University of California , Santa Barbara (UCSB)United States
Dmitri Strukov is a Professor at the University of California, Santa Barbara in the Department of Electrical and Computer Engineering. His work spans material science, electrical engineering, and computer science, focusing on novel computing paradigms using emerging memory devices. Education: PhD in Electrical and Computer Engineering from SUNY Stony Brook, MS in Applied Physics and Mathematics from Moscow Institute of Physics and Technology. Research Interests include neuromorphic computing , non-volatile memory applications , and mixed-signal circuits for machine learning and hardware security. His group develops memristive crossbar arrays and 3D NAND flash for energy-efficient systems. Scientific Leadership features Fellow of IEEE and Distinguished Lecturer roles. His work has been recognized with best paper awards at ASPLOS’19 and Computing Frontiers’13. Students: Mentored PhD graduates in neurocomputing, security, and memristor design including Z. Fahimi, S. Larimian, M.R. Mahmoodi, and X. Guo. Grants: Funded by AFOSR, ARO, DARPA, NSF, and industry leaders like Google and Samsung. Labs: Utilizes UCSB’s nanofabrication center and advanced tools for memristor characterization.
Marc V Fuccillo is an Associate Professor of Neuroscience at the Perelman School of Medicine, University of Pennsylvania, where he leads a research laboratory focused on understanding the neural circuit mechanisms underlying behavioral control. His work bridges molecular, synaptic, and behavioral approaches to investigate how striatal circuits regulate mouse behavior from simple motor patterns to complex goal-directed actions. Fuccillo holds dual appointments in the Neuroscience and Cell and Molecular Biology Graduate Groups at Penn and maintains an active laboratory investigating the synaptic and circuit basis of neuropsychiatric disorders. Education: B.A. in Molecular and Cellular Biology and Music Performance (Violin) from Brown University (1998) Ph.D. in Developmental Genetics from New York University School of Medicine (2007) M.D. from New York University School of Medicine (2008) Fuccillo's research centers on the synaptic and circuit mechanisms of behavioral control, with particular emphasis on striatal circuits. His laboratory employs a range of technologies including mouse genetics, in vitro electrophysiology, in vivo imaging, and quantitative behavioral analysis to explore how neural circuits of the striatum regulate behavior and how disruptions in these circuits contribute to neuropsychiatric disorders. His work has particularly focused on autism-associated abnormalities in behavioral control, examining how synaptic adhesion molecules like neuroligins and neurexins shape circuit function and behavior, with significant findings regarding D1 dopamine receptor positive medium spiny neurons in the nucleus accumbens. Analysis of Fuccillo's recent publications reveals a strong focus on striatal circuit function across multiple dimensions. His work spans molecular neuroscience (examining synaptic adhesion molecules), cellular physiology (studying specific neuron types in striatal circuits), systems neuroscience (mapping circuit connectivity), and behavioral neuroscience (quantifying motor learning and decision-making). A unifying theme is how disruptions in specific molecular pathways lead to circuit-level abnormalities that manifest as behavioral phenotypes relevant to neuropsychiatric disorders, with particular attention to autism, OCD, and schizophrenia models. Scientific Recognition: Publications in high-impact journals including Nature Neuroscience, Current Biology, Cell Reports, and Neuron Research supported by multiple NIH grants including NIMH F32, NIMH K01, and HHMI Gilliam Fellowship awards for lab members Fuccillo actively mentors a diverse group of trainees including postdoctoral fellows, graduate students, and undergraduates. His laboratory has produced numerous successful alumni who have gone on to faculty positions, medical residencies, and graduate programs at prestigious institutions. His mentoring approach emphasizes technical skill development across multiple neuroscience disciplines while fostering independent scientific thinking. Current research in his lab is supported by NIH funding focused on understanding the molecular architecture of striatal circuits and their role in behavioral control, with three major research directions exploring molecular logic of striatal circuits, circuit mechanisms of behavioral control, and striatal dysfunction in neuropsychiatric disease models. The Fuccillo Laboratory operates within the Department of Neuroscience at the University of Pennsylvania, with access to state-of-the-art facilities for molecular, electrophysiological, imaging, and behavioral neuroscience research. The lab maintains active collaborations with other neuroscience research groups at Penn and beyond, creating a rich intellectual environment for studying the neural basis of behavior. Current research directions include investigating whether there is a molecular logic to striatal circuit composition, how striatal circuits shape behavioral control, and what mouse models of autism, schizophrenia, and OCD can reveal about striatal circuit dysfunction in disease pathophysiology.
Dr. Daniel J. Preston is an Assistant Professor of Mechanical Engineering at Rice University, leading the Preston Innovation Laboratory (PI Lab). He holds a B.S. from the University of Alabama (2012), M.S. and Ph.D. from MIT (2014, 2017), and postdoctoral training at Harvard University (2017-2019). His research focuses on energy efficiency, soft materials, fluid mechanics, and robotics, with applications in wearable technologies and sustainable systems. Key achievements include the NSF CAREER Award (2022) and pioneering work on necrobotics using biotic materials. The PI Lab collaborates widely, with projects involving biomimetic surfaces, smart textiles, and fluidic control systems. Education: B.S. Mechanical Engineering, University of Alabama (2012) M.S./Ph.D. Mechanical Engineering, MIT (2014/2017) Research Interests: Energy : Thermal management, heat transfer optimization, and waste heat recovery. Materials : Soft actuators, surface coatings, and functional biomaterials. Fluids : Interfacial phenomena, droplet dynamics, and fluid-structure interactions. Notable Publications: Recent work includes advancements in wearable haptic devices ( Science Advances ), necrobotics ( Advanced Science ), and fluidic logic textiles ( PNAS ). Over 50 peer-reviewed articles span multidisciplinary topics from magnetic levitation to virus decontamination. Honors: In addition to the CAREER Award, Dr. Preston has received the NSF Graduate Research Fellowship, Tau Beta Pi Fellowship, and Wunsch Foundation Award. His lab fosters innovation through grants and industry partnerships. Advising & Grants: Supervises ~15 graduate and undergraduate students, including NSF GRFP recipients. Active in mentoring initiatives like the Randall Research Scholars Program and AATCC grants. Labs & Teams: The PI Lab collaborates with MIT’s DRL, Harvard’s Whitesides Group, and institutions globally. Core projects include smart wearables, energy harvesting fabrics, and autonomous soft robots.
California Institute of Technology (Caltech)United States
Alexei Kitaev is the Ronald and Maxine Linde Professor of Theoretical Physics and Mathematics at the California Institute of Technology (Caltech). His research focuses on quantum computation, topological quantum phases, anyons, topological insulators and superconductors, and the black hole information paradox. He has pioneered the concept of topological quantum computation, where quantum information is protected through topological properties of many-body systems. His recent publications explore quantum error correction, scrambling dynamics, and holographic principles in SYK-like models, reflecting his interdisciplinary impact on quantum physics, computer science, and condensed matter. His work on the Sachdev-Ye-Kitaev model has advanced understanding of quantum chaos and gravitational phenomena. Kitaev has received numerous accolades, including the MacArthur Award (2008), Breakthrough Prize in Fundamental Physics (2012), Dirac Medal (2015), and Oliver Buckley Condensed Matter Prize (2017). He has taught advanced courses such as 'Quantum Computation' and 'Advanced Condensed-Matter Physics' at Caltech. Scientific Awards: MacArthur Award (2008) Breakthrough Prize in Fundamental Physics (2012) Dirac Medal (2015) Oliver Buckley Condensed Matter Prize (2017)
David Allcock is an Assistant Professor in the Department of Physics at the University of Oregon, part of the College of Arts and Sciences. His research focuses on ion trapping, quantum computing, and hybrid quantum systems, with an emphasis on manipulating atomic and molecular systems using electric and magnetic fields for quantum information applications. He leads the Ion Trapping Lab at UO, where he develops scalable quantum technologies and open-source control systems like ARTIQ and Sinara. His work bridges experimental physics with engineering, addressing challenges in qubit control, error mitigation, and large-scale quantum computer design. Education: MPhys from the University of Oxford (2007), D.Phil. in Physics from Oxford (2012). Prior to UO, he was a Lindemann Fellow at the National Institute of Standards and Technology (NIST) in Boulder, CO. His research includes innovations in trapped-ion qubit control, including laser-free entangling gates, scalable architectures, and applications in quantum sensing and dark matter detection. Key research themes include metastable qubit systems, photon scattering error mitigation, and the integration of superconducting detectors for state readout. He collaborates on open-source hardware-software stacks for quantum experiments and mentors students in quantum engineering through programs like the Quantum Technology Master’s Internship. Current projects explore hybrid quantum-classical interfaces and ultra-stable ion trap fabrication. His lab’s contributions span theoretical and experimental domains, with recent advances in geometric phase gates, microwave-driven control, and error-resilient qubit operations. The group also engages in interdisciplinary work linking quantum computing with precision measurement, such as SPUD (SPectroscopy for Ultralight Dark matter) and bosonic sensing tools.
Marco Platzner is a Professor for Computer Engineering at Paderborn University , Germany. He serves as the Dean of Research for the Faculty of Computer Science, Electrical Engineering and Mathematics and heads the Department of Computer Science. Previously, he held research positions at ETH Zurich, Stanford University, GMD (now Fraunhofer IAIS), and Graz University of Technology. Education: Diploma and PhD in Telematics (Graz University of Technology, 1991 and 1996), Habilitation in Hardware-Software Co-Design (ETH Zurich, 2002) Research Interests focus on reconfigurable computing, approximate computing, self-* computing, and embedded systems. His work addresses hardware security, FPGA design, and sustainable AI in data centers. Current projects include energy-efficient AI through deep neural network approximation for FPGAs (EKI-App) and lifecycle sustainability of socio-technical systems (SAIL). Publication Trends show expertise in FPGA security, approximate circuit synthesis, robotics, and hardware acceleration. Collaborations span robotics (ROS 2 integration), AI (transformer optimization), and cybersecurity (Trojan detection). Scientific Awards: ACM SIGDA Hall of Fame (2020) Significant Paper Award (FPL 2015) Best Paper Awards at IEEE ISVLSI (2024), ARC (2018), IEEE ReConFig (2015), and others Weierstraß Prize for Teaching (2008) Leadership Roles include membership in the board of Paderborn Center for Parallel Computing (PC2) and the Jenny Aloni Centre for Early Career Researchers. He has contributed to EU FP7 FET project EPiCS and German priority programs on embedded systems and organic computing.
Christof Paar is a Professor of Embedded Security at Ruhr University Bochum's Faculty of Computer Science. His work focuses on hardware security, cryptography, and embedded systems security. He leads the Embedded Security Group within the CASE Cluster of Excellence (Cyber Security in the Age of Large-Scale Attacks), addressing cutting-edge challenges in hardware Trojans, side-channel attacks, and cryptographic engineering. His research spans FPGA and IoT security, with contributions to physical-layer security, wireless jamming defenses, and tamper-resistant systems. Research Interests: Paar's expertise lies in hardware-software co-design for security, with a focus on embedded systems. His work includes analyzing vulnerabilities in FPGAs, developing countermeasures against side-channel attacks, and exploring physical-layer security mechanisms. He also investigates the human factors in hardware reverse engineering and the implications of adversarial machine learning on security systems. Notable Work: Recent publications highlight breakthroughs in detecting hardware Trojans across CMOS generations, breaking industry-standard IP protection mechanisms (IEEE 1735), and proposing novel defenses like IRShield against adversarial wireless sensing. His group actively contributes to standards for secure embedded systems and IoT devices, emphasizing practical implementations.
Vibhav Gogate is a Professor and Associate Head of Research at University of Texas at Dallas, specializing in machine learning and artificial intelligence. His research focuses on probabilistic graphical models, statistical relational learning, and integrating deep learning with graphical models. Professor Gogate has received numerous awards including the NSF CAREER Award (2017), Outstanding Researcher Award (2022, 2017), and Best Paper awards at top AI conferences. His research funding includes projects from NSF and DARPA. Education: PhD, University of California, Irvine MS, University of Maine BS, University of Mumbai Recent Publications: His research spans probabilistic inference, tractable models, and neural network approaches for efficient reasoning, with publications in NeurIPS, AAAI, UAI, and AISTATS. Recent work focuses on scalable inference methods and explainable AI systems for complex domains. Research Funding: Secured over $8M in grants from DARPA and NSF for projects in explainable AI and probabilistic reasoning. Teaching: Regularly teaches graduate and undergraduate courses in Machine Learning, Artificial Intelligence, and Advanced Statistical Methods.
Manoj Sachdev is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Faculty of Engineering. His research focuses on semiconductor devices, low-power electronics, and secure hardware systems. He leads projects in flexible electronics, nanotechnology, and radiation-hardened circuits, with applications in displays, memory technologies, and biomedical sensors. His work integrates advanced materials science with circuit design to address challenges in energy efficiency and security. Education: Not explicitly stated in provided text. Research interests span thin-film transistors (TFTs), resistive switching memories, neuromorphic computing, and physically unclonable functions (PUFs). Recent efforts include developing low-power circuits for flexible substrates and secure microprocessors. His contributions to semiconductor device physics and integration techniques have advanced applications in wearable electronics and medical diagnostics. Publications highlight innovations in low-power flip-flops, energy-efficient display drivers, and memristor-based systems. He collaborates on interdisciplinary projects combining photonics, nanoelectronics, and biomedical engineering. Awards: None explicitly listed in provided text. Grants and advising: Advises on semiconductor fabrication, secure hardware design, and radiation effects in electronics. Leads research groups focused on next-generation memory technologies and flexible integrated systems. Labs/Teams: Active in the University of Waterloo's semiconductor and flexible electronics research clusters, contributing to both academic and industry partnerships.
Supratik Chakraborty serves as the Bajaj Group Chair Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. He maintains dual affiliations with the Centre for Formal Design and Verification of Software and the Centre for Liberal Education at IIT Bombay, demonstrating his cross-disciplinary engagement. Professor Chakraborty's research spans formal methods with focus on formal verification, rigorous analysis of system models, and automated synthesis of systems from specifications. His work bridges theoretical foundations with practical applications, particularly in developing mathematically provable guarantees for increasingly complex hardware, software, and intelligent systems. Current research interests include constrained counting and sampling, scalable formal verification of software and hardware systems, automated synthesis of programs and circuits, and applications of automata, logic and finite model theory to practical verification challenges. His publication trajectory shows a significant evolution from traditional hardware and software verification toward addressing verification challenges in machine learning and AI systems. Recent work increasingly focuses on interpretability of black-box models, verification of neural networks, and synthesis techniques applicable to intelligent systems. The research demonstrates strong interdisciplinary connections between formal methods, programming languages, and artificial intelligence. IIT Bombay Excellence in Thesis (CSE) Award 2011 (for Bhargav Gulavani's thesis) IIT Bombay Excellence in Thesis (CSE) Award 2017 (for Abhisekh Sankaran's thesis) Best Paper in Algorithms and Architecture track at IEEE International Conference on Computer Design: VLSI in Computers and Processors, 1998 Professor Chakraborty has successfully supervised 11 doctoral students, with research spanning formal verification techniques, Boolean functional synthesis, constrained counting, and applications to hardware and software systems. His students have gone on to positions at major institutions including Microsoft Research, TCS Research, Georgia Tech, and BARC, reflecting the strong industry and academic impact of his mentorship. Current research directions show increasing emphasis on verification challenges posed by machine learning systems and AI. His research group at IIT Bombay, while not explicitly named in the materials, appears to focus on formal methods with strong connections to the Centre for Formal Design and Verification of Software. The group maintains active collaborations with international researchers including Moshe Y. Vardi at Rice University, and has made significant contributions to verification tools like VeriAbs that bridge theoretical advances with practical applications.
Soheil Salehi is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona, with a joint appointment in Systems and Industrial Engineering. He is the Director of the Privacy-preserving, Intelligent, and Secure Computing (PRISM) Lab, established in August 2022. Prior to this, he was an NSF-Sponsored Computing Innovation Fellow and Postdoctoral Research Fellow at the University of California, Davis. Ph.D., Electrical and Computer Engineering, University of Central Florida, 2020 M.S., Electrical and Computer Engineering, University of Central Florida, 2016 B.S., Isfahan University of Technology, Iran, 2014 Dr. Salehi's research focuses on the intersection of hardware, AI, and security. His work spans hardware and AI-enabled security in IoT , Generative AI for hardware design and security , neuromorphic and biologically-inspired AI hardware , emerging spin-based devices , reconfigurable architectures , low-power VLSI circuits , and digital twins and mixed reality for semiconductor workforce development . He also explores the application of Generative AI in personalized education . His recent publications, spanning 2023–2025, reveal a strong trend toward integrating AI and machine learning into hardware security and design. Key themes include automated secure IC design flows , AI-driven hardware obfuscation , firmware and side-channel attack analysis , security in neuromorphic and spiking neural networks , and educational frameworks using digital twins and generative models . His work appears in top venues like DAC, ICCAD, USENIX Security, IEEE TCAS-I, and ISCAS. Outstanding Reviewer Award, IEEE/ACM Design Automation Conference (DAC), 2023 Best Presentation of the Symposium Award, UC Davis Postdoctoral Research Symposium, 2021 UCF Excellence by a Graduate Teaching Assistant (University-Level), 2016 Nominated for 30-under-30 Award, UCF, 2020 Nominated for Postdoctoral Research Excellence Award, UC Davis, 2022 Dr. Salehi has secured significant research funding as PI and Co-PI, including a $300K NSF SaTC EAGER grant on Generative AI-based Personalized Cybersecurity Tutor, a $174,000 University of Arizona PIF Award, and multiple RII grants totaling over $198K. He has also received industry funding from CHEST. He actively mentors students and leads the PRISM Lab, which focuses on privacy-preserving and intelligent secure computing. His service includes roles as Technical Program Committee (TPC) Member and Session Chair at premier conferences such as DAC, ICCAD, CCS, NDSS, and GLSVLSI. The PRISM Lab, under his direction, conducts cutting-edge research in secure and intelligent hardware systems, with applications in IoT, edge computing, and workforce development. The lab emphasizes interdisciplinary collaboration and innovation in both research and education.
Prof. Baker Mohammad serves as Professor and Director of the System on Chip Lab in the Department of Computer and Information Engineering at Khalifa University. With over 15 years of industrial experience at Intel and Qualcomm designing microprocessors and DSP chips, he bridges academic research with real-world engineering challenges in high-performance computing and low-power systems. His educational background includes: Ph.D. in Electrical and Computer Engineering, University of Texas at Austin (2008) M.S. in Electrical and Computer Engineering, Arizona State University B.S. in Electrical Engineering, University of New Mexico Dr. Mohammad's research spans cutting-edge domains where VLSI design converges with AI acceleration and emerging memory technologies . His work pioneers Memristor applications in environmental sensing (radiation, vacuum, glucose) and neuromorphic computing, while advancing energy harvesting systems for wearable electronics. The integration of in-memory computing with security primitives represents a paradigm shift in hardware design, moving beyond traditional CMOS limitations. His publication trajectory reveals accelerating focus on self-powered neuromorphic systems and RRAM-based architectures, with recent work (2021-2023) emphasizing hardware-software co-design for edge AI. Over 75% of his recent publications involve cross-disciplinary collaborations spanning materials science, chemistry, and biomedical engineering. Notable scientific recognition includes: IEEE TVLSI Best Paper Award 2016 IEEE MWSCAS Myrill B. Reed Best Paper Award Qualcomm Qstar Award for Performance Leadership KUSTAR IP Excellence Award Multiple SRC Techon Best Session Papers As a dedicated mentor, he has supervised over 15 graduate students while securing competitive funding from Khalifa University, ADEK, Qualcomm, Tii, and UAE space agencies. His grant portfolio demonstrates exceptional translational impact, converting fundamental research in memristive devices into drone flight computers and medical sensors. Current projects integrate academic rigor with industrial deployment timelines. The System on Chip Lab operates as a multidisciplinary hub where semiconductor physicists collaborate with AI researchers to develop RISC-V-based secure processors and piezoelectric nanogenerator systems. Recent expansions include partnerships with Tii for aerospace applications and medical device startups for glucose monitoring technology.
Jonathan M. Baker is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin, holding the Advanced Micro Devices Chair in Computer Engineering. His research centers on quantum computer architecture with emphasis on practical quantum error correction implementation across the quantum computing stack. His educational background includes a Ph.D. in Computer Science from the University of Chicago (advised by Fred Chong) and dual B.S. degrees in Mathematics and Chemistry and Computer Science from the University of Notre Dame. Baker's research spans quantum compilation, logic synthesis, multi-radix architectures, and error mitigation for both near-term and fault-tolerant quantum systems. His work addresses critical challenges in quantum hardware-software co-design, with particular focus on optimizing quantum circuits for real-world hardware constraints and noise characteristics. Current projects emphasize qudit-based computing, neutral atom architectures, and efficient error correction implementations. His publication record shows strong focus on quantum architecture innovations, with recent work exploring qudit advantages, modular chiplet designs, and dynamic noise adaptation. Key trends include hardware-aware compilation techniques, communication optimization across quantum systems, and practical approaches to fault tolerance. Best Paper Award Runner Up, MICRO 2020 IEEE Micro Top Pick, 2020 (Virtualized Logical Qubits) IEEE Micro Top Pick, 2020 (Extending Frontier with Qutrits) IEEE Micro Top Pick, 2021 (Emerging Technologies) Best Poster Award, MICRO 2018 Baker actively mentors graduate students in quantum computing architecture research and serves on conference review committees including MICRO and ASPLOS. His teaching includes specialized quantum systems courses at UT Austin and online EdX modules covering quantum computation fundamentals and architecture. He collaborates with the Duke Quantum Center and maintains strong industry connections through the AMD Chair position, focusing on bridging academic research with practical quantum computing implementations.
Lauren Andrews serves as Associate Professor and Marvin and Eva Schlanger Faculty Fellow in the Department of Chemical Engineering at the University of Massachusetts Amherst. Her research integrates synthetic biology and genetic engineering to develop programmable cellular systems for biotechnological applications. Education: Postdoctoral Training: Massachusetts Institute of Technology (Biological Engineering and Broad Institute of MIT and Harvard) PhD: University of Colorado Boulder, Chemical Engineering (2012) MS: University of Colorado Boulder, Chemical Engineering (2009) BS: Cornell University, Chemical Engineering (2006) Dr. Andrews' research focuses on establishing genetic design rules for reprogramming cellular regulation and metabolism. Her lab pioneers synthetic gene networks, genetically-encoded biosensors, and high-throughput methodologies for optimizing genetic designs in both model and non-model bacteria. This work enables precise control of cellular sensing, memory, and environmental responses through multiplexed DNA assembly and next-generation sequencing. Analysis of her 15 most recent publications reveals dominant themes in bacterial biosensor development (particularly for bioremediation), quorum sensing engineering, and programmable genetic circuits for probiotic applications. Her research consistently bridges fundamental genetic circuit design with practical implementations in bacterial consortia and non-model organisms. Scientific Awards: Marvin and Eva Schlanger Faculty Fellowship NSF CAREER Award (2020) for "Programmable synthetic microbial consortia for complex multicellular functions" Her grant portfolio demonstrates significant funding for collaborative research in bacterial communication systems and model-guided design of synthetic ecosystems. The Andrews Lab maintains active partnerships with the MIT-Broad Foundry and Cold Spring Harbor Laboratory, where she co-founded the Synthetic Biology Summer Course. Current projects focus on CRISPR-based regulation in non-model bacteria and algorithmic programming of sequential logic in probiotic strains. The Andrews Lab operates within the Life Science Laboratories at UMass Amherst, utilizing advanced facilities for genetic prototyping and high-throughput screening. Her team develops multiplexed tools for exploring genetic design spaces, with particular emphasis on soil bacteria and Gram-positive pathogens for environmental and therapeutic applications.
Swiss Federal Institute of Technology in LausanneSwitzerland
Andras Kis is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI) across multiple institutes including the Institute of Electrical Engineering (IEL), Institute of Materials Science (IMX), and teaching programs in Electrical Engineering (SEL-ENS). He leads the Laboratory of Nanoscale Electronics and Structures (LANES) and serves on the PhD program committee for Microsystems and Microelectronics. PhD, EPFL (2003) MS, Physics, University of Zagreb (1999) Baccalaureate, MIOC High School Research Focus: Pioneering work on 2D materials for electronic and optoelectronic devices, particularly transition metal dichalcogenides like MoS2 and PtSe2. His research spans: Transistor design with atomically thin semiconductors Excitonic devices and valleytronics Nanofluidics and ionic logic Optical properties of 2D heterostructures Scalable fabrication of 2D materials Defect engineering and doping techniques Scientific Impact: Based on analysis of 15 most recent publications, his work focuses on advancing 2D materials for next-generation electronics through innovations in: Van der Waals heterostructures Thermoelectric and optoelectronic applications Nanofabrication techniques Spintronic and quantum transport phenomena Memristive and neuromorphic devices Characterization of electronic and optical properties Awards & Recognition: Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Lotfi A. Zadeh Award for Emerging Technologies (2024) Highly Cited Researcher (Clarivate Analytics) Teaching & Academic Leadership: Currently teaching courses including Lab in Nanoelectronics , Physical Models for Micro and Nanosystems , and Semiconductor Devices II . He has supervised over 20 PhD students in his research group at EPFL. Laboratory & Collaborations: Directs the Laboratory of Nanoscale Electronics and Structures (LANES) which focuses on fundamental and applied research in 2D materials and nanoelectronic devices. His work bridges materials science, condensed matter physics, and microelectronics engineering.
