Dr. Li Chen is an Alfred and Helen Lamson/BORSF Endowed Associate Professor in the School of Computing and Informatics at the University of Louisiana at Lafayette. She leads the CELESTIAL research lab, focusing on distributed systems and networking for machine learning and AI. Her research interests include federated learning, cloud computing, and resource optimization. Dr. Chen holds a Ph.D. from the University of Toronto and has received awards such as the NSF EPSCoR RII Track-4 grant and the BoRSF Endowed Professorship. Education: Ph.D. (2018), M.A.Sc. (2015) in Electrical and Computer Engineering from University of Toronto; B.Eng. (2012) in Computer Science from Huazhong University of Science and Technology. She also visited Hong Kong Polytechnic University (2013-2014). Research spans federated learning frameworks (e.g., SEAFL, FedClust), cloud resource scheduling (e.g., Hadar, HarmonyBatch), and applications in weather forecasting (e.g., MMST-ViT). Her work is supported by NSF, Louisiana BoRSF, and industry partners like XRMedix. Awards include the Alfred and Helen Lamson/BORSF Endowed Professorship (2024-2027), NSF EPSCoR grant (2024-2026), and best paper recognitions at IEEE conferences. She advises a diverse group of graduate students and has supervised alumni now in academia and industry. Teaching includes courses on computer networks, operating systems, and distributed systems. She organizes workshops and tutorials (e.g., 2023 Summer Tutorial on ML & Meteorology) and serves on conference committees such as INFOCOM and IWQoS.
Dr. Madjid Mohseni is a Professor in the Department of Chemical and Biological Engineering at the University of British Columbia's Faculty of Applied Science. He serves as the Scientific Director of the Community Circle on Scaling Business Innovation for Humanity with his office located in CHBE 221. Dr. Mohseni leads the Water Laboratory (http://waterlab.chbe.ubc.ca/), focusing on water quality and advanced treatment technologies for drinking water applications. Dr. Mohseni earned his Ph.D. (1998) and M.A.Sc. (1994) from the University of Toronto, and his B.Sc. from Amirkabir University of Technology in Iran. His educational background has prepared him for his current research in water treatment and environmental engineering. His research program centers on developing, evaluating, and implementing advanced oxidation processes (AOPs), particularly UV-based AOPs, ion exchange, and electrochemical processes. His laboratory conducts both laboratory-scale development and pilot-scale field evaluations at partner community sites. Current work emphasizes PFAS remediation through various approaches including advanced oxidation/reduction processes, ion exchange technologies, and electrochemical methods. He also investigates novel materials like MXenes for water purification and develops practical solutions for municipal and community water systems. Analysis of Dr. Mohseni's recent publications reveals a strong focus on emerging contaminants, particularly PFAS, with significant emphasis on UV-based treatments and vacuum UV technology. His research consistently bridges fundamental science with real-world applications, developing technologies specifically tailored for small community water systems while addressing critical water quality challenges. Dr. Mohseni maintains affiliations with the Clean Energy Research Centre and the Bioproducts Institute at UBC, demonstrating his commitment to interdisciplinary research that addresses environmental challenges through innovative engineering solutions. His work aims to advance the science behind water treatment technologies while offering communities more efficient and cost-effective solutions to protect human health and the environment.
Ambuj Varshney is an Assistant Professor at the National University of Singapore (NUS) School of Computing , leading the WEISER research group . His work bridges electronics, wireless communication, computer science, and AI with a focus on creating ultra-low-power embedded systems for sustainable IoT deployments. University of California, Berkeley: Postdoctoral Scholar (2020-2022) Uppsala University: PhD in Sustainable Networked Systems NXP Semiconductors: Software Engineer (prior to PhD) Bachelors in Information & Communication Technology Research interests center on overcoming wireless systems' energy asymmetry through tunnel diode oscillators , LiFi-RF hybrid networks , and battery-free communication architectures . His group develops STICORS —sticker-like computers for industrial and medical monitoring. Recent publications demonstrate AudioCast 's FM-band utilization for 130m transmission, TunnelSense 's vital monitoring, and PixelGen 's diffusion model cameras. These works combine IoT sustainability, spectrum efficiency, and hardware innovation . 2024: Google Research Scholar Award 2023: MobiSys Best Demonstration 2021: Berkeley FORM+FUND Fellowship 2019: ABB's $300K Research Award As an educator, he teaches CS4222 Wireless Networking and CS5272 Embedded Software Design . Past students include Wenqing Yan (NUS/UCB PhD), Qiao Yukai , and Kunjun Li . His team collaborates with Prabal Dutta (UCB), Christian Rohner (Uppsala), and Prateek Saxena (NUS).
Nabil Alshurafa is an Associate Professor at Northwestern University, holding joint appointments in the McCormick School of Engineering (Computer Science and Electrical and Computer Engineering) and the Feinberg School of Medicine (Preventive Medicine). He directs the HABits Lab, which focuses on developing mHealth systems to address health behaviors such as overeating, stress, and UV exposure. His work integrates wearable sensors, machine learning, and behavioral science to create passive sensing solutions. Education: PhD in Computer Science and Wireless Health (UCLA), MS and BS in Computer Science (UCLA). Research Interests: Body sensor networks, activity recognition, embedded systems, and health informatics. His lab designs wearable devices (e.g., neck-worn sensors, UV patches) and AI frameworks to detect behaviors like eating patterns and stress levels. Collaborations include domain experts in medicine and engineering to translate technical innovations into clinical interventions. Recent Projects: Developing systems for stress monitoring via ECG-PPG patches, UV exposure tracking, and just-in-time interventions for overeating. The lab emphasizes ethical design, privacy preservation, and user-centered technology. Students and Lab Team: Supervises PhD, MS, and undergraduate researchers in areas like machine learning, embedded systems, and health data analytics. Notable advisees include Rawan Alharbi (PhD candidate), Shibo Zhang (PhD student), and Wilson Wang (MS student). Labs/Teams: HABits Lab collaborates with experts in Preventive Medicine, Psychiatry, and Dermatology to advance interdisciplinary health research. Current projects include predictive analytics for weight loss and interventions targeting maternal stress during pregnancy.
Prof. Dr. Christian Breitsamter is a Professor at the Technische Universität München (TUM), leading the Chair of Aerodynamics and Fluid Mechanics within the TUM School of Engineering and Design. He has held this position since 2007 and has been a member of key committees such as the ICAS Programme Committee and STAB-Programmleitung. His research focuses on aerodynamics of aircraft and rotorcraft configurations, including vortex dynamics, aeroelasticity, and fluid-structure interaction. Education: PhD in Aerodynamics (1997) Master’s in Aerospace Engineering (1989) Research Interests: Prof. Breitsamter’s work spans experimental and numerical studies of high-agility aircraft, helicopter aerodynamics, and advanced wing designs. Key areas include leading-edge vortices, gust load mitigation using flexible wings, and flow control techniques. His group investigates cutting-edge topics like deep learning for buffet prediction and hybrid neural networks for aerodynamic modeling. Awards: Willy Messerschmitt Preis (1999) AIAA Associate Fellow (2007) Advising & Grants: While specific student names are not listed, his research involves collaborative projects with industry partners (e.g., RACER Compound Helicopter) and EU initiatives like the FURADO program. His team contributes to the NFDI4ING infrastructure for engineering data. Labs/Teams: Active in the Aerodynamics Wind Tunnel facilities (Windkanäle A/B/C) and leads the SAGITTA flying wing demonstrator project. His group also explores membrane wings and elasto-flexible morphing technologies.
Ian Horrocks is a Professor of Computer Science at the University of Oxford and a Fellow of Oriel College. His research focuses on knowledge representation, description logics, automated reasoning, and semantic web technologies. He has held academic positions at the University of Manchester (2003–2007) and served as Chief Scientist at Cerebra Inc. (2001–2006). Horrocks earned his BSc (1st class), MSc, and PhD in Computer Science from the University of Manchester (1981–1997). His work includes foundational contributions to ontology languages (e.g., OWL) and reasoning systems such as HermiT and ELK. He has supervised over twenty doctoral students and postdoctoral researchers. His honors include Fellowships from the Royal Society (2011), ECCAI (2009), and the British Computer Society (2005). He serves as Editor-in-Chief of the Transactions on Graph Data and Knowledge and leads initiatives in semantic web standards and knowledge graph applications. Key Roles: Editor-in-Chief (Journal of Web Semantics), Co-Chair (W3C OWL Working Group) Grants: EPSRC Senior Research Fellowship (2005), numerous international collaborations Labs: Oxford Semantic Technologies, involvement in projects like RDFox and PAGOdA
Gerd Bruder is an Associate Professor at the University of Central Florida , affiliated with the Department of Computer Science . He previously held positions as a Research Associate Professor at UCF's Institute for Simulation and Training (2016-2022), Post-Doc at University of Hamburg (2014-2016), and Post-Doc at University of Würzburg (2011-2014). Habilitation : Computer Science, University of Hamburg (2017) Ph.D. : Computer Science, University of Münster (2011) M.Sc. : Computer Science (minor in Mathematics), University of Münster (2009) Research Interests : Gerd Bruder's work focuses on Extended Reality (XR) systems, spanning Virtual Reality (VR) , Augmented Reality (AR) , and Mixed Reality (MR) . Key areas include human factors , digital twins , 3D user interfaces , and perceptual modeling , with applications in healthcare, military operations, architecture, and smart environments. Recent Publications highlight innovations in XR locomotion , trust calibration with autonomous agents, spatial cognition , and haptic-visual integration . His 2025 articles address task-switching efficiency in XR and collaborative mixed reality design. Scientific Awards : Best Paper at ACM VRST 2023 2021 TechConnect Innovation Award Best Demo at ACM SUI 2021 Multiple Best Paper Awards (2016-2023) Best Poster & Demo Awards (2015-2020) Patents include systems for medical simulation , smart environment interruption management , and adaptive AR interfaces . His work bridges academic research and practical applications across VR, AR, and MR domains.
Lin Ma is currently an Assistant Professor at the University of Michigan, Ann Arbor in the Department of Electrical Engineering and Computer Science (College of Engineering). Previously, they served as a Post Doctoral Fellow at Carnegie Mellon University (2021-2022) and as a Software Engineer at Databricks, Inc. (2022-2023). Research Interests focus on the intersection of database systems and machine learning, particularly in developing self-driving database management systems . Key areas include workload forecasting , automated index optimization , query execution acceleration , and machine learning integration for database automation. Their work explores GPU-accelerated analytics, memory optimization, and transactional consistency models. Academic Contributions span 15+ publications in top venues like VKDB , SIGMOD , and CIDR , including recent 2025 papers on Vortex (GPU memory optimization) and Scompression (workload compression). Earlier work introduced QueryBot 5000 , a workload forecasting framework, and explored anti-caching for storage optimization in OLTP systems. Teaching includes courses like EECS 584: Advanced Database Management Systems and EECS 484: Database Management Systems at the University of Michigan (2023-2025), and 15-445/645 Database Systems at Carnegie Mellon University. Service involves program committee roles for SIGMOD (2023-2025), VLDB (2022-2025), and CIDR (2024-2025). They also served on admissions and search committees at both institutions. Advising includes supervising PhD and MS students: Siyuan (Doug) Dong , Zhongwei Xu , and Haotian (Jack) Gong (co-advised with Barzan Mozafari), among others.
John MacLaren Walsh is a Professor in the Department of Electrical and Computer Engineering at Drexel University, where he leads the Adaptive Signal Processing and Information Theory Research Group. He holds BS, MS, and PhD degrees from Cornell University, all completed under Dr. C. Richard Johnson, Jr. His research spans information theory, network coding, distributed computing, and machine learning applications in patent analysis. His work focuses on: Bounding entropic vectors and their impact on communication networks Rate region computation for network coding and distributed storage Information theory for distributed function computation Machine learning-enhanced patent processing systems Publications emphasize entropy geometry, network coding complexity, distributed algorithms, and patent analysis, with consistent themes of optimization and combinatorial methods. Recent work (2016-2019) shows increased focus on probabilistic supports and computational efficiency in network coding. Awards: 2011 NSF CAREER Award for 'Entropy Geometry in Variational Inference Signal Processing' He has advised PhD students on topics like entropy region mapping, network coding, and distributed control. Key grants include NSF CAREER and AFOSR funding for wireless network overhead control. He directs the Adaptive Signal Processing and Information Theory Research Group, which develops algorithms for network coding, distributed storage, and patent analysis systems.
Yayue Pan is a Professor at the Department of Mechanical and Industrial Engineering, University of Illinois Chicago (UIC) , and serves as the Director of NASA MIRO Center for In-Space Manufacturing: Recycling and Regolith Processing (CISM-R2) . Her research focuses on advancing Additive Manufacturing (AM) technologies for applications in biomedical engineering , energy storage , and smart structures . Ph.D., Industrial and Systems Engineering, University of Southern California (2014) M.S., Mechanical Manufacturing and Automation, Zhejiang University, China (2010) B.S., Industrial Engineering, Zhejiang University of Technology, China (2007) Her work addresses technical challenges in AM such as multi-material printing , multi-scale fabrication , and field-assisted processes . Notable projects include: Development of electrostatically-assisted direct ink writing (eDIW) for high-speed, high-resolution printing Continuous projection stereolithography for rapid solid object manufacturing Acoustic field-assisted particle patterning for smart composites Light-curable hydrogels for corneal repair applications Her 15 most recent publications (2022–2025) span topics in: Multi-material AM (conductive polymers, hierarchical composites) Biomedical applications (soft robotics, corneal repair) Energy components (battery electrolytes, supercapacitors) Field-assisted processes (acoustic, electrostatic, magnetic) Scientific Awards : 2024 ASME Chao and Trigger Young Manufacturing Engineer Award 2022 UIC Researcher of the Year Rising Star Award 2020 ASME CIE TC Leadership Award 2019 UIC Outstanding Teaching Award 2017 SME Outstanding Young Manufacturing Engineer Award NSF REU Supplements (2023–2024) Advising : Mentored 24+ graduate/undergraduate researchers, including 17 NASA/GPIP interns. Former advisees hold academic positions at University at Buffalo and University of North Carolina at Charlotte , and industry roles at Apple , GE Healthcare , and ANSYS . Grants : Recipient of a $4.65M NASA grant and multiple NSF awards. Collaborations include Northwestern University, University of Michigan, and NASA centers.
Ashwani K. Gupta is a Distinguished University Professor at the University of Maryland, holding the Minta Martin Professorship in Engineering. He serves as Professor in the Department of Mechanical Engineering, Professor at the Institute of Physical Science and Technology, and Affiliate Professor in the Department of Aerospace Engineering. With over 45 years of experience in combustion engineering since graduating from Southampton University in 1970, Gupta has established himself as a leading authority in advanced combustion technologies. Dr. Gupta earned his Ph.D. from the University of Sheffield in 1973, followed by a D.Sc. from the same institution in 1986 and another D.Sc. from Southampton University in 2013. His academic journey includes six years at MIT as a research staff member and three years at Sheffield University as an independent research worker before joining the University of Maryland in 1983. Gupta's research focuses on revolutionizing combustion technology through innovations in swirl flows, high-temperature air combustion (HiTAC), and distributed combustion systems. His pioneering work on 'colorless distributed combustion' has enabled ultra-low emission combustion processes with significant applications in gas turbine engines and waste-to-energy conversion. His research spans biofuels, CO2 utilization, sulfur chemistry, waste conversion, and advanced laser diagnostics, addressing critical challenges in sustainable energy and environmental protection. Analyzing his recent publications reveals a strong emphasis on waste-to-energy conversion, biomass processing, and CO2-assisted technologies. Gupta's work demonstrates a clear trajectory toward sustainable energy solutions, with increasing integration of artificial intelligence for combustion optimization and emission control. His research bridges fundamental combustion science with practical engineering applications for cleaner energy systems. Among Gupta's numerous accolades are: Election to Fellowship of the Royal Academy of Engineering (2023) Honorary Fellowship of the Royal Aeronautical Society (2020) Recognition as one of the top 2% of scientists worldwide by Stanford University (2022-2024) Multiple prestigious medals from ASME and AIAA including the Soichiro Honda Medal (2018) and AIAA Air Breathing Propulsion Award (2014) Honorary doctorates from three international universities Gupta has secured substantial research funding throughout his career, resulting in over 850 technical papers, three books, 18 edited books, and 22 book chapters. He has delivered over 100 plenary/keynote/invited presentations at international conferences. His mentorship has shaped numerous graduate students who continue to contribute to the field of combustion engineering. Gupta directs the Combustion Laboratory at the University of Maryland, which serves as a hub for cutting-edge research in sustainable combustion technologies. The Combustion Laboratory, under Gupta's leadership, has become a center of excellence for advanced combustion research, particularly in distributed combustion systems, waste-to-energy conversion, and alternative fuels. The lab maintains strong collaborations with industry partners and international research institutions, facilitating technology transfer and practical implementation of research findings. Gupta's team employs state-of-the-art diagnostics and computational tools to advance fundamental understanding while developing practical engineering solutions for cleaner energy systems.
Hadi Esmaeilzadeh is an Associate Professor at the University of California, San Diego in the Department of Computer Science and Engineering . He leads the Alternative Computing Technologies (ACT) Laboratory and serves as Associate Director of the Center for Machine Integrated Computing and Security (MICS) . Previously, he was an Assistant Professor at Georgia Institute of Technology. Ph.D., Computer Science and Engineering, University of Washington (2013) Research focuses on computer architecture , machine learning acceleration , and approximate computing His work has produced 15+ publications spanning IEEE Micro Top Picks , CACM Research Highlights , and ISCA . Key projects include: Tabla : Cross-stack ML acceleration framework DnnWeaver : Open-source DNN acceleration platform Major honors include: IEEE TCCA Young Computer Architect Award ISCA Hall of Fame Qualcomm Innovation Fellowship Georgia Tech PURA Award Teaching roles: CSE 141: Introduction to Computer Architecture CSE 240D: Accelerator Design for Deep Learning CSE 240A: Principles of Computer Architecture
Juan Zhai is an Assistant Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. She co-directs the Laboratory for Advanced Software Engineering Research (LASER) and is a member of the UMass NLP group. Her research advances software engineering through automated techniques for building high-quality systems with emphasis on behavioral specifications, AI safety, and trustworthy AI. Her work addresses the fundamental challenge of aligning software behavior with intended specifications through two main directions: automated specification synthesis (translating natural language comments to formal specifications via tools like C2S and LLMCup) and defect detection/repair (developing frameworks for AI system testing, bias mitigation, and training diagnostics). Her vision integrates these into end-to-end assurance systems that continuously validate, repair, and audit evolving software in dynamic environments. Recent publications (2024-2025) reveal dominant trends at the software engineering/AI intersection: formal specification synthesis for IoT and code generation, comment maintenance using LLMs, deep learning framework testing (DevMuT, Citadel), bias detection in LLMs, and automated training repair (AutoTrainer, DREAM). These contributions appear in top venues including ICSE, FSE, ASE, ISSTA, and ACL. Professor Zhai currently advises PhD student Gehao Zhang (focusing on Software Engineering and AI Safety) and actively recruits new PhD/Master's students. Her LASER lab develops practical tools for specification inference, LLM-driven synthesis, and trustworthy AI, while collaborating with the UMass NLP group on language-centric software analysis. The LASER lab, co-directed by Zhai, pioneers techniques for behavioral specification enforcement across traditional and AI-powered systems. Key projects include CPC for bidirectional code-comment analysis, ModelMeta for deep learning framework testing, and frameworks for bias mitigation across the ML lifecycle. The lab emphasizes practical, scalable tools that enhance correctness, robustness, and fairness in critical AI applications.
Tim Ibell is a Professor in the Department of Architecture & Civil Engineering at the University of Bath, affiliated with the Centre for Climate Adaptation & Environment Research (CAER). He has held leadership roles including Head of Department, Associate Dean (Graduate Studies), and Associate Dean (Research). His career includes a Fulbright Distinguished Scholar Award (2002) and a stint as Sir Kirby Laing Professor of Civil Engineering at the University of Cambridge. He is a Fellow of the Royal Academy of Engineering and has received six best journal-paper awards. Education: Postdoctoral research at the University of Cambridge following industry experience. Joined Bath in 1997. Research focuses on sustainable construction materials, FRP reinforcement, and embodied carbon reduction in concrete structures. His work aligns with UN SDGs on climate action and sustainable cities. Key innovations include fabric-formed concrete structures and low-carbon pile foundations. Recent projects include EPSRC-funded studies on pile optimization, creative design teaching, and resource efficiency in industrial strategy. Collaborations span UK and international institutions. Awards include the Institution of Structural Engineers' recognition and Royal Academy of Engineering Fellowship. Advising and grants: Supervises PhD students in sustainable construction. Leads multi-million-pound research projects addressing embodied carbon and material efficiency. Labs/Teams: Directs CAER and collaborates with interdisciplinary teams on projects like ACORN (Automating Concrete Construction) and MEICON (Minimising Energy in Construction).
Conor Ryan is a Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a Science Foundation Ireland-funded Investigator since 2002 and a member of multiple research centres including Lero – the Irish Software Research Centre and the Limerick Digital Cancer Research Centre. His research focuses on Genetic Programming, Grammatical Evolution, and their applications in domains like healthcare analytics, digital circuit design, and financial modeling. He has authored over 250 publications, with recent work emphasizing automated feature selection in medical diagnostics, neural architecture search, and blockchain ecosystems. Teaching includes courses on Foundations of Computer Science and Computer Games Programming. Research interests span evolutionary computation, machine learning, and interdisciplinary applications. Collaborations involve global institutions, reflecting his work's impact across computer science, engineering, and healthcare. His research has addressed challenges in breast cancer diagnosis via genetic algorithms, cryptocurrency volatility prediction using random forests, and automated generation of digital circuits. Ongoing projects explore interpretability in AI, energy-efficient computing, and sustainable transport systems through predictive analytics. Professional memberships include roles in the Centre for Research Training in Foundations of Data Science and the Data-Driven Computer Engineering Research Centre, underscoring his commitment to interdisciplinary innovation.
