Diego Donzis is a Professor in the Department of Aerospace Engineering at Texas A&M University, affiliated with the College of Engineering. He holds the Presidential Impact Fellow title. His work focuses on high-performance computing for fluid dynamics, particularly compressible turbulence, turbulent mixing, and shock-turbulence interactions. Donzis earned his Ph.D. and M.S. in Aerospace Engineering from the Georgia Institute of Technology. Research interests include large-scale simulations of turbulent flows, thermal boundary condition effects on turbulence, and the development of advanced numerical methods like Selected-Eddy Simulations (SES) for extreme-scale computing. His studies explore universality in turbulence scaling, energy spectra dynamics, and the interplay between compressibility and fluid mixing. Publications emphasize turbulence decay laws, shock-turbulence interactions, and the role of thermal non-equilibrium in turbulent flows. Notable contributions include advancing asynchronous algorithms for exascale CFD and analyzing density gradient statistics in compressible turbulence. Awards include the Presidential Impact Fellow distinction. Donzis collaborates on grants such as the Frontera Travel Grant for compressible turbulence research. His work bridges computational methods with fundamental fluid dynamics, addressing challenges in both numerical accuracy and physical modeling.
Jeyavijayan 'JV' Rajendran is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He is an ASCEND Fellow and leads the Secure and Trustworthy Hardware (SETH) Lab. His research focuses on hardware security, computer security, and novel applications of AI in secure hardware design. Education: PhD in Electrical Engineering (NYU 2015), MS in Computer Engineering (NYU Tandon 2010), BE in Electronics and Communication Engineering (Anna University 2008). Research Interests: Hardware Security, Computer Security, Logic Locking, Hardware IP Protection, and Reinforcement Learning for Security. He explores AI-driven approaches to detect vulnerabilities, protect intellectual property, and enhance secure hardware design through fuzzing, obfuscation, and formal verification. Notable Awards: 2022 Office of Naval Research Young Investigator Award, 2021 IEEE CEDA Ernest Kuh Early Career Award, 2017 NSF CAREER Award. Lab and Teams: The SETH Lab focuses on trustworthy hardware design, developing techniques to secure integrated circuits against reverse engineering and IP theft. Current projects include LLM-based hardware code generation, formal approaches for hardware fuzzing, and AI-driven vulnerability detection.
Diane Guignard is an Assistant Professor in the Department of Mathematics and Statistics at the University of Ottawa. Her research focuses on numerical analysis, partial differential equations, and computational methods with applications to mechanics and stochastic systems. She holds a position in a leading mathematics department and can be contacted at dguignar@uOttawa.ca . Her research interests include finite element methods, model reduction, uncertainty quantification, and optimal transport-based mesh adaptation. She explores nonlinear approximation theories for high-dimensional anisotropic functions and develops computational frameworks for thin structures and colloidal flow simulations. Her work bridges numerical analysis with practical engineering challenges, emphasizing adaptive algorithms and error estimation techniques. Her recent publications (2021-2024) highlight contributions to goal-oriented mesh adaptation, stochastic field approximations on surfaces, and large deformation analyses of prestrained plates. These studies emphasize interdisciplinary approaches combining mathematical rigor with computational innovation. Dr. Guignard has not been explicitly noted for awards in the provided materials. Her advising record is currently unspecified, though her research group likely engages in advanced numerical methods and computational mechanics projects.
Riyadh Baghdadi is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Tandon School of Engineering, NYU. He is also a Research Affiliate at MIT, where he previously completed a postdoctoral fellowship. His academic journey includes a PhD and Master’s from Sorbonne University (INRIA/UPMC) and an engineering degree from Ecole Supérieure d’Informatique in Algiers. Assistant Professor, NYU Abu Dhabi Global Network Assistant Professor, Tandon School of Engineering, NYU Research Affiliate, MIT His research lies at the intersection of compilers, programming languages, and applied machine learning, with a focus on developing advanced compiler techniques for deep learning, high-performance computing, and data-parallel algorithms. He is the lead developer of the Tiramisu compiler , a polyhedral compiler designed to optimize dense and sparse deep learning workloads across diverse architectures including CPUs, GPUs, and FPGAs. Riyadh’s recent publications demonstrate a strong trend toward integrating machine learning into compiler optimization—particularly in cost modeling, loop scheduling, and automatic code generation. His work addresses critical challenges in optimizing sparse neural networks and enabling efficient execution on resource-constrained platforms like smartphones and autonomous vehicles. Outstanding Paper Award, MLSys 2021 He has mentored 18 students and taught core courses such as Computer Systems Organization and Machine Learning at NYUAD. His service to the academic community includes program committee roles at MLSys, IPDPS, ECOOP, and PACT, as well as organizing workshops on polyhedral compilation and machine learning for hardware-software co-design. Riyadh actively contributes to open-source projects and collaborates with industry leaders including Google, Facebook, NVIDIA, and Intel. He leads the development of Tiramisu and collaborates on DSLs like GraphIt and Halide, focusing on performance portability and automation in compiler design.
Rastko Sknepnek is a Chair of Biological Physics at the University of Dundee , affiliated with both the School of Science and Engineering (Physics department) and the School of Life Sciences (Computational Biology). His research focuses on pattern formation in complex geometries, physics of biological/artificial membranes, active matter systems, and computational biophysics. PhD in Physics (2004, Missouri S&T) Postdoctoral training at McMaster, Iowa State/Ames Lab, Northwestern, and Syracuse University Joined University of Dundee in 2013 as Lecturer and Dundee Fellow Current work explores cellular homeostasis , actomyosin dynamics , and collective cell behavior , with recent publications analyzing epithelial monolayers, active matter models, and developmental mechanics. His 2025 projects include AI applications for drug resistance and cell shape quantification. Scientific Awards : Distinguished University Postdoctoral Fellowship (Syracuse, 2012) Dundee Fellow (2013) He leads the Computational Soft Condensed Matter and Biophysics Group , collaborating with institutions like University of Oxford, UCL, and University of Bristol. Grants include £2.1 million from UKRI for embryonic self-organization research and BBSRC funding for cell dynamics studies.
Peng Gao is a Professor in the Department of Geography and the Environment at Syracuse University, affiliated with the Maxwell School of Citizenship and Public Affairs. His work bridges river geomorphology and urban geospatial analysis, leveraging GIS, remote sensing, and UAV technologies to address environmental and social challenges. Education: Ph.D., Physical Geography, State University of New York at Buffalo (2003) M.S., Physical Geography, Lanzhou University (1993) B.S., Solid Mechanics, Lanzhou University (1990) Professor Gao specializes in river morphodynamics—particularly in the Qinghai-Tibet Plateau—and geospatial applications for urban planning. His research examines braided/meandering river systems, peatland hydrology, and how urban built environments influence social inequities and public health outcomes through spatial analysis. His 2020-2024 publications reveal a dual focus: (1) fluvial processes in high-altitude regions (e.g., neck cutoff dynamics, braided river discharge estimation using Landsat), and (2) urban applications (e.g., green building design, lead poisoning exposure mapping). This reflects a strategic integration of field geomorphology with computational geospatial modeling. Professor Gao actively mentors through SOURCE undergraduate research grants and PhD committees. Current funded projects include peatland mapping in the Andean Altiplano, I-81 Viaduct impact analysis in Syracuse, and studies on urban built environments affecting childhood lead poisoning. His work utilizes UAVs for BVLOS operations and collaborates with Syracuse CoE on urban environmental simulations, emphasizing technical innovation in geospatial data acquisition and analysis.
Dr. George C Tseng serves as Professor and Vice Chair for Research in the Department of Biostatistics at the University of Pittsburgh School of Public Health, with secondary appointments in Human Genetics and Computational and Systems Biology. His educational background includes a BS (1997) and MS (1999) in Mathematics from National Taiwan University and an ScD (2003) in Biostatistics from Harvard School of Public Health. Dr. Tseng's research focuses on developing statistical methodologies for genomic and bioinformatic applications to advance precision medicine. His work spans multiple high-impact areas including multi-omics data integration, machine learning for high-dimensional data, cluster analysis for disease subtyping, and statistical methods for experimental design in omics studies. His approach emphasizes close collaboration with biological and clinical researchers to ensure methodological relevance to real-world problems. His publication record demonstrates consistent contributions to top statistical and bioinformatics journals, with recent work focusing on congruence analysis between animal models and humans, outcome-guided clustering methods, and high-dimensional causal mediation analysis. Elected Fellow, American Statistical Association (2017) Statistician of the Year, ASA Pittsburgh Chapter (2017) Provost's Award for Excellence in PhD Mentoring, University of Pittsburgh (2019) Clinical Research Scholar (K12) Award, NIH (2007-2009) Elected Member, International Statistical Institute (2012) Dr. Tseng has successfully mentored over 25 PhD students who have secured positions in academia, industry, and government agencies. His laboratory has maintained continuous NIH funding as principal investigator since 2012, including current grants R01CA285337 (2025-2030) and R01LM014142 (2023-2026). The Tseng Lab operates as a collaborative research environment focused on translating statistical innovations into practical solutions for biological and medical challenges, with strong connections to multiple research centers and clinical departments at the University of Pittsburgh.
Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.
Sean Welleck is an Assistant Professor at Carnegie Mellon University's School of Computer Science, specifically within the Language Technologies Institute (LTI). He leads the L3 Lab and serves as an advisor for the AI for Math Fund. His academic journey includes a PhD from New York University under Kyunghyun Cho and postdoctoral positions at the Allen Institute for Artificial Intelligence and the University of Washington with Yejin Choi. Dr. Welleck's educational background shows a strong foundation in computer science. He earned his PhD in Computer Science from New York University, where he worked under the mentorship of Kyunghyun Cho and Zheng Zhang. Prior to this, he completed his MSE and BSE in Computer Science from the University of Pennsylvania, demonstrating a long-standing commitment to the field. Dr. Welleck's research focuses on bridging informal and formal reasoning with AI, with particular emphasis on developing learning, inference, and evaluation algorithms for large language models. His work spans multiple cutting-edge areas including mathematical reasoning , code generation , inference algorithms , and AI reasoning agents . A significant portion of his recent work involves combining AI with formal methods for mathematics, where he has developed frameworks like Llemma (an open-source language model for mathematical reasoning) and meta-generation (for inference-time algorithms). His research is characterized by a strong theoretical foundation coupled with practical applications that push the boundaries of what AI systems can achieve in formal reasoning domains. Analysis of Dr. Welleck's recent publications reveals a clear research trajectory focused on enhancing language models' capabilities in formal reasoning and mathematical problem-solving. His work demonstrates an evolution from foundational research in neural text generation to increasingly sophisticated approaches that integrate formal methods with deep learning. Key trends include the development of inference-time algorithms that improve model performance without additional training, frameworks for mathematical reasoning that connect informal and formal proofs, and novel evaluation methodologies for language models. His publications consistently appear in top-tier conferences including NeurIPS, ICLR, ICML, and ACL, reflecting the high impact of his contributions to the field. Dr. Welleck's scientific achievements have been recognized with several prestigious awards: NAACL 2025 Best Paper Award ICLR 2025 Oral Presentation (Top 2%) ICLR 2025 Spotlight Presentation (Top 5%) NeurIPS 2021 Outstanding Paper Award (Top 0.1%) for MAUVE NVIDIA AI Labs Pioneering Research Award (2017 and 2018) As an educator and mentor, Dr. Welleck actively guides the next generation of AI researchers. He currently advises multiple PhD students including Pranjal Aggarwal, Weihua Du, Andre He, and Seungone Kim (some co-advised with other faculty), along with MS students Riyaz Ahuja, Jiewen Hu, Qinyue Tan, and Thomas Zhu, and undergraduate Tate Rowney. At CMU, he teaches advanced courses such as Neural Code Generation and Advanced NLP, and has previously taught at New York University and the University of Washington. His commitment to education extends to creating resources like the Thesis Review Podcast and developing tutorials on neural theorem proving that have been presented at major conferences. Dr. Welleck leads the L3 Lab at CMU, which focuses on the intersection of language, learning, and logic. The lab brings together students and researchers to tackle challenging problems in AI reasoning, with particular emphasis on mathematical reasoning and code generation. Recent initiatives include the development of Llemma, an open-source language model specialized for mathematical reasoning, and work on inference-time algorithms that enable language models to improve their performance through additional computation during inference rather than through additional training.
Flavio Esposito is an Associate Professor in the Computer Science Department at Saint Louis University's School of Engineering. He also serves as a Research Institute Fellow and CS Graduate Coordinator. His office is located in ISE 234D at 3450 Lindell Blvd, St. Louis, MO. Dr. Esposito's research focuses on cyber-physical systems and networked systems, including network virtualization, network management, Software-Defined Networks (SDN), network architectures, and wireless networks. He has a strong interest in interdisciplinary applications of these technologies to medicine and agriculture. His work bridges theoretical networking concepts with practical implementations. His publications span key areas in networking research, with recent work focusing on congestion control algorithms, virtual network embedding, recursive network architectures, and edge computing applications. The research trends show a progression from foundational networking protocols toward more sophisticated applications integrating machine learning, edge computing, and cyber-physical systems, with increasing emphasis on real-world applications in diverse domains. Outstanding Graduate Mentoring Faculty Award from the School of Engineering (2021) Finalist for the Undergraduate Mentoring Award in the College of Arts and Sciences Multiple NSF research awards including US Ignite, ICE-T, CNS Core, CC* Integration, CPS:TTP, and ModernCARE projects COMCAST Innovation Fund Award (January 2020) International Center for Responsible Gaming (ICRG) Award ($150K) Dr. Esposito actively mentors PhD and MS students, with numerous current and past students who have gone on to positions at major tech companies, universities, and research institutions. He has been a Principal Investigator on multiple significant research grants totaling millions of dollars. He co-founded Spaghetti Code Labs with former PhD student Alessandro Sangiorgi, whose cybersecurity educational app WeeNet has achieved 5.7M+ downloads. He leads several research labs and teams focused on cyber-physical systems, with current openings for PhD students, visiting researchers, and postdocs working on networks, learning, edge computing, and applications to medicine and agriculture. His teams have developed numerous software systems including Software Mutant, Neighborhood Method Prototype, VINEA, ProtoRINA, and BUtorrent.
Koroush Shirvan is the Atlantic Richfield Career Development Professor in Energy Studies and a tenured faculty member in MIT's Department of Nuclear Science and Engineering within the School of Engineering. Joined in July 2017, he directs the Reactor Technology Course for Utility Executives and leads the Fission Materials in Extreme Environments Lab. His work bridges nuclear engineering with practical industrial applications for decarbonization. His research focuses on reactor design economics, materials testing under irradiation, nuclear safety, and boiling heat transfer. He accelerates innovations in nuclear fuels, small modular reactors, and space propulsion through multi-scale physics integration. Current projects include accident-tolerant fuels, high-temperature materials for microreactors, and AI-driven optimization of reactor systems. His approach combines experimental irradiation testing at MITR with advanced computational modeling. Recent publications reveal strong trends toward economic nuclear deployment via advanced fuel technologies and small modular reactors. AI/ML applications dominate optimization research, particularly for core reload and uncertainty quantification. Materials science under extreme conditions remains central, with growing emphasis on space nuclear applications and horizontal reactor configurations for cost reduction. His scientific recognition includes: Nuclear News 40 under 40 (2024) American Nuclear Society Landis Young Member Engineering Achievement Award (2023) American Nuclear Society Reactor Technology Award (2022) Teaching responsibilities span Sustainable Energy (22.811/081), Graduate Reactor Physics, and Nuclear Design courses. Research grants support experimental programs at MIT Reactor Lab and computational frameworks for reactor-to-repository analysis. He mentors students through senior design projects and graduate research in nuclear fuel cycles. He directs the Fission Materials in Extreme Environments Lab and co-leads MIT's Space Nuclear initiative with AeroAstro. The team conducts irradiation experiments using MITR's high-temperature hydrogen flow capabilities and advanced diagnostics for post-irradiation examination. Current thrusts include nuclear thermal rocket materials testing and fission surface power development for lunar/Mars missions.
Marat I. Latypov serves as Assistant Professor in the Department of Materials Science and Engineering at the University of Arizona's College of Engineering. He is also a member of the Applied Mathematics Graduate Interdisciplinary Program and leads the Materials Informatics Lab. His research spans computational materials science, sustainable alloy design, and machine learning applications for materials development. Dr. Latypov holds a PhD in Materials Science and Engineering from Pohang University of Science and Technology (POSTECH, South Korea, 2014) and a Dipl.-Ing. in Engineering Physics from Ufa State Aviation Technical University (Russia, 2011). His postdoctoral training included appointments at Georgia Tech/CNRS in France and the University of California, Santa Barbara. His research focuses on materials informatics , physics-informed machine learning , and sustainable structural alloys . Key methodologies include graph neural networks for polycrystal mechanics, vision transformers for microstructure representation, and adaptive experimental design for materials optimization. Recent work emphasizes circular economy applications through construction waste recycling and copper mine tailings valorization. Analysis of his publication record reveals strong emphasis on computational microstructure-property linkages (35% of recent work), machine learning for materials design (30%), and sustainable materials processing (25%), with growing integration of large language models for materials knowledge extraction. NSF CAREER Award (2025) : For damage control in recycled aluminum alloys ISTI Distinguished Faculty Scholar (2024) : At Los Alamos National Laboratory Novelis Hackathon First Prize (2021) : Computer vision application Acta Materialia Outstanding Reviewer (2018) Young Researcher Award (2017) : NanoSPD7 Conference Dr. Latypov advises PhD students including Herbold Fellow Zhuocheng Huang and leads projects funded by NSF and the Grantham Foundation. Current initiatives include chalcopyrite leaching optimization for copper mining and graph neural network development for fatigue prediction. His Materials Informatics Lab maintains collaborations with Los Alamos National Laboratory, MIT, and industry partners including Novelis. The lab operates at the intersection of metallurgy , machine learning , and high-performance computing , with capabilities spanning deep learning, Bayesian inference, and cloud-based computational infrastructure. Recent news highlights participation in CODAS-HEP summer school and publication of vision transformer work in Acta Materialia.
Irena Koprinska is a prominent researcher at the University of Sydney with over 150 publications from 1996 to 2025. Her work spans multiple interdisciplinary domains with significant contributions to machine learning applications in educational technology, time series forecasting, and health informatics. She maintains strong research collaborations, particularly with Kalina Yacef (38 joint publications), Mashud Rana (26 papers), and Bryn Jeffries (22 papers), indicating leadership in her research group. Her research interests focus on practical applications of machine learning across diverse domains. In educational data mining, she has pioneered methods for predicting student performance in programming courses, analyzing syntax errors, and developing automated hint generation systems. Her work in time series forecasting has made significant contributions to solar power prediction using advanced neural network architectures. Additionally, she has applied machine learning techniques to medical domains, particularly in sleep disorder detection and analysis. The analysis of her 15 most recent publications (2022-2025) reveals a continued focus on educational technology and time series analysis, with increasing attention to interpretable methods and health applications. Her work demonstrates a consistent trajectory of applying sophisticated machine learning techniques to solve real-world problems across multiple domains, with particular emphasis on creating practical tools for education and renewable energy management. Notable Research Contributions: Development of the HINTS framework for automated programming hint generation Innovative approaches to multistep-ahead time series forecasting Applications of deep learning to sleep disorder detection Methods for predicting student performance in programming education Her publication record in top venues including Machine Learning journal, AIED, EDM, and IJCNN demonstrates significant impact in both machine learning and educational technology communities. The consistent output of high-quality research over nearly three decades indicates sustained scholarly productivity and leadership in her fields of expertise.
Prof. Dr. Jürgen Biela serves as Full Professor at ETH Zurich within the Department of Information Technology and Electrical Engineering, where he leads the Laboratory for High Power Electronic Systems. His academic career at ETH Zurich has progressed from doctoral studies to his current position as head of his research laboratory, with significant contributions to power electronics research and education. Biela earned his diploma with honors from Friedrich-Alexander University in Erlangen, Germany in 2000 and completed his Ph.D. at ETH Zurich in 2005, both in electrical engineering. His educational background includes specialized work on resonant DC-link inverters at Strathclyde University and active control of series connected IGCTs at the Technical University of Munich. His research program focuses on multi-physics modeling, design and optimization of power electronic systems , with particular emphasis on applications for future energy distribution and transmission, pulsed power systems, and advanced medium voltage power electronics based on novel semiconductor technologies like silicon carbide (SiC). He also investigates integrated passive components for ultra-compact and ultra-efficient high-power converter systems, pushing the boundaries of power density and efficiency in electronic power conversion. Analysis of his recent publications reveals strong trends in high-frequency power conversion , with significant work on transformer and inductor design, insulation systems for medium-frequency applications, thermal management of power components, and advanced modeling techniques for electromagnetic phenomena. His research bridges fundamental electromagnetic theory with practical engineering applications, particularly in high-voltage and high-power scenarios where traditional approaches face limitations. As a prolific researcher, Biela has published over 85 journal papers and 210 conference papers while holding more than 35 patents. He serves as an Associate Editor for the IEEE Transactions on Power Electronics and regularly reviews for leading journals and conferences in the field. His work demonstrates consistent contributions to advancing power electronic systems through rigorous theoretical analysis combined with practical implementation. Biela has supervised numerous doctoral and master's students, with recent publications indicating active mentorship of researchers working on advanced power electronic components and systems. His laboratory at ETH Zurich serves as a hub for innovation in high-power electronics, with connections to industry research projects that translate theoretical advances into practical applications. Current research directions include developing cost-effective alternatives to traditional components like Litz wire, improving insulation systems for high-voltage applications, and creating more accurate models for predicting thermal and electromagnetic behavior in power electronic systems.
Zhidan Zheng is a researcher at the Technical University of Munich (TUM), working within the Chair of Electronic Design Automation led by Prof. Ulf Schlichtmann. His office is located in room 0509.05.911 at Arcisstr. 21, 80333 Munich, with direct contact available via email zhidan.zheng@tum.de and phone +49 (89) 289 - 23692. Zheng holds a Master of Science degree as indicated by his academic title M.Sc. and has been actively contributing to the field of optical interconnects and network-on-chip design. Zheng's research focuses on wavelength-routed optical networks-on-chip, with particular expertise in network topology optimization, fault tolerance mechanisms, waveguide routing algorithms, and bandwidth allocation strategies. His work addresses critical challenges in photonic integrated circuit design, including thermal variation effects, crosstalk mitigation, and lifetime extension for communication-intensive systems. Zheng has developed several innovative methodologies including ToPro+ for topology projection, LightR for fault-tolerant architectures, and WROXIM for network-level simulation. Analysis of Zheng's publication trends from 2021-2025 reveals a consistent focus on practical implementation challenges of optical networks-on-chip. His research has evolved from foundational topology design (Light, 2021) to increasingly sophisticated solutions addressing reliability (LightR, 2023) and comprehensive system integration (ToPro+, 2025). The work demonstrates strong collaboration with researchers including Mengchu Li, Tsun-Ming Tseng, and Ulf Schlichtmann across multiple high-impact venues including DAC, DATE, ICCAD, and ASP-DAC. Zheng actively contributes to the Electronic Design Automation research group at TUM, participating in projects related to analog EDA, emerging technologies, and optical networks. His research is situated within TUM's broader initiatives in photonic integration and high-performance computing architectures, working closely with Prof. Schlichtmann's team on funded projects in the optical NoC domain.
