Dr. Alister Smith is a Reader (Associate Professor) in Geotechnics at Loughborough University, where he directs the £1M National Engineered Slope Simulator (NESS) – the world’s first climate-controlled facility testing clay slope deterioration. His £6.7M research portfolio addresses geotechnical infrastructure resilience under climate change. Innovations include acoustic emission (AE) landslide early warning systems deployed across five continents, with the Community Slope SAFE project recognized in REF2021. Current work includes the RAINDROP doctoral training cluster on infrastructure prognosis and the £4.9M EPSRC ACHILLES programme on climate impacts. Awards: Philip Leverhulme Prize, ICE Thomas Telford Premium (twice), and Hawley Award for Engineering Innovation. He serves on the Lilly Fellows Program Board and edits for the International Journal of Christianity and Education.
Dr. José del R. Millán is a Professor and holds the Linda Steen Norris & Lee Norris Endowed Chair in Neuroengineering at The University of Texas at Austin's Chandra Family Department of Electrical and Computer Engineering. He also serves as a Professor in Dell Medical School's Department of Neurology, a courtesy Professor in Biomedical Engineering, and is affiliated with the Mulva Clinic for the Neurosciences, Institute for Neuroscience, Texas Robotics, and the UT CARE Initiative. His work focuses on brain-machine interfaces (BMI), neuroprosthetics, and translating BMI technologies for individuals with motor/cognitive disabilities and able-bodied users. Education: PhD in Computer Science (1992, Technical University of Catalonia). Previous roles include Defitech Foundation Chair in Brain-Machine Interface at EPFL (Switzerland) and visiting scholar positions at Berkeley, Stanford, and the International Computer Science Institute. Research Interests: Neuroengineering, BMI applications in healthcare and assistive robotics, statistical machine learning for neural signals, and neurorehabilitation. Key contributions include EEG-based BMI systems, closed-loop neurostimulation, and wearable neurotechnology. Awards: IEEE Fellow (2017), Norbert Wiener Award (2011), and Fellow of the International Academy of Medical and Biological Engineering (2020). Grants & Labs: Co-director of UT CARE, leader in clinical neuroprosthetics and neurorobotics. Active in developing BMI-driven wheelchairs, VR integration for BCI, and EEG-based speech prosthetics. Research outputs emphasize translational neurotechnology, with projects funded by industry and governmental agencies. Labs/Teams: Clinical Neuroprosthetics & Brain Interaction Lab, Texas Robotics, Wireless Networking and Communications Group (WNCG).
K. Rajibul Islam is an Associate Professor at the University of Waterloo, affiliated with the Institute for Quantum Computing (IQC) and the Department of Physics and Astronomy. He holds a joint appointment with the Perimeter Institute for Theoretical Physics and co-founded Open Quantum Design and Lightflow Optics Inc. His research focuses on quantum information processing, quantum simulation, and trapped ion systems, with applications in quantum computing and entanglement studies. Education: Ph.D. in Physics (2012, University of Maryland), M.Sc. in Physics (2007, Tata Institute of Fundamental Research), B.Sc. in Physics (2005, Jadavpur University). Postdoctoral research at Harvard University (2012–2015) and MIT (2015–2016). Research Interests : Quantum simulation of spin models, quantum computing with trapped ions, entanglement measurement, frustrated spin systems, and quantum materials. His lab, QITI (Quantum Information with Trapped Ions), develops scalable quantum simulators and open-access quantum computers like 'QuantumIon.' Awards : Fellow of the American Physical Society (2024), VAIBHAV Fellowship (2024), Excellence in Teaching Award (2024), Early Researcher Award (2019), and Distinguished PhD Dissertation Award (2012–13). Teaching : Courses include PHYS 701 (Graduate Quantum Physics), PHYS 234 (Quantum Physics I), PHYS 393 (Physical Optics), and PHYS 256 (Geometrical and Physical Optics). He emphasizes outreach via initiatives like Bigyan.org.in , a Bengali-language science platform. Lab and Collaborations : Active in developing trapped-ion quantum hardware, including ion trap designs, optical addressing systems, and holographic control methods. Collaborates on quantum algorithms, machine learning for quantum systems, and experimental quantum thermodynamics.
Bert DE REYCK is the Dean of the Lee Kong Chian School of Business at Singapore Management University (SMU) and holds the rank of Professor of Operations Management. He holds a PhD in Business Economics from the University of Leuven, Belgium, along with an MSc in Business Information Systems and a BSc in Business Engineering from the same institution. His career includes roles as Founding Director and Professor at UCL School of Management (2015–2021), Professor and Head of Department at University College London (2009–2015), and various visiting professorships at institutions like London Business School and the University of Ghent. He has held academic positions since 1998, including Assistant and Associate Professor roles at prestigious institutions such as the London Business School and Rotterdam School of Management. DE REYCK’s research focuses on Operations Management, Business Analytics, and Project Management, with notable contributions to AI applications, transportation systems, and strategic sourcing. His work bridges theoretical rigor and practical impact, addressing challenges in project valuation, contract design, and operations optimization. He has received numerous accolades, including INFORMS’ AAS Best Paper Award (2022), multiple Daniel H. Wagner Prize recognitions, and teaching awards from London Business School and UCL. His research has been supported by grants from organizations like Eurocontrol, NHS, and private sector firms such as Vungle and Noble Group. DE REYCK has led significant industry collaborations, including projects on airport operations optimization, pharmaceutical R&D valuation, and AI-driven advertising strategies. His advisory work spans sectors like aviation, healthcare, and transportation, emphasizing real-world problem-solving through analytics and decision science.
Ruonan Han is a Professor of Electrical Engineering and Computer Science at MIT and serves as Associate Director of the Microsystems Technology Laboratories (MTL) and Director of the MIT-MTL Center for Integrated Circuits and Systems . His research focuses on ultra-high-frequency microelectronic circuits , particularly addressing the 'terahertz gap' in sensing, metrology, security, and communication. He leads the Terahertz Integrated Electronics Group at MIT's MTL, established in 2014. Education: B.S. in Microelectronics, Fudan University (2007) M.S. in Electrical Engineering, University of Florida (2009) Ph.D. in Electrical and Computer Engineering, Cornell University (2014) Research Interests: Terahertz (THz) integrated circuits and systems High-frequency CMOS technologies Quantum sensing and magnetometry RF systems for imaging, radar, and molecular sensing Energy-efficient communication systems His work bridges electronic circuits , electromagnetics , and quantum physics , with applications in defense, healthcare, and environmental monitoring. Awards & Recognition: 2023 IEEE SSCS New Frontier Award 2020 NSF CAREER Award 2019 Intel Outstanding Researcher Award 3× IEEE RFIC Best Student Paper Awards (2012, 2017, 2021) Advising & Leadership: Ph.D. advisor to over 10 students (many recipients of MIT MTL Dissertation Awards) Co-advises with Prof. Anantha Chandrakasan and Prof. Tomás Palacios Editorial roles at IEEE Transactions on Quantum Engineering and VLSI Systems Technical committee member for ISSCC, RFIC, and IMS Labs & Teams: Terahertz Integrated Electronics Group at MIT MTL: Focuses on chip-scale THz systems, quantum devices, and next-generation RF circuits Collaborations with industry (e.g., Apple, MediaTek) and academic groups (e.g., D. Englund's lab at MIT)
Esteban G. Tabak is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a Ph.D. in Mathematics from MIT (1992) and a Hydraulic Engineer degree from the University of Buenos Aires (1988). His research spans fluid dynamics, data science, and optimization, with notable contributions to optimal transport theory, atmospheric and ocean modeling, and machine learning methodologies. He leads the Research and Training Group in Mathematical Modeling and Simulation at NYU. Research Interests include Data Analysis, Optimal Transport, Applied Mathematics, and Physics, particularly in fluid dynamics and geophysical flows. His work bridges theoretical advancements with practical applications, such as sea ice dynamics, internal waves, and turbulence modeling. Publications highlight innovations in density estimation, constrained optimization, and energy spectrum analysis of oceanic internal waves. Collaborations span disciplines, including biomedical applications (e.g., heart transplant diagnostics) and climate science. His methodologies, such as dual ascent algorithms and prototypal analysis, emphasize data-driven solutions to complex systems. Teaching includes courses on partial differential equations, fluid dynamics, and mathematical modeling. His work has been supported by grants addressing stratified flows, internal wave energy spectra, and turbulent mixing.
Dr. Michelle J. Johnson is an Associate Professor at the University of Pennsylvania, affiliated with the Perelman School of Medicine in the Department of Physical Medicine and Rehabilitation. She holds secondary appointments in Bioengineering and Mechanical Engineering and Applied Mechanics at the School of Engineering and Applied Science (SEAS). She directs the Rehabilitation Robotic Lab and is actively involved in global health initiatives, particularly in Botswana, focusing on neurorehabilitation for stroke and HIV-related impairments. Bachelor of Science in Mechanical Engineering and Applied Mechanics, University of Pennsylvania PhD in Mechanical Engineering, Stanford University Her research centers on rehabilitation robotics, neurorehabilitation, and the application of technology to address motor and cognitive impairments after brain injury. She is particularly committed to making robotic rehabilitation accessible in low- and middle-income countries. Her work bridges engineering, clinical medicine, and global health equity. The recent publications highlight a strong trend in inclusive, globally applicable rehabilitation robotics. Her work spans clinical trials in Mexico, theoretical frameworks for inclusive design, and comprehensive analyses of global disparities in access to robotic therapy. The focus is on stroke, HIV-related neurological impairments, and technological solutions that are affordable and scalable in resource-limited settings. Dr. Johnson has received significant recognition for her work, including: Fulbright Scholar IEEE Distinguished Lecturer She leads the Rehabilitation Robotic Lab, where she mentors students and develops innovative technologies. She has secured funding and collaborative support for international projects, including data collection in Botswana and the development of a global webinar series (GPMRR) with Elsevier to disseminate knowledge in rehabilitation robotics. Her work includes grant-funded research on characterizing impairment in populations with HIV, stroke, or both, and designing robot-assisted strategies tailored to these needs. Dr. Johnson leads the Rehabilitation Robotic Lab, which focuses on developing and testing robotic systems for neurorehabilitation. The lab emphasizes inclusive design, global applicability, and clinical validation across diverse populations and healthcare settings.
Umakishore Ramachandran is a Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research spans edge computing, distributed systems, and real-time video analytics, with significant contributions to fog computing infrastructure, mobile systems, and sensor networks. Over a prolific 38-year career, he has authored 142 publications with major contributions in 2022-2025. His research interests focus on bridging the gap between cloud and edge computing, with pioneering work in video analytics systems like EVA and MicroEdge. He investigates resource optimization for latency-sensitive applications, developing novel approaches for load shedding, data management, and container runtime efficiency at the network edge. His work addresses fundamental challenges in distributed camera networks, autonomous vehicle systems, and real-time stream processing. Ramachandran's recent publications reveal a strong emphasis on practical edge computing solutions, with 75% of his 2021-2025 work focusing on video analytics and infrastructure optimization. His research shows increasing collaboration with industry partners while maintaining academic rigor, with publications appearing in top venues like SIGMOD, Middleware, and DEBS. The work consistently addresses real-world constraints of resource-constrained edge environments. Ramachandran has mentored numerous researchers who have become principal investigators on edge computing projects, with notable collaborators including Harshit Gupta, Enrique Saurez, and Zhuangdi Xu appearing as first authors on multiple papers. His work has received significant grant support for projects related to mobile fog computing and distributed video analytics. He leads research in the Edge Computing Laboratory at Georgia Tech, focusing on the development of practical frameworks for real-world deployment of edge infrastructure. Current projects include eCAV for connected autonomous vehicles and MicroEdge for multi-tenant camera processing systems.
Li Tang is an Associate Professor with tenure at École polytechnique fédérale de Lausanne (EPFL), affiliated with the Institute of Bioengineering (IBI) and the Institute of Materials Science and Engineering (IMX) within the School of Engineering (STI). She leads the Laboratory of Biomaterials for Immunoengineering, focusing on developing innovative strategies at the intersection of immunology, materials science, and cancer therapy. Her work bridges fundamental research and clinical translation, with multiple ongoing clinical trials based on CAR-T cell therapies developed in her lab. B.S. in Chemistry, Peking University (2003–2007) Ph.D. in Materials Science and Engineering, University of Illinois at Urbana-Champaign (2007–2012) Postdoctoral Fellow, MIT (2013–2016) Her research lies at the forefront of immunoengineering, integrating chemical, metabolic, and mechanical approaches to modulate immune responses. Key areas include cancer immunotherapy, immune metabolism, mechano-immunology, and biomaterials. She investigates how physical and biochemical cues can reprogram T cells, overcome exhaustion, and enhance tumor targeting. Her work emphasizes multidimensional immunity-disease interactions, aiming to develop safer and more effective therapies for cancer and autoimmune diseases. The recent publications highlight a strong trend in engineering immune cells (especially CAR-T) for enhanced durability and function, using advanced biomaterials and metabolic reprogramming. There is a clear focus on overcoming challenges in solid tumors, modulating the tumor microenvironment, and translating findings into clinical applications. The use of nanoparticle delivery, single-cell analysis, and biomechanical cues are recurring themes across her work. Notable scientific awards include: Friedrich Miescher Award (2025) ERC Starting Grant (2018) MIT TR35 Innovators Under 35 (China Region, 2020) Nano Research Young Innovator Award (2018) Biomaterials Science Emerging Investigator (2019) Materials Horizons Emerging Investigator (2020) Li Tang actively mentors PhD students across multiple doctoral programs (EDBB, EDMS, EDMX) and has advised numerous graduates who have gone on to prestigious postdoctoral and faculty positions. She is involved in significant research grants, including an Innosuisse project with Novochizol SA, and her lab is supported by competitive funding. She teaches core courses such as Immunoengineering and Next-Generation Biomaterials, shaping the next generation of scientists. Her lab fosters interdisciplinary collaboration and innovation, with active projects in chemical, metabolic, and mechanical immunoengineering, as well as CAR-T cell development. She is the Principal Investigator of the Tang Lab, which includes postdoctoral fellows, PhD students, and technical staff. The lab is actively recruiting and has a strong publication and clinical translation record. Tang Lab is also involved in multiple MA/BA training projects and promotes student engagement in cutting-edge research. The lab’s discoveries are being translated into clinical trials, reflecting a strong commitment to translational science.
Jihyun Lee is an Assistant Professor in the Department of Mechanical and Manufacturing Engineering at the Schulich School of Engineering, University of Calgary. She was awarded the Anna Boyksen Fellowship by the Technical University of Munich Institute for Advanced Study (TUM-IAS) in 2021, hosted by Prof. Michael Zäh. Doctorate in Mechanical Engineering from University of Michigan-Ann Arbor (2016) Prior post at Korea Institute of Machinery and Materials (2016-2019) Her research focuses on mechatronics, robotics, manufacturing automation, and control systems , with applications in machine tools, additive manufacturing, and precision measurement. She integrates artificial intelligence and optimization to enhance industrial automation. Recent publications highlight work on vibration control , sensor fusion , and flexible manufacturing systems . Her team explores dynamic modeling , nanocomposite sensors , and human-in-the-loop robotics for industrial and marine applications. 2020 Remote Teaching Award, Schulich Engineering 2020 Early Achievement Award, Association of Korean-Canadian Scientists and Engineers 2018 Best Achievement Award, KIMM She supervises doctoral and master’s students at the University of Calgary, emphasizing hands-on experience and MATLAB/Python simulation skills in her lab. Her work bridges quantum logic and industrial robotics through interdisciplinary collaborations.
James R. Fienup is the Robert E. Hopkins Professor of Optics at the University of Rochester's Institute of Optics, with additional appointments as Distinguished Scientist at the Laboratory for Laser Energetics, Professor at the Center for Visual Science, Professor of Electrical and Computer Engineering, and Affiliated Faculty at the Goergen Institute for Data Science and Artificial Intelligence. His office is located at Wilmot 410, 275 Hutchison Rd., Rochester, NY. Education PhD in Applied Physics from Stanford University (1975) MS in Applied Physics from Stanford University (1972) BA in Physics & Mathematics (magna cum laude) from Holy Cross College (1970) Research Focus Professor Fienup's research specializes in imaging science , with emphasis on phase retrieval algorithms, unconventional imaging techniques, and wavefront sensing. His work spans computational methods for image reconstruction, sparse-aperture systems, and synthetic-aperture imaging. Recent innovations include applying machine learning to wavefront control and developing advanced digital holography techniques for 3D imaging through atmospheric turbulence. Publication Trends His recent articles (2018-2024) demonstrate a strong focus on computational imaging techniques, particularly phase retrieval algorithms applied to optical metrology and wavefront correction. Key themes include multi-plane digital holography, coronagraphic wavefront control for astronomical applications, machine learning-enhanced sensing, and novel approaches for segmented-aperture systems. His work consistently bridges theoretical optics with practical instrumentation challenges. Awards and Honors Lifetime Achievement Award, Hajim School of Engineering (2019) Emmett N. Leith Medal, Optical Society of America (2013) National Academy of Engineering Member (2012) Distinguished Visiting Scientist, JPL (2009) Fellow of OSA and SPIE International Prize in Optics (1983) Rudolf Kingslake Medal (1979) NSF Graduate Fellow (1970-1972) Professional Activities Professor Fienup has served as Editor-in-Chief of the Journal of the Optical Society of America A (1998-2003) and held editorial roles at Applied Optics and Optics Letters . He consults for NASA (James Webb Space Telescope, Hubble), national laboratories, and aerospace companies, and holds five patents in optical systems design.
Prof. Dan Jiao is the Synopsys Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School. She leads the Rapid-Heterogeneous Integration (Rapid-HI) Design Institute and serves as Editor-in-Chief of the IEEE Journal on Multiscale and Multiphysics Computational Techniques. Her research focuses on computational electromagnetics, multiphysics modeling, and AI-driven design automation for advanced integrated circuits and quantum systems. She has held academic positions since 2005, progressing from Assistant to Full Professor, and has extensive industry experience at Intel Corporation (2001–2005). Education: PhD in Electrical Engineering, University of Illinois at Urbana-Champaign (2001) Senior Staff Engineer at Intel Corporation (2001–2005) Research Interests: Fast numerical methods for large-scale electromagnetic analysis AI/ML integration in design automation (EDA/MDA) Quantum circuits and spin qubit systems Heterogeneous integration and advanced packaging Multiphysics co-simulation for nano-scale devices Signal/power integrity in high-speed systems Key Projects: Leads the NSTC AIDRFIC program (first NSTC R&D Jump Start project), the DARPA NGMM Rapid-HI Design Institute, and the GENIE-RFIC generative design tool initiative. Also directs the Consortium for Electromagnetic Science and Technology. Awards & Honors: 2022 ACES Computational Electromagnetics Award IEEE Fellow (2016) Intel Outstanding Researcher Award (2019) MTT-S Distinguished Microwave Lecturer (2020–2023) 2013 Schelkunoff Prize Paper Award Advising & Grants: Advised over 30 PhD/master's students and led projects funded by NSF, DARPA, Intel, SRC, and industry partnerships. Key grants include NSF CAREER (2008), ONR Young Investigator (2006), and multiple industry-sponsored initiatives. Labs & Teams: Rapid-HI Design Institute (DARPA NGMM) Quantum device co-design group Multiphysics modeling team
Nick Bansback is an Honorary Visiting Professor in the School of Population and Public Health at the University of British Columbia (primary affiliation) and affiliated with NUS Saw Swee Hock School of Public Health. His research focuses on maximizing healthcare value through economic evaluation, patient preference measurement, and decision analysis. He holds a PhD in Health Economics and Decision Sciences (University of Sheffield, 2010), an MSc in Health Economics (2004), and a BSc in Mathematics (2001). Key research areas include health technology assessment, patient-reported outcomes, and cost-effectiveness analysis of treatments for chronic conditions like rheumatoid arthritis and multiple sclerosis. He has pioneered methods for incorporating patient preferences into healthcare decisions, including developing decision aids and discrete-choice experiments. His work has been recognized with awards such as the UBC Killam Research Award (2020) and the Canadian Agency for Drugs and Technology in Health Rising Star Award (2016). He has contributed to over 30 peer-reviewed publications, emphasizing practical applications of health economics to improve healthcare resource allocation and patient-centered care. Prof. Bansback’s career spans postdoctoral research at UBC, health economics roles at the Centre for Health Evaluation and Outcomes Sciences, and early research at the University of Sheffield. His interdisciplinary approach bridges clinical practice, policy, and quantitative methods to address complex healthcare challenges.
Yuri Bazilevs is the E. Paul Sorensen Professor of Engineering at Brown University's School of Engineering and Co-Director of the Mechanics of Undersea Science and Engineering Center. His research focuses on computational mechanics, isogeometric analysis, fluid-structure interaction, and high-performance computing. Prior to Brown, he held positions at UC San Diego, where he advanced to Full Professor in 2014 after a rapid tenure. He earned his PhD in 2006 and postdoc training in computational engineering at UT Austin's ICES. Research interests span computational fluid dynamics, solid mechanics, and advanced discretization methods like isogeometric analysis (IGA) and meshfree approaches. He has developed novel formulations for complex phenomena such as underwater explosions, composite material failure, and hypersonic flow dynamics. His work integrates cutting-edge numerical methods with practical engineering applications in aerospace, energy, and biomedical systems. Recent publications highlight advancements in IGA for architected materials, RKPM-based crack modeling, and stabilized formulations for compressible flows. His contributions bridge theoretical mechanics with computational innovation, addressing challenges in multiphysics coupling and large-scale simulations. Collaborations span academia and industry, emphasizing practical validation and real-world impact. Bazilevs' expertise includes variational multiscale methods, peridynamics for fracture mechanics, and immersive particle methods for fluid-structure interaction. His work has been applied to wind turbine aerodynamics, gas turbine optimization, and cardiovascular flow analysis. He actively contributes to computational infrastructure development, such as the tIGAr software framework for IGA automation.
Mehmet Esat Belviranli is an Assistant Professor in the Computer Science Department at the Colorado School of Mines, where he directs the High Performance Systems and Software Lab (HyperSys). His research focuses on increasing resource utilization in heterogeneous architectures through runtime systems, scheduling algorithms, and performance modeling, with publications in top venues including MICRO, PPoPP, and SC. Education: Ph.D. in Computer Science, University of California, Riverside (2016) M.S. in Computer Science, Bilkent University (2009) B.S. in Computer Science, Bilkent University (2006) Belviranli's research spans heterogeneous architectures, runtime systems, performance modeling, parallel programming, autonomous computing, deep learning acceleration, cyber-physical systems, and edge-cloud platforms. His work develops analytical models and programming abstractions to address resource management, scheduling, and security challenges in diversely heterogeneous systems, with applications in edge computing, autonomous systems, and machine learning acceleration. Recent projects emphasize real-world constraints and security implications. His publication trends reveal increasing focus on edge-cloud resource management (e.g., HARNESS), security vulnerabilities in heterogeneous systems (e.g., MC3), and deep learning acceleration under resource constraints. Key themes include memory contention modeling, scheduling for cyber-physical systems, and concurrent DNN execution, reflecting a shift toward practical deployment in security-sensitive edge environments. Scientific Awards: U.S. Air Force Research Lab Summer Faculty Fellowship Award (2022) U.S. Air Force Research Lab Summer Faculty Fellowship Award (2021) Oak Ridge National Laboratory Significant Event Award (2019) Best Paper Finalist, IEEE HPEC 2018 Outstanding Paper Award, DATE 2024 Belviranli mentors Ph.D. students Ismet Dagli (MLCommons Rising Star 2024, CGO'24 SRC finalist) and Justin Davis (DATE'24 Outstanding Paper Award winner). He has secured $2M+ in funding from NSF, DoE, and SRC, including an NSF-SaTC grant on mobile security (2024), a DoE grant on superconductive systems (2023), and an NSF FuSe grant on graphene nanoribbons (2023), often leading multi-institutional teams from Rochester, Virginia, Arizona, and Minnesota. The HyperSys Lab develops ecosystems for high-performance heterogeneous systems, with recent projects including HARNESS for edge-cloud resource management and MC3 for mobile SoC security. The lab has received equipment donations from Google Coral.ai and Xilinx, and collaborates with national labs on security challenges and next-generation semiconductor technologies.
