Manufacturing Technology More than 25 conference and student paper awards Twelve issued US Patents Dr. Bakir leads the Integrated 3D Systems Lab (I3DS) at Georgia Tech, which is actively researching advanced packaging, interconnects, electrical and thermal design, and system integration. His team has received significant recognition for their work, including multiple best paper awards from major conferences like ECTC, IITC, and CICC. The lab is currently seeking postdoctoral researchers and research faculty to advance next-generation electronics through collaborative research. His lab focuses on enabling the next phase of Moore's Law through polylithic integration, which concatenates heterogeneous ICs of various functionalities (digital, analog, photonic, and mm-wave) using advanced off-chip '2.5D' and '3D' heterogeneous interconnects and packaging. This work impacts applications in high-performance computing, machine learning, edge intelligence, autonomous vehicles, augmented/virtual reality, and healthcare.
Nuno F. Loureiro is Professor of Nuclear Science and Engineering and the Herman Feshbach (1942) Professor of Physics at MIT, and serves as Director of MIT's Plasma Science and Fusion Center (PSFC) since May 2024. He holds joint appointments in MIT's School of Engineering and School of Science, and is affiliated with the MIT Kavli Institute for Astrophysics and Space Research and the MIT Energy Initiative. Director, Plasma Science and Fusion Center (2024-present) Professor of Nuclear Science and Engineering (2016-present) Herman Feshbach (1942) Professor of Physics (current) Loureiro earned his MEng in Physics from Instituto Superior Técnico in Lisbon (2000) and his PhD in Physics from Imperial College London (2005). He completed postdoctoral work at Princeton Plasma Physics Laboratory (2005-07) and UKAEA Culham Centre for Fusion Energy (2007-09) before returning to lead the Theory and Modeling Group at the Institute for Plasmas and Nuclear Fusion at IST Lisbon. Loureiro's research focuses on fundamental aspects of magnetized plasma dynamics, with particular emphasis on magnetic reconnection, magnetic field generation and amplification, confinement and transport in fusion plasmas, and turbulence in strongly magnetized, weakly collisional plasmas. His work bridges theoretical physics with computational simulations using state-of-the-art tools like the Viriato code, which he developed for reduced-gyrokinetic modeling. His research has significant implications for both understanding cosmic phenomena and advancing practical fusion energy solutions. Analysis of Loureiro's recent publications reveals a strong focus on magnetic reconnection mechanisms across multiple scales, from electron-only reconnection to relativistic plasma turbulence. His work increasingly incorporates computational innovations, including quantum computing approaches for plasma modeling. The research spans applications from solar physics and astrophysical phenomena to practical fusion energy challenges, demonstrating the unifying nature of plasma physics across disciplines. NSF Presidential Early Career Award for Scientists and Engineers (PECASE) (2025) American Physical Society Fellow (2022) NSF CAREER Award (2017) Thomas H. Stix Award for Outstanding Early Career Contributions (2015) Loureiro leads the Loureiro Group at MIT, which conducts research at the interface of analytical theory and numerical simulations on supercomputers. His laboratory has developed the Viriato code for plasma simulations and investigates phenomena relevant to both fusion energy and astrophysical plasmas. As Director of the PSFC, he oversees one of MIT's largest research laboratories with over 250 full-time researchers, staff members, and students working across 250,000 square feet of lab space.
Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.
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.
