جست‌وجوی استاد و پژوهشگر

Appala Raju Badireddyمشاهده پروفایل

دانشیار

• Water Quality Engineering
• Membrane Separation Processes
• Environmental Nanotechnology
• +۵ مورد دیگر

Dr. Appala Raju Badireddy is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Vermont (UVM), and Director of the Water Treatment & Environmental Nanotechnology (WTEN) Laboratory. He is also co-founder and CTO of Secure Surgical Solutions LLC, and a founding member of Vermont Initiative for Biological and Environmental Surveillance (VIBES). His research focuses on sustainable membrane processes, environmental nanotechnology, nanometrology, and water security. Education: Ph.D., Environmental Engineering, University of Houston (2003-2009) M.Tech., Chemical Engineering, Indian Institute of Technology Madras (2001-2003) B.Tech., Chemical Engineering, Jawaharlal Nehru Technological University Hyderabad (1997-2001) Postdoctoral Research, Duke University (2009-2014) under Prof. Mark Wiesner Research Interests: Sustainable Membrane Processes: Water/wastewater treatment, desalination, anti-fouling strategies, and resource recovery. Environmental Nanotechnology: Nano-enabled sensors, remediation, and implications of nanomaterials in ecosystems. Environmental Chemodynamics: PFAS fate/transport, nutrient cycling, and contaminant toxicity. Water Security: Real-time monitoring systems and soil health assessments. His work integrates lab-scale innovations with field applications, emphasizing interdisciplinary collaboration. Recent Research Trends: Recent publications highlight advancements in PFAS remediation, electric-field enhanced filtration, and living lab approaches to precision agriculture. He explores nanomaterials for water treatment while addressing their environmental implications through novel detection methods like ED-HSI microscopy. Labs & Initiatives: WTEN Lab: Focuses on nanotechnology-driven water solutions. VIBES: Develops environmental surveillance tools for public health. Secure Surgical Solutions: Applies nanotechnology to medical devices.

Angela Di Fulvioمشاهده پروفایل

دانشیار

• Nuclear Engineering
• Radiation Detection
• Nonproliferation Technologies
• +۳ مورد دیگر

Angela Di Fulvio is an Associate Professor and Donald Biggar Willett Faculty Scholar at the University of Illinois at Urbana-Champaign, holding joint appointments in the Department of Nuclear, Plasma, and Radiological Engineering and the Center for Digital Agriculture at NCSA. She leads the Nuclear Measurement Laboratory (NML), focusing on radiation detection technologies for nonproliferation, medical physics, and nuclear security. Her academic journey includes a Ph.D. in Nuclear Engineering and Industrial Safety from the University of Pisa (2012), preceded by M.Sc. and B.Sc. degrees in Bioengineering. Her research emphasizes neutron detection instrumentation, radiation protection in therapy, and safeguards applications. Key areas include next-generation thermal neutron detectors, boron neutron capture therapy dosimetry, and spent nuclear fuel imaging. She has pioneered work on pulse shape discrimination using commercial ASICs and developed algorithms for neutron-gamma discrimination in harsh environments. Di Fulvio’s 15+ peer-reviewed articles span advanced detection systems, Monte Carlo modeling, and machine learning for radiation imaging. Notable contributions include a physics-based forward model for spent fuel imaging and variational autoencoder-based pulse discrimination. Her work has been recognized with the Dean’s Award for Excellence in Research. Professional roles include Associate Editor of Radiation Measurements and editorial board member of Nature Scientific Reports . She chairs APS’s Instrumentation and Measurement Science group and ANS’s Nuclear Nonproliferation Policy Division. Recent courses taught include NPRE 451-452 labs, Nuclear Safeguards, and Student Research Seminars.

Amy Childressمشاهده پروفایل

استاد

• Membrane processes
• Desalination
• Water treatment
• +۶ مورد دیگر

Amy Childress is Dean's Professor of Civil and Environmental Engineering at the University of Southern California's Viterbi School of Engineering. She serves as director of the Civil and Environmental Engineering Department's environmental engineering program and leads the Center for Water Reuse (ReWater). Dr. Childress has been with USC since summer 2013 and previously served as professor and chair of the Civil and Environmental Engineering Department at the University of Nevada, Reno. Dr. Childress earned her educational degrees from the following institutions: Bachelor's Degree in Civil Engineering from the University of Maryland College Park Master's Degree in Civil Engineering from the University of California - Los Angeles Doctoral Degree in Civil Engineering from the University of California - Los Angeles For over 20 years, Professor Childress' research has focused on membrane processes for addressing global water scarcity challenges. Her current research interests include membrane contactor processes for innovative solutions to contaminant and energy challenges; pressure-driven membrane processes as industry standards for desalination and water reuse; membrane bioreactor technology; and colloidal and interfacial aspects of membrane processes. She emphasizes process sustainability through reduction of discharge by-products, limiting chemical and material consumption, and minimizing energy, carbon, and infrastructure footprints of treatment systems. Her work explores the water-energy nexus to develop holistic solutions for finite water and energy resources. Professor Childress' recent publications demonstrate a consistent focus on advancing membrane technologies for water treatment and desalination. Her research spans fundamental studies of membrane properties and performance to applied research on system integration and optimization. Key themes include improving membrane wetting resistance, developing mathematical models for water blending, understanding morphological changes in membranes, exploring power density limitations in osmotic processes, and investigating long-term operational effects on membrane performance. Her work consistently addresses the technical challenges of water scarcity while considering sustainability and energy efficiency. Her scientific achievements have been recognized with numerous awards and honors: National Science Foundation CAREER Award (2001) NAE Frontiers of Engineering Invited speaker (2007) AEESP President (2008) Multiple fellowships and scholarships from UCLA, AWRA, ACS, and water districts UNR Student Chapter of AWRA Excellence in Teaching Honorable Mention Award (2001) Clair A. Hill Scholarship (1995) Larson Aquatic Research Support (LARS) Scholarship (1996) Professor Childress has directed research projects funded by numerous prestigious organizations including the U.S. Bureau of Reclamation, NSF, NASA, Office of Naval Research, U.S. Department of Energy, California Energy Commission, California Department of Water Resources, the U.S. EPA, and SERDP, as well as local and private agencies. She leads a productive research group that has generated numerous peer-reviewed publications, proceeding papers, and patents. Her leadership extends to service on the AEESP Foundation Board of Directors and previously as AEESP President. She also serves on the Advisory Board of Desalination journal. Dr. Childress leads the Childress Research Group at USC, which focuses on fundamental and applied aspects of membrane processes for water treatment and desalination. The group maintains laboratory facilities in Biegler Hall (BHE) at USC and conducts both experimental and modeling research to advance water treatment technologies. Their work addresses critical challenges in southern California and around the world related to wastewater reclamation and seawater desalination.

• Transportation Engineering
• Road Safety
• Remote Sensing
• +۵ مورد دیگر

Dr. Karim El-Basyouny is a Killam Laureate Professor and City of Edmonton Urban Traffic Safety Research Chair at the University of Alberta's Faculty of Engineering, where he serves as Associate Dean (Research Infrastructure and Innovation) in the Civil and Environmental Engineering Department. A licensed Professional Engineer in Alberta, he holds advanced degrees in Transportation Engineering from the University of British Columbia and has dedicated his career to advancing road safety through data-driven management frameworks. His academic credentials include: Doctor of Philosophy, Civil Engineering, University of British Columbia, 2011 Engineering Management Sub-specialization, Civil Engineering, University of British Columbia, 2010 Master of Applied Science, Civil Engineering, University of British Columbia, 2006 Bachelor's degree (ABET Equivalent), Civil & Environmental Engineering, United Arab Emirates University, 2003 El-Basyouny's research pioneers the integration of remote sensing, machine learning, and statistical modeling to enhance transportation safety. His work develops automated tools for infrastructure digitization, collision prediction, and speed management, treating safety as a systemic product requiring management frameworks. Key contributions include LiDAR-based road feature extraction, network-level safety evaluations, and frameworks for vision-zero outcomes that address both human-driven and autonomous vehicle contexts. His recent publications demonstrate a cohesive research trajectory centered on leveraging point cloud data and computational intelligence for safety management. Over 15 major publications since 2021 focus on automated infrastructure assessment (light pole detection, clear zone mapping, vertical clearance evaluation), weather-impact modeling, and enforcement resource optimization. This body of work bridges transportation engineering with computer vision and operations research to create scalable safety solutions. His scientific contributions have been recognized with prestigious honors including: 2024 Killam Annual Professorship Award 2024 Road Safety Achievement Award from TAC 2023 Donald Stanley Award for environmental engineering 2022 Faculty of Engineering Graduate Teaching Award 2021 Daniel B. Fambro Student Paper Award As an academic leader, El-Basyouny actively mentors graduate students and secures significant research funding through his endowed chair position. He currently recruits fully-funded PhD and postdoctoral candidates specializing in remote sensing applications, machine learning, and geomatics for road digitization projects. His research group collaborates with national safety committees and municipal agencies to translate findings into policy, while he serves on editorial boards for Transportation Research Record and Analytic Methods in Accident Research. The research group operates at the intersection of transportation engineering and computational science, developing automated frameworks that merge sensor technologies with data processing tools. Current projects focus on semantic segmentation of 3D point clouds, safety implications of infrastructure digitization, and machine learning applications for road feature extraction in both urban and rural environments.

Carlos Torres-Verdinمشاهده پروفایل

استاد

• Borehole Geophysics
• Petrophysics
• Formation Evaluation
• +۸ مورد دیگر

Carlos Torres-Verdín is a Professor and holds the Brian James Jennings Memorial Endowed Chair and Zarrow Centennial Professorship in Petroleum Engineering at The University of Texas at Austin's Hildebrand Department of Petroleum and Geosystems Engineering, within the Jackson School of Geosciences. He earned a B.S. in Geophysical Engineering from the National Polytechnic Institute of México (1983), an M.Sc. in Electrical Engineering from UT Austin (1985), and a Ph.D. in Engineering Geoscience from UC Berkeley (1991). His research focuses on petrophysical and geophysical characterization of subsurface regions using well logging, seismic, and multi-physics data. Key areas include borehole geophysics, rock physics, reservoir characterization, and hydraulic fracturing. He has received numerous accolades, including the 2020 Virgil Kauffman Gold Medal (SEG) and the 2017 Conrad Schlumberger Award (EAGE). His work integrates advanced numerical methods and machine learning to enhance reservoir evaluation and CO2 sequestration monitoring. Torres-Verdín teaches courses such as PGE 358 (Formation Evaluation) and directs the Formation Evaluation Joint Industry Research Consortium, fostering industry-academia collaboration. Awards & Honors 2020 Virgil Kauffman Gold Medal, SEG 2019 Anthony F. Lucas Gold Medal, SPE 2017 Conrad Schlumberger Award, EAGE 2017 Lockheed Martin Excellence in Engineering Teaching Award Research & Teaching His recent studies address challenges in unconventional reservoirs, fluid dynamics in nanoporous media, and real-time geosteering. He has published over 150 peer-reviewed articles, emphasizing innovation in inversion techniques, NMR applications, and reservoir simulation.

Daniel M. Wolpertمشاهده پروفایل

استاد

• Motor Control
• Computational Neuroscience
• Sensorimotor Learning
• +۴ مورد دیگر

Daniel M. Wolpert is a Professor of Neuroscience at Columbia University and Principal Investigator at the Zuckerman Institute. His research focuses on computational models of movement, integrating sensory cues and cognitive elements to understand motor control, memory, and rehabilitation strategies for cerebellar disorders. Key Research Areas: Sensorimotor integration, probabilistic inference, reinforcement learning, predictive modeling of movement, and aging effects on motor learning. Selected Awards: Royal Society Fellow (2012), Minerva Golden Brain Award (2010), Fulbright Scholarship (1992-1995). Recent publications highlight his work on contextual learning, motor memory formation, and the computational basis of sensorimotor uncertainty. His lab develops robotic interfaces to study human motor behavior and collaborates on clinical applications for movement disorders. Current opportunities include postdoctoral fellowships in sensorimotor control and decision-making.

Zhibo Pangمشاهده پروفایل

استاد مدعو

• Embodied Intelligence
• Artificial Intelligence/ML
• Robotics
• +۷ مورد دیگر

Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.

Gautam Kamathمشاهده پروفایل

استادیار

• Differential Privacy
• Machine Learning
• Statistics
• +۴ مورد دیگر

Gautam Kamath is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, a Faculty Member at the Vector Institute, and a Canada CIFAR AI Chair. He leads The Salon, a research group focused on statistics, algorithms, machine learning, and optimization. His work bridges theoretical foundations with practical applications in data privacy and robustness, contributing to both academic research and real-world deployments that impact millions of users. Dr. Kamath earned his PhD and SM degrees in Electrical Engineering and Computer Science at MIT, where he was advised by Costis Daskalakis. Prior to MIT, he graduated from Cornell University in May 2012 with a degree in Computer Science and Electrical and Computer Engineering, where he worked with Bobby Kleinberg. His academic journey reflects a strong foundation in both theoretical computer science and practical applications. His research focuses on developing solutions for trustworthy and reliable machine learning and statistics, with particular emphasis on data privacy and robustness. He addresses fundamental problems in these areas, revitalizing statistical toolkits for the modern data era where privacy preservation is paramount. His work spans theoretical foundations of differential privacy to practical applications that have been deployed at scale, including contributions to systems that protect the sensitive information of hundreds of millions of individuals. Analysis of his recent publications reveals a strong trend toward addressing the interplay between privacy, robustness, and machine learning performance. His work increasingly examines practical deployment challenges while maintaining theoretical rigor, with growing emphasis on diffusion models, generative AI, and the legal implications of AI systems. There's a clear trajectory from foundational privacy theory toward addressing real-world implementation challenges across diverse application domains. Canada CIFAR AI Chair Ontario Early Researcher Award 2024 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies STOC Best Student Presentation Award ICML 2024 Best Paper Award Microsoft Research Fellow at the Simons Institute for the Theory of Computing Dr. Kamath actively mentors students, with notable successes including Valentio Iverson winning the Germain-Erdős Undergraduate Award and Chris Trevisan receiving the CRA Outstanding Undergraduate Researcher Award. His service to the research community is extensive, serving as Editor-in-Chief of TMLR, on the Executive Committee of the Learning Theory Alliance, and on steering committees for major conferences including ICML, COLT, and ALT. He has organized numerous workshops focused on privacy-preserving machine learning and differential privacy. Through The Salon research group, Dr. Kamath fosters a collaborative environment where postdoctoral fellows, graduate students, and undergraduates work together on cutting-edge problems at the intersection of statistics, algorithms, machine learning, and optimization. The group maintains strong connections with industry partners and participates in major research initiatives, including the Vector Institute's privacy and security research efforts. Looking forward, Dr. Kamath will be moving to the Computer Science department at NYU's Courant Institute of Mathematical Sciences in September 2026, where he plans to expand his research program.

Zsolt Kiraمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Zsolt Kira serves as an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology's College of Computing, with additional affiliations at the Georgia Tech Research Institute and as Associate Director of ML@GT. He leads the Robotics Perception and Learning (RIPL) Lab, driving research at the intersection of machine learning and robotics. Dr. Kira earned his Ph.D. in 2010 under Professor Ron Arkin, establishing foundational expertise in robotics and AI before transitioning to his current academic role after industry experience at SRI International Sarnoff. His research pioneers beyond supervised learning through unsupervised, semi-supervised, self-supervised, and continual/lifelong learning frameworks, while advancing distributed perception via multi-modal fusion and cross-robot information integration. This dual focus addresses core challenges in robotic autonomy and sensor processing. Analysis of his 15 most recent publications reveals dominant trends in embodied AI systems, robust foundation model adaptation, and multimodal learning architectures. Key themes include neural radiance field applications, reinforcement learning for locomotion, and novel benchmarks for memory evaluation in agents. While specific advisees and grants aren't documented in the source material, his RIPL Lab leadership implies active graduate mentorship and research funding acquisition. The lab's work directly enables next-generation robotic systems through algorithmic innovation in perception and learning. The RIPL Lab operates as a hub for developing machine learning techniques that solve difficult perception problems in robotics, with particular emphasis on unsupervised learning paradigms and distributed multi-robot systems that push the boundaries of autonomous operation.

Sam Kirshnerمشاهده پروفایل

دانشیار

• Behavioral Decision Making
• Operations Management
• Technology Management
• +۶ مورد دیگر

Associate Professor Sam Kirshner is a faculty member at the University of New South Wales within the School of Information Systems and Technology Management . His research focuses on behavioral decision making , algorithmic impact on operations , and artificial intelligence applications in business contexts. PhD in Management Science from Queen’s University, Canada Teaching expertise in data visualization, predictive analytics, and AI ethics Co-author of Business Analytics: A Management Approach Member of the Ethical AI Advisory His research explores how psychological distance and construal level theory influence decisions in supply chains, technology management, and consumer behavior. Recent work examines ChatGPT's decision biases , algorithm aversion , and sustainable operations under financial constraints. Key publication trends show focus areas: AI ethics and human-AI collaboration Behavioral supply chain analysis Temporal/spatial psychological distance effects CO2 forecasting with sparse data Consumer behavior in digital platforms Virtual reality and cognitive processing Supervision roles include mentoring 2 PhD students and 6 honors students, contributing to the next generation of scholars in business analytics and technology management.

• Applied Mathematics
• Climate Science
• Oceanography
• +۳ مورد دیگر

Dr Jordan Pitt is an academic affiliated with the University of Sydney's Faculty of Science. He holds the role of Associate Dean (Indigenous Strategy and Services) while also actively contributing as an applied mathematician. His work emphasizes interdisciplinary research and Indigenous inclusion in academia. Dr Pitt completed his PhD at the Australian National University in 2019, focusing on tsunami and storm surge modeling. He pursued post-doctoral research at the University of Adelaide, where his studies expanded into ocean wave-sea ice interactions. Current research investigates how wave dynamics influence sea ice growth/melt cycles, a critical factor in Earth's climate systems. Research Interests: Mathematical modeling of environmental systems Ocean wave mechanics Climate change impacts on sea ice Computational fluid dynamics While no specific scientific awards are explicitly listed, Jordan has been involved in initiatives like co-convening the Academy of Science’s 'Looking Back, Moving Forward' Public Lecture Series (2023) and organizing the ANZIAM 2024 Early Career Workshop. His efforts bridge Indigenous communities with STEM fields through programs like Science Pathways. As an educator and researcher, Jordan has not listed formal advisees or grants in the provided texts. His professional networks include roles in Indigenous strategy and academic service. Jordan’s work is associated with laboratories and teams focused on environmental fluid dynamics and climate modeling, though specific lab names are not mentioned.

• Operations Management
• Manufacturing Systems
• Supply Chain Management
• +۵ مورد دیگر

Sunder Kekre is the Vasantrao Dempo Professor of Operations Management at Carnegie Mellon University’s Tepper School of Business , where he has held academic appointments since 1984. His research focuses on manufacturing systems, global supply chains, and healthcare operations management. Research Expertise : New product development structures, strategic costing, lean innovation, knowledge sharing in enterprise networks, and business analytics for coordinated supply chains. Key Publications : Contributions to journals like Management Science , Operations Research , and Production and Operations Management , with work on LNG storage valuation, RFID logistics, and healthcare disparities. Scientific Recognition : Dempo Chair Professorship Bosch Chair Professorship Best Paper Award (1995) Teaching : Courses in Operations Management, Strategic Management of the Enterprise, and Information Systems Project. Non-Academic Experience : Prior engineering roles at Tata Steel (1974-1980) and consulting engagements with Fortune 500 firms like Bosch, Caterpillar, and IBM.

Karen Livescuمشاهده پروفایل

استاد

• Speech Processing
• Language Processing
• Machine Learning
• +۵ مورد دیگر

Karen Livescu is a Professor at the Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed graduate institute for computer science located on the University of Chicago campus. She also serves as a courtesy faculty member in the Department of Computer Science at the University of Chicago and is an Affiliated Scholar at the Data Science Institute there. Her research focuses on advancing speech and language processing through innovative machine learning approaches. Education: PhD in Electrical Engineering and Computer Science from MIT (2005) S.M. from MIT Department of Electrical Engineering and Computer Science (1999) A.B. in Physics from Princeton University (1996) Karen's research spans multiple dimensions of speech and language processing with particular emphasis on speech recognition, spoken language understanding, and multimodal processing. She has made significant contributions to articulatory feature-based speech recognition, self-supervised learning for speech representation, and sign language processing. Her work consistently bridges machine learning techniques with linguistic and speech science knowledge, focusing on creating more robust, interpretable, and inclusive speech processing systems that can handle diverse languages and modalities. Her recent publication trajectory reveals a strong focus on self-supervised learning for speech representation, multilingual speech processing, and sign language understanding. She has been instrumental in developing benchmark frameworks like SUPERB and ML-SUPERB that have become standard evaluation tools in the speech community. Her work increasingly addresses critical challenges in low-resource language scenarios, language disparities in speech technology, and ethical considerations in real-world deployment. Scientific Awards: Best Paper award at EMNLP 2024 for 'Towards robust speech representation learning for thousands of languages' Best Student Paper Award at ASRU 2023 Best Short Paper Award at CRAC 2021 Top system at WMT-SLT 2023 Karen has successfully advised numerous PhD students and postdoctoral researchers who have gone on to faculty positions at institutions like University of Waterloo, University of Edinburgh, and Stellenbosch University, as well as industry roles at major technology companies including Google, Meta, and NVIDIA. Her research group has secured significant funding for projects including the development of the SLUE benchmark for spoken language understanding and the SUPERB framework for evaluating self-supervised speech models. She has been actively involved in organizing workshops and symposia that bring together researchers in speech and language processing. Karen leads the Speech and Language at TTIC (SL@TTIC) research group, which maintains a strong collaborative relationship with researchers at the University of Chicago and other institutions. The group has been particularly active in advancing sign language processing through projects like ChicagoFSWild and OpenASL, while also making significant contributions to spoken language understanding and multilingual speech recognition. Her team regularly participates in community challenges and benchmarks, helping to push the field forward through open science and collaborative evaluation frameworks.

Dominika Ignasiakمشاهده پروفایل

پژوهشگر

• Biomechanics
• Musculoskeletal Modeling
• Spinal Surgery
• +۳ مورد دیگر

Dr. Dominika Ignasiak is a Researcher affiliated with the Institute of Biomechanics at ETH Zürich. Her work focuses on spinal biomechanics, musculoskeletal modeling, and computational analysis of spinal pathologies. She contributes to understanding the biomechanical implications of surgical interventions, spinal deformities, and age-related changes in spinal alignment and loading. Her research integrates clinical data with advanced musculoskeletal modeling techniques, particularly in predicting postoperative outcomes and assessing spinal load distributions under dynamic conditions. Key areas include spinal stenosis, idiopathic scoliosis, and the biomechanics of spinal fusion surgery. Dr. Ignasiak collaborates on translational studies bridging computational simulations with clinical applications. Her publications emphasize the role of personalized models in optimizing surgical strategies and understanding degenerative spinal conditions. While no formal awards are listed, her contributions to spinal biomechanics research are evident through her active publication record in high-impact journals. Dr. Ignasiak is based at ETH Zürich’s Institute of Biomechanics, where she engages in cutting-edge research and contributes to both academic and clinical advancements in orthopedic biomechanics.

Ye Zhishengمشاهده پروفایل

دانشیار

• Inventory Control
• Reliability Engineering
• Emergency Response Systems
• +۴ مورد دیگر

Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.