جست‌وجوی استاد و پژوهشگر

T. S. Eugene Ngمشاهده پروفایل

استاد

• Computer Networks
• Networking Architecture
• Optical Networking
• +۵ مورد دیگر

T. S. Eugene Ng is a Professor of Computer Science and Electrical & Computer Engineering at Rice University. He holds appointments in both departments and chairs the CS Grad Committee. His research focuses on network architectures, optical networking, and machine learning applications in distributed systems. Education: B.S. in Computer Engineering (with distinction and magna cum laude), University of Washington M.S. and Ph.D. in Computer Science, Carnegie Mellon University Research Interests: Developing robust network infrastructure, optical circuit-switched systems, congestion control, and efficient machine learning frameworks. Current projects include BOLD (Big data and Optical Lightpaths Driven) networking, telemetry systems like Söze, and gradient compression techniques for distributed training. Awards: IEEE Fellow (2023) Alfred P. Sloan Research Fellow (2009) National Science Foundation CAREER Award (2005) IBM Faculty Award (2009) Kavli Fellow Professional Activities: Chair of the 2018 ACM SIGCOMM Distinguished Dissertation Award Committee, Associate Editor for IEEE Transactions on Big Data, and organizer of multiple networking conferences/workshops. Active in program committees for SIGCOMM, NSDI, and CoNEXT. Teaching: Courses include Introduction to Computer Networks, Advanced Computer Networks, and seminars in distributed computing and network systems.

Kyle Jamiesonمشاهده پروفایل

استاد

• Wireless Networks
• 5G Technology
• Quantum Computing in Communications
• +۳ مورد دیگر

Dr. Kyle Jamieson is a Professor of Computer Science at Princeton University, leading the Princeton Advanced Wireless Systems (PAWS) lab within the Department of Computer Science. He is also Affiliated Faculty in the Department of Electrical and Computer Engineering. His research focuses on wireless networking systems, 5G architecture, IoT networks, and quantum computing applications in wireless communication. He has pioneered work in reconfigurable intelligent surfaces, MIMO detection algorithms, and metamaterials for millimeter-wave networks. Dr. Jamieson has developed courses such as COS 597S: Recent Advances in Wireless Networks (graduate seminar), COS 463: Wireless Networks , and COS 418: Distributed Systems . His teaching emphasizes interdisciplinary approaches to networking challenges, including physical-layer design, computational structures for wireless processing, and cross-layer optimization. His lab’s research spans smart surfaces for 5G networks, quantum annealing for MIMO processing, and edge computing for live video analytics. Recent work includes deploying reconfigurable metamaterials for enhanced mmWave networks and developing tools like NR-Scope for 5G telemetry. While no awards are listed in the provided text, his contributions to wireless systems have advanced both academic and industrial applications in areas such as network resilience, IoT scalability, and quantum-enabled wireless processing. Dr. Jamieson’s advising focuses on graduate and undergraduate students working in wireless systems, though specific advisee names are not provided. His lab collaborates on projects like Wall-Street for roadside networking and Spider for multi-hop mmWave video analytics. External collaborations include work with Microsoft Research and guest lecturing roles at Berkeley. His research bridges theoretical foundations with practical implementations, often addressing real-world challenges in wireless infrastructure and next-generation communication systems.

Lingpeng Kongمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Dr. Lingpeng Kong is an Assistant Professor in the Department of Computer Science at the University of Hong Kong (HKU), affiliated with the School of Computing and Data Science. He obtained his PhD from Carnegie Mellon University in 2017, co-advised by Noah Smith and Chris Dyer. Previously, he was a research scientist at Google DeepMind (2017–2020). His research focuses on natural language processing (NLP), machine learning, and deep learning, particularly in structured prediction and representation learning. He co-directs the HKU NLP Lab. Notable contributions include work on syntactic parsing, neural architecture design, and lifelong learning. He has received an Outstanding Paper Award at EACL 2017. Dr. Kong teaches courses such as Natural Language Processing (COMP3361/COMP7607) and Machine Learning (COMP3314), and has advised multiple research projects at HKU.

Tiancheng Zhaoمشاهده پروفایل

پژوهشگر

• Multimodal Foundation Models
• AI Agents
• Vision-Language Models
• +۳ مورد دیگر

Tiancheng Zhao is a principal researcher at the Binjiang Institute of Zhejiang University and founder of the Om Artificial Intelligence Laboratory (Om AI Lab), dedicated to frontier open multimodal AGI research for building next-generation agents that transform work and life through advanced human-machine interaction. His academic credentials include: Ph.D. in Computer Science from Carnegie Mellon University (2016-2019) under Prof. Maxine Eskenazi, Prof. Louis-Philippe Morency, Prof. William W. Cohen, and Dr. Dilek Hakkani-Tur, with pioneering dissertation “Learning to Converse With Latent Actions” in end-to-end generative conversational models M.S. in Computer Science from Carnegie Mellon University (2014-2016) B.S. in Electrical Engineering from UCLA (2010-2014) with Summa Cum Laude, focusing on speech signal processing under Prof. Abeer Alwan Dr. Zhao’s research centers on multimodal foundation models and agents, tackling three core challenges: Multimodal Models for cross-modal representation learning in high-dimensional data, Learning to Learn for effective skill acquisition from diverse signals (supervised labels, rewards, meta-learning), and AI Agents for open-world understanding and complex decision-making. His work bridges computer vision, natural language processing, and real-world applications including healthcare analytics and remote sensing. Analysis of his 50+ publications reveals accelerating innovation in multimodal large language models (2024-2025), with emphasis on stable vision-language architectures (VLM-R1), agent orchestration frameworks, and domain-specific applications in geospatial analysis and healthcare. Key trends include solving long-tail distribution challenges in satellite imagery, developing human-like zooming capabilities for multimodal LLMs, and creating unified benchmarks for autonomous GUI testing. His scientific recognition includes: National Breakthrough Technology Award by Ministry of Science and Technology (2021) Microsoft Research Best & Brightest PhD (2018) BEST PAPER AWARD at SIGDIAL 2018 Best Paper Nomination at SIGDIAL 2016 Top 1 Outstanding Bachelor of Science Award at UCLA (2014) As Om AI Lab founder, Dr. Zhao leads research teams developing computational building blocks for human-AI collaboration. While specific student mentorship details aren’t public, his extensive publication record with junior co-authors indicates active research supervision. Current projects focus on practical system implementations for real-world multimodal agent deployment across diverse domains.

Di Shiمشاهده پروفایل

دانشیار

• Power System Data Analytics
• Energy Storage Systems
• Artificial Intelligence
• +۳ مورد دیگر

Di Shi is an Associate Professor at the Klipsch School of Electrical and Computer Engineering , New Mexico State University (NMSU), holding the Paul W. and Valerie Klipsch Distinguished Professorship. He previously founded the AI energy startup AInergy, LLC and held leadership roles at GEIRI North America, NEC Laboratories America, and Arizona State University. Education: PhD in Electrical Engineering, Arizona State University (2012) MS in Electrical Engineering, Arizona State University (2009) BS in Electrical Engineering, Xi'an Jiaotong University (2007) His research focuses on power system data analytics , energy storage , artificial intelligence , and IoT applications for grid stability and renewable integration. His work bridges theoretical innovation with real-world deployment, including software adopted by 15 utility companies. Recent publications highlight his leadership in deep reinforcement learning for grid control, blockchain frameworks for energy management, and tensor decomposition for efficient load modeling. He has secured a $6M NSF grant for AI-driven digital twinning to address climate-aware energy resilience. Awards & Recognition: 2025 Paul W. and Valerie Klipsch Distinguished Professorship 2024 University Research Council Mid-Career Award 2024 IET Fellow Multiple IEEE Best Paper Awards (2019–2022) 2019 L2RPN AI Competition Championship He serves as an editor for IEEE Transactions on Power Systems , IET Generation, Transmission & Distribution , and other journals, and leads the IEEE Task Force on IoT for Power Systems . His team’s patents cover AI-driven load modeling , energy storage scheduling , and state estimation , with 42 granted or pending.

• Language Models
• Diffusion Models
• Theory of Deep Learning
• +۶ مورد دیگر

Song Mei is an Assistant Professor in the Department of Statistics and Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She received her Ph.D. from Stanford University in 2020 under Andrea Montanari and maintains active research collaborations with institutions including Amazon (as a 2023 Amazon Research Award recipient) and OpenAI (where she is currently on leave). Her research spans the intersection of statistics, machine learning, information theory, and computer science, with particular emphasis on foundational theories for modern AI systems. Key interests include language models, diffusion models, quantum algorithms, and high-dimensional statistics, often leveraging insights from statistical physics literature. Analysis of her recent publications reveals a strong focus on theoretical underpinnings of generative AI, with significant contributions to understanding contrastive pre-training (CLIP), attention mechanisms in LLMs, and mathematical foundations of diffusion models. Her work demonstrates consistent interdisciplinary connections between statistical theory and practical AI development. Sloan Research Fellowship (2025) Noether Early Career Scholar Award (2025) Google Research Scholar Award (2024) Amazon Research Award (2024) She actively advises graduate students through MA programs and has secured significant research grants including Amazon Research Awards. Her work with AGI Labs at Amazon demonstrates applied impact of theoretical research. Current projects focus on mechanistic interpretability of large language models and mathematical frameworks for generative AI. Professor Mei leads research on the statistical principles behind frontier AI models, with particular focus on developing rigorous theoretical frameworks for understanding emergent behaviors in large-scale systems.

• Scientific Machine Learning
• Numerical Linear Algebra
• Numerical Multilinear Algebra
• +۷ مورد دیگر

Peter Benner is a Professor and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, where he leads the Computational Methods in Systems and Control Theory group. He also holds an Honorarprofessor position for Mathematics at Otto-von-Guericke Universität Magdeburg since 2011. Benner has previously served as Managing Director of the Max Planck Institute during multiple periods (2013-2014, 2021-2022, and 2025-2026), demonstrating his leadership in the field. Benner's research focuses on Scientific Machine Learning, Numerical Linear and Multilinear Algebra, Model Order Reduction and Reduced-order Modeling, Numerical Methods in Systems and Control Theory, PDE Constrained Optimization, High-performance and Power-aware Computing, and Mathematical Software development. His work bridges theoretical mathematics with practical engineering applications, particularly addressing challenges in large-scale dynamical systems. Analysis of his recent publications reveals a strong emphasis on developing efficient computational methods for complex systems. Benner has pioneered approaches combining model order reduction with tensor methods to tackle high-dimensional problems in uncertainty quantification and PDE-constrained optimization. His work shows a consistent trend toward integrating data-driven techniques with traditional model-based approaches, particularly for nonlinear and parametric systems. Throughout his career, Benner has actively mentored students and collaborated with researchers worldwide, delivering numerous invited talks at prestigious institutions and conferences across Europe, North America, and Asia. His research has received significant funding, supporting the development of mathematical software and computational methods for industrial applications. Benner leads the Computational Methods in Systems and Control Theory department at the Max Planck Institute, which focuses on developing and implementing advanced numerical methods for large-scale dynamical systems. The group maintains strong connections with both theoretical mathematics and practical engineering applications, particularly in fluid dynamics, energy systems, and control theory.

• Environmental Impact Assessment
• Life Cycle Assessment (LCA)
• Industrial Ecology
• +۴ مورد دیگر

Prof. Dr. Stefanie Hellweg serves as a Full Professor for Ecological Systems Design at the ETH Zürich , Department of Civil, Environmental and Geomatic Engineering. She is Deputy Head of the Institute of Environmental Engineering and leads groundbreaking research at the intersection of industrial ecology, life cycle assessment, and circular economy systems. PhD in Environmental Engineering (ETH Zürich, 2000) Industrial Engineering Degree (Karlsruhe University, 1996) Her research develops advanced methodologies for environmental impact assessment of products and technologies, focusing on: Life Cycle Assessment (LCA) with correlated uncertainty modeling Circular economy implementation in chemical and energy sectors Industrial symbiosis optimization for heat and nutrient flows Urban wastewater systems redesign for resource recovery Chemical pollution analysis in plastic materials Key publication trends reveal expertise in: Hybrid wastewater treatment systems Energy-efficient greenhouse cultivation Exergy analysis for sustainability metrics Machine learning applications in environmental modeling Policy-relevant industrial ecology studies She actively participates in global sustainability initiatives as: President of the International Society of Industrial Ecology (ISIE) Member of UN Environment's International Resource Panel Editorial board member for leading environmental journals

• Theoretical Computer Science
• Graph Algorithms
• Dynamic Algorithms
• +۷ مورد دیگر

Nicole Wein is an Assistant Professor in the Computer Science and Engineering Division of the Department of Electrical Engineering and Computer Science (EECS) at the University of Michigan, College of Engineering. Her research lies in theoretical computer science, focusing on graph algorithms, dynamic algorithms, parameterized algorithms, distributed algorithms, online algorithms, and fine-grained complexity. She is part of the Theory of Computation Lab and advises both PhD and undergraduate researchers. PhD, Massachusetts Institute of Technology (MIT), advised by Virginia Vassilevska Williams Postdoctoral Fellow, DIMACS Research Fellow, Simons Institute, UC Berkeley MS, Stanford University BS, Computer Science/Math, Harvey Mudd College Her research explores fundamental algorithmic questions in combinatorial settings, particularly how algorithms handle dynamic data, extract information efficiently (e.g., in linear time), and understand shortest path structures in graphs—especially directed ones. She investigates problems in distance estimation, spanners, hopsets, dynamic graph algorithms, and hardness of approximation. Her work combines theoretical depth with practical implications for algorithm design. The recent publications reflect a strong trend in fine-grained complexity and graph algorithm design, with a focus on proving tight bounds, developing efficient approximations, and understanding structural limitations in directed and dynamic graphs. Her work frequently appears in top venues such as STOC, FOCS, SODA, and ICALP, often in collaboration with leading researchers in the field. Scientific Awards and Recognition: Invited to special issue of SIAM Journal on Computing (SICOMP) (FOCS 2022 paper) Invited to Highlights of Algorithms (HALG) (FOCS 2022 paper) Invited to minisymposium at CANADAM (ESA 2022 paper) Work featured in Quanta Magazine Nicole Wein actively mentors students, including current PhD student Jubayer Nirjhor and former undergraduate researchers like Sam Hiken (now pre-doc at MIT). She has served on program committees for major conferences including SODA, FOCS, ICALP, and ITCS, and co-organized the DIMACS workshop on Modern Techniques in Graph Algorithms (2023). She also contributes to the academic community through outreach, such as her article offering reassurance to early-stage PhD students in theoretical computer science. She leads and participates in collaborative research groups and workshops, emphasizing supercollaboration and interdisciplinary communication in algorithms. Her lab fosters a strong research environment in theoretical computer science at the University of Michigan.

Frede Blaabjergمشاهده پروفایل

استاد

• Power Electronics
• Wind Turbines
• Photovoltaic (PV) Systems
• +۶ مورد دیگر

Frede Blaabjerg is a Professor at Aalborg University (AAU Energy) , affiliated with the Faculty of Engineering and Science . Since 1998, he has pioneered power electronics research in applications such as wind turbines , photovoltaic (PV) systems , reliability engineering , and Power-2-X technologies. Education : PhD in Electrical Engineering (1995, Aalborg University) Honorary Degrees : Honoris Causa at University Politehnica Timisoara (2017) and Tallinn Technical University (2018) His research focuses on power electronics control , system optimization , and reliability for renewable energy and electric mobility . Recent work includes grid-forming converters , virtual synchronous generators , and smart EV charging systems. Key publication trends span 15+ years , with over 3,733 peer-reviewed articles and 900+ journal papers in power electronics , renewables , and energy storage . Notable book series: Control of Power Electronic Converters and Systems (4 volumes, Elsevier). Scientific Awards : 46 IEEE Prize Paper Awards 2020 IEEE Edison Medal 2019 Global Energy Prize 2014 IEEE William E. Newell Power Electronics Award Leadership Roles : Editor-in-Chief, IEEE Transactions on Power Electronics (2006–2012) Chairman, Danish Council for Research and Innovation Policy (2020–) President, IEEE Power Electronics Society (2019–2020)

• Optimization
• Machine Learning
• Statistics
• +۳ مورد دیگر

Damek Davis serves as an Associate Professor of Statistics and Data Science and Co-Academic Director of the Dual Master's Degree in Statistics at the Wharton School, University of Pennsylvania. His academic base is the Department of Statistics and Data Science within the Wharton School, with his office located at the Academic Research Building in Philadelphia, PA. His research expertise centers on optimization theory for data science, with deep specialization in nonsmooth and stochastic optimization problems. Key focus areas include convergence analysis of first-order methods, variance reduction techniques, and theoretical guarantees for algorithms in nonconvex settings. His work bridges mathematical rigor with practical applications in machine learning and statistical inference, particularly in developing efficient computational frameworks for large-scale data analysis. Analysis of his 2022-2024 publications reveals dominant themes in optimization for modern data challenges: nonsmooth stochastic approximation, linear convergence under sharpness conditions, and global optimality in mixture models. His research consistently appears in premier venues across optimization (Mathematical Programming, SIAM Journal), statistics (The Annals of Statistics), and machine learning (IEEE Transactions), demonstrating cross-disciplinary impact in both theoretical foundations and computational methodologies.

• Machine Learning
• Artificial Intelligence
• Data Science
• +۷ مورد دیگر

Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University. Previously, Xu was a Postdoctoral Scholar Research Associate at Caltech's Department of Computing and Mathematical Science and earned a Ph.D. in Computer Science from UCLA. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong empirical performance and theoretical guarantees. Xu's research interests center around Machine Learning with broad applications in Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics. The research specifically targets real-world problems in Bioinformatics and Healthcare, with recent work emphasizing distributionally robust decision making, efficient exploration strategies, and multi-agent systems. Xu has developed novel algorithms that address the challenges of exploration in sequential decision making and robustness to distributional shifts between training and deployment environments. Xu's recent publications demonstrate a strong trend toward developing theoretically grounded yet practical algorithms for reinforcement learning and bandit problems, with particular emphasis on distributionally robust methods, efficient exploration techniques, and applications to healthcare. The work spans both theoretical analysis (providing minimax optimal regret bounds) and practical implementations (validated on benchmarks like Atari games and real healthcare datasets). Whitehead Scholar award from Duke University School of Medicine (2023) Best Paper Award at ACM FAccT 2023 for Queer In AI paper PIMCO Postdoctoral Fellowship in Data Science (2022) TMLR Featured Certification (2023) NSF award on approximate sampling based exploration (2023) Xu actively mentors multiple Ph.D. students across Duke's Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering programs, with several alumni now pursuing doctoral studies at top institutions. The research group has secured competitive funding including an NSF award for approximate sampling based exploration for sequential decision making. Xu serves as an action editor for TMLR and as an area chair for major conferences including ICML, NeurIPS, AAAI, ICLR, and AISTATS. Xu leads a dynamic research group focused on sequential decision making, with projects spanning theoretical algorithm development, implementation of practical systems, and applications to healthcare and bioinformatics. The group maintains active collaborations across Duke's medical and engineering schools, with recent work applying machine learning to epidemic forecasting during the pandemic.

John W. Edmundsمشاهده پروفایل

استاد

• Infectious Disease Modelling
• Epidemiology
• Public Health Policy
• +۶ مورد دیگر

Professor John W. Edmunds is a leading academic in Infectious Disease Modelling at the London School of Hygiene and Tropical Medicine (LSHTM). Holding a Professor position since 2013 and serving as Dean of the Faculty of Epidemiology and Population Health (2013-2019), he combines mathematical, statistical, and economic models to inform public health policy. His part-time role at the Health Protection Agency (HPA) since 2008 underscores his policy advisory contributions. PhD in Infectious Disease Modelling (Imperial College, 1994) MSc in Health Economics (University of York, 1995) BSc in Biology (Imperial College, 1989) His research focuses on understanding disease transmission and optimizing control strategies. He has pioneered methods integrating social contact surveys , participatory surveillance (e.g., Influenzanet), and economic analysis to evaluate vaccines and interventions. Recent work includes SARS-CoV-2 dynamics , Ebola spatial forecasting , and typhoid vaccine prioritization . His 15 most recent publications highlight social contact patterns (Reconnect, CoMix studies), vaccine impact (HPV, typhoid), and real-time outbreak analytics (Ebola, cholera). Key trends include digital epidemiology , behavioral surveillance , and cross-country modeling for global health. Knighthood (2024) for services to epidemiology Weldon Memorial Prize (2022) for biostatistics contributions FMedSci (2018) for medical science excellence OBE (2016) for public service As an educator, he teaches "Modelling and the Dynamics of Infectious Diseases" and co-organizes "Pandemics: Emergence, Spread and Response" . Current grants include National Institute for Health and Care Research projects on post-pandemic surveillance and Bill & Melinda Gates Foundation funding for polio eradication. He leads collaborations with the Centre for Mathematical Modelling of Infectious Diseases , Vaccine Centre , and Health in Humanitarian Crises Centre , while advising UK and WHO committees on zoonotic influenza , variants , and testing programs .

Sebastian Steinhorstمشاهده پروفایل

دانشیار

• Embedded Systems
• Internet of Things (IoT)
• Cyber-Physical Systems
• +۵ مورد دیگر

Prof. Dr. Sebastian Steinhorst is an Associate Professor (W3-level with tenure) at the Technical University of Munich (TUM) within the Embedded Systems and Internet of Things group at the TUM School of Computation, Information and Technology . His research focuses on advancing the security, predictability, reliability, and interoperability of smart connected and autonomous systems, particularly for applications in Internet of Things (IoT) , Industry 4.0 , and automotive systems . PhD in Computer Science (2011) from Goethe University Frankfurt Postdoctoral roles at TUMCREATE Singapore (2011-2016) and Aarhus University (2016) Joined TUM in 2016 as Rudolf Moessbauer Tenure Track Professor His research areas include decentralized embedded systems, hardware/software co-design, modeling and verification of cyber-physical systems, security protocols for automotive networks, and time-sensitive networking (TSN) for industrial applications. Recent work explores blockchain-based data sovereignty, zero-knowledge proofs for vehicle authentication, and resilient architectures for autonomous systems. Key scientific contributions include the 2019 ACM TODAES Best Paper Award and pioneering work on CyberSecDome , LeapChain , and Simutack frameworks. He serves on editorial boards and conference committees, including co-organizing the Autonomous Systems Design initiative at DATE. His teaching portfolio spans lectures on System Design for IoT , Software Architecture for Distributed Systems , and IoT Security across multiple semesters. He also leads advanced seminars on embedded systems and IoT.

Alfred O. Hero, IIIمشاهده پروفایل

استاد

• Data Science
• Statistical Machine Learning
• Distributed Optimization
• +۷ مورد دیگر

Alfred O. Hero, III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS), with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is also affiliated with the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS), which he co-founded from 2015-2018. Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization that use statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His recent research interests include high dimensional spatio-temporal data analysis, multimodal data integration, statistical signal processing, and machine learning, with particular emphasis on predictive mathematical models for biological and physical sciences, social networks, network security and forensics, and personalized health and disease. His recent publications demonstrate a strong focus on high-dimensional statistical methods, contrastive learning, neural network optimization, change detection in temporal graphs, and applications in microbiome analysis and epidemic modeling. The research spans theoretical foundations in information theory and statistical learning while addressing practical applications across multiple domains. Scientific Awards: IEEE Signal Processing Society Best Paper Award (1998) Best Original Paper Award from Journal of Flow Cytometry (2008) Best Magazine Paper Award from IEEE Signal Processing Society (2010) SPIE Best Student Paper Award (2011) IEEE ICASSP Best Student Paper Award (2011) IEEE Signal Processing Society Technical Achievement Award (2014) IEEE Signal Processing Society Society Award (2015) IEEE Fourier Award (2020) University of Michigan Distinguished Faculty Achievement Award (2011) Hero has advised over 60 PhD students and 30 postdocs in areas including modeling, computation, and inference for large scale time varying data in the biosciences. He has received significant research funding from the Department of Energy, Army Research Office, Air Force Office of Scientific Research, and National Science Foundation. He has held leadership positions including President of the IEEE Signal Processing Society (2006-2007), Director of Division IX (Signals and Applications) on the IEEE Board of Directors (2009-2011), and Chair of the Committee on Applied and Theoretical Statistics of the US National Academies (2018-2020).