Stephen W. Keckler is an Adjunct Professor at the Department of Computer Science , The University of Texas at Austin , and serves as Vice President of Architecture Research at NVIDIA . He is an ACM Fellow , IEEE Fellow , and Sloan Foundation Research Fellow . Education: BS in Electrical Engineering, Stanford University (1990) SM in Computer Science, Massachusetts Institute of Technology (1992) PhD in Computer Science, MIT (1998) Research Interests focus on computer architecture for deep learning , GPU computing , and energy-efficient systems . His work explores memory compression , network-on-chip designs , and heterogeneous computing . Publication Trends highlight advancements in deep learning accelerators , GPU memory systems , and energy-efficient architectures . Notable themes include sparsity exploitation , multi-chip modules , and fault-tolerant GPU pipelines . Scientific Recognition : ACM Fellow IEEE Fellow Sloan Foundation Research Fellow Best Paper Awards at ASPLOS 2009 and ISPASS 2011 Laboratory Affiliations : Computer Architecture and Technology Laboratory (CART) TRIPS Project (Tera-Op Reliable Intelligently adaptive Processing System) NVIDIA Research
James Anderson is an Assistant Professor in the Department of Electrical Engineering at Columbia University, with affiliations to the Data Science Institute (DSI) and multiple research centers including the Computing Systems for Data-Driven Science and Foundations of Data Science. Prior to Columbia, he was a Senior Research Scientist at Caltech’s Computing + Mathematical Sciences division (2016–2019) and held a Junior Research Fellowship at the University of Oxford’s Department of Engineering Science (pre-2012). He earned his DPhil (PhD) in Engineering Science from Oxford in 2012. His research focuses on optimal/robust control theory, mathematical programming, data privacy, and cyber-physical systems, with applications in smart grids, systems biology, and power systems. Recent work emphasizes energy storage strategies, distributed control algorithms, and cybersecurity in critical infrastructure. His publications span advanced control methodologies (e.g., reinforcement learning for LQR problems), energy market dynamics, and resilient system designs. Notable contributions include frameworks for decision-focused energy storage arbitrage and defenses against false data attacks in power grids. He actively collaborates on federated learning approaches for distributed systems and has pioneered techniques for system-level synthesis in cyber-physical architectures. Anderson’s affiliations include the Data Science Institute (DSI) and specialized centers focused on data-driven science and energy systems. His work bridges theoretical control advancements with real-world applications in energy and healthcare.
Bo Chen is a postdoctoral researcher at the Siebel School of Computing and Data Science and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. His work focuses on AI-system co-design for immersive computing, particularly in extended reality (XR) and multi-modal content delivery over wireless networks. Ph.D. in Computer Science (2022), advised by Klara Nahrstedt B.S. in Computer Science from Shanghai Jiao Tong University (2016) His research integrates AI techniques with system-level optimizations to address challenges in XR infrastructure , including multi-view video streaming , NeRF-based content delivery , and uncertainty management in video transmission . He has pioneered methods like Loose Frame Referencing for learned codecs and Context-Aware NeRF Serving for mobile XR applications. Bo Chen's recent publications span top venues like ACM MobiSys, ACM SenSys, and USENIX NSDI. Key themes include AI-driven compression , dynamic 3D rendering , and reliable streaming over mobile networks . He has received recognition including the Rising Star Best Presentation Award (ACM MobiSys 2025) and Best Student Paper Award (ACM MMSys 2022). Bayesian optimization for XR systems Multi-view video aggregation at edge networks 3D Gaussian Splatting for immersive media
Catherine Polling is an NIHR Clinical Lecturer in General Psychiatry at King’s College London (KCL), affiliated with the Institute of Psychiatry, Psychology & Neuroscience (IoPPN) and the Health Inequalities Research Group . Her work focuses on mental health inequities, self-harm epidemiology, and mixed-methods research, particularly in urban and marginalized communities. Education: MBBS from University College London, MSc in Epidemiology from the London School of Hygiene and Tropical Medicine, PhD from KCL's Department of Psychological Medicine Research Interests: Catherine investigates health disparities in mental health services using advanced statistical methods (multi-level modeling, Bayesian disease mapping) and GIS-based data visualization. Her clinical work with the South London and Maudsley NHS Foundation Trust complements her academic focus on systemic racism and mental health outcomes. Teaching & Engagement: She lectures on urban mental health for KCL’s Urban Informatics MSc and co-leads training on racism in mental health services. She also facilitates reflective practice groups accredited by the Balint Society and contributes to curriculum reform via the Maudsley Cultural Psychiatry Group. Scientific Awards: NIHR Clinical Lecturer Fellowship Wellcome Training Fellowship Research Trends: Her publications emphasize self-harm risk factors, pandemic impacts on healthcare workers, ethnic disparities in mental health services, and the intersection of urban environments with mental health outcomes. She integrates large-scale clinical datasets, qualitative interviews, and spatial analysis to address systemic inequities.
Abhijit Sarkar is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa. His work centers on computational dynamics and probabilistic modeling, with office MC 3076 in the Minto Centre for Advanced Studies in Engineering and contact details including phone (613) 520-2600 x6320 and email abhijit_sarkar@carleton.ca . Education: D.Phil. from University of Oxford M.Sc. from Indian Institute of Science (IISc) B.E. from Calcutta University Professional Engineer (P.Eng.) designation His research drives innovation in uncertainty quantification for complex engineering systems. Core interests include dynamics of nonlinear structures, probabilistic mechanics for stochastic finite element methods, and Bayesian inference frameworks for parameter estimation. He pioneers scalable high-performance computing solvers for large-scale systems and sparse learning algorithms to address overfitting in statistical modeling. Recent publications (2022-2024) reveal three dominant trends: (1) Bayesian model calibration for stochastic compartmental systems applied to epidemiology and aerospace, (2) domain decomposition techniques for scalable uncertainty quantification in stochastic PDEs, and (3) sparse learning methods for nonlinear aerodynamic encoding. Key applications span wind turbine vibration analysis, flutter margin prediction, MEMS resonator optimization, and geospatial pandemic modeling. Scientific awards: No awards, fellowships, or medals listed in the source material Graduate supervision includes 6 current students (Ajay Kumar, John Clarabut, Nastaran Dabiran, Sakhi Mittal, Michael Pantano, Brandon Robinson) and 18 graduated students across 17 years (2006-2023). His research leverages high-performance computing for projects in structural dynamics, aeroelasticity, and computational epidemiology, frequently co-supervised with Dominique Poirel and Chris Pettit. Notable grants focus on wind tunnel validation for nonlinear systems and pandemic spread modeling. Based in the Minto Centre for Advanced Studies in Engineering, his computational mechanics group develops algorithms for stochastic dynamics using Carleton University's high-performance computing infrastructure. Collaborations span aerospace engineering (flutter analysis), civil infrastructure (seismic wave propagation), and public health (Covid-19 modeling).
Dr. Xin Zhou is an Oxford-Bristol Myers Squibb Fellow at the Department of Computer Science, University of Oxford. Her research integrates computational modeling, clinical data, and experimental findings to investigate cardiac disease mechanisms and develop human-based simulations for drug evaluation. BSc and MSc in Life Sciences, Beijing Normal University DPhil in Computational Biology, University of Oxford Her work focuses on multi-scale cardiac modeling , particularly in ischemic heart disease and heart failure, exploring ionic currents, tissue conduction, and organ-level dynamics. She develops electromechanical simulations to study cardiac alternans and arrhythmic risks, translating these into clinical applications for patient stratification and pharmaceutical testing. Recent publications emphasize in silico clinical trials , sex-specific cardiometabolic analysis, and Purkinje network modeling. Collaborative efforts with clinicians and pharmaceutical partners highlight her translational approach to regulatory science. Model of the Year 2024, BioModels EPSRC Impact Acceleration Account Microsoft Research Project Award Recognition Award, University of Oxford She supervises PhD and MSc students in computational cardiology, while serving on the editorial board of Frontiers in Physiology . Her current projects involve digital twinning and predictive cardiac safety models to reduce animal testing reliance.
Dr. Chunyan Lai is an Associate Professor at the Department of Electrical and Computer Engineering, Concordia University. Her research focuses on electric drives, motor control, power electronics, electrified vehicles, and vehicle-to-grid solutions. She contributes to both graduate and undergraduate education through courses such as Controlled Electric Drives and Hybrid Electric Vehicle Power Systems . Research Emphasis : Electric motor drives and control systems, electrified transportation, power electronics innovations, and energy management strategies. Publications : Specializes in sensorless control techniques for Permanent Magnet Synchronous Motors (PMSM), thermal management in electric machines, and advanced energy trading frameworks for smart grids. PhD Opportunities : The Power Electronics and Energy Research (PEER) Group under Dr. Lai offers positions for developing efficient motor drives for EVs and grid-connected power converters. Collaboration : Industry-adjacent research with requirements for professional communication, patent development, and technical dissemination.
Professor Alexander J. Hartemink holds dual appointments in the Department of Computer Science and Department of Biology at Duke University, Trinity College of Arts & Sciences. He is also a Bass Fellow in Computer Science. His research focuses on computational biology, machine learning, and systems biology, with applications to genomics, epigenomics, and transcriptional regulation. Hartemink leads the Duke Office of University Scholars and Fellows and has directed the Computational Biology and Bioinformatics graduate program. He earned a PhD from MIT (2001), MPhil from the University of Oxford (1996), and BS from Duke (1994). Research Interests His work integrates computational methods to study chromatin dynamics, transcriptional networks, and epigenetic mechanisms. Key areas include modeling chromatin accessibility, predicting transcription factor binding, and understanding cell-cycle regulation. Techniques employed include Bayesian networks, dynamic systems modeling, and machine learning algorithms. Publications & Trends Recent work emphasizes single-cell multi-omics integration, chromatin occupancy modeling (RoboCOP framework), and transcriptional regulation in response to genetic perturbations. Themes include epigenetic plasticity, disease-associated enhancers, and systems-level analysis of gene expression. Awards & Grants Hartemink has received the Sloan Research Fellowship (2005) and NSF CAREER Award (2004). Active grants include NIH funding for chromatin-transcription interplay studies and NSF support for regulatory genome research. He collaborates on projects like the Data+ initiative, promoting interdisciplinary data science. Affiliations & Labs Associated with Duke’s Center for Genomic and Computational Biology and Center for Advanced Genomic Technologies. His lab develops computational tools for genomic analysis, including software for chromatin modeling and epigenetic data integration.
Sha Yang serves as the Ernest Hahn Professor of Marketing at the Marshall School of Business, University of Southern California, where she has held full-time faculty positions since 2017 after progressing from Assistant to Associate Professor roles at New York University and UC-Riverside. Her research examines interdependencies in consumer preferences, social influences on decision-making, and competitive dynamics in advertising, pricing, and platform growth. Her educational background includes a PhD in Marketing (2000) and MA in Statistics (1998) from Ohio State University, complemented by an MA in Economics (1995) and BA in International Economics (1994) from Renmin University of China. Her methodological expertise spans Bayesian methods, structural modeling, and data analytics applied to consumer behavior. Yang's research portfolio reveals consistent focus on digital marketing phenomena, with recent work analyzing cross-category spillovers in advertising, review impacts under negotiated pricing, and psychological pricing effects in luxury markets. Her publications in Journal of Marketing , Management Science , and Marketing Science demonstrate interdisciplinary approaches bridging econometrics and behavioral insights. Among her recognitions is the Marketing Science Institute Young Scholar award. She has served as Associate Editor for Journal of Marketing (2017-present) and Marketing Science (2017-2024), reflecting her scholarly impact. Marketing Science Institute Young Scholar Associate Editor, Journal of Marketing (2017-present) Associate Editor, Marketing Science (2017-2024) VP, INFORMS Society for Marketing Science Administratively, Yang served as Vice Dean and Senior Vice Dean for Faculty and Academic Affairs at Marshall School of Business (2020-2023), overseeing faculty development and academic strategy. Her current research integrates causal inference methods with media and entertainment industry applications, supported by grants from marketing research institutions.
Carolina Osorio is a Professor at HEC Montréal, holding the Scale AI Research Chair in Artificial Intelligence for Urban Mobility and Logistics. She is affiliated with the Department of Decision Sciences and is a member of the Group for Research in Decision Analysis (GERAD) and the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). Her research focuses on transportation optimization, urban mobility, and data-driven simulation-based methods. She has been recognized among the world’s most influential researchers in 2023 and 2024. Education: Ph.D. in Mathematics, École Polytechnique Fédérale de Lausanne (EPFL) M.Sc. in Statistics, University College London (UCL) Bachelor’s in Engineering, École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble (ENSIMAG) Research Interests: Her work emphasizes scalable transportation modeling, simulation-based optimization, and AI applications for urban logistics. She develops methods for large-scale network analysis, traffic demand estimation, and sustainable urban mobility solutions. Key areas include traffic signal optimization, car-sharing service design, and high-dimensional stochastic systems. Publications: Recent articles highlight advancements in scalable traffic demand estimation, Bayesian optimization for transportation systems, and simulation-based toll optimization. Her work addresses challenges in global highway networks, urban congestion dynamics, and multi-city calibration. Awards: Scale AI Research Chair (Artificial Intelligence for Urban Mobility and Logistics) Recognition as a world-leading researcher in transportation science Advising & Grants: Osorio collaborates on projects funded by Scale AI and leads research initiatives through GERAD and CIRRELT. Her supervision activities include teaching courses such as Decision Analysis and Sample Efficient Optimization at HEC Montréal. Labs & Teams: She contributes to interdisciplinary teams at GERAD and CIRRELT, focusing on integrating advanced analytics into urban transportation systems.
Thomas Berger is a Professor at the University of Hohenheim , affiliated with the Faculty of Agricultural Sciences and leading the Department of Economics of Land Use . He also contributes to the Computational Science Hub and Hohenheim Tropics initiatives. Focus Areas: Climate change adaptation, land-use modeling, biodiversity-productivity trade-offs, agent-based simulation, and machine learning in agricultural systems. Key Projects: Simulation frameworks for smallholder resilience in Ethiopia, bioeconomic modeling in the Amazon, and hybrid intelligence applications in European agricultural policy. Recent Publications: 2025 study on climate change effects on insecticide reduction in Germany, 2024 work on reconciling biodiversity with productivity via hybrid models, and 2023 methodological contributions to surrogate modeling and seasonal forecast integration. Research Trends: Interdisciplinary integration of climate science, agricultural economics, and computational modeling, with increasing emphasis on AI-assisted decision support systems and sustainability policy validation. Teaching & Outreach: Offers Agricultural Economics seminars and Hohenheim Tropics discussions, requiring advance email registration for office hours.
Amir Shaikhha is an Associate Professor (Reader) in the School of Informatics at the University of Edinburgh. He was previously an Assistant Professor (Lecturer) at the same institution from 2020 to 2024 and a Departmental Lecturer at the University of Oxford until August 2020. He is also a Junior Research Fellow at University College, Oxford. His academic journey began with a Ph.D. from EPFL in 2018, where he was awarded the Google Ph.D. Fellowship in structured data analysis and a Ph.D. thesis distinction. His research centers on the design and implementation of data-analytics systems, drawing upon techniques from databases, programming languages, compilers, and machine learning. He develops high-performance systems such as SDQL.py, StructTensor, and VecHT, focusing on the compilation of data science workloads and optimization of tensor operations. His work bridges the gap between high-level abstractions and efficient execution, particularly in sparse and probabilistic computing domains. The recent publications highlight a strong trend in compiler-driven optimizations for data-intensive applications, including automatic differentiation, loop fusion, probabilistic programming, and domain-specific language (DSL) restaging. His research integrates machine learning for systems decisions and emphasizes reproducibility and performance. He has published consistently in top venues like PLDI, OOPSLA, SIGMOD, and CGO, reflecting sustained impact in programming languages and database systems. Dahl-Nygaard Junior Prize, 2025 Google Research Scholar Award, 2025 Most Influential Paper Award, GPCE 2024 Best Paper Award, GPCE 2017 Most Reproducible Paper Award, SIGMOD 2017 Google Ph.D. Fellowship, 2017 Amir Shaikhha has advised PhD students including Hesam Shahrokhi and has been nominated for Best Supervisor of the Year at the University of Edinburgh. He leads research projects that have received recognition and support through awards and grants, including the Google Research Scholar Award. He actively serves the community through program committees (e.g., GPCE, DBPL, DRAGSTERS), editorial roles, and peer review for premier journals. His leadership in organizing workshops and conferences underscores his role as a central figure in the programming languages and databases research communities. He leads a research group focused on compiler and database systems, with recent open-source releases such as StructTensor and VecHT. His team collaborates with researchers from institutions like MIT, EPFL, and TU Berlin, and he co-chairs workshops like Sparse@PLDI and DRAGSTERS. His lab emphasizes innovation in how data-intensive programs are compiled and executed efficiently across modern hardware.
George Kesidis is a Professor in Computer Science and Engineering and Electrical Engineering at Penn State University. His research spans deep learning security, virtual reality optimization, and cloud computing. College of Engineering (Penn State University) Research Focus: Backdoor Attacks, DNN Robustness, Edge Caching Active in NSF and U.S. Navy-funded projects (2022-2026) His work addresses backdoor data poisoning , test-time evasion attacks , and DNN overfitting mitigation . He develops techniques like activation clipping, perturbation analysis, and statistical defense models. Recent projects include edge caching systems for VR and security-driven AI frameworks. Key article trends reveal expertise in adversarial deep learning, immersive media delivery, and cloud resource optimization. Current grants focus on multi-user VR, GPU scheduling, and serverless-cloud hybrid architectures. He collaborates extensively with researchers like David J. Miller and Xinyu Li, particularly on cloud-based adversarial defense mechanisms and VR streaming benchmarks.
Prof. Dr.-Ing. Ralf Beck serves as Professor for Control and Regulation Technology and Automation Technology at Hochschule Düsseldorf University of Applied Sciences within the Faculty of Electrical Engineering & Information Technology. His academic responsibilities span multiple degree programs including BEng Electrical Engineering, BEng Industrial Engineering, and MSc Electrical Engineering and Information Technology. His educational background includes Mechanical Engineering studies at TU Braunschweig (1998-2004), followed by doctoral research at RWTH Aachen's Institute of Control Engineering where he earned his Dr.-Ing. in 2010 with a dissertation on predictive energy management for hybrid vehicles. Prior to his current professorship, he held progressive roles at FEV Europe GmbH from 2009-2018, culminating as Senior Project Manager for Vehicle and Powertrain Electronics. Beck's research focuses on control engineering systems with particular emphasis on automation technology, regulation systems, and model-based development approaches. His work bridges theoretical control methodologies with practical automotive applications, especially in hybrid vehicle energy management, multi-robot systems, and intelligent air path control. The Modellfabrik Fab21 serves as his primary experimental platform for model-based development applications. His publication record since 2005 demonstrates consistent contributions to control engineering, particularly in hybrid vehicle systems, emission control optimization, and calibration methodologies. Recent work shows increasing focus on distributed robotics and intelligent transportation systems, reflecting evolving research directions while maintaining core expertise in control theory applications. As an educator, Beck teaches foundational and advanced courses including Electrical Engineering III, Control and Regulation Technology, Model-Based Development, Technical Mechanics, and Advanced Control Engineering at the Master's level. His teaching integrates theoretical concepts with practical laboratory applications through the university's Moodle platform, emphasizing hands-on implementation of control algorithms and system modeling techniques.
Jacob Gardner is an Assistant Professor in the Department of Computer & Information Science at the School of Engineering and Applied Science, University of Pennsylvania. His research bridges machine learning and scientific discovery with emphasis on computational biology and molecular design. His primary research interests include: Machine Learning Bayesian Optimization Computational Biology Molecular Design Artificial Intelligence Gaussian Processes Analysis of his 2024-2025 publications reveals a dominant focus on Bayesian optimization techniques integrated with large language models for biological applications. Key trends include therapeutic design using knowledge distillation from scientific literature, RNA splicing prediction, antibiotic development, and scalable Gaussian process methods. His work consistently addresses dimensionality challenges in molecular modeling while improving computational efficiency for high-dimensional biological data. No scientific awards were mentioned in the provided text. No information regarding student advising or research grants was provided in the source material. His research appears supported by institutional initiatives including Penn AI, Innovation in Data Engineering and Science (IDEAS), and the Data Driven Discovery Initiative (DDDI).
