Serkut Ayvasik is a Researcher at the Chair of Communication Networks at Technical University Munich (TUM). He joined TUM in March 2019 as a research and teaching associate, following his M.Sc. in Communications Engineering (2019) and B.Sc. in Electrical and Electronics Engineering (2016) from Middle East Technical University. His research focuses on: Wireless Network Resource Management for heterogeneous latency-critical 5G applications Channel State Information Prediction using depth images Network Slicing and Quality of Service optimization Machine Learning for proactive network configuration Telemedicine Applications in cross-border communication Key article trends include 5G/6G technology , IoT sustainability , digital twins , and haptic feedback systems . He contributes to IEEE and ACM journals, with recent work on Digiot (2025) and OCTOPUS (2024). Collaborations include researchers like Wolfgang Kellerer (Chair), Edwin Babaians , Alba Jano , and Fidan Mehmeti . His work spans projects such as 6G Future Lab Bavaria , DFG GGI QCDE , and ERC FlexNets .
Peter Dinda is a Professor in the Department of Computer Science at Northwestern University , with a secondary appointment in the Department of Electrical and Computer Engineering . He has authored over 130 scientific papers, holds five patents, and is a Fellow of the IEEE . As the former head of the Computer Engineering and Systems division, he has contributed extensively to experimental computer systems. Education: B.S. in Electrical and Computer Engineering from the University of Wisconsin Ph.D. in Computer Science from Carnegie Mellon University Research Focus: Experimental computer systems, particularly parallel and distributed systems , virtualization , operating systems , and empathic systems that integrate user satisfaction with systems-level decision-making. His work also spans compiler design, memory management, and hardware-software co-design for performance optimization. Recent Trends: His publications emphasize virtualization efficiency, memory protection frameworks, parallel programming language design, and power management in heterogeneous computing environments. Key areas include exascale systems, IoT privacy, and physiological sensor-based user modeling. Scientific Awards: Fellow, IEEE Leadership: Served as Director of Graduate Studies and previously led the Computer Engineering and Systems division.
Roy Dong is an Assistant Professor at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research bridges Control Theory Economics Statistics Optimization to address challenges in cyber-physical systems and the Internet of Things, focusing on data manipulation, privacy, and strategic behavior in interconnected systems. His academic journey includes a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2017) and dual B.S. degrees in Economics and Computer Engineering from Michigan State University (2010). At Illinois, he teaches courses ranging from Control Systems to Convex Optimization , with multiple teaching excellence awards. Roy's research explores Closed-loop effects of machine learning Causality in decision systems Incentive design for strategic agents Privacy-utility tradeoff optimization Human behavior modeling with applications in smart grids, transportation networks, and semi-autonomous vehicles. His work formulates privacy-preserving mechanisms as optimization problems, balancing data utility against user privacy in dynamic systems. Article trends show expertise in Game theory for strategic data sources Energy disaggregation techniques Nonlinear basis pursuit algorithms Privacy-aware control systems with a focus on cyber-physical systems and human-in-the-loop applications. Scientific recognition includes 'Teacher Ranked as Excellent' awards (ECE 120, ECE 486, ECE 515) Contributions to smartSDH building control and CPRL compressive sensing Roy leads the Privacy-aware Control Systems research group, collaborating with institutions like UC Berkeley and Michigan State University , and directs projects funded by grants including the New USDA NIFA grant for agricultural robot autonomy .
Dr. Zaheer Nasar is a Reader in Atmospheric Aerosols at Cranfield University's School of Aerospace, Transport and Manufacturing. His work focuses on real-time bioaerosol characterization, indoor/outdoor air quality dynamics, and environmental health impacts of particulate matter. He leads the NERC-funded Light-Induced Fluorescence sensor project and contributes to the BioAirNet network (NE/V002171/1) as Co-I. Research Interests Physico-chemical and biological characterization of aerosols Spatio-temporal dynamics of particulate matter (PM) and bioaerosols Quantitative microbial risk assessment (QMRA) methodologies Low-cost air quality sensor networks and machine learning calibration Urban green infrastructure effects on air pollution Policy development in Hindu Kush Himalayan air quality Recent publications emphasize machine learning-enhanced sensor calibration (2024 IEEE paper), wastewater plant bioaerosol risks (2024 Water Research), and urban air quality interventions across the UK and Lahore. He has secured over £1.6M in grants from NERC, STFC, and UKRI GCRF, with significant work on BTEX exposure in Nigeria and SARS-CoV-2 risks in wastewater facilities. Scientific Recognition Fellow of the Higher Education Academy (FHEA) Co-investigator in multiple NERC/UKRI projects Active participant in BSI bioaerosol standards committee As an advisor, he mentors five postgraduate researchers including Reece Dillon and Hathaikarn Tathong, with a strong publication record in journals like Environmental Science: Atmospheres , Risk Analysis , and BJPsych Open . His work bridges environmental science, public health, and policy implementation through interdisciplinary research.
Kaka Ma is an Associate Professor in the Department of Materials Science & Engineering at Texas A&M University, specializing in advanced materials processing for energy systems and extreme environments through powder-based synthesis, additive manufacturing, and sintering technologies. Educational Background: Ph.D. in Materials Science and Engineering, University of California, Davis (2010) B.S. in Materials Science and Engineering, University of Science and Technology of China (2006) His research focuses on powder-based synthesis of metals/ceramics, laser directed energy deposition, field-assisted sintering technology (FAST), thermionic/thermoelectric energy conversion materials, and ultrahigh-temperature/hypersonic environment applications, with strong emphasis on sustainability in materials engineering. Recent publications demonstrate expertise in creating functionally graded materials via controlled thermal gradients and powder morphology optimization. Analysis of 2021-2025 publications reveals dominant trends in spark plasma sintering parameter optimization, additive manufacturing of titanium alloys, high-entropy carbide development, and nanoparticle synthesis for energy applications, consistently linking processing parameters to microstructure-property relationships in extreme-condition materials. Scientific Awards: TMS Light Metals/Extraction & Processing Subject Award – Recycling (2020) Professional memberships include The Minerals, Metals and Materials Society (TMS) and America Makes. While specific advising details and grant information are not documented in the provided materials, his extensive collaborative publication record indicates active mentorship of graduate researchers and successful acquisition of research funding. No dedicated laboratory facilities or research team structures are specified in the source documentation.
Phillip B. Gibbons is a Professor in both the Computer Science Department and Electrical & Computer Engineering Department at Carnegie Mellon University. He received his Ph.D. in Computer Science from the University of California at Berkeley in 1989 and has held research positions at AT&T Bell Laboratories, Lucent Bell Laboratories, and Intel Research Pittsburgh before joining CMU's faculty. His research spans parallel computing, distributed systems, databases, computer architecture, and machine learning. Gibbons' work bridges theory and systems, with publications in top-tier conferences including SOSP, OSDI, SIGMOD, VLDB, NeurIPS, and many others across computer science and engineering disciplines. His research has been supported by significant funding from NSF, Intel, and other organizations. Gibbons has made substantial contributions to streaming algorithms, parallel computing frameworks, distributed systems security, and large-scale machine learning systems. His work on data stream algorithms with Alon, Matias, and Szegedy has been particularly influential in the field. He has served in numerous leadership roles including Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018) and on the editorial boards of Journal of the ACM and IEEE Transactions on Cloud Computing. He has also been active on program committees for major conferences in systems, databases, and theory. IEEE Fellow (2014) - For contributions to parallel computing and databases ACM Fellow (2006) - For contributions to parallel computing, databases, and sensor networks Selected for Oral Presentation at NeurIPS '13 (only 20 selected out of 1420 submissions) Co-winner of the best paper award for NSDI '06 Gibbons has advised numerous students and mentored researchers who have gone on to make significant contributions in academia and industry. His research has been supported by major grants including the $15M Intel Science and Technology Center for Cloud Computing (2011-2015) where he served as Co-PI/Co-Director. He currently leads research projects on write-efficient algorithms, big learning systems, and visual cloud systems. His laboratory work focuses on bridging theoretical computer science with practical systems implementation, particularly in the areas of parallel and distributed computing. Current research directions include adapting algorithms for emerging memory technologies and optimizing machine learning systems for large-scale deployment.
Rohit Kannan is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He holds a Ph.D. and M.S. in Chemical Engineering from MIT and a B.Tech. from IIT Madras. His research focuses on integrating machine learning with global optimization and optimization under uncertainty, emphasizing energy systems applications. Previous roles include postdoc positions at Los Alamos National Laboratory and the Wisconsin Institute for Discovery. Education: Ph.D., Chemical Engineering, Massachusetts Institute of Technology, 2018 M.S., Chemical Engineering Practice, MIT, 2014 B.Tech., Chemical Engineering, IIT Madras, 2012 Research Interests: Global optimization, optimization under uncertainty, computational optimization, energy systems, and machine learning integration. Recent Highlights: Recipient of the Excellence in Teaching Spotlight Award (2024) Lead researcher in stochastic optimization and energy systems (e.g., hybrid polygeneration systems) Developed algorithms for chance-constrained nonlinear programs and distributionally robust optimization Service & Leadership: Elected Vice-Chair of Global Optimization, INFORMS Optimization Society (2025–2026) Reviewer for top journals like Operations Research and Mathematical Programming Advisor to ISE InclusiveVT and Graduate Admissions Committee Labs & Collaborations: Directs a research group advancing optimization and machine learning for energy and engineering systems. Active in interdisciplinary projects with LANL and UW-Madison.
Dr. Sara McMenamin is an Associate Professor at the Herbert Wertheim School of Public Health & Human Longevity Science at UC San Diego. She serves as the Associate Director of the MPH program and Interim Assistant Dean for Undergraduate Education . Her research focuses on state-level policies addressing tobacco use and preventive services, particularly health insurance coverage for tobacco dependence treatment in California and nationally. She also co-chairs the California Health Benefits Review Program , evaluating proposed health insurance legislation's medical, cost, and public health impacts. Her work spans public health policy analysis , health insurance mandate evaluations, and tobacco control strategies. Key areas include analyzing the effects of tobacco tax increases (e.g., Proposition 56), tracking smoking trends among youth and adults, and investigating the interplay between e-cigarette use and smoking cessation. She has contributed to studies on fertility preservation mandates, telemedicine for substance use disorders, and health equity in insurance access. Recent research highlights include assessing declines in youth smoking despite e-cigarette surges, evaluating tobacco policy coverage changes in California, and projecting future cigarette consumption trends. Her work often bridges policy implementation and population health outcomes, emphasizing evidence-based approaches. Dr. McMenamin collaborates with interdisciplinary teams and state agencies to translate research into actionable policies. Her contributions span over 150 peer-reviewed publications, with frequent media and policy engagement (e.g., cited in 14+ news outlets for key studies).
Dr. Linda De Caestecker is a Visiting Professor at the School of Health & Wellbeing, Glasgow Caledonian University. Her work focuses on public health policy, healthcare management, and organizational change within clinical and public sector contexts. She has contributed to studies on smoking cessation interventions during pregnancy, the role of lay-workers in healthcare settings, and systemic organizational dynamics affecting public institutions. Her research spans disciplines including public health, clinical policy, and management theory, with notable contributions to understanding barriers to organizational change and healthcare delivery improvements. Dr. De Caestecker's publications highlight interdisciplinary approaches to complex health challenges, such as balancing economic incentives with health outcomes and addressing paradoxical tensions in institutional reform.
Hyosang Lee is an Assistant Professor in the Robotics Section of the Mechanical Engineering Department at Eindhoven University of Technology (TU/e). He holds a PhD from KAIST and has held research positions at the Max Planck Institute and University of Stuttgart. His work focuses on tactile sensing technologies, including artificial skin development, soft robotics, and integration of sensory systems with AI. Bachelor's: Mechanical Engineering, Korea University Master's: Robotics and Mechanical Engineering (double major) PhD: Mechanical Engineering, KAIST (2017) Research interests span tactile sensor design, electrical impedance tomography (EIT), and human-robot interaction. His group emphasizes creating scalable, flexible tactile systems for robots. Recent work includes air pressure sensing for force estimation and biomimetic skin materials. Publications highlight innovations in multi-directional force sensing, soft component technologies, and haptic interfaces for autism therapy. He teaches 'Dynamics and Control of Robotic Systems' and serves on the editorial board of npj Robotics . No formal student advisees are listed, though his lab, the Tactile Sensing and Robotic Skin Group , likely involves graduate researchers. His research contributes to UN Sustainable Development Goals related to health and technology.
James Tung is an Associate Professor at the University of Waterloo’s Faculty of Engineering, Department of Mechanical and Mechatronics Engineering. His research focuses on assistive technology, rehabilitation engineering, and mobility solutions for individuals with disabilities. He leads the Neural and Rehabilitation Engineering (NRE) Lab, which develops wearable sensors, robotics, and machine learning tools to enhance mobility and monitor motor rehabilitation. He teaches courses including BME 355 (Physiological Systems Modelling), BME 540 (Neural and Rehabilitation Engineering), and ME/MTE engineering modules. The lab collaborates with clinical and industry partners to translate research into practical solutions, addressing real-world mobility challenges and aging demographics. His research spans real-world gait analysis, fall risk assessment, and prosthetic design, with a focus on pediatric neurodevelopmental disorders and elderly mobility. The NRE Lab emphasizes interdisciplinary work, combining biomechanics, robotics, and data science to improve healthcare outcomes. Lab Alumni: Includes researchers like Robin Murdock (Myant Inc.), Andrew Hart, and Raj Senthilkumar, contributing to prosthetics and gait analysis. Partnerships: Engages clinical and industry stakeholders for knowledge translation and commercialization. Current projects include developing smart rollators, biofeedback prosthetics, and sensor-based assessment tools to address mobility limitations in aging populations and individuals with disabilities.
Andrew Warfield is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Principal Engineer at Amazon. His research focuses on computer systems software, including virtualization, distributed storage, security, and high-availability systems. He previously held roles as Associate Professor at UBC, CTO at Coho Data, and Technical Director at Citrix Systems. His work has led to projects like Remus (high-availability replication), Tralfamadore (execution analysis), and secure hypervisor development with Xen. Warfield's education includes a PhD from the University of Cambridge's Computer Laboratory, where he researched I/O device virtualization under Steven Hand. He has held visiting roles at Intel Research Cambridge and internships at AT&T Research and Nortel Networks. Research Grants: Supported by Intel Research, NSERC, Network Appliance, and the Communications Security Establishment. Professional Activities: Technical Advisory Board Member at Teradici, and involvement in program committees for conferences like HotOS, EuroSys, and FAST. His research emphasizes practical systems, aiming to bridge the gap between theoretical computer science and real-world applications. Notable contributions include innovations in storage for virtualized environments, secure hypervisor architectures, and disaster-tolerant systems like SecondSite. Warfield is affiliated with UBC's Department of Computer Science and maintains active collaboration with industry partners. Though currently not actively recruiting students, his prior mentorship has influenced many in systems research.
James Anderson is an Assistant Professor in the Department of Electrical Engineering at Columbia University, with affiliations to the Data Science Institute (DSI) and multiple research centers including the Computing Systems for Data-Driven Science and Foundations of Data Science. Prior to Columbia, he was a Senior Research Scientist at Caltech’s Computing + Mathematical Sciences division (2016–2019) and held a Junior Research Fellowship at the University of Oxford’s Department of Engineering Science (pre-2012). He earned his DPhil (PhD) in Engineering Science from Oxford in 2012. His research focuses on optimal/robust control theory, mathematical programming, data privacy, and cyber-physical systems, with applications in smart grids, systems biology, and power systems. Recent work emphasizes energy storage strategies, distributed control algorithms, and cybersecurity in critical infrastructure. His publications span advanced control methodologies (e.g., reinforcement learning for LQR problems), energy market dynamics, and resilient system designs. Notable contributions include frameworks for decision-focused energy storage arbitrage and defenses against false data attacks in power grids. He actively collaborates on federated learning approaches for distributed systems and has pioneered techniques for system-level synthesis in cyber-physical architectures. Anderson’s affiliations include the Data Science Institute (DSI) and specialized centers focused on data-driven science and energy systems. His work bridges theoretical control advancements with real-world applications in energy and healthcare.
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.
Syed S.H. Rizvi is a Professor of Food Process Engineering at Cornell University's Department of Food Science, affiliated with the College of Agriculture and Life Sciences. He holds the title of International Professor and advises over 65 graduate and 70 undergraduate students. His expertise spans food engineering, processing, and international development. Education: Ph.D., The Ohio State University (1976) MEng, Chemical Engineering, University of Toronto (1988) B.Tech., Panjab University (1968) Research Focus: Physical, chemical, and engineering aspects of food and biomaterials Supercritical fluid technology, liposome synthesis, and sustainable processing International value-chain developments and food security Articles Trends: Recent works emphasize supercritical fluid extrusion, liposome encapsulation, and novel food processing innovations. Key themes include material characterization, functional food development, and sustainable technologies. Awards: Distinguished Professor (2017), Chinese Academy of Agricultural Science Excellence in Teaching Award (2014) Stanley Watson Award (2012) Advising & Grants: Mentor to numerous students globally. Collaborates internationally under sponsorships like FAO, UNIDO, and World Bank. Teaches courses on food engineering, international agriculture, and bioprocessing systems. Labs/Teams: Leads the Rizvi Lab, focusing on novel food processes and engineering. Active in developing sustainable technologies for bioactive delivery and food security.
