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Naim DahnounView profile

Teaching Professor

• Signal Processing
• Digital Signal Processing
• mmWave Radar
• +2 more

Naim Dahnoun serves as Professor of Teaching and Learning in Signal Processing at the University of Bristol's School of Electrical, Electronic and Mechanical Engineering, where he contributes to the Visual Information Laboratory and Photonics and Quantum research theme. His career uniquely integrates advanced technical research with pedagogical innovation in engineering education. He holds an Ingenieurd'Etat degree from Oran and a Ph.D. from the University of Leicester, establishing his foundation in both theoretical and applied engineering. His academic journey reflects continuous engagement with cutting-edge signal processing technologies and educational methodologies. Research interests center on Signal Processing with specialized expertise in Digital Signal Processing implementation , mmWave Radar systems for human monitoring, Biomedical Signal Processing (particularly foetal EEG and vital sign detection), and Engineering Education pedagogy. His work develops real-time radar-based solutions for posture estimation, fall detection, and non-contact health monitoring while pioneering FPGA-based DSP teaching approaches. Analysis of his 91 publications reveals a distinct dual trajectory: since 2020, approximately 60% of his work focuses on mmWave radar applications in healthcare (including neonatal monitoring and elderly care), while the remainder advances DSP education through project-based FPGA implementations and e-learning tools. This synergy between technical innovation and teaching methodology defines his scholarly contribution. His recognition includes: Students’ Award for Outstanding Teaching (Engineering), 2018 As a research supervisor, he has guided 7 students through advanced projects while securing funding as co-investigator on interdisciplinary grants including a national database study on neonatal outcomes (2018-2019) and Bristol Harbour water quality monitoring (2017). His grant portfolio demonstrates consistent translation of signal processing techniques to healthcare and environmental applications. Within the Engineering Education Research Group, he leads initiatives in DSP pedagogy and digital learning tools, while his technical work in the Visual Information Laboratory drives radar-based sensing innovations. Current projects emphasize low-cost, real-time implementations for both medical monitoring systems and educational platforms.

• Microelectronics
• Radiation Effects on Electronics
• Semiconductor Reliability
• +3 more

Jean-Luc Autran is an Exceptional University Professor (PRCE2) at Aix-Marseille University, affiliated with the Department of Detection, Radiation and Reliability (DETECT) within the Faculty of Sciences. Since July 2023, he has been temporarily assigned to the University of Rennes for managerial roles. His research focuses on radiation effects in microelectronics, particularly soft errors caused by atmospheric neutrons, muons, protons, and terrestrial radiation in nanoscale devices. Key Research Areas: Microelectronics Reliability, Single-Event Effects, Atmospheric Radiation, Neutron Interactions, Muon Physics, Radiation-Hardened Design. Scientific Leadership: As an Honorary Member of the Institut Universitaire de France (since 2003), he leads multidisciplinary efforts from radiation metrology to multi-physics circuit simulation via tools like GEANT4, SRIM, and NGSPICE. His work spans 25+ years, evolving from quantum transport in nano-MOSFETs (1998-2008) to atmospheric radiation effects (2005-present). Recent Contributions: 2025 studies on ultrawide-bandgap semiconductors, deep learning-based radiation simulations, and JET Tokamak neutron experiments. He pioneers a multi-scale Single-Event Effect simulation framework for decananometer CMOS, integrating particle physics, device modeling, and system-level error rate prediction. Scientific Awards: Honorary Member, Institut Universitaire de France (2003 class). Educational Impact: Teaches graduate courses in quantum mechanics simulation, nanoelectronics reliability, and radiation detection at Aix-Marseille University, with lectures delivered in English for international audiences.

• Embedded Systems
• Hardware/Software Co-design
• Energy-efficient AI Accelerators
• +7 more

Oliver Bringmann is a full Professor and head of the Chair of Embedded Systems at the University of Tübingen, Germany, and a member of the board of directors at the FZI Research Center for Information Technology. His research integrates embedded-system design, energy-efficient AI accelerators, dependable automotive perception, and medical AI for capsule endoscopy. Education & Career Ph.D. in Computer Science, University of Tübingen, 2001 Diploma in Computer Science, University of Karlsruhe (KIT) Head, Chair of Embedded Systems, University of Tübingen (since 2012) Deputy spokesperson & spokesperson, Dept. of Computer Science, University of Tübingen (2014-2022) Board of Directors, FZI Research Center for Information Technology Research Interests Bringmann’s group pioneers hardware/software co-design for ultra-low-power Edge-AI , developing RISC-V based accelerators, compiler-aware neural-architecture search, and real-time perception systems for autonomous driving and medical devices. Key topics include: Energy-efficient AI architectures (“Edge AI”) and custom accelerator generation Robust collective perception under adverse weather (LiDAR, camera, V2X fusion) Timing/power-predictable embedded software and system-on-chip design automation Hardware-assisted security and safety for automotive & IoT systems AI-driven capsule endoscopy localization and anomaly detection Recent Publication Trends His 2024-2025 articles reveal a strong shift toward robust multimodal perception for automated driving (snow, fog, collective LiDAR fusion) and Edge-AI medical devices (capsule endoscopy with multi-task CNNs). Core contributions span dataset generation (SCOPE, SnowyLane), safety metrics (LSM), and fast performance modeling for DNN accelerators. Professional Service & Projects Executive/Steering Committees: IEEE/ACM DATE, CODES+ISSS, CASES, ITSS conferences EU CATRENE EDA roadmap chapter lead (Embedded Software & ESL-to-RTL) Principal investigator in Scale4Edge, OCEAN12, enerDAG and other national projects on energy-efficient sensorics and secure energy trading. His group maintains extensive collaborations with automotive and semiconductor industry, focusing on dependable, energy-aware embedded intelligence.

Debarshi SenView profile

Assistant Professor

• Structural Dynamic Systems
• Infrastructure Monitoring and Resilience
• Statistical and Machine Learning in Monitoring
• +2 more

Dr. Debarshi Sen is an Assistant Professor in the Department of Civil Engineering at Southern Illinois University Carbondale (SIU), specializing in structural engineering. His research focuses on structural dynamic systems, infrastructure monitoring and resilience, and the application of statistical and machine learning techniques in monitoring and seismic response control. Education: Ph.D. in Civil Engineering (2018), Rice University, Houston, TX M.S. in Civil Engineering (2013), Indian Institute of Technology Kharagpur, India B.S. in Civil Engineering (2011), Indian Institute of Technology Kharagpur, India Research Interests: Dr. Sen's research integrates advanced computational techniques with structural engineering challenges. His work spans: Development of dynamic systems for infrastructure monitoring Application of machine learning algorithms for structural health assessment Seismic response control and regional fragility assessment Innovative approaches using crowdsourced data and mobile sensing for bridge monitoring Publications Overview: Dr. Sen has an extensive publication record, including over 20 journal articles and numerous conference presentations. His recent work focuses on leveraging AI and machine learning for bridge condition assessment, with significant contributions to crowdsourced mobile sensing technologies. His research also explores experimental and analytical studies on seismic protection systems, particularly using negative stiffness devices. Labs and Teams: Dr. Sen is affiliated with the Structural Engineering research group at SIU and maintains collaborations with institutions such as MIT (as a Research Affiliate since 2020) and Lehigh University (as a Postdoctoral Research Associate from 2020-2022).

Ahmed H. ElsheikhView profile

Professor

• Subsurface Engineering
• Artificial Intelligence in Energy Systems
• Machine Learning Applications
• +5 more

Professor Ahmed H. Elsheikh is a faculty member at Heriot-Watt University's School of Energy, Geoscience, Infrastructure and Society, within the Institute for GeoEnergy Engineering. He holds a Professor title since 2021, previously serving as Associate (2017-2021) and Assistant Professor (2013-2017). His educational background includes a Ph.D. (2010) and MASc (2002) from McMaster University, and a BASc from Al-Azhar University (1999). Research Interests: Fluid control via AI, predictive machine learning, generative modeling, data assimilation, Bayesian uncertainty quantification, and subsurface engineering. His work addresses challenges in reservoir modeling, CO2 sequestration, seismic inversion, and geophysical data analysis. Publications span 2004-2025 with a focus on machine learning applications in energy systems, stochastic field generation, and subsurface flow modeling. Notable contributions include AI-driven seismic inversion, deep learning for reactive transport, and ensemble-based history matching. Key Achievements: Developed novel methodologies for uncertainty quantification, GAN applications in geological modeling, and real-time reservoir monitoring systems. His work contributes to UN Sustainable Development Goals related to climate action (SDG 13) and affordable clean energy (SDG 7).

• Mobile and wireless systems
• Internet of Things (IoT)
• Edge computing
• +7 more

Mahanth Gowda is an Associate Professor in the Department of Computer Science and Engineering, leading a prolific research program at the intersection of mobile systems, wireless sensing, and human-computer interaction. His work has been continuously funded by the U.S. National Science Foundation since 2019, serving as Principal Investigator on four awards and Co-PI on two others, collectively spanning edge computing for XR, sign-language recognition, next-generation wireless networking, and healthcare-oriented wearables. Research Interests Millimetre-wave and ultra-wideband sensing for 3-D finger motion tracking and speech eavesdropping Edge-IoT platforms for real-time sign-language recognition and translation Neural-augmented game streaming and super-resolution on commodity mobile devices Open-source wearable systems for sports analytics and rehabilitative healthcare Security and privacy implications of motion sensors in smartphones and IoT devices Over the past five years his publication trajectory has concentrated on leveraging emerging radio modalities—especially millimetre-wave radar, Wi-Fi, and UWB—to extract fine-grained human-centric information such as finger gestures, facial micro-motions, and spoken content. A complementary thread develops edge-native machine-learning frameworks that push intelligence to resource-constrained devices, enabling immersive AR/VR experiences and assistive technologies for the Deaf and hard-of-hearing communities. Grants & Projects CAREER: Sign-to-Speech – NSF, $500k, 2021-2026; Edge-IoT platform for real-time ASL recognition. SHF: Medium: Next-Gen XR Edge Platform – NSF, $1.2M, 2022-2025; Co-PI with Das, Sivasubramaniam, Kandemir. CNS Core: IoTScope – NSF, $450k, 2020-2025; Sensing physical materials via low-cost IoT radios. CNS Core: Medium: ML-driven Next-G Wireless – NSF, $800k, 2020-2024; Co-PI with Yang and Mahdavi. I-Corps: Smart Ring for Healthcare Analytics – NSF, $50k, 2023-2025; Commercialization of finger-motion wearables. Labs & Teams Gowda directs a research group that operates at the confluence of wireless networking, embedded systems, and applied machine learning. The lab maintains active collaborations with faculty in computer architecture, augmented reality, and accessibility studies, and routinely mentors graduate researchers whose work appears in top-tier venues such as ACM MobiCom, IEEE INFOCOM, ISCA, and ACM IoTDI.

Chung Hwan KimView profile

Assistant Professor

• Computer Systems Security
• Cyber-Physical Security
• Software Security and Reliability
• +3 more

Chung Hwan Kim serves as an Assistant Professor in the Department of Computer Science at the University of Texas at Dallas, where he directs the Software & Systems Security Laboratory (S³ Lab). His research focuses on critical security challenges in cyber-physical systems, embedded devices, and cloud infrastructure, with recognition including the NSF CAREER Award and UT Dallas New Faculty Research Symposium Grant. His expertise spans Computer Systems Security , Cyber-Physical Security , and Software Security and Reliability , emphasizing practical solutions for robotic vehicles, autonomous systems, and trusted execution environments. Current projects address signal injection attacks, resilience testing, and confidential computing through innovative fuzzing frameworks and hardware-assisted protections. Recent publications (2020-2026) reveal three dominant research thrusts: (1) Security for autonomous/robotic systems ( DriveFuzz , IMUFUZZER ), (2) Trusted execution in constrained environments ( Vessels , GEVisor ), and (3) Automated vulnerability discovery ( HFL , TZ-DATASHIELD ), consistently appearing in top venues like IEEE S&P and USENIX Security. Key honors include: NSF CAREER Award (premier early-career recognition) UT Dallas New Faculty Research Symposium Grant Top 10 finalist for CSAW Best Applied Research Paper Award (2018) As principal investigator of the S³ Lab, Kim mentors graduate researchers and secures competitive funding for projects spanning robotic vehicle security, embedded systems hardening, and confidential computing. His teaching portfolio includes Operating Systems, Information Security, and specialized courses on CPS/IoT security. The S³ Lab develops deployable security tools like TZ-DATASHIELD for embedded data protection and IMUFUZZER for resilience testing of aerial vehicles, collaborating with industry partners to translate research into real-world solutions for autonomous systems and critical infrastructure.

• Machine Learning Theory
• Statistical Physics
• Neuroscience
• +5 more

Stefano Sarao Mannelli is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. He also holds a Visiting Lecturer position at the University of the Witwatersrand. His research group focuses on fundamental aspects of learning in biological and artificial systems, with emphasis on bias generation, optimization dynamics, and comparative neuroscience. Education: Ph.D. in Theoretical Physics, Université Paris-Saclay (2020) M.Sc. in Electronic Engineering, Politecnico di Torino (2017) M.Sc. in Physics of Complex Systems, Politecnico di Torino/SISSA (2016) M2 in Physique Théorique, Paris Diderot/UPMC/ENS Cachan (2016) B.Sc. in Mathematics for Engineering, Politecnico di Torino (2014) Research: Dr. Mannelli develops model-based approaches to reduce complex machine learning problems into analytically tractable frameworks. His core interests include: 1) Bias amplification mechanisms in AI systems, 2) Learning differences between biological and artificial neural networks (continual/transfer/curriculum learning), and 3) Optimization in high-dimensional landscapes. His work bridges statistical physics, neuroscience, and deep learning theory. Publication Trends: Recent articles (2024-2025) predominantly analyze curriculum learning dynamics, bias propagation in optimization, and theoretical comparisons between biological and artificial learning systems. Methodologically, they combine statistical physics frameworks with control theory and high-dimensional analysis. Awards: Academic Grant (CM Lerici Foundation, 2025) Travel Grants (Guarantor of Brains, G-Research 2024) UK–IT Trustworthy AI Exchange Programme (Alan Turing Institute, 2023) SCGB Conference Award (Simons Foundation, 2023) Ph.D. Scholarship (CEA, 2017-2020) Team & Funding: Leads a research group with 2 PhD students and 1 postdoc. Secured significant funding for international workshops including Analytical Connectionism (£42K, 2023; $152K, 2024) and High-Dimensional Methods (135,500 SEK, 2025).

Stephen T. LamView profile

Assistant Professor

• Nuclear materials
• Advanced reactors
• Materials chemistry
• +10 more

Stephen T. Lam is an Assistant Professor of Chemical Engineering at the University of Massachusetts Lowell, where he also serves as Director of the Nuclear Engineering Program and RHSA Mentor within the Francis College of Engineering. His research focuses on accelerating materials development for nuclear and clean energy applications through computational methods. His educational background includes a Ph.D. in Nuclear Science and Engineering (2020) and an MS in Nuclear Science and Engineering (2017) from the Massachusetts Institute of Technology, and a BS in Chemical Engineering (2013) from the University of British Columbia. Prior to his academic career, Lam worked in the petroleum and chemical processing industries and holds a Professional Engineer license in Canada. Ph.D.: Nuclear Science and Engineering (2020), Massachusetts Institute of Technology MS: Nuclear Science and Engineering (2017), Massachusetts Institute of Technology BS: Chemical Engineering (2013), University of British Columbia Lam's research integrates multi-scale simulation, experimental validation, and data analytics to address materials challenges in nuclear and clean energy systems. His work spans molten salt chemistry for advanced reactors, neural network interatomic potentials, and fusion energy materials. He leads the Lam Research Group, which combines predictive simulation, data analytics, and experiments to accelerate materials development. His recent publications demonstrate expertise in molten salt structure, neural network potentials, and fusion materials, with emphasis on fluoride and chloride salt systems for nuclear applications. The research shows strong interdisciplinary collaboration across computational chemistry, materials science, and nuclear engineering. Among his notable recognitions are the Early Career Research Award (2024) from the U.S. Department of Energy and the Distinguished Faculty Advancement Award (2024) from the U.S. Nuclear Regulatory. He has also received multiple early-career research awards from national laboratories and professional societies. Early Career Research Award (2024), U.S. Department of Energy Distinguished Faculty Advancement Award (2024), U.S. Nuclear Regulatory Best Presentation (2017), Tokyo Institute of Technology Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship (2017) Lam actively mentors graduate students and postdoctoral researchers, with current advisees working on AI-assisted materials design, molten salt chemistry, and fusion energy applications. He teaches courses in nuclear materials, nuclear science and engineering, transport phenomena, and fundamentals of electricity. The Lam Research Group maintains strong collaborations with national laboratories and industry partners, focusing on developing computational tools to improve understanding of material properties and accelerate materials discovery for next-generation energy technologies.

• Optical Engineering
• Biomedical Engineering
• Machine Learning
• +4 more

Demetri Psaltis is a **Professor honoraire** at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (STI) and the Department of Physics (PH-STI). He holds roles as **Chargé de cours** (Lecturer) across multiple departments including Microengineering (SMT-ENS), Electrical and Electronics Engineering (SEL-ENS), and serves as **Professeur hôte** (Host Professor) at the Laboratoire d'hémodynamique et de technologie cardiovasculaire (LHTC). His research focuses on advanced optical systems, biomedical imaging, nonlinear optics, and the integration of machine learning with optical technologies. Key affiliations include the Institute of Bioengineering (IBI-STI) and administrative roles in the IBI-STI-GE management unit. He has advised over 20 PhD students at EPFL, contributing significantly to their thesis work. His laboratories develop cutting-edge tools for applications in medical diagnostics, energy systems, and optical computing. Research interests span computational optical imaging, optical computing architectures, 3D printing with light, and AI-driven wavefront shaping. Recent publications emphasize innovations in hybrid neural networks, optical diffusion models, and scalable optical circuit switching. His work bridges fundamental physics with practical applications in healthcare and renewable energy sectors. Labs: Laboratoire d'hémodynamique et de technologie cardiovasculaire (LHTC), IBI-STI Institute Teaching:** Courses include Computational Optical Imaging, Optical Computing, and 3D Printing with Light.

George DrettakisView profile

Researcher

• Computer Graphics
• Rendering
• Image-Based Rendering
• +12 more

George Drettakis is a Senior Researcher at INRIA Sophia-Antipolis and leads the GRAPHDECO research group. He has held professorial roles at institutions including MIT, University of Reims, University of Toronto, and École Normale Supérieure. His research focuses on rendering for computer graphics and sound, with emphasis on image-based rendering, perceptual rendering, and audio-visual cross-modal effects. He has also explored interactive illumination, shadows, relighting, and generative models. Current Students: G. Kopanas (Neural Rendering), N. Violante (Generative Models), A. Petitjean (co-supervised), Y. Poirier-Ginter (co-supervised), P. Panantonakis (starting fall 2023). Postdoctoral Researchers: A. Gauthier at INRIA. His recent work includes 3D Gaussian Splatting , Diffusion-based Relighting , and Neural Radiance Fields . He has received the Eurographics Outstanding Technical Contributions Award (2007) and was named an Eurographics Fellow . He manages projects like ERC Advanced Grant FUNGRAPH and has participated in H2020 EMOTIVE , ANR SEMAPOLIS , and CROSSMOD . His group collaborates internationally and has hosted researchers from institutions such as UC Berkeley, Imperial College London, and TU Wien.

• Laser Scanning
• Point Cloud Processing
• Geospatial Information Systems
• +3 more

Tamás Lovas serves as Associate Professor at the Department of Photogrammetry and Geoinformatics, Faculty of Civil Engineering, Budapest University of Technology and Economics. He teaches advanced courses in Building Information Modeling, Laser Scanning, Remote Sensing, and Intelligent Transportation Systems while supervising diploma theses in Surveying and Geoinformatics Engineering. Education: 1994: High school graduation, Városmajor High School, Budapest 1999: Certified Surveyor and Geoinformatics Engineer, Budapest University of Technology, Faculty of Civil Engineering 2005: PhD (Earth Sciences), Budapest University of Technology and Economics, Faculty of Civil Engineering Research Interests: Dr. Lovas specializes in laser scanning technologies and geospatial data processing with emphasis on airborne and terrestrial point cloud analysis. His work bridges civil engineering applications and computational methods, particularly in infrastructure monitoring and digital representation. Processing, classification, and modeling of airborne laser scanned data Accuracy testing of terrestrial laser scanning Processing and modeling of terrestrial laser scanned data Comparative study of spatial data acquisition technologies His 2022-2025 publications demonstrate accelerating integration of artificial intelligence in point cloud processing, with significant contributions to road surface extraction, urban land cover classification, and BIM automation. Current research trends show strong focus on digital twin development for autonomous vehicles and infrastructure management. Scientific Awards: Republic Scholarship (1998-1999) Karlsruhe Chancellor's Scholarship (1999) ERASMUS scholarship (2000) Korányi Fellowship (2001-2002) János Bolyai Research Scholarship (2008-2011) OHV 1st place (2008) Dean's commendation for ERASMUS committee work (2014) For Students Award - Teaching Department (2017) Advising and Grants: Dr. Lovas mentors students through diploma theses and TDK research projects on topics including object survey with amateur sensors and hull modeling. His research funding includes the prestigious János Bolyai Research Scholarship and international fellowships supporting collaborations with institutions like The Ohio State University. Labs and Teams: As founding member and supervisory board member of the Hungarian BIM Association, he drives industry-academia collaboration. His leadership roles include Deputy Dean of Education at the Faculty of Civil Engineering and responsibility for English language training programs, facilitating international academic exchange.

Yang SuiView profile

Researcher

• Efficient AI
• Machine Learning Systems
• Model Compression
• +5 more

Yang Sui is a Postdoctoral Research Associate in the Department of Computer Science at Rice University, collaborating with Professors Xia (Ben) Hu and Hanjie Chen. His research focuses on Efficient AI and Trustworthy AI, including deep neural networks, large language models (LLMs), diffusion models, and algorithm-hardware co-design. He holds a PhD from Rutgers University (2024), an MS from Jilin University (2019), and a BS from Jilin University (2016). Education: PhD, Computer Science, Rutgers University, 2024 MS, Computer Science, Jilin University, 2019 BS, Computer Science, Jilin University, 2016 Research Interests: Efficient AI: Model Compression (pruning, quantization, low-rank decomposition), Generative AI (diffusion models, LLMs), and algorithm-hardware co-design. Trustworthy AI: Adversarial robustness (backdoor attacks, vulnerability detection). He has interned at Snap Research (2024), Tencent America (2022), and Baidu (2018), contributing to projects like BitsFusion quantization and Paddle-Lite framework. Awards: Paul Panayotatos Scholarship (2024) Best Paper Runner-Up Award (DCAA Workshop at AAAI 2023) First Place in ESWEEK Classification Track (2023) SGS Travel Award (2023) Advising & Grants: Advises students on topics like LLM quantization and multimodal models. Collaborates with industry and academia on grants related to efficient AI and hardware co-design. Led projects like Rice’s “Efficient Deep Learning Reading Group” (2023). Labs & Teams: Contributes to Snap’s Creative Vision team, Rutgers’ research groups, and co-design initiatives with industry partners like Baidu and Tencent.

• Neuroscience
• Consciousness Studies
• Neuroimaging
• +4 more

Aya Khalaf is an Associate Research Scientist at the Yale School of Medicine, affiliated with the Blumenfeld Lab and the Janeway Society. Her research focuses on understanding neural mechanisms of consciousness, brain-computer interfaces, and machine learning applications in healthcare. She collaborates with leading institutions and researchers globally on studies involving EEG, fTCD, and neuroimaging techniques. Key research interests include auditory and visual perception networks, impaired consciousness in epilepsy, and hybrid BCI systems. Her work bridges cognitive neuroscience with engineering, aiming to translate findings into clinical tools. Notable collaborations include projects with Hal Blumenfeld, Dennis Spencer, and international teams testing consciousness theories. Publications highlight advancements in neural activity analysis, BCI calibration optimization, and multimodal signal processing. Her contributions span theoretical neuroscience frameworks and applied biomedical engineering solutions.

• Health Economics
• Applied Microeconomics
• Industrial Organization

Assoc Prof Yang Zhang is an Associate Professor in the School of Economics at the National University of Singapore , Department of Economics. He has been affiliated with the university since 2012, progressing from Lecturer (2012-2015) to Senior Lecturer (2016-2021) and currently Associate Professor since 2022. Education: PhD in Managerial Economics and Strategy (Northwestern University, 2011) BA in Economics (Peking University, 2005) Research Interests: Health Economics Applied Microeconomics Industrial Organization Recent Research Trends focus on leveraging deep learning and structural biology tools for protein structure prediction, RNA modeling, and computational drug design. His work integrates advanced algorithms with large-scale metagenomic data to improve accuracy in complex biological system modeling.