Associate Professor Joshua San Miguel leads research in computer architecture and systems at the University of Wisconsin-Madison, with an affiliate role in Computer Sciences. His work focuses on energy-efficient computing for IoT devices, microarchitecture innovations, and networks-on-chip. He holds a PhD (2017) and BASc (2012) from the University of Toronto. Education: PhD in Electrical & Computer Engineering, University of Toronto (2017) BASc in Engineering Science (ECE), University of Toronto (2012) Research Interests: Approximate computing for energy harvesting systems Branch prediction and value prediction in processors Cache architectures and networks-on-chip for many-core processors Intermittent computing resilience His recent work emphasizes value-level parallelism (Carat/uSystolic), RTL simulation acceleration (TaroRTL), and personalized neural network inference (CAP’NN). His research has been recognized with the NSF CAREER Award (2021) and multiple IEEE Micro Top Picks. Grants & Advising: Active in supervising advanced independent studies and master’s/dissertation research. Extensive grant funding includes the NSF CAREER Award and the Grainger Faculty Scholarship. Labs & Teams: Leads research groups focused on approximate computing and energy-efficient architectures within the Electrical & Computer Engineering department.
Monika Neda is a Professor in the Department of Mathematical Sciences and Associate Dean for Research at the University of Nevada, Las Vegas (UNLV). She holds a Ph.D. in Mathematics from the University of Pittsburgh (2007) and a B.S. in Mechanical Engineering from the Technical Faculty Mihajlo Pupin in Serbia (2001). Her research focuses on theoretical and computational fluid dynamics, turbulence modeling, and numerical methods for partial differential equations, with particular emphasis on Navier-Stokes equations and sensitivity analysis. Her work explores model derivation, numerical stability, finite element error estimates, and computational simulations of fluid flow problems. She also investigates educational strategies to improve STEM students' foundational math skills through game-based and activity-based instruction. Neda is affiliated with UNLV's Center for Applied Math & Statistics and has contributed to interdisciplinary projects involving engineering education and photovoltaic panel efficiency. Recent research trends highlight advancements in stabilization techniques for fluid models (e.g., time relaxation, deconvolution operators) and innovative pedagogical approaches leveraging technology platforms like Canvas. Her publications span computational mathematics, turbulence closure models, and education-focused interventions. Neda's academic leadership includes roles in curriculum development and institutional research, reflecting her dual commitment to scholarship and academic administration.
Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.
Sandhya Dwarkadas is the Walter N. Munster Professor and Chair of the Department of Computer Science at the University of Virginia. Her research focuses on the intersection of computer hardware and software, particularly in parallel computing, computer architecture, and compiler/runtime-architecture interaction. She holds dual roles as department chair and active researcher, balancing leadership with contributions to energy-efficient and reconfigurable computing systems. Education: B.Tech. in Electrical Engineering, Indian Institute of Technology (1986) M.S. and Ph.D. in Electrical and Computer Engineering, Rice University (1989, 1993) Research interests emphasize parallel and distributed computing architectures, with a focus on energy efficiency, cache coherence, and security in multicore systems. Recent work explores mitigating side-channel attacks via innovations like TimeCache and RollingCache. Her publications span 30+ years, addressing both foundational and applied challenges in computer systems. Awards highlight her impact: ACM and IEEE Fellowships (2018/2017), University of Rochester’s Hajim Award (2020), and AAAS Fellowship (2024). She actively mentors students through courses like CS 6190 and leads interdisciplinary projects like TriForce. Labs/Teams: Her work is anchored in the University of Virginia’s Computer Science Department, collaborating across academia and industry to advance next-generation computing systems.
M. Hadi Amini is an Assistant Professor at Florida International University's Knight Foundation School of Computing and Information Sciences. He founded and directs the Sustainability, Optimization, and Learning for InterDependent networks (SOLID) laboratory, focusing on cyber-physical-social systems and distributed AI applications. Ph.D., Electrical and Computer Engineering (2019), Carnegie Mellon University M.Sc., Electrical and Computer Engineering (2015), Carnegie Mellon University M.Sc. (2013), Tarbiat Modares University B.Sc. (2011), Sharif University of Technology His research spans federated learning, interdependent network optimization, and AI applications in smart cities , energy systems , and healthcare . Recent work emphasizes privacy-preserving techniques, quantum encryption, and blockchain integration for secure distributed learning. The 15 most recent publications highlight trends in large language models , edge computing , medical imaging security , and infrastructure resilience , with interdisciplinary emphasis across computer science, systems engineering, and urban planning. Best Paper Award, IEEE Conference on Computational Science & Computational Intelligence (2019) Best Journal Paper Award, Springer Nature Operations Research Forum (2021) Excellence in Teaching Award, FIU (2020) Multiple Best Reviewer Awards, IEEE Transactions NSF Travel Awards (2019) As Associate Editor for Frontiers in Communications and Networks and book series editor for Sustainable Interdependent Networks , he actively shapes research discourse. His lab has secured $3.6M in federal/state funding for AI-driven infrastructure projects.
Ian Sellers is Professor in Electrical Engineering at University at Buffalo's School of Engineering and Applied Sciences. He specializes in next-generation solar cells and materials for space photovoltaics, with appointments including Marie Curie Fellow (2004-2006) and Visiting Academic Fellow at Oxford (2009-2012). His research examines: Novel photovoltaic materials and architectures Ultra-low power electronic systems MEMS-based sensors and energy harvesters Beyond-CMOS computing technologies Recent publications demonstrate advances in ultra-low power sensor design (MEMS accelerometers), energy-efficient computing (MESO technology), and miniaturized imaging systems. The work shows consistent focus on optimizing power efficiency through innovations in circuit design, materials integration, and system architecture. Before joining UB, Sellers held positions as Presidential Professor at University of Oklahoma and Senior Research Scientist at Sharp Labs of Europe. His international collaborations include extended research stays in France and the UK.
Guofu Niu is a Professor in the Department of Electrical and Computer Engineering at Auburn University, holding the Ed and Peggy Reynolds Family endowed chair. His research specializes in semiconductor device physics, compact modeling of SiGe heterojunction bipolar transistors (HBTs), FinFETs, and cryogenic electronics for RF and power applications. Key research areas include RF linearity characterization, avalanche effects, thermal noise, and low-temperature device performance. He has developed advanced models (e.g., Mextram) for circuit simulation tools, enhancing the accuracy of semiconductor design workflows. Publications focus on nano-scale device reliability, tunneling currents, and optimization of RF amplifiers, with applications in 5G technology and extreme-environment electronics. Collaborative projects span industry and academia to advance semiconductor modeling frameworks.
Pedro Fonseca is an Assistant Professor at the Department of Computer Science, Purdue University. He leads the Reliable and Secure Systems Lab, focusing on building reliable and secure core software systems such as operating systems, hypervisors, and distributed systems. His research has been recognized with awards including the NSF CAREER Award and Google Faculty Research Awards. Before Purdue, he completed a postdoc at the University of Washington, working with Arvind Krishnamurthy, Hank Levy, and Xi Wang. He earned his PhD from MPI-SWS and the University of Saarland under Rodrigo Rodrigues. His academic contributions span over 30 peer-reviewed publications in top-tier conferences like SOSP, OSDI, EuroSys, and ASPLOS. He teaches courses including CS503 (Operating Systems), CS592 (Reliable and Secure Systems), and CS408 (Software Testing). He actively serves on program committees for major systems conferences including SOSP, OSDI, EuroSys, and ASPLOS.
Dr. Stephen V. Chenoweth is an Associate Professor of Computer Science and Software Engineering at Rose-Hulman Institute of Technology. He holds a PhD in Computer Science and Engineering from Wright State University and has nearly 30 years of industry experience, including roles at NCR Corporation and Bell Laboratories. At Rose-Hulman, he has spearheaded initiatives in software engineering and robotics, including developing online program components and coordinating the senior capstone design course. He is a faculty member of the Home for Environmentally Responsible Engineers, fostering sustainability-focused STEM professionals. His teaching interests span systems architecture, software project management, and interdisciplinary teamwork. Dr. Chenoweth also mentors high school students through Rose-Hulman’s Operation Catapult program. Education: PhD, Wright State University, Computer Science and Engineering (AI Search), 1990 MS, Wright State University, Computer Science (Mathematical Foundations), 1986 MBA, Wright State University, Management, 1980 MS Ed, Butler University, Mathematics, 1970 BS, Butler University, Mathematics, 1966 Professional Contributions: Member of IT Advisory Board, City of Terre Haute Academic administrator for Purdue University’s EPICS program Consultant for Rose-Hulman Ventures and Millennium Services Research & Teaching Focus: Dr. Chenoweth emphasizes social skills in engineering education, advocating for holistic student development and alignment with industry needs. His work includes advancing ethical practices, team dynamics, and innovative pedagogical methods like service learning and flipped classrooms. He is also involved in curriculum design, including the Rose-Hulman MSSE program and software architecture courses.
Alva L. Couch is an Associate Professor at Tufts University's School of Engineering, Department of Computer Science, with a career spanning over 30 years. His work bridges network/system administration, autonomic computing, and hydrologic data science, focusing on scalable solutions for data management and automated system administration. Education: Ph.D. in Mathematics (1988), B.S. in Architecture (1978), and B.A. in Bassoon/Contrabassoon Performance (1978). Research Interests His research centers on: Network and System Administration: Tools like SLINK, Maelstrom, and Babble for dependency analysis, cloud migration, and policy enforcement. Geo-informatics: MEDFORD metadata language and HydroShare platform for hydrologic data curation and discovery. Autonomic Computing: Promise theory, convergent operators, and closure models for self-managing systems. Recent Work Trends His 2024-2018 publications emphasize: Cloud-based hydrologic data management (AnVILMEDFORD, HydroShare) Metadata standards for interdisciplinary research Machine learning for system administration Agent-based resource sharing models Scientific Awards Liebner Teaching Award (1996) Seymour Simches Advising Award (2017) Best Paper Awards: LISA 1996, AIMS 2008, LISA 2001 LISA 2000 Best Student Paper (with Michael Gilfix) Contributions He developed key software like Peep (network auralization) and Slink (configuration management), supported by NSF grants and industry partnerships. His work with CUAHSI's Water Data Center shapes national hydrologic data infrastructure. He also advocates for science education and privacy in computing.
Salmaan A. Keshavjee, MD, PhD, ScM, serves as Professor of Global Health and Social Medicine at Harvard Medical School and Director of the Center for Global Health Delivery. He concurrently holds appointments as Associate Professor of Medicine at Brigham and Women's Hospital and Faculty Dean of Adams House at Harvard University. His leadership targets critical global health delivery challenges in regions including the Middle East, North Africa, and sub-Saharan Africa through research, training, and policy engagement. Dr. Keshavjee's interdisciplinary foundation combines medical (MD), anthropological (PhD), and public health (ScM) training. This unique background informs his approach to health systems analysis and policy implementation across diverse cultural contexts. As a leading expert in drug-resistant tuberculosis treatment and the anthropology of health policy, his research examines the intersection of infectious disease control, social determinants of health, and structural barriers to care. Additional interests include mental health integration in TB programs, health economics, and digital health applications for disease screening. His fieldwork spans 16 years in Russia, Lesotho, and Pakistan, with extensive collaboration through Partners In Health. Analysis of his recent publications reveals consistent focus on tuberculosis prevention and treatment optimization, with growing attention to mental health comorbidities and artificial intelligence applications. His work addresses diverse populations including prisoners and household contacts across multiple continents, emphasizing regimen feasibility, health system barriers, and economic consequences of inadequate TB control. Key initiatives include the Zero TB Cities project aiming for tuberculosis elimination. Dr. Keshavjee has held influential policy roles including chair of the WHO/Stop TB Partnership’s Green Light Committee for MDR-TB Treatment (2007-2010) and co-authorship of U.S. Institute of Medicine policy papers, though no formal scientific awards are specified in source materials. Through the Center for Global Health Delivery, he mentors fellows in programs such as the Paul Farmer Global Surgery Research Fellowship and Global Mental Health Delivery Fellowship. These initiatives support implementation research in resource-limited settings through structured training and field placements. The Center operates six core programs—Infectious Disease, Mental Health, Noncommunicable Disease, Primary Care, Public Policy, and Surgery—developing evidence-based interventions through partnerships with institutions worldwide. Current priorities include scaling depression screening in TB care, optimizing preventive therapy regimens, and strengthening health systems in the Middle East and North Africa.
Lillian Ratliff is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Washington, holding additional Adjunct Associate Professor positions in the Allen School of Computer Science and Engineering and the Department of Aeronautics and Astronautics. She specializes in research at the intersection of game theory, optimization, machine learning, and control theory, with a focus on decision-making in intelligent systems with learning-enabled components and strategic agents. Her research interests span game theory and economics, optimization, machine learning, and control theory. She develops theoretical frameworks for understanding how intelligent systems make decisions when interacting with strategic agents. Her work bridges theoretical foundations with practical applications in multi-agent learning systems, where she examines how learning algorithms converge to equilibria in strategic environments. She has made significant contributions to understanding the dynamics of gradient-based learning in games, Stackelberg games, and decision-dependent distributions. Her recent publications demonstrate a strong focus on theoretical foundations of game-theoretic learning, with particular attention to convergence properties, equilibrium analysis, and strategic behavior in multi-agent systems. Her work spans zero-sum games, matrix games, Stackelberg games, and decision-dependent learning scenarios, with applications across multiple domains including human-machine interaction and networked systems. NSF Graduate Research Fellowship (2009) NSF CISE Research Initiation Initiative award (2017) NSF CAREER award (2019) ONR Young Investigator award (2020) UW CoE Junior Faculty Award (2021) Invited speaker at NAE China-America Frontiers of Engineering Symposium (2019) Dhanani Endowed Faculty Fellowship (2020) Professor Ratliff's research is supported by multiple NSF grants (current: CNS-1736582, CNS-1836819, CNS-1931718, CNS-1907907, CNS-1844729, CNS-1952011; previous: CNS-1634136, CNS-1646912, CNS-1656873) and an Office of Naval Research Young Investigator award. She has mentored numerous students and collaborators across her extensive publication record, with work appearing in top venues including NeurIPS, ICML, AISTATS, and IEEE conferences.
Joe Geunes is a Professor and Associate Department Head for Graduate Affairs in the Department of Industrial & Systems Engineering at Texas A&M University, holding the Mike and Sugar Barnes Professorship. His research focuses on production planning, supply chain management, logistics, and operations optimization. He earned his Ph.D. in Business Administration (Management Science & Operations Research) and M.B.A. from The Pennsylvania State University in 1999 and 1993, respectively. Dr. Geunes has received notable accolades including Fellow of the Institute of Industrial Engineers (2015), Marilyn and L. David Black Faculty Fellow (2022), and Best Reviewer Award from Omega (2022). His work spans infrastructure network restoration, supply chain resilience, and optimization algorithms for logistics systems. Recent projects address railcar operations, distribution network fortification, and disaster response strategies. Education: Ph.D., Business Administration (Management Science & Operations Research), The Pennsylvania State University – 1999 M.B.A., The Pennsylvania State University – 1993 Awards: Fellow, Institute of Industrial Engineers – 2015 Marilyn and L. David Black Faculty Fellow – 2022 Best Reviewer Award, Omega – 2022 Best Application Paper, IISE – 2018 His research integrates mathematical modeling and computational methods to address real-world challenges in supply chain design, inventory management, and infrastructure resilience. Recent publications emphasize multi-modal logistics, robust optimization under uncertainty, and post-disaster network recovery strategies.
Di Zhou is a Researcher specializing in aerospace engineering and fluid dynamics, focusing on turbulence modeling, aeroacoustics, and computational fluid dynamics (CFD). His work integrates advanced numerical methods such as large-eddy simulation (LES) with machine learning techniques like reinforcement learning to address challenges in wall modeling and flow prediction. Current affiliations are not explicitly stated, but his research involves collaborations in turbulence, rotor noise, and high-Reynolds-number flows. Research interests span turbulent boundary layers, adverse pressure gradients, and rotor aeroacoustic response. He has pioneered the application of multi-agent reinforcement learning for wall modeling in LES, advancing accuracy in simulating complex flows over periodic hills and Gaussian bumps. His studies also explore optimal sensor placement for lift prediction under gust loads and noise generation mechanisms in rotor systems. Recent trends in his publications highlight machine learning-driven turbulence modeling, sensitivity analysis of LES closure models, and computational analysis of rotor ingestion noise. Despite no listed awards, his work contributes significantly to both fundamental fluid dynamics and applied aerospace problems. No specific advising roles or labs are mentioned, but his research likely involves collaboration with experimental and numerical groups in aerodynamics and acoustics.
P. (Saday) Sadayappan is a Professor in the School of Computing at the University of Utah. He serves as a lead researcher in high-performance computing, with a focus on compiler optimization and algorithm-architecture co-design. His current projects include NIH SBIR Phase 2 funding for large-scale image analysis and NSF grants for tensor applications and cyber-infrastructure for AI. Research Interests : Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Research Trends in Publications : His work emphasizes optimizing computational workflows for emerging hardware architectures, with a focus on accelerating machine learning and scientific computing through compiler-level innovations. Recent trends include co-design for CNNs, sparse matrix optimizations, and distributed algorithms. Scientific Awards : ACM SIGPLAN Most Influential PLDI Paper Award (2018) Grants & Projects : NSF (2022–2027): Comprehensive Framework for Tensor Applications NSF AI Institute ICICLE (2021–2026): Cyber-infrastructure for environmental AI NIH SBIR (2023–2025): Next-gen machine learning for image analysis Labs & Teams : Collaborates with institutions like Ohio State University and RNET Technologies on projects involving parallel computing, sparse algorithms, and compiler design.
