Professor Mohammad FARD is a faculty member at RMIT University's School of Engineering, specializing in Mechanical Engineering and Intelligent Systems. He leads research in autonomous vehicles, crash safety, and driver monitoring using AI. His industry experience includes six years at Nissan Technical Centre, focusing on vehicle body design. He holds a PhD from Tohoku University and has collaborated across Engineering, Health, and Science disciplines, achieving international media coverage for work on driver drowsiness and road safety. Research Interests: Autonomous Vehicles Advanced Crash Safety Driver State Monitoring AI in Noise/Vibration Human Factors/Ergonomics Teaching & Projects: Teaches Advanced CAE, Vehicle NVH, and research supervision in areas like crash simulation and vibration control. Current projects include Formula One safety barriers and driver education for autonomous vehicles. Awards & Labs: No awards listed. Active in cross-disciplinary teams and labs addressing automotive innovation and safety.
Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment in the Cheriton School of Computer Science. He is a member of the Waterloo Artificial Intelligence Institute (WAII) and the Waterloo Institute for Complexity and Innovation (WICI), and serves as National Secretary of the Canadian Artificial Intelligence Association (CAIAC). His educational background includes a Ph.D. and M.Sc. in Computer Science from the University of British Columbia, where he worked in the Laboratory for Computational Intelligence, and a B.A. in Computer Science from York University. He completed a postdoctoral fellowship at Oregon State University working with Tom Dietterich's machine learning group. Crowley's research focuses on developing dependable and transparent algorithms to augment human decision-making in complex domains with multiple agents, spatial structure, or uncertainty. His work spans Reinforcement Learning , Deep Learning , Ensemble Methods , and Manifold Learning . He frequently collaborates with researchers in applied fields including Computational Sustainability, Sustainable Forest Management, Autonomous Driving, Medical Imaging, and Material Design. His research is motivated by both theoretical opportunities and real-world challenges such as forest fire management, automotive applications, and medical imaging. His recent publications demonstrate a strong focus on addressing challenges in reinforcement learning, particularly around observation costs, multi-agent systems, and causal representation learning. His work on ChemGymRL provides a significant contribution to digital chemistry and material design through reinforcement learning frameworks. The textbook Elements of Dimensionality Reduction and Manifold Learning represents a major contribution to the theoretical foundations of machine learning. Crowley actively supervises graduate students, with recent thesis completions including Shayan Shirahmadi Gale Bagi (PhD, Feb 2025) and Oleksandra Nahorna (MASc, Dec 2024). His lab, UWECEML (Waterloo ECE Machine Learning Lab), focuses on developing new algorithms at the intersection of Machine Learning, Optimization, and Probabilistic Modeling. He teaches courses including ECE 457C (Reinforcement Learning), ECE 657A (Data & Knowledge Modelling & Analysis), and ECE 457B (Fundamentals of Computational Intelligence). His blog Computationally Thinking explores AI, machine learning, and the societal impact of these technologies.
Ian Greer is a Research Fellow at Cornell University's School of Industrial and Labour Relations , with a career focused on marketisation , industrial relations , and transnational solidarity . He holds a PhD from Cornell University and has previously studied at Bard College. His work examines labor dynamics in globalised industries , welfare states , and multinational corporations , often through qualitative comparative methods . Education : BSc (Bard College), MSc/PhD (Cornell University) Greer's research spans marketisation of employment services , social movement unionism , and labour policy in crisis situations . His publications include studies on European welfare-to-work schemes , healthcare privatisation , and labour migration . He is associated with the Centre for Employment Relations, Innovation and Change (CERIC) at the University of Leeds. Greer's recent work analyses precarious labor and project-based employment in social services across Slovenia and France. He also contributes to debates on social dumping , work-first welfare states , and activist labor strategies in sectors like healthcare and automotive manufacturing. He has no listed scientific awards. His work often involves collaboration with scholars like Marco Hauptmeier , Charles Umney , and Nathan Lillie , though no formal advisees are named in the provided text. His email is icg2@cornell.edu .
Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk
Ankit Saxena serves as Assistant Professor in the Department of Mechanical Engineering at the University of Wyoming since 2024, focusing on innovative applications of additive manufacturing in structural engineering and materials science. His work bridges theoretical design with practical implementations in energy, aerospace, and robotics systems. Education: Ph.D. in Mechanical Engineering, Penn State University (2024) M.S. in Mechanical Engineering, Penn State University (2020) B.S. in Mechanical and Automotive Engineering, Delhi Technological University (2016) Dr. Saxena's research centers on developing adaptive stiffness structures , meta-materials , and functionally graded systems through advanced additive manufacturing techniques. His work specifically targets energy applications (nuclear, wind, hydrogen, oil/gas) and aerospace challenges, with emphasis on structural health monitoring and vibration damping. The SUMMIT Lab under his direction creates multi-functional materials enabling shape morphing and self-strengthening properties for next-generation engineering solutions. His publication record (2020-2024) reveals a consistent trajectory toward multi-physics meta-material design , with dominant themes in TPMS lattice optimization, fluid-structure interaction systems, and medical robotics applications. Key methodological contributions include novel fluid accumulator integration, laser powder bed fusion parameterization, and non-pneumatic tire architectures. Scientific Recognition: ASME Graduate Teaching Fellow (2022-2024) Harold F. Martin Graduate Assistant Outstanding Teaching Assistant Award (Penn State, 2023) Dr. Saxena teaches core materials courses (ME 3450: Properties of Materials; ME 4150: Mechanical Behavior of Materials) while expanding his research group through active recruitment of PhD candidates for 2026. His teaching philosophy emphasizes practical applications of theoretical concepts, recognized through multiple Penn State teaching fellowships. The SUMMIT Lab operates at the intersection of Wyoming's energy priorities and cutting-edge manufacturing research, maintaining strategic focus on renewable energy infrastructure and aerospace applications through metal additive manufacturing innovations.
Xiaoming Liu is the Anil K. and Nandita Jain Endowed Professor of Engineering and MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University . Holding a Ph.D. from Carnegie Mellon University (2004), he leads cutting-edge research in computer vision and machine learning. Research Interests : Computer Vision Pattern Recognition Image and Video Processing Machine Learning Medical Image Analysis Multimedia Retrieval Recent Research Trends : Focus on 3D object detection and depth estimation Development of robust biometric recognition systems Integration of radar-camera fusion for autonomous systems Advancements in self-supervised and multimodal learning Exploration of adversarial AI security Creation of interpretable forgery detection frameworks Teaching : Spring 2013: CSE891-006 Computer Vision Seminar Fall 2012-2015: CSE803 Computer Vision Spring 2014-2017: CSE 471 Media Processing and Multimedia Contact Information : Email: liuxm@cse.msu.edu Office: EB 3137, Michigan State University Phone: +1 (517) 355-2359
Aline Eid is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Michigan Ann Arbor, directing the Beam Dynamics research group. Her work bridges electromagnetics, wireless systems, and autonomous technologies, with a focus on mmWave/sub-THz sensing, backscatter communications, and wireless power transfer. PhD in Electrical and Computer Engineering (Georgia Tech, 2021) MS in Electrical and Computer Engineering (American University of Beirut, 2017) Postdoctoral Associate at MIT Media Lab (Signal Kinetics group) Research Interests: She pioneers systems that use electromagnetic waves to address societal challenges in Sustainable energy networks Smart cities/infrastructures Autonomous vehicles/robots Her group develops mmID tags for radar vision, batteryless sensors, and 5G-based wireless power grids. Scientific Impact: Recognized with awards including IEEE RFID Best Paper (2023) Proceedings of the IEEE Best Paper (2023) IEEE MTT-S Graduate Fellowship (2020) She leads commercialization efforts for warehouse robotics via MTRAC grant and startup Atheraxon. Advising: Mentors 8 students across PhD (Skanda Harisha, Sepideh Ghasemi, Yunfei Liu), Master's (Longyu Guo), and undergraduates (Mohamed Safawi, Katherine Shih, Andy Wang, Adrian Velazquez) in projects spanning robotics, IoT, and electromagnetic engineering. Labs: Beam Dynamics group at U-M Radiation Laboratory, collaborating with Atheraxon and MTRAC for technology translation.
Dr. Xinwei Ye serves as a Researcher in the Inorganic Chemistry and Catalysis division at Utrecht University's Faculty of Science. His primary affiliation is with the Department of Chemistry, where he conducts cutting-edge research on heterogeneous catalysis for environmental applications, particularly focusing on selective catalytic reduction (SCR) systems for automotive emissions control. With a strong background in inorganic materials and advanced characterization techniques, Dr. Ye contributes significantly to understanding catalyst structure-performance relationships. Educational Background: Master of Science (MSc) - Institution not specified in source Doctor of Philosophy (PhD) in Chemistry, Utrecht University (2022) Dr. Ye's research program centers on the development and mechanistic investigation of copper-exchanged zeolite catalysts for NH 3 -SCR processes. His work integrates multiple advanced characterization methodologies including operando spectroscopy, scanning transmission X-ray microscopy (STXM), and atom probe tomography to probe catalyst behavior under working conditions at nanometer resolution. This multi-technique approach enables unprecedented insights into active site speciation, reaction mechanisms, and deactivation pathways in emission control catalysts. Analysis of Dr. Ye's publication record from 2018-2022 reveals a cohesive research trajectory focused on copper-zeolite SCR catalysts. His work consistently addresses critical challenges in catalyst durability and performance optimization through fundamental understanding of structure-activity relationships. The publications demonstrate increasing sophistication in experimental approaches, moving from membrane synthesis (2018) to nanoscale deactivation studies (2020) and ultimately to comprehensive structure-performance correlations in his doctoral thesis (2022). As a core member of Utrecht University's catalysis research community, Dr. Ye collaborates extensively with the renowned Weckhuysen group. His research is conducted within well-equipped laboratories featuring state-of-the-art instrumentation for catalyst synthesis, testing, and characterization, including access to synchrotron radiation facilities for advanced X-ray techniques.
Elahe Soltanaghai is an Assistant Professor in the Department of Computer Science and a Faculty Affiliate in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. She is also a 2022 NCSA Fellow and received her PhD in Computer Science from the University of Virginia (2019), MS in Computer Engineering from Sharif University of Technology (2014), and dual BS degrees in Computer and Information Technology Engineering from Amirkabir University of Technology (2011, 2013). PhD: University of Virginia, Computer Science, 2019 MS: Sharif University of Technology, Computer Engineering, 2014 BS (Computer Engineering): Amirkabir University of Technology, 2011 BS (Information Technology Engineering): Amirkabir University of Technology, 2013 Her research spans wireless sensing and communication, focusing on Millimeter-wave Radar Sensing (for automotive, mixed reality, structural monitoring), Machine Learning for Wireless Systems (adaptive sensing/communication), Forest IoT (through-canopy biomass and soil sensing), Metaverse Technologies (gaze-based VR/AR), and Low-Power Backscatter Communication (WiFi/power-line tags). She directs the Wireless, Sensing & Embedded Networked Systems (iSENS) Lab and co-directs the Illinois Center for IoT. Her work bridges wireless networking with cyber-physical sensing , emphasizing environmental monitoring (e.g., wildfire fuel detection via radar tags) and human-computer interaction (e.g., gaze-tracking in VR). Recent articles include innovations in passive radar profiling , through-canopy biomass characterization , and integrated communication-sensing protocols . Scientific Awards: Google Research Scholar Award (2022) N2Women Rising Star (2021) ACM SIGMOBILE Dissertation Award (2020) EECS Rising Stars (2019) NCSA Faculty Fellowship (2023) Best Demo Runner-up, IPSN (2023) Teaching Excellence Award (2023) Grants: NASA FireTech Program Grant (2025) NSF Grant for Radar-based Perception (2024) Insper-Illinois Grant for VR Research (2024) Keysight Research Gifts (2022, 2023) T-Mobile Research Gift (2022)
Prof. Dr. Steffen Marburg is a Full Professor at the Chair of Acoustics of Mobile Systems within the TUM School of Engineering and Design at the Technical University of Munich. His research focuses on numerical methods in vibroacoustics, structural optimization, and acoustic modeling for applications in automotive, maritime, and musical instrument domains. Education: PhD from Technical University of Dresden (1998). Academic Career: Junior Professor at TU Dresden (2004), Chair of Technical Dynamics at University of the Federal Armed Forces Munich (2010), Full Professor at TUM (2015–present). Editorial Roles: Co-Editor-in-Chief of Journal of Theoretical and Computational Acoustics, Associate Editor of Journal of the Acoustical Society of America, Editor of Acoustics Australia and Mechanical Systems and Signal Processing. His research integrates computational acoustics, boundary element methods, and machine learning to address noise control and structural optimization challenges. Recent work explores acoustic metamaterials, viscothermal losses, and data-driven modeling. He has co-authored over 150 publications and led advancements in multifrequency solution methods and noise-insulating structures. Scientific awards include the Innovation Award of the Industrieclub Sachsen e.V. (1999). His editorial contributions and leadership in journals highlight his influence in computational acoustics and structural dynamics.
Dr. Binbin Xie is an Assistant Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). She holds a Ph.D. from the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, where she was a recipient of the prestigious Google PhD Fellowship in Mobile Computing. Her research focuses on wireless sensing, mobile health, cyber-physical systems, smart IoT, and wireless networking. Dr. Xie’s academic journey includes a tenure-track position at UTA since September 2024. She has received notable awards such as the Emerging Rockstar (IEEE Pervasive Computing, 2025), Rising STARs Award (UT System, 2024), and multiple scholarships including the Krithi Ramamritham Computer Science Scholarship (2023). Her work spans innovative applications in wireless sensing, leveraging LoRa, mmWave radar, and RFID technologies for real-world challenges like in-vehicle sensing and indoor localization. Her recent publications highlight advancements in LoRa sensing coexistence with communication, mmWave radar human sensing using secondary reflections, and combating interference in multi-target localization. She actively contributes to the academic community through service roles, including Technical Program Committee memberships for MobiSys 2025 and EWSN 2025, and as a reviewer for top-tier conferences like IMWUT/UbiComp and journals such as ACM Transactions on Sensor Networks. Dr. Xie’s teaching includes courses like CSE 4321 (Software Testing & Maintenance). Her research and educational efforts aim to bridge theoretical innovations with practical implementations in wireless and IoT systems.
Prof. Dr. Markus Zimmermann leads the Chair of Product Development and Lightweight Design at the Technical University of Munich (TUM). With a background in mechanical engineering from TU Berlin and the University of Michigan, and a doctorate from MIT on solid-state singularities, he bridges academic rigor with industrial application. His career spans 12 years at BMW focusing on vehicle development before transitioning to academia. Specializes in solution space engineering for robust design Expert in additive manufacturing and systems engineering Develops methodologies for managing design complexity and uncertainty His research focuses on multidisciplinary design optimization and lightweight structures , particularly in robotics and automotive systems . His team applies digital twin frameworks and attribute dependency graphs to enhance design processes. Recent publications emphasize topology optimization in robotic systems and thermal management for medical X-ray sources. Key trends in his 2024-2025 publications include: Topological optimization for additive manufacturing and robotics Application of solution spaces to manage design uncertainty Development of compact X-ray systems for medical therapy Integration of digital twin technologies in industrial contexts
Jie Bai is an Associate Professor of Public Policy at Harvard Kennedy School (HKS), focusing on firms and markets in developing economies. His research addresses barriers to firm growth, market frictions, and policy design for private sector development in regions like China, East Africa, and Southeast Asia. Methodologically, he combines randomized control trials, quasi-experiments, and structural modeling from industrial organization and international trade. He holds a Ph.D. in Economics from MIT (2016) and previously worked at Microsoft Research New England before joining HKS in 2017. His work emphasizes collaboration with governments and NGOs to evaluate industrial and trade policies. Key research themes include collective reputation in trade, environmental policy impacts, corruption dynamics, and child labor economics. His recent publications analyze China's dairy industry reputation, Vietnam's firm corruption patterns, and Zambia's product choice perceptions. He co-founded initiatives like the China Econ Lab and China and the Global Economy project to foster research on China's role in global economics. Teaching includes advanced microeconomic analysis and game theory. No grants or labs are explicitly listed in the provided texts.
ONG Soh Khim is a Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS) and a member of the NUS Graduate School. She is a Fellow of CIRP (International Academy for Production Engineering), the first Asian and fourth female Fellow globally. Her research focuses on augmented reality (AR) and digital twins in manufacturing, with applications in assistive technology and sustainable design. She has authored/co-edited 6 books, over 300 journal/conference papers, and holds an H-index of 66 (Google Scholar) and 52 (Scopus). She has held leadership roles, including serving as a Nominated Member of the Singapore Parliament (2005–2006) and on multiple NUS governance boards. Her honors include the Singapore Youth Award (2004), M. Eugene Merchant Award (2004), and the 2021 Long Service Medal. She advocates for gender diversity in STEM, leading initiatives like the Girls2Pioneers program. Her current roles include Senior Principal Investigator at NUS Research Institutes in Suzhou and Guangzhou, and Visiting Professorships at Chinese universities.
Daniel M.G. Raff is an Associate Professor of Management at the Wharton School of the University of Pennsylvania, where he has been affiliated since 1994. His research bridges business history and strategy, focusing on urban history, public policy, and historical demography. PhD, Massachusetts Institute of Technology, 1987 BPhil, Oxford University, 1978 MPA, Princeton University, 1976 BA, New College, 1973 Raff’s work explores the historical evolution of business institutions, with recent publications analyzing topics such as pre-modern Chinese real estate markets , industrial routines , and strategic risk management . His research often employs archival data and interdisciplinary methodologies. His publications span business history, economic performance, and transactional engineering, reflecting trends in historical demography , corporate strategy , and market evolution . Raff has also contributed to academic leadership as a Trustee of the Business History Conference and Chair of committees at the Economic History Association. Daniel Raff actively engages in teaching courses like Value Creation & Val Cap and Deals: Econ Struc Trans , emphasizing critical historical thinking in strategic business analysis. He is affiliated with institutions such as the National Bureau of Economic Research and the Wharton Financial Institutions Center.
