جست‌وجوی استاد و پژوهشگر

Zhi Daمشاهده پروفایل

استاد

• Asset Pricing
• Behavioral Finance
• Market Microstructure
• +۷ مورد دیگر

Zhi Da is the Howard J. and Geraldine F. Korth Chair in Finance and Professor of Finance at the University of Notre Dame , Mendoza College of Business, Department of Finance. He completed his Ph.D. in Finance at Northwestern University’s Kellogg School of Management (2006), preceded by an M.Sc. in Financial Engineering from the National University of Singapore (2001) and a B.B.A. with First-Class Honors (1999) from the same institution. Holding editorial roles at Journal of Finance , Management Science , Review of Financial Studies and several other top journals, he is a leading voice in empirical finance research. Education Ph.D. in Finance, 2006 – Kellogg School of Management, Northwestern University M.Sc. in Financial Engineering, 2001 – National University of Singapore B.B.A. (1st Class Honors), 1999 – National University of Singapore Research Interests Zhi Da’s scholarship sits at the intersection of asset pricing , behavioral finance , and market microstructure . He investigates how investor attention, institutional trading, liquidity frictions, and information flows jointly determine the cross-section of expected returns. His work delves into retail margin trading, the role of pension-fund flows in exchange-rate dynamics, the informational content of SEC filings, and the efficiency of short-selling mechanisms. By combining large-scale data analytics, textual analysis, and structural modeling, he uncovers novel predictors of returns ranging from presidential approval ratings to real-time attention measures. Recent projects explore fractional trading ’s impact on price efficiency, hedging demand as a driver of intraday momentum, and the hidden effort problem in delegated portfolio management. These themes collectively advance our understanding of limits to arbitrage and the formation of extrapolative beliefs. Publication Landscape Spanning 2025 back to 2009, his 15 most recent articles in Journal of Finance , Review of Financial Studies , Management Science , Journal of Financial Economics , and Journal of Financial and Quantitative Analysis converge on three broad motifs: (1) micro-level trading frictions—liquidity costs, margin requirements, and short-selling constraints; (2) macro-finance linkages—exchange rates, fiscal policy, and global capital flows; and (3) information economics—attention allocation, media analytics, and regulatory disclosures. The collective evidence demonstrates that seemingly small trading or informational frictions aggregate into large, persistent cross-sectional return predictability. Honors and Awards 2017 William F. Sharpe Award for Best Paper, Journal of Financial and Quantitative Analysis Lead-article distinctions in Journal of Finance , Review of Financial Studies , and Management Science Featured coverage in SmartMoney and CNBC Teaching & Mentorship At Notre Dame’s Mendoza College, Professor Da teaches Investments (undergraduate and MBA) and Fixed Income Securities , integrating cutting-edge research insights into the curriculum. While specific advisees are not listed, his extensive co-author network (22+ recurring collaborators) attests to a vibrant mentoring environment. Laboratory & Data Resources He publicly distributes the NAT (Net Arbitrage Trading) dataset, a stock-quarter panel of arbitrage positions used in Chen, Da & Huang (2019). This resource has become a standard tool for researchers studying arbitrage capital movements.

Damir Filipovicمشاهده پروفایل

استاد

• Quantitative Finance
• Risk Management
• Machine Learning
• +۷ مورد دیگر

Academic Profile: Damir Filipovic is a Full Professor and the Swissquote Chair in Quantitative Finance at the College of Management of Technology (CDM) of École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He previously held academic positions at the University of Vienna, University of Munich, and Princeton University, and served as Head of the Vienna Institute of Finance. Research Focus: Quantitative finance, risk management, stochastic processes, term structure modeling, volatility risk, and machine learning applications in financial markets. Industry Collaboration: Co-developed the Swiss Solvency Test for insurance capital requirements while consulting for the Swiss Federal Office of Private Insurance. Publications: Contributed extensively to journals like Journal of Financial Economics, Mathematical Finance, and Annals of Applied Probability, with a textbook on Term-Structure Models. Academic Service: Editorial board member of multiple journals and organizer of advanced workshops on systemic risk and financial technology. Recent Research: His work emphasizes machine learning for portfolio risk management, kernel-based yield curve estimation, and robust stochastic modeling. Keynote speaker at international conferences on finance and insurance mathematics, with over 15 recent publications in 2023-2025 addressing high-dimensional financial problems, neural control systems, and causal inference in market data. Education: Ph.D. in Mathematics from ETH Zurich (2000). Graduate of ETH Zurich and University of Vienna. Teaching & Mentorship: Supervises current and former EPFL Ph.D. students in quantitative finance, including Nicolas Camenzind, Joshua Hayes, Andrea Ruglioni, and ten others. Former students like Damien Ackerer and Lotfi Boudabsa now lead research in risk management. Labs & Programs: Directs EPFL's Finance and Technology Programme, leads the Computational Finance Group (CSF) at EPFL, and contributes to Swiss Finance Institute initiatives. Scientific Leadership: Served on EPFL Committee of Academic Evaluation and Doctoral Program Finance committee.

Petter N. Kolmمشاهده پروفایل

استاد بالینی

• Quantitative Trading Strategies
• Delegated Portfolio Management
• Financial Econometrics
• +۵ مورد دیگر

Petter N. Kolm serves as a Clinical Professor of Mathematics and Program Director at New York University, with his office located in Warren Weaver Hall (520). He can be contacted at petter.kolm@nyu.edu or 212-998-4855, and holds an editorial board position at the Journal of Portfolio Management. His academic qualifications include: Doctorate in Mathematics from Yale University M.Phil. in Applied Mathematics from the Royal Institute of Technology in Stockholm M.S. in Mathematics from ETH Zurich Dr. Kolm's research centers on quantitative finance, with primary focus areas including quantitative trading strategies, delegated portfolio management, financial econometrics, risk management, and optimal portfolio strategies. His work integrates advanced mathematical modeling with practical investment applications, bridging theoretical frameworks and real-world market dynamics through rigorous empirical analysis. Analysis of his 15 most recent publications reveals consistent emphasis on portfolio optimization techniques—particularly Bayesian methods and the Black-Litterman model—alongside significant contributions to algorithmic trading systems, factor-based equity portfolio construction, and machine learning applications for financial sentiment analysis. His scholarly output demonstrates evolution from foundational portfolio theory toward contemporary computational finance challenges. As Program Director, Dr. Kolm oversees academic programming and likely mentors graduate students in quantitative finance, though specific advisee details are not documented. His prior industry role at Goldman Sachs Asset Management provided direct experience in developing hedge fund strategies, informing his applied research approach. Dr. Kolm's professional trajectory includes significant industry engagement through his tenure in Goldman Sachs' Quantitative Strategies Group, where he developed quantitative investment systems. His current academic leadership position leverages this practical experience to shape quantitative finance education and research at NYU.

• Wireless Communications
• Optical Communications
• Radar Signal Processing
• +۶ مورد دیگر

Saleh A. Alshebeili is a Professor in the Department of Electrical Engineering at King Saud University's College of Engineering, Riyadh, Saudi Arabia. With over 139 publications spanning from 1991 to 2024, his research demonstrates significant contributions across multiple engineering disciplines. His academic profile shows consistent collaboration with Saudi research institutions and international partners, particularly in communications and signal processing fields. Dr. Alshebeili's research interests span wireless communications, optical networks, radar systems, and biomedical signal processing. His work bridges theoretical signal processing with practical applications in 5G/6G communications, IoT security systems, and healthcare monitoring. The interdisciplinary nature of his research connects electrical engineering with computer science, particularly through machine learning applications for signal analysis and system optimization. His publications demonstrate expertise in both traditional signal processing techniques and emerging AI-driven approaches to engineering problems. Analysis of his recent publications (2021-2024) reveals a strong focus on next-generation communication technologies including 6G systems, optical wavelength conversion, and OAM-SDM communication. Simultaneously, he maintains active research in biomedical applications, particularly EEG signal processing for seizure detection and biometric authentication using physiological signals. His work consistently appears in top IEEE journals including IEEE Access, IEEE Transactions on Wireless Communications, and IEEE Journal of Biomedical and Health Informatics, reflecting the high quality and relevance of his research. Dr. Alshebeili has established extensive collaborations with researchers across King Saud University, particularly with Fathi E. Abd El-Samie (29 co-authored papers), Turky N. Alotaiby (22 papers), and Amr Ragheb (21 papers). These long-term collaborations suggest leadership in research groups focusing on communications systems and biomedical signal processing. His work spans theoretical development, simulation, and experimental validation, as evidenced by publications with 'Experimental Investigation' and 'Experimental Demonstration' in their titles.

Christoph Freiمشاهده پروفایل

استاد

• Quantitative Finance
• Risk Management
• Mathematical Economics
• +۴ مورد دیگر

Christoph Frei is a Professor and Chair of the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a PhD in mathematical finance from ETH Zurich and previously worked as a researcher at École Polytechnique in Paris. His research focuses on quantitative finance, risk management, and mathematical economics, with applications to algorithmic trading, credit risk, and digital currencies. Education: PhD in Mathematical Finance, ETH Zurich Postdoctoral Researcher, École Polytechnique (Paris) Bachelor/Master in Mathematics, ETH Zurich His work bridges academia and industry through collaborations with institutions like ATB Financial, Canadian Western Bank, and the Federal Reserve System. Key research interests include over-the-counter markets, financial regulation, and machine learning applications in risk prediction. Frei has received notable recognition, including the Best Paper in Asset Pricing Award (2019). Current industry partnerships include AI-driven customer risk prediction projects with ATB Financial and credit risk analysis with Canadian Western Bank. He actively contributes to professional organizations like PRMIA Edmonton and advises on financial technology innovation. Research grants come from NSERC, SSHRC, and Mitacs. His consulting roles have spanned risk modeling at UBS and Credit Suisse, emphasizing practical applications of theoretical frameworks.

Abolfazl Asudehمشاهده پروفایل

دانشیار

• Algorithm Design
• Data Science
• Artificial Intelligence
• +۴ مورد دیگر

Abolfazl Asudeh is an Associate Professor in the Department of Computer Science at the University of Illinois Chicago and director of the Innovative Data Exploration Laboratory (InDeX Lab) . He is a Senior Member of ACM and IEEE , serving as Associate Editor for IEEE Transactions on Knowledge and Data Engineering , VLDB Ambassador , and VLDB Endowment Liaison to NSF . His research focuses on Algorithm Design for Data and AI problems , emphasizing efficient, accurate, and responsible solutions through Approximation Algorithms , Randomized Methods , and Computational Geometry . Recent work explores LLM optimization ( Needle ), fair data structures ( FairHash ), and responsible AI frameworks ( Chameleon ). Scientific awards include Communications of the ACM Research Highlight Google Research Scholar Award SIGMOD 2019 Research Highlight Best of VLDB 2020 SIGMOD 2017 Reproducibility Award Grants: NSF IIS-2348919 (2024-2027): Fairness-aware Data Structures NSF IIS-2107290 (2021-2024): Collaborative Fairness Research The InDeX Lab develops systems like Needle (image retrieval) and RSR (matrix multiplication). His work integrates fairness , reliability , and computational efficiency across data structures , LLMs , and responsible AI implementations.

Ivan Guoمشاهده پروفایل

دانشیار

• Mathematical Finance
• Optimal Transport
• Stochastic Control
• +۳ مورد دیگر

Associate Professor Ivan Guo is a faculty member at Monash University's School of Mathematics, where he leads research in mathematical finance and stochastic modeling. He obtained his PhD in Mathematics from the University of Sydney in 2014 and currently accepts PhD students. His work bridges theoretical mathematics and practical financial applications, with active projects spanning 2022-2026. Research Focus Dr. Guo's research centers on three interconnected areas: Optimal Transport Applications : Developing transport-based methods for financial model calibration and derivatives pricing Market Microstructure : Analyzing market-making strategies, liquidity, and high-frequency trading dynamics Sustainable Finance : Modeling green investment impacts and energy market transitions using game-theoretic approaches Active Projects Can green investors drive transition to a low-emission economy? (2022-2026) Integrating energy storage into electricity markets (2022-2024) Data61 CRP #46 - Risklab mathematical sciences (2020-2023) Efficient computational techniques for econophysics (2019-2021) The role of liquidity in financial markets (2017-2020) His research consistently addresses model uncertainty, volatility dynamics, and computational methods across 18+ publications since 2012.

Hyejin Kuمشاهده پروفایل

استاد

• Mathematical Finance
• Machine Learning
• Risk Management
• +۴ مورد دیگر

Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on the intersection of Mathematical Finance and Machine Learning, addressing challenges in risk measurement, portfolio optimization, and quantitative finance. She develops advanced mathematical models to enhance decision-making through reinforcement learning and data analytics. Notable projects include novel algorithms for credit rating prediction using neural networks and sequence-based clustering for credit risk assessment. Her work integrates applied mathematics with real-world financial applications, such as systemic risk reduction in multi-layer networks and option pricing under liquidity constraints. She holds a prominent position in mathematical finance, contributing to both theoretical advancements and practical solutions for financial markets. Her research trends emphasize interdisciplinary approaches, combining machine learning techniques with financial modeling to solve complex problems in risk management and asset valuation. Her publications span over two decades, showcasing contributions to portfolio optimization, derivatives pricing, and computational finance. Dr. Ku is affiliated with York University’s Department of Mathematics and Statistics, where she contributes to academic leadership and research mentorship. Her office is located in DB 2025, and she can be reached at hku@yorku.ca.

Md. Zoheb Hassanمشاهده پروفایل

استادیار

• Spectrum sharing and management
• Wireless communication systems
• Communications and network control and regulation systems
• +۱ مورد دیگر

Md. Zoheb Hassan serves as an Assistant Professor in the Department of Electrical Engineering and Computer Engineering at Laval University, where he leads cutting-edge research in wireless communications and spectrum management. His academic role includes graduate recruitment and active participation in the university's research ecosystem, particularly through the Establishment of the Next Generation of Professors program funded by FRQNT. Dr. Hassan's research centers on spectrum sharing and management, wireless communication systems, and communications network control systems. He pioneers the integration of digital twin technology and machine learning to solve critical challenges in next-generation networks, including interference management in 5G/6G aerial corridors, Internet of Vehicles, and satellite-terrestrial integration. His work emphasizes practical implementations such as proof-of-concept demonstrations for tactical networks and proactive resource allocation in dynamic environments. Analysis of his 2024-2025 publications reveals a dominant trend toward AI-driven wireless resource optimization, with 12 of 15 recent papers featuring digital twins for interference management, spectrum sharing, and energy efficiency. Key thematic clusters include vehicular communications (4 papers), underwater IoT networks (2 papers), and hardware-impairment resilient designs (3 papers), demonstrating his focus on bridging theoretical advances with real-world deployment challenges across diverse network topologies. Dr. Hassan has secured significant competitive funding for his research initiatives: Digital Twin-Enhanced Interference Management for Next-Generation Radio Access Networks in the FR3 Band (FRQNT, 2025-2027) Center for Radio Frequency and Communications Systems, Technologies and Applications (FRQNT, 2024-2030) Context-Aware Spectrum Sharing and Management for Next Generation Wireless Networks (NSERC, 2024-2029) Development of innovative technologies for modeling predictive systems in urban mobility (MITACS, 2022-2026) Springboard to Discovery supplement for Context-Aware Spectrum Sharing (NSERC, 2024-2025) He actively mentors doctoral candidates, currently supervising Mahima Karim (PhD in Electrical Engineering, expected 2025) and Mohammadamin Parhizgar (PhD in Electrical Engineering, expected 2024). His supervisory approach combines theoretical rigor with practical problem-solving, focusing on spectrum management algorithms and digital twin implementations for next-generation networks. While specific laboratory affiliations aren't detailed in the source material, his projects indicate strong alignment with Laval University's wireless research infrastructure and the Center for Radio Frequency and Communications Systems.

• Mechanism Design
• Auction Theory
• Blockchain Economics
• +۴ مورد دیگر

Mallesh M Pai is the Lay Family Associate Professor in the Department of Economics at Rice University and a CEPR Research Fellow. His research bridges mechanism design, auction theory, and blockchain economics, with applications to decentralized finance (DeFi) and privacy economics. Prior to Rice, he was a Janice and Julian Bers Assistant Professor at the University of Pennsylvania. PhD in Managerial Economics and Strategy, Kellogg School of Management, Northwestern University Bachelor's in Computer Science and Engineering, Indian Institute of Technology, Delhi His work focuses on designing robust mechanisms in environments with information asymmetry, privacy constraints, and decentralized systems. Recent publications address dynamic transaction fees, blockchain centralization, and algorithmic collusion. He serves on program committees for major conferences in computer science and economics. Indirect Persuasion (Journal of Political Economy) Dynamic Transaction Fee Mechanism Design (with Max Resnick) Collusive Outcomes via Pricing Algorithms (Marketing Science) Mallesh has received significant recognition for his work, including the Best Paper award at the American Economic Journal: Microeconomics (2021) and Best Pricing Paper from the American Marketing Association RAPSIG (2023). His research is supported by NSF grants on fair data analysis and digital privacy foundations. He teaches graduate and undergraduate courses in microeconomic theory and market design at Rice, emphasizing practical applications of theoretical models. His Erdős number is 3, reflecting interdisciplinary collaborations.

Sonia Lopez Alarconمشاهده پروفایل

دانشیار

• Quantum Computing
• Computer Architecture
• Heterogeneous Computing
• +۲ مورد دیگر

Sonia Lopez Alarcon is an Associate Professor in the Department of Computer Engineering at the Kate Gleason College of Engineering, Rochester Institute of Technology (RIT). She has been a faculty member since 2009, teaching core courses like Computer Organization and developing quantum computing curricula including the new CMPE-257 undergraduate course and CMPE-757 graduate course. Her research bridges computer architecture and quantum computing with emphasis on practical quantum circuit implementation. Her educational background includes a Bachelor of Physics and Master's in Device Physics from the University Complutense of Madrid (2002), followed by a PhD in Computer Engineering (2009) where she researched cache hierarchy in simultaneous multithreaded architectures. During her studies, she gained industry experience at Lucent Technologies and Fundetel working on integrated circuit design. Dr. Lopez Alarcon's primary research focuses on Quantum Computing and heterogeneous hardware solutions, specifically quantum circuit compilation processes, scalability challenges, and error resilience techniques. She investigates how to translate theoretical quantum algorithms into executable circuits while managing noise and resource constraints, with applications in optimization problems and physics simulations. Her work connects computer engineering principles to emerging quantum technologies. Analysis of her publication timeline shows a strategic shift from traditional computer architecture (2015-2018 cache/HLS research for GPU/heterogeneous systems) to quantum computing (2019-2021). Recent work explores quantum algorithms for combinatorial optimization (Grover's), quantum simulation of physical systems, and machine learning applications, reflecting her adaptation to the rapidly evolving quantum landscape while maintaining her architectural expertise. Her teaching excellence has been recognized through multiple awards: Kate Gleason College of Engineering Exemplary Performance in Teaching Award (2016, 2017, 2020) Computer Engineering Most Effective Teacher Award (2016) She actively mentors graduate students including Mark Danza (MS Computer Engineering candidate 2025), with whom she collaborated on quantum machine learning research featured in Quantum Zeitgeist (May 2025). She contributes to RIT's quantum information science minor launched in 2022, developing curriculum and supervising student research in this emerging field. Dr. Lopez Alarcon leads quantum computing research efforts within RIT's Department of Computer Engineering, collaborating with colleagues like Cory Merkel on quantum algorithm applications. Her work is supported through her personal research website and integration into university-wide quantum initiatives, positioning her at the forefront of academic quantum computing education and research.

Shahin Kamaliمشاهده پروفایل

دانشیار

• Algorithms Design
• Algorithms Analysis
• Online Algorithms
• +۹ مورد دیگر

Shahin Kamali is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. Previously, he served as an Assistant Professor at York University (2022-2024) and the University of Manitoba (2017-2022), where he continues to hold an Adjunct Professor position. He completed his Ph.D. in Computer Science at the University of Waterloo in 2014 and was a postdoctoral fellow at MIT's CSAIL lab from 2015-2017. Dr. Kamali received his B.Sc. from the University of Tehran and his M.Sc. from Concordia University, both in Computer Science. He has earned teaching certifications including the Kaufman Teaching Certificate Program from MIT and the Certificate in University Teaching from the University of Waterloo. His research focuses on algorithms' design, analysis, and limitations, with particular emphasis on online problems such as bin packing, paging, list update, and k-Server. His work extends to big-data applications of algorithms in data compression, graph partitioning, resource allocation in the cloud, and algorithmic aspects of blockchain technology. His recent publications (2023-2025) demonstrate a strong focus on learning-augmented algorithms, online computation with predictions, and novel applications in blockchain and graph theory. His research has been supported by multiple NSERC grants since 2010, with recent projects investigating models, applications, and limitations of online algorithms. He has successfully supervised numerous graduate students and serves on various committees related to algorithms conferences and academic governance. University of Waterloo Doctoral Thesis Completion Award (2014) University of Waterloo Mathematics Graduate Experience Award (2008) Dr. Kamali teaches advanced courses in algorithms and data structures and is scheduled to teach Design and Analysis of Algorithms (EECS 3101) in Fall 2025 and Advanced Data Structures (EECS 4101/5101) in Winter 2026. He is actively involved in organizing major conferences, including CCCG and WADS 2025 at York University.

Hanqing Jinمشاهده پروفایل

دانشیار

• Mathematical Finance
• Stochastic Analysis
• Portfolio Optimization
• +۳ مورد دیگر

Dr. Hanqing Jin is an Associate Professor in the Mathematical Institute at the University of Oxford, specializing in Mathematical Finance and Applied Stochastic Analysis. His research focuses on investor decision-making under non-utility behavior, dynamic portfolio optimization, and stochastic control problems in financial markets. Prior to Oxford, he held an Assistant Professorship at the National University of Singapore. He has contributed to foundational work on mean-variance portfolio selection, behavioral finance, and the application of stochastic processes to financial modeling. His academic background includes extensive work on continuous-time portfolio optimization, risk management frameworks, and the intersection of behavioral economics with quantitative finance. Notable contributions include studies on optimal lockdown policies during pandemics, blockchain consensus algorithms analysis, and the development of novel biomarker detection techniques using Raman spectroscopy. Dr. Jin's research integrates advanced mathematical techniques with real-world financial challenges, bridging theoretical models with practical applications in areas such as robo-advising systems and cryptocurrency frameworks. His work on transaction cost analysis and dynamic mean-variance strategies has been widely cited in both academic and industry contexts.

Sanjay K Dharمشاهده پروفایل

استاد

• Strategic Marketing Management
• Brand Management
• New Product Development
• +۱۲ مورد دیگر

Sanjay K. Dhar, the James M. Kilts, Jr. Professor of Marketing at the University of Chicago Booth School of Business, is a leading scholar in strategic marketing management and retail dynamics. His work bridges empirical economic modeling with actionable business insights. Education: B.Tech (IIT), MBA (IIM), PhD (UCLA Anderson School) Affiliation: Faculty member at Chicago Booth since 1992 Research interests span brand management , consumer behavior , and retail strategy , with a focus on how economic factors and marketing mix variables influence household budget allocations and brand performance . His empirical work leverages massive datasets like Nielsen Homescan to uncover geographic patterns and long-term consumer trends . Recent publications examine on-screen cigarette product placement effects, WIC program impacts on cereal consumption, and retail format competition . His 2025 under-review paper extends these insights to multi-category budget reallocation during macroeconomic shifts. Scientific Recognition includes: William O’Dell Award Finalist (2012) Paul Green Award (2008) John D. C. Little Award (1995) McKinsey Teaching Excellence Award (2000) Hillel Einhorn Teaching Awards (2008, 2012) Dhar’s teaching philosophy emphasizes mastering fundamentals to solve complex marketing problems. He advises firms on strategic marketing and new product development while maintaining active research in consumer packaged goods (CPG) industries.

• Computer Architecture
• High-Performance Computing
• Multicore and Manycore Systems
• +۷ مورد دیگر

Miquel Moreto Planas is a Senior Lecturer in the Department of Computer Architecture at the Barcelona School of Informatics, Universitat Politècnica de Catalunya (UPC). He is also affiliated with the Barcelona Supercomputing Center (BSC-CNS), a leading institution in high-performance computing. His academic profile is deeply rooted in computer architecture and high-performance computing, with a strong emphasis on practical and theoretical advancements in multicore systems, memory management, and hardware acceleration. His research interests span a wide range of topics including computer architecture, high-performance computing, multicore and manycore systems, cache and memory management, hardware acceleration for genomics and AI, RISC-V processor design, processing-in-memory, interconnection networks, and real-time systems. These interests are reflected in his extensive publication record and collaborative projects. The most recent articles highlight a significant trend toward interdisciplinary research, particularly the application of advanced computer architecture techniques to bioinformatics and healthcare. Key themes include the acceleration of genomic sequence alignment using novel hardware such as processing-in-memory, the development of benchmarks for ARM-based HPC systems in genomics, and the creation of AI-based 3D decision support tools for neurosurgical applications. His work also continues to advance core computer architecture topics like cache management, power-aware resource allocation in heterogeneous systems, and the design of secure, post-quantum cryptographic hardware based on RISC-V. Fulbright Award 2011 HiPEAC Paper Award HiPEAC Paper Award 2024 HiPEAC Paper Award Moreto has been a principal investigator or key contributor to multiple competitive R&D+i projects, such as the STRATUM project for neurosurgical tools, REDIOH for open hardware, and the Laboratorio Zettaescala de Barcelona. He has advised several doctoral students, including López, G., Kostalampros, I., and Haghi, A., and is a core member of the CAP (High Performance Computing) research group at UPC. His work is characterized by strong collaborations with leading researchers like Mateo Valero, Eduard Ayguadé, and Jesús Labarta, often bridging the gap between UPC and BSC-CNS. His laboratory and team affiliations are centered around the CAP group and the Barcelona Supercomputing Center, where he contributes to cutting-edge research in high-performance and embedded computer architectures. His recent work on the BIMSA accelerator and the STRATUM project demonstrates a clear future direction toward applying high-performance computing solutions to critical problems in genomics and medicine.