Martin Larsson is a Professor in the Department of Mathematical Sciences at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. He holds a Ph.D. from Cornell University and completed a postdoctoral appointment at the Swiss Finance Institute at EPFL, Lausanne, Switzerland. His research focuses on Mathematical Finance, stochastic analysis, probability, and statistics, with emphasis on affine and polynomial processes, stochastic portfolio theory, and sequential statistics. Key research domains include modeling interest rate term structures, large-scale equity market dynamics, and statistical testing in online settings. He serves as the Departmental representative for the Master of Science in Computational Finance (MSCF) program at CMU. Larsson has received the Bruti-Liberati Visiting Fellowship from the University of Technology Sydney. His work bridges theoretical probability with applications in finance, including contributions to stochastic volatility modeling, optimal contracts in trading, and robust portfolio optimization under uncertainty. Publications span topics such as martingale exit times, Wasserstein distance convergence, and ergodic control in stochastic systems, reflecting his interdisciplinary approach to mathematical finance and probability theory. His research often combines analytical techniques with stochastic control and geometric flows.
Kevin A. Shinpaugh is Collegiate Professor in the Department of Aerospace and Ocean Engineering at Virginia Tech’s College of Engineering. Since 2019 he has led instruction and research in spacecraft design and propulsion, leveraging decades of experience in high-performance computing and space-systems engineering. Education Ph.D., Aerospace Engineering, Virginia Tech (1994) M.S., Aerospace Engineering, Virginia Tech (1989) B.S., Aerospace Engineering, Virginia Tech (1986) Research Focus Dr. Shinpaugh’s scholarship centers on the intersection of high-performance computing (HPC) and space systems engineering . He develops and applies advanced computational techniques to spacecraft design, propulsion analysis, and mission planning. His work spans numerical simulation of complex aerospace systems, optimization of propulsion architectures, and creation of scalable HPC frameworks that enable rapid design iteration for spacecraft and launch vehicles. Publication Trends Across more than thirty refereed papers and design-competition reports, a clear trajectory emerges: early contributions in experimental fluid-mechanics instrumentation (laser-Doppler velocimetry, fiber-optic sensors) evolved into large-scale computational studies of space systems, and most recently into student-led mission-concept designs for CubeSats, lunar exploration, and interplanetary missions. Keywords consistently include spacecraft design, propulsion, deployable structures, and mission architecture. Service & Committees Chair, Virginia Tech HPC User Committee (2004–2011) Member, VT HPC Advisory Board (2007–present) NSF TeraGrid/XSEDE Campus Champion for Virginia Tech (2006–2013) IBM HPC/AI Customer Advisory Council DC (2019–present) Member, VT AOE Seminar Committee (2019–present) Laboratory & Computing Resources Dr. Shinpaugh has long stewarded Virginia Tech’s high-performance computing ecosystem. He directs students and collaborators in leveraging the university’s Advanced Research Computing (ARC) clusters, as well as national facilities through XSEDE and DoD HPCMP, to execute spacecraft-design simulations and propulsion analyses at scale.
Rainer Watermann is a Full Professor for Empirical Research in Education at the Free University of Berlin since 2011, previously serving as Full Professor for Education and Empirical Research in Schools at Georg-August University of Göttingen (2005-2011). His academic career includes significant research positions at the Max Planck Institute for Human Development in Berlin (1997-2005). Watermann earned his Diploma in Educational Science from the University of Münster (1996), followed by a Dr. phil (2002) and Habilitation (2005), both from Freie Universität Berlin. His educational background established the foundation for his extensive research in educational transitions and disparities. His research focuses on educational transitions, particularly from primary to secondary school and into higher education, examining motivational factors, social background influences, and achievement goal development. Watermann's work consistently addresses how social disparities affect educational opportunities and outcomes, with particular attention to gender differences and longitudinal developmental patterns. His methodological expertise includes latent class analysis, structural equation modeling, and large-scale assessment design. Watermann's publication portfolio reveals consistent research trajectories examining motivational frameworks (particularly expectancy-value theory), educational transitions, social disparities in education, and political socialization. His work spans both theoretical development and practical applications for educational policy and practice, with increasing focus on intervention effectiveness in recent years. As an active member of the academic community, Watermann serves on multiple editorial boards including the Swiss Journal for Educational Sciences and Empirical Educational Science , and regularly reviews for major educational and psychological journals. He has also contributed to significant research centers, including serving as spokesman for the Center for Empirical Research on Teaching and Learning in Schools (ZeUS) at the University of Göttingen (2008-2010). Watermann maintains an active research program with numerous collaborations across Germany and internationally, evidenced by his consistent publication record through 2025. His work bridges educational psychology, sociology of education, and policy-relevant research, maintaining strong connections between theoretical frameworks and practical educational contexts.
Carla P. Gomes is a Professor of Computer Science at Cornell University with joint appointments in the Department of Computer Science and the Dyson School of Applied Economics and Management. She holds a PhD in computer science from the University of Edinburgh and an M.Sc. in applied mathematics from the University of Lisbon. Her research focuses on artificial intelligence, constraint reasoning, optimization, and computational sustainability. As Director of the Institute for Computational Sustainability (ICS) and co-director of the Cornell University AI for Science Institute, she leads efforts to integrate AI with sustainability challenges. Her research themes include the integration of constraint reasoning, machine learning, and operations research to solve large-scale problems. She pioneered the field of Computational Sustainability, addressing environmental, economic, and societal challenges through AI. Gomes directed two NSF Expeditions in Computing awards and established CompSustNet, a large-scale sustainability research network. Key awards include the 2021 ACM–AAAI Allen Newell Award, AAAI Feigenbaum Prize, and fellowships from AAAI, ACM, and AAAS. Her work spans over 200 publications, with contributions to AI, sustainability, and materials discovery. She advises numerous PhD students and oversees postdocs in AI, sustainability, and interdisciplinary projects. Gomes' lab focuses on AI for scientific discovery, including autonomous materials synthesis and crystal-structure phase mapping. She collaborates with institutions like JCAP and the Materials Project, advancing AI-driven solutions for energy and environmental challenges. Current projects include Schmidt AI in Science postdoc initiatives and AI-driven materials discovery platforms like DRNets and SARA.
Xuezhe Ma is an Assistant Professor in the Department of Computer Science at the University of Southern California's Viterbi School of Engineering. Previously, he was a Ph.D. student at Carnegie Mellon University's Language Technologies Institute, where he worked under the supervision of Professor Eduard Hovy. His academic journey includes a Master's degree from Shanghai Jiao Tong University's Center for Brain-like Computing and Machine Intelligence and a Bachelor's degree in Computer Science from the same institution. Ph.D. in Computer Science, Carnegie Mellon University (completed ~2020) M.S. in Brain-like Computing, Shanghai Jiao Tong University B.S. in Computer Science, Shanghai Jiao Tong University Dr. Ma's research spans multiple areas at the intersection of Natural Language Processing and Machine Learning, with particular focus on structured prediction, syntactic and semantic parsing, machine translation, language generation, and deep generative models. His recent work has expanded into vision-language models, large language model architectures, and applications across computer vision tasks. His research combines theoretical foundations with practical implementations, as evidenced by his development of tools like NeuroNLP2 and MaxParser. His publication record shows a clear trajectory from foundational NLP work during his PhD (including papers on dependency parsing and sequence labeling) to more recent contributions in generative models and large language systems. The 15 most recent publications reveal a strong focus on addressing fundamental challenges in generative modeling, context handling, and multimodal integration, with applications spanning literary translation, medical imaging, and news diffusion analysis. AI2 Outstanding Intern Award (2018) Dr. Ma has secured research funding supporting his work in generative models and language technologies, with projects focusing on improving the efficiency and capabilities of large language models. His research group at USC is actively working on next-generation language understanding and generation systems, with particular emphasis on context-aware modeling and multimodal integration. He has established collaborations with industry partners including the Allen Institute for AI and has contributed to open-source projects like Texar. At USC, Dr. Ma leads research in the Information Sciences Institute, directing projects on efficient large language model architectures and multimodal reasoning systems. His lab focuses on developing novel approaches to context handling, model efficiency, and multimodal integration, with applications across diverse domains including healthcare, literary analysis, and news media.
Dr. Arnab Bhattacharya is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Kanpur since December 2020. He previously served as Associate Professor (2014–2020) and Assistant Professor (2007–2014) at IIT Kanpur. Education: PhD in Computer Science (2007), University of California, Santa Barbara MS in Computer Science (2007), University of California, Santa Barbara Bachelor of Computer Science and Engineering (2001), Jadavpur University Research Focus spans Databases , Data Mining , Information Retrieval , and Artificial Intelligence . His work emphasizes graph analytics , skyline queries , probabilistic data , and knowledge graph management . Article Trends reveal expertise in graph neural networks , trajectory-aware systems , statistical significance in databases , and legal/medical data mining . His methodologies often integrate chi-square statistics , approximate indexing , and provenance tracking . Scientific Awards: IBM Faculty Research Award Yahoo! Faculty Research and Engagement Program Award Best Paper at COMAD 2011 Best Student Paper at COMAD 2010 Top-Five Student Paper at ICDM 2005 ICDM Student Travel Award sponsored by IBM Contact: Office RM 409, Department of Computer Science and Engineering, IIT Kanpur Email: arnabb@iitk.ac.in | Phone: +91-512-259-7650
About Marin Litoiu is a Professor at York University, holding dual affiliations in the Department of Electrical Engineering and Computer Science at the Lassonde School of Engineering and the School of Information Technology in the Faculty of Liberal Arts and Professional Studies. He is a Fellow of the Canadian Academy of Engineering and a recipient of the 2020 IBM Faculty of the Year Award. His research focuses on cloud computing, self-adaptive systems, DevOps, IoT, and machine learning-driven performance engineering. Research & Awards Litoiu leads the Dependable Internet-of-Things Applications (DITA) program, funded by NSERC, and co-founded Bitnobi Inc., acquired by Myant. His notable awards include the CASCON 2019 Most Influential Paper Award and Best Paper Awards at multiple conferences. His work emphasizes practical applications of adaptive systems, cybersecurity, and smart infrastructure integration. Grants & Projects NSERC CREATE Program: $1.65M for the DITA program (2018) York Innovation, TIAP, NSERC, and OCI-funded Bitnobi incubation Leadership in multiple CASCON workshops on cloud computing and AIOps Labs & Teams Litoiu’s lab has produced impactful startups like Bitnobi and pioneered research in self-driving systems, edge computing, and AI-driven operations. His team collaborates with industry partners like IBM and explores cutting-edge topics such as LLMs in performance optimization and fault detection.
Stephen Bach is an Assistant Professor in the Computer Science Department at Brown University, where he leads the BATS (Bach's Awesome Team of Students) research group. His research focuses on improving how humans teach computers through programmatic weak supervision and methods for learning from fewer examples like zero-shot and few-shot learning. His primary research interests include weak supervision, data programming, probabilistic soft logic (PSL), statistical relational learning (SRL), information extraction, zero-shot learning, and few-shot learning. Bach's work often focuses on exploiting high-level, symbolic or semantically meaningful domain knowledge, with applications in information extraction, image understanding, scientific discovery, and data science. Bach's recent publications show a strong focus on language models, weak supervision techniques, and multimodal learning, particularly examining the capabilities and limitations of models like CLIP. His research has increasingly emphasized practical applications in low-resource settings and cross-lingual scenarios. Best Paper Award at NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) 2023 Larry S. Davis Doctoral Dissertation Award Selected for oral presentation at ICLR 2024 Best of VLDB 2018 paper selection Bach advises numerous Ph.D., Master's, and undergraduate students, many of whom have gone on to positions at leading tech companies, research institutions, and graduate programs. His research group has developed several influential frameworks including Snorkel (for weak supervision), PSL (Probabilistic Soft Logic), T0 (for zero-shot task generalization), ZSL-KG (for zero-shot learning with knowledge graphs), TAGLETS (for semi-supervised learning with auxiliary data), and WISER (for programmatic weak supervision in sequence tagging).
John C. Doyle is the Jean-Lou Chameau Professor of Control and Dynamical Systems, Electrical Engineering, and BioEngineering at the California Institute of Technology (Caltech), where he holds appointments in the Division of Engineering and Applied Science with primary affiliation in the Control and Dynamical Systems Department. His research bridges theoretical foundations with applications across biological, technological, medical, and ecological networks. He earned a BS and MS in Electrical Engineering from MIT (1977) and a PhD in Mathematics from UC Berkeley (1984), followed by consultancy at Honeywell Systems and Research Center (1976-1990). MIT: BS & MS in Electrical Engineering (1977) UC Berkeley: PhD in Mathematics (1984) Doyle's research centers on universal laws and architectures in complex systems, emphasizing robustness-efficiency tradeoffs, speed-accuracy tradeoffs (SATs), diversity-enabled sweet spots (DeSS), bowtie/hourglass structures, and evolvability. His work pioneers System Level Synthesis (SLS) for control systems with sparse, local, saturating, delayed, noisy, quantized, and distributed (SLSDNQD) components, integrating control theory, computation, communication, and machine learning to address challenges from neural networks to infrastructure resilience. Key concepts include virtualization, horizontal transfer, and virality in multiscale systems. Analysis of his publication trends reveals consistent interdisciplinary impact across neuroscience (brain connectivity modeling), systems biology (metabolic oscillations), network science (internet topology), and physics (turbulence, earthquakes), with recurring themes of robust-efficiency limits and architectural principles governing complex networks. His work demonstrates exceptional translation from abstract theory to practical tools like the Matlab Robust Control Toolbox and Systems Biology Markup Language (SBML). His scientific recognition includes: 1990 IEEE Baker Prize (ranked among top 10 most important mathematics papers 1981-1993) Three IEEE Automatic Control Transactions Awards (1998, 1999, 2021) ACM Sigcomm Paper Prize (2004) and Test of Time Award (2016) IEEE Control Systems Field Award (2004) Multiple early-career honors including IEEE Centennial Outstanding Young Engineer (1984) Doyle has mentored generations of students whose contributions include foundational software tools adopted globally. His research has secured sustained funding from NSF, NIH, and other agencies supporting theoretical advances in control frameworks and their applications to biomedical systems, network infrastructure, and environmental modeling. The SBML initiative exemplifies his group's impact in standardizing computational biology research. He leads a highly collaborative research ecosystem at Caltech that integrates engineers, biologists, neuroscientists, and computer scientists to develop universal principles for complex networks. Current efforts focus on translating theoretical insights into health technologies, resilient infrastructure, and climate-responsive systems through the application of robust-efficiency frameworks to emerging challenges in cyber-physical and biological domains.
Pedram Mortazavi is an Assistant Professor at the University of Minnesota, based in the Civil Engineering Building, Minneapolis, with contact details including email pmortaza@umn.edu and office address 236 Civil Engineering Building, 500 Pillsbury Drive SE. His research centers on structural resilience through: Steel structures and large-scale experimental testing Cast steel energy dissipative systems and passive control devices (damping/isolation) Self-centering systems to mitigate residual deformations Advanced simulation methods including hybrid and multi-platform techniques Validation and codification of new structural systems for seismic applications Analysis of his 2023-2025 publications reveals concentrated work on eccentrically braced frames with replaceable cast steel links, hybrid simulation methodologies, and low-cost re-centering solutions. His research consistently targets enhanced seismic performance, ductility, and resilience via experimental validation and practical design innovations, with significant focus on friction mitigation in multi-axial testing and ultra-low cycle fatigue of structural components.
Noah A. Smith is an Adjunct Professor of Computer Science and Engineering at the University of Washington. His work focuses on computational linguistics, machine learning, and natural language processing. He holds a Ph.D. in Computer Science from Johns Hopkins University (2006). His research explores ethical AI applications, multimodal systems, and foundational aspects of language models. Key research areas include: Ethical considerations in NLP, such as detecting rights abuses through text analysis Efficient decoding and alignment strategies for large language models Large-scale evaluation frameworks for multitask and multimodal generation Understanding pretraining dynamics and data composition effects Recent work emphasizes transparency in language models (e.g., tracing outputs to training data) and improving alignment through human feedback. He has contributed to open-source projects like OLMo and Dolma, advancing reproducibility in NLP research. No awards explicitly listed in provided texts. No specific advising or grant details available, though extensive publication output indicates active research involvement.
Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.
Lili Zheng is an Assistant Professor in the Department of Statistics at the University of Illinois. Her research focuses on statistical methodology, machine learning, and high-dimensional data analysis with applications in neuroscience and network science. Key areas of expertise include graphical models, stochastic processes, and algorithmic optimization. She collaborates extensively on projects involving functional connectivity analysis, neuronal data imputation, and interpretable machine learning frameworks. Her work bridges statistical theory and computational practice, addressing challenges in model inference, feature importance assessment, and low-rank tensor completion. Notable contributions include techniques for distribution-free inference, spectral clustering in patchwork learning, and Gaussian process parameter estimation using mini-batch stochastic gradient descent. Dr. Zheng's research emphasizes interdisciplinary applications, particularly in neuroimaging (calcium imaging, functional connectivity) and multi-modal data integration. She actively explores statistical challenges in big data contexts, emphasizing robust methodologies for real-world datasets.
John S. McCartney is a Professor and Hal Sorenson Endowed Chair in the Department of Structural Engineering at the University of California San Diego (UCSD). He directs the Englekirk Structural Engineering Center and holds editorial roles at journals such as ASCE Journal of Geotechnical and Geoenvironmental Engineering and Computers and Geotechnics. His research focuses on unsaturated soil mechanics, energy geotechnics, and geosynthetics engineering, with applications in thermal energy systems, landfill covers, and seismic response analysis. Education: B.S. and M.S. in Civil Engineering from University of Colorado Boulder (2002), Ph.D. in Civil Engineering from University of Texas at Austin (2007). Research interests include thermo-hydro-mechanical behavior of soils, geothermal energy piles, tire-derived aggregates, and seismic performance of geotechnical systems. His work combines laboratory testing, centrifuge modeling, and numerical simulations to address challenges in sustainable infrastructure and energy systems. Key awards include the Walter L. Huber Research Prize (2016), NSF CAREER Award (2011), and multiple teaching and service recognitions. He actively contributes to ASTM standards and serves as President of the IGS-NA chapter. Lab facilities are located in the Structural and Materials Engineering Building (SME 409). Courses taught include advanced soil mechanics, energy geotechnics, and geotechnical earthquake engineering.
Parviz Moin holds the Franklin P. and Caroline M. Johnson Professorship in Stanford University's School of Engineering. As founding director of the Center for Turbulence Research (CTR)—a NASA-Stanford consortium established in 1987—he has pioneered computational methods for turbulence physics, including direct numerical simulation and Large Eddy Simulation (LES) techniques. CTR serves as an international hub for turbulence studies across engineering, mathematics, and physics disciplines. Moin's research encompasses computational physics of turbulent flows, with emphasis on boundary layer control, hypersonic aerodynamics, propulsion systems, and aircraft icing. His recent work advances high-fidelity simulations for aerospace applications, particularly developing wall models for LES that accurately capture separation phenomena under complex pressure gradients and Reynolds number effects. Recent publications demonstrate extensive applications of LES to aircraft design challenges, including transonic buffet prediction, high-lift configuration analysis, and icing aerodynamics. Investigations consistently address fundamental turbulence physics while developing practical computational tools for aerospace engineering, with particular focus on hypersonic boundary layers, flow separation mechanisms, and conjugate heat transfer in iced environments.
