Laura Munoz is an Associate Professor in the School of Mathematics and Statistics at the Rochester Institute of Technology (RIT), within the College of Science. She holds a BS from the California Institute of Technology and a Ph.D. from the University of California at Berkeley. Her research focuses on mathematical biology, dynamical systems, applied control theory, and cardiac electrophysiology, with a particular emphasis on understanding mechanisms underlying cardiac arrhythmias through mathematical modeling and computational methods. Her work explores topics such as ephaptic coupling in cardiac tissue, controllability of cardiac alternans, and the role of calcium dynamics in arrhythmogenesis. Recent studies include analyzing discordant alternans mechanisms and their link to ventricular fibrillation, as well as developing state estimation techniques for cardiac ionic models using Kalman filters. Munoz has published extensively in journals like Physical Review Letters , Chaos , and Computers in Biology and Medicine , and has presented at conferences such as the SIAM Conference on the Life Sciences. Munoz currently teaches courses such as Linear Algebra, Complex Variables, Mathematical Modeling I, and Mathematical Biology, emphasizing the application of mathematical tools to real-world biological systems. Her research integrates principles from applied mathematics, control theory, and computational biology to advance understanding of cardiac physiology and disease.
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.
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.
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.
Shantanu Dutt is a full Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago , located in Chicago, IL. His office is in 930 SEO at 851 S. Morgan St, and he can be reached at dutt@uic.edu . Education Ph.D. in Computer Science and Engineering, University of Michigan, Ann Arbor (1990) M.Tech. in Computer Engineering, Indian Institute of Technology Kharagpur (1984) B.E. in Electronics and Communication Engineering, Maharaja Sayajirao University of Baroda (1983) Research Interests Professor Dutt’s research agenda centers on VLSI Computer-Aided Design (CAD) , with particular emphasis on physical design and incremental synthesis targeting both ASICs and FPGAs. He explores discrete optimization techniques to solve placement, routing, and partitioning problems. Another major thrust is FPGA built-in self-test (BIST) and trusted design , ensuring provable diagnosability and security against hardware Trojans. He also investigates fault-tolerant computing at both chip and system levels, and develops parallel and distributed computing algorithms for scalable performance. Scientific Awards 1996 Best Paper Award , ACM/IEEE Design Automation Conference, for “A probability-based approach to VLSI circuit partitioning” 1995 Most Influential Paper Award , Fault-Tolerant Computing Symposium, for “Design and reconfiguration strategies for near-optimal k-fault-tolerant tree architectures” Research Impact and Funding Professor Dutt has published extensively in top-tier journals (IEEE TVLSI, ACM TRETS, IEEE TCAD) and premier conferences (ICCAD, DAC, DATE, FTCS). His work has shaped incremental placement, timing-driven routing, FPGA security, and fault-tolerant multiprocessor architectures. While specific grant numbers are not listed, the breadth and longevity of his publication record indicate sustained funding from NSF, industry, and other agencies. Laboratory and Collaborations Although no formal laboratory name is provided, Professor Dutt’s research is conducted within the ECE department’s VLSI CAD and Fault-Tolerance groups, collaborating with graduate students and colleagues across the US and internationally.
Professor Kevin Sivula is a Full Professor of Chemical Engineering at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Laboratory of Molecular Engineering of Optoelectronic Nanomaterials (LIMNO) and serves as Director of the Institute of Chemical Sciences and Engineering (ISIC). He teaches courses on Transport Phenomena, Chemical Engineering Practicals, Product design, and solar energy conversion systems. His research focuses on photoelectrochemical solar energy conversion, particularly developing novel nanomaterials for renewable energy applications. Professor Sivula's work bridges chemical engineering, materials science, and renewable energy technologies with emphasis on solar water splitting and hydrogen production. His research group explores organic semiconductors, transition metal dichalcogenides, perovskite materials, and metal oxide photoelectrodes for solar fuel generation. Professor Sivula's recent publications (2023-2025) demonstrate his leadership in advancing perovskite solar cell technology, developing novel photoelectrochemical systems for hydrogen production, and engineering semiconductor interfaces for improved energy conversion efficiency. His work spans fundamental materials characterization to device engineering, with particular emphasis on stability and scalability of solar fuel production systems. As an academic leader, Professor Sivula has supervised numerous PhD students whose research spans organic semiconductor nanoparticles for solar hydrogen production, perovskite materials engineering, photoelectrochemical CO 2 reduction, and nanomaterials for energy conversion. His research has been supported by various grants focused on advancing sustainable energy technologies.
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.
Rahul Sarpeshkar is a Professor of Engineering, Microbiology & Immunology, Physics, and Molecular & Systems Biology at Dartmouth College, holding the Thomas E. Kurtz Professorship and chairing the Neukom Computational Science Cluster. His research bridges analog circuits with quantum physics, synthetic biology, and ultra-low-power systems. BS in Electrical Engineering and Physics from MIT (1995) PhD in Computation and Neural Systems from Caltech (1998) His research focuses on analog synthetic biology , quantum circuit design , and bio-inspired supercomputing , emphasizing noise, thermodynamics, and energy efficiency. He develops cytomorphic chips to model biochemical networks and quantum-inspired circuits for spectrum analysis. Recent work integrates quantum and classical computation for biological simulations, drug cocktail formulation , and ATP energy measurement in living cells. Patents highlight innovations in quantum emulation and medical devices. Scientific awards include: Fellow, National Academy of Inventors (2018) IEEE Fellow (2018) NSF CAREER Award ONR Young Investigator Award Packard Fellow Award Junior Bose Teaching Award, MIT He leads a wet lab for synthetic microbial circuit implementation and a dry lab for quantum and nanoelectronics, mentoring a multidisciplinary team of physicists, bioengineers, and computer scientists.
Cathy Wu is an Associate Professor at MIT, with affiliations in the Laboratory for Information and Decision Systems (LIDS), Department of Civil and Environmental Engineering (CEE), and Institute for Data, Systems, and Society (IDSS). Her research group focuses on integrating machine learning with model-based optimization to solve complex problems in transportation systems and cyber-physical systems. Academic Leadership: Class of 1954 Career Development Associate Professor (MIT) Research Grants: NSF CAREER Award, Amazon Robotics, Mathworks, MIT Mobility Initiative, US DOT, Microsoft Research, Cintra, Symbotic Research Interests : Wu's work bridges AI and engineering challenges in transportation. Key areas include: Hybrid ML/Model-based Optimization (large neighborhood search, branch-and-cut) Sustainable Mobility (Project Greenwave, eco-driving) Multi-Agent Coordination (warehouse automation, cooperative driving) Cyber-Professional Systems (generalization in RL, transfer learning) Recent work demonstrates significant advances in eco-driving (11-22% emissions reduction), large-scale multi-agent path finding (1000+ agents), and foundational RL methods for traffic control. Her group has produced 15+ major publications since 2015, with notable media coverage in Science, Wired, and NewScientist. Selected Scientific Awards NSF CAREER Award (2023) Ole Madsen Mentoring Award (2025) IEEE ITSS WiE/YP Fellowship (2024) Harold L. Hazen Teaching Award (2022) Her lab has advised 12+ graduate students and postdocs, including: Vindula Jayawardana (PhD '24, now at Anthropic) Sirui Li (PhD '25, now at Microsoft Research) Yining Ma (Postdoc, active researcher) Zhongxia Yan (PhD '24, now at Anthropic)
J. Edward Colgate is the Walter P. Murphy Professor of Mechanical Engineering and Director of the Human Augmentation via Dexterity (HAND) Engineering Research Center at Northwestern University's McCormick School of Engineering. He also holds the title of Breed Senior Professor of Design. His academic career includes leadership roles as founding co-Director of the Segal Design Institute and director of the Master of Science in Engineering Design and Innovation program. Colgate earned his Ph.D. (1988), S.M. (1986), and S.B. in Physics (1983) from the Massachusetts Institute of Technology. Colgate's research focuses on physical human-robot interaction with specialization in surface haptic interactive design and electroadhesion technology development. His work spans three interconnected domains: haptic interfaces (including wearable haptic arrays and Touchbot systems), robot dexterity through Shape-Based Remote Manipulation (SBRM) for overcoming communication delays, and high-speed electroadhesive actuators. The Northwestern Haptics Lab under his direction aims to create realistic virtual environments by merging these research vectors. His publications demonstrate consistent focus on tactile perception mechanisms, electroadhesion applications, and haptic rendering algorithms. Recent work explores texture playback fidelity, wearable electroadhesive arrays, robotic manipulation, and human-swarm control systems, reflecting interdisciplinary integration of mechanical engineering, materials science, and neuroscience principles. Awards: Elected to National Academy of Engineering (2021) for contributions to haptics, human-robot systems, and design education Inducted into National Academy of Inventors (2015) Educational initiatives include developing Northwestern's Design Thinking and Communication curriculum, establishing the Certificate in Engineering Design, and creating the Master of Science in Engineering Design and Innovation. He teaches ME 390: Introduction to System Dynamics using a flipped classroom model. Colgate directs the Northwestern Haptics Lab within the Center for Robotics and Biosystems, focusing on fundamental haptics research with applications in virtual reality, prosthetics, and human-assistive devices. The lab maintains active industry partnerships for technology transfer of haptic innovations.
Prashant Mehta is a Professor of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign , affiliated with the Coordinated Science Laboratory . His research focuses on controlled interacting particle systems and machine learning applications , particularly in human activity recognition using motion sensors. Education: Ph.D. in Mathematics, Cornell University (2004) M.S. in Electrical & Computer Engineering, University of Massachusetts Amherst (1996) B.E. in Electrical & Electronics Engineering, Birla Institute of Technology & Sciences (1993) Mehta's work has pioneered the feedback particle filter (FPF) algorithm for nonlinear estimation, applied in robotic systems and gesture recognition. His research spans control of combustion instabilities in jet engines, mean-field games , and dynamical systems in aerospace engineering. His publications emphasize nonlinear control theory and stochastic filtering , with recent trends in sensor data pattern recognition and cyber-physical systems . He has received multiple scientific awards , including the MURI award for the Cyberoctopus project and Excellence in Undergraduate Advising Awards . Scientific Honors: MURI Award (2019) for Cyberoctopus Excellence in Undergraduate Advising (2010, 2008) Outstanding Teaching Assistant Award (1994) Senior Member, IEEE Control Systems Society Member, ASME Energy Systems Subcommittee Member, SIAM Dynamical Systems Group Mehta has supervised students like Jin Kim (IEEE CDC Best Student Paper, 2019) and co-founded the startup Rithmio , acquired by Bosch Sensortec . His laboratory develops gesture-detection filters for applications in soft robotics and human-machine interfaces .
Simo Hostikka is a Professor in the Department of Civil Engineering at Aalto University's School of Engineering. His research focuses on fire safety engineering , utilizing numerical fire simulations to address critical challenges in building and infrastructure safety. Key Expertise: Fire Dynamics Simulator (FDS) development, thermal radiation heat transfer, pyrolysis modeling, fire toxicity calculations, and probabilistic risk analysis. Leadership: Supervises advanced fire safety research and contributes to international fire safety standards. Research Trends: Recent publications emphasize fire toxicity modeling , hydrogen fire safety , radiation heat transfer , and fire retardancy of polymeric materials . His work bridges computational methods with real-world fire safety applications. Scientific Awards: Philip Thomas Medal of Excellence (2008, 2005) Sjölin Award (2012) Interflam Trophy (2007) Harmathy Award (2020, 2019) Dean’s Award for Best MSc Thesis (2020) Best Paper in Rakenteiden Mekaniikka (2009) Advising: Supervised Topi Sikanen, who received the Young Talent Award from the International Water Mist Association.
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.
