Jennifer Dy is a Distinguished Professor at Northeastern University with joint appointments in Electrical and Computer Engineering and Khoury College of Computer Sciences. As Director of AI Faculty at the Institute for Experiential AI, she leads research in machine learning, computer vision, and explainable AI. Her work spans biomedical applications (COPD phenotyping, neuroimaging) and fundamental algorithms (active learning, continual learning). She holds a PhD from Purdue University and is an AAAI Fellow. Research Focus: Dy develops methodologies for robust and interpretable machine learning, including techniques for model stability in continual learning, dependency-aware active learning, and axiomatic explanation frameworks. Her applied research advances diagnostic tools using Raman spectroscopy, CT imaging, and multi-omics biomarker discovery. Awards: Recognized with the NSF CAREER Award, Faculty Research Team Award, and AAAI Fellowship for contributions to unsupervised learning and medical AI. Publication Trends: Recent articles demonstrate strong cross-disciplinary integration, combining theoretical advances in explainability/robustness with applications in healthcare, wireless systems, and particle physics. Methodological themes include optimal transport theory, probabilistic modeling, and transformer architectures.
Thomas C. M. Lee is a Distinguished Professor of Statistics and Associate Dean of the Faculty in Mathematical and Physical Sciences at the University of California, Davis, within the College of Letters and Science. He holds a prominent position in the Department of Statistics and serves as a key academic leader at UC Davis. Education: B.App.Sc. (Math) from University of Technology, Sydney, Australia (1992) B.Sc. (Hons) (Math) with University Medal from University of Technology, Sydney, Australia (1993) Ph.D. from Macquarie University and CSIRO Mathematical and Information Sciences, Sydney, Australia (1997) Professor Lee's research spans multiple areas of statistics with a focus on developing innovative methodologies. His work particularly emphasizes nonparametric and semiparametric modeling , statistical learning , and statistical image and signal processing . He has made significant contributions to applying statistical methods across various scientific disciplines, demonstrating the versatility and power of statistical approaches in solving complex real-world problems. His research often bridges theoretical developments with practical applications, creating methodologies that are both mathematically sound and practically useful. Scientific Awards and Honors: Elected Fellow of the American Association for the Advancement of Science (AAAS, 2019) Elected Fellow of the American Statistical Association (ASA) Elected Fellow of the Institute of Mathematical Statistics (IMS) Elected Senior Member of the IEEE Professor Lee has held significant editorial roles including serving as Editor-in-Chief for the Journal of Computational and Graphical Statistics (2013-2015) and currently as Review Editor for the Journal of the American Statistical Association. From 2015 to 2018, he chaired the Department of Statistics at UC Davis. He has taught numerous statistics courses including STA 13 (Elementary Statistics), STA 131C (Introduction to Mathematical Statistics), STA 243 (Computational Statistics), and STA 401 (Statistical Consulting). His leadership extends beyond research to academic administration, where he has shaped statistics education and departmental direction at UC Davis.
Francis Y. Yan is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), holding an affiliate appointment in Electrical & Computer Engineering within the Grainger College of Engineering. He leads the Illinois Networked Systems and AI (NSAI) research group, focusing on building intelligent networked systems that are safe, robust, and performance-optimized through practical machine learning integration. Prior to joining UIUC in January 2025, he served as a Senior Researcher at Microsoft Research Redmond under Victor Bahl. His educational background includes: Ph.D. in Computer Science from Stanford University (2020), advised by Keith Winstein and Philip Levis B.S. in Computer Science (Yao Class) and B.A. in Economics from Tsinghua University (2015) Additional undergraduate studies at MIT Yan's research adopts a holistic approach to practical machine learning for networked systems, emphasizing judicious application rather than indiscriminate use. He builds real-world systems and research platforms to lay ML foundations, devises deployable algorithms using domain insights, and validates performance through extensive empirical evidence. His work consistently addresses operator concerns regarding ML deployment—focusing on safety, robustness, generalization, and efficiency—while strategically combining ML with classical networking and systems techniques. Analysis of his 15 most recent publications (2023-2025) reveals dominant themes in resource allocation for microservices (DeDe, Autothrottle), real-time video optimization (Mowgli, GRACE), and LLM-driven network algorithm design. His work bridges theoretical advances with industrial deployment, evidenced by platforms like Puffer (400,000+ users) and OpenNetLab that have become community standards for validating congestion control algorithms. His research has been recognized with top honors: USENIX NSDI Outstanding Paper Award (2024) for Autothrottle APNet Best Paper Award (2022) IRTF Applied Networking Research Prize (2021) USENIX NSDI Community Award (2020) USENIX ATC Best Paper Award (2018) for Pantheon Yan actively recruits master's and undergraduate researchers for his NSAI group, prioritizing self-motivated students for projects in networked systems and AI. His research is supported by industry collaborations (notably Microsoft) and manifests in deployable platforms like Puffer—which has enabled award-winning research at NSDI and SIGCOMM—and OpenNetLab for real-time communications. His work directly impacts production systems including Microsoft Teams and Bing. He founded and directs the Illinois Networked Systems and AI (NSAI) research group, which operates critical infrastructure including Puffer (a live TV service and research platform) and OpenNetLab. These platforms facilitate community-wide validation of novel algorithms, with Puffer alone supporting multiple best-paper awards at top conferences. Current workstreams span cloud resource management (Teal, Autothrottle, DeDe), low-latency video (Puffer, Tambur, Mowgli), and LLM-augmented systems (Nada, Designing Network Algorithms via LLMs).
Yang Kaidi is an Assistant Professor in the Department of Civil and Environmental Engineering at the National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA) within NUS’s Smart Nation Research Cluster. Their research focuses on intelligent transportation systems, traffic control, shared mobility, and machine learning applications in mobility. Key interests include connected and automated vehicles, privacy-preserving data sharing, and reinforcement learning for traffic optimization. Research highlights include developing parameter privacy-preserving strategies for mixed-autonomy platoons, enhancing safety in autonomous driving via transformer-based trajectory prediction, and optimizing traffic signal timing using connected vehicle data. Their work bridges theoretical control systems with practical urban mobility challenges, addressing issues like ridesourcing-public transit integration, modular transit service operations, and weaving section management in mixed traffic environments. Recent publications emphasize real-time control frameworks, cooperative safety mechanisms, and data-driven solutions for urban and highway systems. Yang’s interdisciplinary approach integrates robotics, optimization, and cybersecurity to advance smart transportation infrastructure. Their contributions are particularly notable in privacy-preserving techniques for traffic state estimation and federated learning applications. While no specific awards or grants are listed, their research aligns with Singapore’s Smart Nation initiatives through IORA’s strategic focus areas. Yang’s work has implications for future traffic management systems, autonomous vehicle coordination, and sustainable urban mobility solutions.
Dr. John Hastings is a Professor in the Department of Computer Science at Dakota State University, part of the Beacom College of Computer & Cyber Sciences. With nearly 35 years of experience, he specializes in teaching and curriculum development in computer science, combining academic rigor with industry insights from his roles as an AI/ML engineer, team leader, and business owner. Education: Ph.D., Computer Science, University of Wyoming M.S., Computer Science, University of Wyoming B.S., Computer Science, University of Wyoming Research interests include machine learning, AI applications in natural language processing (LLMs), generative AI, computer vision, ecological/environmental AI, AI in games, and gamification in education. Recent publications focus on cybersecurity challenges, AI ethics, and insider threat detection. His work has been recognized with awards such as the AAAI’s Innovative Applications of Artificial Intelligence (IAAI) Award and an International IPM Award for Excellence related to the CARMA AI tool. Teaching emphasizes active learning and practical skills, including courses on programming, data structures, AI, and cybersecurity. He advocates for gamification in education to enhance student engagement and success.
Sandra Paterlini is a Full Professor in the Department of Economics and Management at the University of Trento, Italy. She holds academic roles including Co-Chair of the ERCIM Working Group on Optimization Heuristics and Vice-Chair of the IEEE Task Force on Portfolio Optimization. Her career includes visiting positions at institutions such as the University of Minnesota and Ludwig-Maximilians-Universität München. She earned a PhD in Computational Methods for Financial and Economic Decisions from the University of Bergamo, an MSc in Financial Mathematics from the University of Warwick, and a Laurea in Economics from the University of Modena and Reggio E. Her research focuses on quantitative finance, risk management, portfolio optimization, and network analysis, with applications to ESG, systemic risk, and financial stability. Key research contributions include methodologies for sparse graphical modeling, systemic risk analysis, and ESG scoring frameworks. She has received multiple awards for research excellence and serves on editorial boards of journals like Computational Statistics & Data Analysis and Frontiers in Applied Mathematics and Statistics . Her work bridges academia and policy, with contributions to the European Central Bank’s Financial Stability Directorate and involvement in global conferences on computational finance and econometrics.
Mustafa A. Mustafa is a Senior Lecturer (Associate Professor) in the Department of Computer Science at The University of Manchester, where he leads the Trusted Digital Systems Cluster as part of the university-wide Centre for Digital Trust and Society. His academic journey spans prestigious institutions including The University of Manchester, where he completed his PhD, and KU Leuven in Belgium, where he served as a post-doctoral research fellow. Dr. Mustafa earned his educational qualifications through an impressive academic path: a B.Sc. in communications from the Technical University of Varna, Bulgaria (2007), an M.Sc. in communications and signal processing from Newcastle University, UK (2010), and a Ph.D. in computer science from The University of Manchester, UK (2015). His doctoral research focused on "Smart Grid Security: Protecting Users' Privacy in Smart Grid Applications," laying the foundation for his subsequent research career. Dr. Mustafa's research expertise centers on information security, data privacy, and applied cryptography with particular focus on smart grid systems, smart city applications, e-health, and IoT. His work addresses critical challenges in securing peer-to-peer electricity trading markets, smart metering infrastructure, electric vehicle charging systems, and health data management. He has developed innovative solutions for keyless car sharing systems, frictionless authentication mechanisms, and privacy-preserving protocols for data collection and distribution. His scholarly contributions demonstrate a consistent trajectory toward increasingly sophisticated privacy-preserving techniques applied across multiple domains. Recent work shows a growing integration of artificial intelligence and machine learning approaches with traditional cryptographic methods, particularly in federated learning systems and large language model verification. His research bridges theoretical cryptography with practical implementations in energy systems and healthcare applications. Dr. Mustafa's scientific achievements have been recognized with several prestigious awards: Winner of the Student Video Competition at IEEE SmartGridComm 2017 for "Secure and Privacy-friendly Local Electricity Trading" Best Paper Award at SECURWARE 2017 Distinguished Achievement Award as Postgraduate Research Student of the Year nominee by the School of Computer Science of The University of Manchester (2015) Dame Kathleen Ollerenshaw Research Fellowship (2018-2023) As an academic supervisor, Dr. Mustafa has mentored numerous graduate students through their PhD and Master's research, with a particular focus on privacy and security challenges in emerging technologies. His current supervision portfolio includes research on privacy-friendly multi-agent systems for smart grids, security for IoT in e-health, vulnerability detection in IoT cryptography, and bot detection systems. He has secured significant research funding through multiple competitive grants including EnnCore: End-to-End Conceptual Guarding of Neural Architectures (EPSRC, 2020-2024), SCorCH: Secure Code for Capability Hardware (EPSRC, 2019-2023), and SNIPPET: Secure and Privacy-friendly Peer-to-peer Electricity Trading (FWO-SBO project, 2019-2023). Dr. Mustafa leads the Trusted Digital Systems Cluster within the Centre for Digital Trust and Society at The University of Manchester. His research group comprises PhD students, postdoctoral researchers, and collaborators working on cutting-edge security and privacy solutions. The team maintains strong international collaborations, particularly with KU Leuven in Belgium, and contributes to standards development as evidenced by Dr. Mustafa's role as an expert in the IEC/SYC/WG 3 "IEC Smart Energy Roadmap."
Dr. Olga Vysotska is a Researcher affiliated with the Professorship for Robotic Systems at ETH Zurich's Department of Mechanical and Process Engineering. Her work focuses on advancing robotic systems through research in sensor-based navigation, SLAM (Simultaneous Localization and Mapping), and autonomous systems. She holds a doctoral degree and is based in Zurich, Switzerland. Her email is olga.vysotska@inf.ethz.ch. Research Interests: Olga's research spans robotics, computer vision, and autonomous navigation. She specializes in LiDAR-based place recognition, SLAM algorithms, and cross-modal localization using 3D scene graphs. Her work addresses challenges such as environmental changes, sensor fusion, and data association in dynamic environments like agriculture and underground exploration. Key themes include robust localization, loop closure detection, and adaptive algorithms for real-world robotic applications. Publications Overview: Olga's recent work emphasizes diffusion-based LiDAR place recognition (2025), 4D spatial-temporal mapping for agricultural robots (2023), and SceneGraphLoc for cross-modal localization (2024). Her research trends highlight innovation in sensor integration, algorithmic robustness, and practical applications in challenging environments. Earlier contributions include exploration of catacombs with mobile robots (2013) and SLAM enhancements using public map data (2017). Grants & Advising: While specific grants or student advisement details are not listed, her active publication record suggests involvement in funded research projects. Her work often collaborates with industry and academic partners to advance robotic autonomy in complex scenarios. Labs/Teams: As part of the Robotic Systems Professorship, she likely contributes to ETH Zurich's robotics labs focused on SLAM, sensor systems, and autonomous navigation. Her projects may intersect with the Department's broader initiatives in mechanical and process engineering.
Can Firtina is a Lecturer at ETH Zurich's Department of Information Technology and Electrical Engineering and a Senior Researcher in the SAFARI Research Group. His research focuses on accelerating genome analysis through algorithm-architecture co-design, particularly leveraging hardware-software integration for bioinformatics workloads. He holds a PhD in Electrical and Computer Engineering from ETH Zurich and degrees from Bilkent University. As of Fall 2025, he will join the University of Maryland, College Park (UMD) as an Assistant Professor of Computer Science. Education: PhD in Electrical and Computer Engineering (D-ITET), ETH Zurich MSc in Computer Engineering, Bilkent University BSc in Computer Engineering, Bilkent University Research Interests: His work bridges bioinformatics and computer architecture, emphasizing real-time, accurate, and energy-efficient genome analysis. Key areas include raw nanopore signal processing (e.g., RawHash, Rawsamble), hardware-software co-design for bioinformatics, and scalable metagenomic analysis. His algorithms address noise mitigation and accelerate applications like assembly polishing (Apollo) and alignment remapping (AirLift). Labs & Collaborations: He leads research within the SAFARI Group, collaborating with institutions like NVIDIA, AMD, and Huawei. His contributions span tools like GenASM (approximate string matching) and BLEND (fuzzy seed matching). He also organizes workshops on bioinformatics acceleration and serves on review boards for venues like ISMB and RECOMB. Future Directions: Future work includes end-to-end raw signal analysis without basecalling, reference-free genome assembly, and leveraging emerging hardware for real-time field applications. He will expand these efforts at UMD, hiring students in Fall 2025.
Weiwei Lin is an Associate Professor in the Department of Civil Engineering at Aalto University, specializing in structural engineering with a focus on bridge systems, composite materials, and structural health monitoring. His research explores fatigue behavior of steel structures, seismic performance of composite systems, and innovative repair techniques. He holds a PhD from Waseda University (2012), MSc from Southeast University (2009), and BEng from Southwest Jiaotong University (2006). Key research areas include: steel-concrete composites, bridge redundancy evaluation, replaceable energy dissipaters, and AI-driven infrastructure diagnostics. Lin leads projects like CCU Structure (EU Horizon Europe) and RCF Mobility initiatives, focusing on sustainable construction and material recyclability. He has published 120+ peer-reviewed articles and secured 6 major grants. Lin has received prestigious awards including the IABMAS Young Award (2014) and Outstanding Reviewing Award (2017). His lab collaborates globally, hosting researchers from institutions like Israel Institute of Technology and Tsinghua University. Current work emphasizes crowdsourcing-based bridge monitoring and physics-guided AI frameworks for infrastructure diagnostics.
Lei Lei is an Associate Professor at the University of Guelph, specializing in Computer Engineering. Her research focuses on Machine Learning/Deep Reinforcement Learning, Internet of Things (IoT)/Internet of Vehicles (IoV), Mobile Edge Computing, and Smart Grid Optimization. She explores cutting-edge applications in energy-efficient systems, autonomous vehicles, and intelligent transportation networks. Her work integrates advanced AI techniques with real-world challenges in communication and control systems. Key research areas include optimizing electric vehicle charging schedules using hierarchical deep reinforcement learning and enhancing vehicular networks through 6G communication protocols. She has pioneered methods for joint communication-control systems, securing federated learning models, and developing robust resource allocation strategies in IoT and edge computing environments. Lei Lei’s publications emphasize interdisciplinary solutions, bridging computer science, electrical engineering, and transportation systems. Her recent work addresses challenges in smart grid security, multitimescale control systems, and the application of AI tools like ChatGPT in connected vehicles. She is affiliated with the AI Affiliated Faculty at the University of Guelph, reflecting her contributions to artificial intelligence research.
Baoyu Zhou is an Assistant Professor of Industrial Engineering at Arizona State University (ASU), School of Computing and Augmented Intelligence. He holds a PhD in Industrial and Systems Engineering from Lehigh University (2018–2022), an M.S. in Industrial Engineering from Lehigh University (2016–2018), and a B.E. in Mechanical Engineering from Shanghai Jiao Tong University (2012–2016). His research focuses on developing efficient algorithms for large-scale, stochastic, and constrained optimization problems, with contributions to sequential quadratic programming, nonsmooth optimization, and derivative-free methods. Before joining ASU, Zhou was a postdoctoral researcher at the University of Michigan (Department of Industrial and Operations Engineering) and the University of Chicago (Booth School of Business). He has received the Van Hoesen Family Best Publication Award and the Elizabeth V. Stout Dissertation Award. His work bridges optimization theory and practical applications, emphasizing scalability and robustness in complex systems. Zhou teaches courses such as IEE 470: Stochastic Operations Research at ASU and has guest-lectured at the University of Michigan. He actively contributes to the academic community through organizing conference sessions, reviewing for top journals, and participating in workshops at NeurIPS and SIAM. His group currently advises three PhD students focusing on optimization algorithms and their applications. Key research areas include large-scale continuous optimization, constrained stochastic optimization, and derivative-free methods. His publications span journals like SIAM Journal on Optimization and INFORMS Journal on Optimization, addressing challenges in nonlinear systems, variance reduction, and algorithmic convergence.
Josh McDermott is a Professor in the Department of Brain and Cognitive Sciences at MIT and an Associate Investigator at the McGovern Institute. He holds roles as Associate Department Head and Principal Investigator of the Laboratory for Computational Audition. His work bridges psychology, neuroscience, and engineering to study auditory perception, with a focus on sound interpretation, hearing impairment treatments, and machine hearing systems. Education includes a B.A. from Harvard (summa cum laude), an MPhil from University College London, and a PhD from MIT. Postdoctoral training included NYU and the University of Minnesota. Research interests encompass computational principles of sound perception, natural sound statistics, music cognition, and machine hearing. Key areas include sound localization, auditory scene analysis, and the role of generative models in perception. Recent publications highlight advancements in auditory neural networks, cross-cultural music perception, and noise schema processing. Awards include the Troland Research Award, BCS Excellence in Advising, and NSF CAREER Award. Advising includes over 20 graduate students and postdocs, with notable contributions to auditory neuroscience and machine learning. Major grants support projects on auditory models and sensory systems. The lab develops tools like cochleagram generation and headphone screening software. The Laboratory for Computational Audition operates at MIT, focusing on biological and computational approaches to hearing. Collaborations span engineering, psychology, and neuroscience to advance understanding of auditory processing.
Lorenzo Cavallaro is a Full Professor of Computer Science at University College London (UCL), specializing in Trustworthy AI for Systems Security. His research focuses on developing learning-based methods that are robust against adversaries by understanding the interplay between program analysis, representations, and machine learning models. His research interests span multiple critical areas in cybersecurity, including adversarial machine learning, malware detection, program analysis, and security evaluation. Cavallaro's work particularly emphasizes the challenges of concept drift in security systems and the development of robust defenses against evolving threats. His research has significant implications for Android security, binary analysis, and memory safety in embedded systems. Analysis of his recent publications (2024-2025) reveals a strong focus on addressing fundamental challenges in ML-based security systems. His work spans malware detection systems that maintain reliability under distribution shifts, adversarial attacks in the problem space, context-driven approaches using LLMs for security applications, and temporal invariance in malware detection. A recurring theme is the critical examination of whether ML-based security systems are truly robust and reliable in real-world scenarios. Cavallaro serves in significant editorial and advisory roles including the NDSS Steering Group (2023-2026), Associate Editor for Computer & Security and ACM TOPS, and Scientific Advisory Board for SERICS. He has been actively involved in program committees for top security conferences including IEEE S&P, USENIX Security, CCS, and NDSS from 2021-2025. He teaches Malware (COMP0060; 2022—ongoing), Research in Information Security (COMP0057; 2021—23), and Computer Security 2 (COMP0055; 2021—ongoing) at UCL, contributing to the next generation of security researchers and practitioners.
Roummel F. Marcia is a Professor and current Chair of the Department of Applied Mathematics at the University of California, Merced, within the School of Natural Sciences. He received his Ph.D. from UC San Diego under Professor Philip Gill and previously held postdoctoral positions at the San Diego Supercomputer Center and University of Wisconsin-Madison, as well as a research scientist position in electrical engineering at Duke University. His research spans multiple areas in optimization and its applications, with a focus on signal processing, data science, machine learning, linear algebra, and mathematical biology. Dr. Marcia's work has significant interdisciplinary impact, particularly in biomedical imaging, computational biology, and quantum computing applications. His research methodology often combines theoretical optimization approaches with practical applications in data-intensive fields. Dr. Marcia's recent publications demonstrate a strong trend toward integrating optimization theory with deep learning architectures, particularly in applications requiring sparse data handling, biomedical imaging, and quantum computing. His work shows increasing focus on developing novel optimization algorithms specifically designed for machine learning contexts, including quasi-Newton methods adapted for deep learning and specialized techniques for handling non-convex optimization problems. School of Natural Sciences Faculty Award for 'Developing or Improving Academic Programs and Tracks' (2021-22) Leadership roles in SIAM Activity Group on Applied Mathematics Education Recognition as a Math Alliance Mentor for supporting underrepresented students Dr. Marcia has successfully mentored numerous doctoral students to completion, with graduates moving to positions at Meta, Johns Hopkins University Applied Physics Laboratory, Lawrence Livermore National Laboratory, and other prestigious institutions. His research has been consistently funded by major agencies including NSF (with grants IIS 1741490, DMS 1840265, DMS 2229495, CCF 2343610), DARPA, and ARPA-E. As the current graduate chair of the Applied Math Graduate Program, he plays a key role in shaping the next generation of mathematical scientists. His work with the SMaRT (Scientific Mathematics Research and Training) team demonstrates his commitment to collaborative, interdisciplinary research.
