Greg Durrett is an Associate Professor in the Department of Computer Science at University of Texas at Austin, leading the TAUR Lab (Text Analysis, Understanding, and Reasoning ). His research focuses on advancing Large Language Models (LLMs) for knowledge-intensive tasks in medical information processing scientific discovery legal reasoning . He received his B.S. in Computer Science and Mathematics from MIT (2010) and Ph.D. in Computer Science from UC Berkeley (2016). His work develops techniques to train LLMs with new capabilities augment models for reliability assess model outputs improve reasoning frameworks . His 15 most recent publications (2021-2025) span knowledge propagation in LLMs chain-of-thought reasoning code generation benchmarks multi-modal reasoning fact verification discourse analysis . Scientific honors include NSF CAREER Award (2024) NSF grants (2018, 2024) Bloomberg Data Science Grant (2017) Facebook Fellowship (2014) Best Paper Finalist (EMNLP 2013) . Teaching: CS388: Natural Language Processing (graduate) CS371N: NLP (undergraduate) High school NLP module .
Kenneth P. Birman is the N. Rama Rao Professor of Computer Science at Cornell University, where he has had a long and impactful career in distributed systems, cloud computing, and AI/ML infrastructure. He is known for foundational contributions to reliable and scalable distributed systems, and for leading high-impact projects such as Cascade, Vortex, and Derecho. He is also the author of a widely used textbook on reliable distributed systems and has founded multiple companies based on his research. Education: Ph.D. in Computer Science, University of California, Berkeley M.S. in Computer Science, University of California, Berkeley B.A. in Computer Science, Columbia University Research Interests: Professor Birman's research focuses on building reliable, secure, and scalable distributed systems . His current emphasis is on AI and ML infrastructure , particularly in reducing latency and improving performance through hardware acceleration, RDMA-based communication, and edge computing. He explores how to eliminate data movement bottlenecks in AI pipelines and how to support real-time, mission-critical applications in domains like healthcare, smart grids, and industrial IoT. His work spans systems programming, cloud computing, fault tolerance, and formal verification . He has designed systems that have been deployed in high-stakes environments such as the New York Stock Exchange, the Swiss Exchange, and the French Air Traffic Control system. Scientific Awards: ACM Fellow (1999) IEEE Fellow (2014) IEEE Tsutomu Kanai Award for innovations in distributed computing Teaching and Mentorship: Professor Birman teaches two courses in the fall semester: CS4414: Systems Programming and CS5416: Cloud and ML Systems Programming . He has advised numerous Ph.D. and M.S. students, including Alicia Yang, Tiancheng Yuan, Yifan Wang, Weijia Song, Edward Tremel, Sagar Jha, Jonathan Behrens, and Mae Milano. He has announced that Fall 2025 will be his last semester teaching, and he is no longer recruiting new students, though he will continue supervising current ones. Labs and Projects: He leads the Derecho Project and the Cascade/Vortex Project , both focused on high-performance distributed systems. These projects are collaborative efforts with students and industry partners, and the software is released under open-source licenses. He also maintains strong ties with Cornell's systems group and collaborates with faculty across CS, ECE, IS, and the Cornell Tech NYC campus.
Priyanka Raina is an Assistant Professor of Electrical Engineering at Stanford University, with a courtesy appointment in Computer Science. She leads the Stanford Accelerate research group, focusing on domain-specific hardware architectures and agile hardware-software co-design. Her work emphasizes high-performance, energy-efficient accelerators for emerging technologies. Education: B.Tech. from IIT Delhi (2011), M.S. and Ph.D. from MIT (2013/2018). Postdoctoral experience includes NVIDIA Research (2018) and Amazon Visiting Academic (2023–present). Awards include the Sloan Research Fellowship (2024), NSF CAREER Award (2023), and Terman Faculty Fellowship (2018). Research Interests: Domain-specific architectures, near-memory computing, design productivity, and machine learning acceleration. Her group develops frameworks like AHA (Agile Hardware) for efficient accelerator design and compilers. Awards: Over 10 major awards, including best paper recognitions at VLSI, MICRO, and JSSC. Active roles as Associate Editor for IEEE JSSC and Program Chair for IEEE Hot Chips (2020). Advising & Teaching: Supervises multiple PhD and MS students in hardware design, CGRAs, and ML accelerators. Teaches VLSI design courses (EE271/272/372) and oversees independent studies in embedded systems and chip design. Labs & Collaborations: Affiliated with Stanford PORTAL Center, AHA Center, and SystemX Alliance. Projects include MINOTAUR (edge AI accelerator), Amber (CGRA-based SoC), and EMBER (RRAM macros).
Kaiyuan Yang is an Associate Professor in the Department of Electrical and Computer Engineering at Rice University, leading the Secure and Intelligent Micro-Systems (SIMS) Lab. His research focuses on low-power integrated circuits and bioelectronic implants for applications like the Internet of Everything and medical devices. He holds a B.S. from Tsinghua University (2012) and M.S./Ph.D. degrees from the University of Michigan (2017). Research Interests: Low-power digital/analog/mixed-signal systems Bioelectronics and implantable devices Hardware security and PUF design Mixed-signal computing and emerging materials Recent work emphasizes magnetoelectric-powered implants, secure backscatter communication, and in-memory computing architectures. His publications span top venues like IEEE ISSCC, IEDM, and ACM MobiCom. Awards: 2022 NSF CAREER Award 2022 IEEE Top Picks in Hardware Security 2016 IEEE SSCS Predoctoral Achievement Award Dr. Yang serves on editorial boards for IEEE TVLSI and program committees for ISSCC/CICC. His lab develops miniature, secure, and energy-efficient systems for healthcare and IoT applications.
Christoph Koch is a Full Professor in the School of Computer and Communication Sciences at EPFL (Ecole Polytechnique Federale de Lausanne) , Switzerland. He has held academic positions at Cornell University (2007-2010, 2006), Saarland University (2005-2007), and TU Vienna (2001-2005). His research focuses on database systems, logic, programming languages, and data management.
Bahar Asgari is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park. She holds appointments in CS, UMIACS, and ECE, and directs the Computer Architecture and Systems Lab (CASL). Her research focuses on domain-specific architecture design, near memory processing, and reconfigurable computing. Before joining UMD, she worked at Google, contributing to system optimization and memory utilization strategies. Education: Ph.D., Georgia Institute of Technology, 2021. Research Interests: Developing energy-efficient architectures for sparse problems, scientific computing acceleration, FPGA optimizations, and reconfigurable hardware systems. Her work addresses challenges in memory efficiency, parallelization, and hardware-software co-design across domains like machine learning and IoT. Key Awards: 2024 UMD Excellence in Teaching Award 2023 DOE Early Career Award Advising & Grants: Supervises 9 graduate students and leads projects sponsored by NSF, DOE, and industry partnerships. Her lab explores systolic arrays, near-data processing, and fault-tolerant computing. Labs/Teams: Directs CASL, which builds next-generation computing systems through hardware innovation and algorithm-architecture co-design.
Subhasish Mitra is the William E. Ayer Professor of Electrical Engineering and Computer Science at Stanford University, holding dual appointments in both departments. He leads the Stanford Robust Systems Group and serves on the leadership team of the Microelectronics Commons AI Hardware Hub under the US CHIPS and Science Act. His research spans Robust Computing, NanoSystems, Electronic Design Automation (EDA), and Neurosciences, with breakthroughs in X-Compact test compression, carbon nanotube computing, and 3D integration. He has held international roles like the Carnot Chair at CEA-LETI and Visiting Professorships globally. Education & Honors: Recipient of over 40 awards including the IEEE Computer Society’s Harry H. Goode Memorial Award, ACM/IEEE’s A. Richard Newton Technical Impact Award, and the Intel Achievement Award. He earned top academic accolades from IIT Kharagpur and Jadavpur University, and is a Fellow of ACM and IEEE. Research Impact: Pioneered first-of-their-kind systems like the carbon nanotube computer and monolithic 3D integration. His work on robust computing techniques like QED validation and X-Compact compression has industry-wide adoption, saving billions in manufacturing costs. Collaborates with industry leaders like Intel, Google, and Samsung. Publications & Grants: Over 400 publications, including award-winning papers in DAC, ISSCC, and IEEE journals. Leads grants from NSF, DoE, and industry partnerships. His lab explores cutting-edge topics like neuromorphic computing, 3D thermal scaffolding, and AI hardware acceleration. Administration & Outreach: Serves as Associate Chair (Faculty Affairs) for Stanford’s Computer Science Department. Recognized by students for mentorship, and frequently invited to global forums like the World Economic Forum and National Academy of Engineering.
Haitong Li is an Assistant Professor in the School of Electrical and Computer Engineering at Purdue University's College of Engineering, joining the faculty in 2022. His research bridges nanoelectronic devices, integrated circuits, and nanotechnology-inspired AI hardware to address critical challenges in energy-efficient artificial intelligence systems. Education: Ph.D. in Electrical Engineering, Stanford University Research Interests: Dr. Li pioneers emerging memory technologies—particularly Resistive RAM (RRAM)—for in-memory computing and neuromorphic systems. His work focuses on 3D monolithic integration of RRAM and gain cell memory with CMOS to enable edge AI, with recent breakthroughs in hardware acceleration for large language models and sustainable computing. Key innovations include carbon footprint prediction for LLMs and zeroth-order fine-tuning techniques. Publication Trends: Dr. Li's 2023-2025 publications reveal a strategic shift toward sustainable AI hardware, emphasizing carbon-aware LLM inference and edge deployment. His research consistently targets data movement reduction through memory-centric architectures, spanning photonic accelerators, neuro-symbolic computing, and heterogeneous 3D integration. Awards: No scientific awards were documented in the provided sources. Advising and Grants: Current advisees and grant funding details were not specified in the available materials. Labs and Teams: Research group composition and laboratory facilities were not described in the source text.
Michael McAlpine is a Professor in the Mechanical Engineering department at the University of Minnesota . He also holds affiliations with the Biomedical Engineering and Electrical and Computer Engineering departments. His research focuses on 3D printing functional materials & devices , Nanoscale inks , Biomedical devices , Bioelectronics , and Flexible Microsystems . Research Interests : 3D Printing, Biomedical Engineering, Nanotechnology, Flexible Electronics, Microfluidics Labs : ME 361/363 Contact : mcalpine@umn.edu , (612) 626-3303, ME 117 Recent Research Trends include 3D Printed Biomedical Devices , Flexible Electronics , and Bioprinting Applications . His work spans from Spinal Organoid Formation to Programmable Drug Release Capsules . Scientific Award : Circulation Research 2020 Best Manuscript Award
Dr. Akhilesh Jaiswal serves as Assistant Professor of Electrical and Computer Engineering at the University of Wisconsin-Madison, where his research pioneers device-circuit co-design for next-generation computing systems. His work focuses on enabling extreme-edge intelligence through processing-in-pixel technology, in-memory computing architectures, and bio-inspired neuromorphic systems. His academic credentials include: PhD in Nano-electronics from Purdue University (2019) MS from the University of Minnesota (2014) Bachelor of Technology from Shri Guru Gobind Singhji Institute of Engineering and Technology (2011) Dr. Jaiswal's research program centers on revolutionizing edge computing through hardware innovations that integrate sensing and processing. His device-circuit co-design approach leverages alternate state variables to create energy-efficient systems for real-time applications, with particular emphasis on retina-inspired sensors and photonic memory architectures. This work bridges semiconductor physics with AI acceleration needs, targeting applications from autonomous systems to biomedical devices. Analysis of his 2023-2025 publications reveals dominant themes in photonic SRAM-based in-memory computing (40% of output), retina-inspired motion processing (30%), and secure hardware architectures (20%). His team consistently develops novel bitcell designs that enable XOR logic execution within memory arrays while maintaining compatibility with CMOS fabrication processes. Recent work shows increasing focus on biomedical applications of processing-in-pixel technology. His distinguished recognition includes: Three consecutive ISI Exploratory Research Awards (2020-2023) IEEE Brain Community Best Paper Award (2022) 27 issued US patents with multiple pending applications Nomination for USC Moore Inventor Fellowship (2022) Dr. Jaiswal actively mentors graduate researchers through ECE 790/890/990 courses while securing exploratory funding through ISI and Keston Foundation awards. His patent portfolio demonstrates exceptional translational impact, with industry game-changer classifications from USPTO. The 2022 VLSI-SoC nomination and multiple research highlights in major outlets validate his contributions to hardware security and neuromorphic vision sensors. His laboratory develops integrated hardware platforms combining magnetic tunnel junctions, photonic memory, and CMOS image sensors to create unified processing-in-sensor systems. Current projects include retina-inspired motion segmentation for event cameras and electro-optic frequency transducers for quantum computing interfaces, with strong industry collaboration through patent licensing.
Enamul Hoque Prince is an Associate Professor and Director of the School of Information Technology at York University. He leads the Intelligent Visualization Lab, funded by the Canada Foundation for Innovation (CFI) and Ontario Research Funds (ORF). He holds a PhD in Computer Science from the University of British Columbia and completed postdoctoral work at Stanford University. His research integrates information visualization, human-computer interaction (HCI), and natural language processing (NLP) to address information overload challenges. Dr. Prince's educational background includes a PhD from UBC, an MSc from Memorial University of Newfoundland, and a BSc from Chittagong University of Engineering & Technology. He has conducted research at institutions like Tableau Software and the Qatar Computing Research Institute and serves on committees for top conferences like ACL and IEEE Vis. His work is supported by grants from NSERC, CFI, and others. Research interests focus on NLP-driven visual analytics, user-adaptive visualization, and accessible interfaces. Notable projects include Evizeon (natural language interfaces for visual analytics), ConVisIT (topic modeling for online conversations), and CIDER (concept-based image search). Publications highlight trends in multimodal systems, chart comprehension, and accessibility. Key awards include the NSERC Discovery Grant (2019) and a Best Paper Honorable Mention at DIS 2021. He supervises graduate and undergraduate students in areas like visualization, NLP, and HCI. Labs and collaborations emphasize interdisciplinary approaches, with the Intelligent Visualization Lab advancing tools for data exploration and user-centered design. Teaching includes courses on design principles and information visualization.
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.
Nadia Polikarpova is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego . She earned her PhD from ETH Zurich in 2014 under Bertrand Meyer , followed by postdoctoral research at MIT CSAIL with Armando Solar-Lezama . Her academic contributions have been recognized with prestigious awards including the 2020 Sloan Fellowship , 2020 Intel Rising Stars Award , and 2020 NSF CAREER Award . Polikarpova's research focuses on program synthesis , program verification , and type systems . She leads the Programming Systems group at UCSD and contributes to the IFIP Working Group 2.8 on Functional Programming since 2022. Her work spans foundational research and practical tools, including projects like Synquid , SuSLik , and Laurel that combine formal methods with machine learning for code generation. Her recent publications in venues like OOPSLA , NeurIPS , and ICFP reveal trends in AI-assisted programming , live programming environments , and formal verification . She has advised numerous PhD and Master’s students including Shraddha Barke , Zheng Guo , and Tristan Knoth , many of whom have moved to prominent academic and industry positions. Notable artifacts from her lab include tools like ColDeco for spreadsheet inspection and Superfusion for eliminating intermediate data structures. 2020 : Sloan Fellow 2020 : Intel Rising Stars Award 2020 : NSF CAREER Award 2021 : Distinguished Paper at POPL 2023 : Distinguished Artifact at PLDI 2023 : Distinguished Paper at OOPSLA Polikarpova actively contributes to academic service, serving on program committees for PLDI , POPL , and OOPSLA , and co-chairing the OOPSLA Review Committee in 2023. She has delivered keynotes at APLAS'20 and PLDI'24 , emphasizing the integration of large language models with formal methods.
José F. Martínez holds the Lee Teng-hui Professorship of Engineering at Cornell University's College of Engineering, where he leads research in computer architecture and systems. His roles include Vice Chair of ACM SIGARCH, IEEE Fellow, and past leadership roles in ISCA, MICRO, and IEEE Computer Society journals. He has received prestigious awards such as the NSF CAREER Award, IBM/Qualcomm Faculty Awards, and multiple teaching accolades including Tau Beta Pi Professor of the Year (2011). Education: Licenciado en Informática de Sistemas (1996), Universidad Politécnica de Valencia M.S. (1999) and Ph.D. (2002) in Computer Science, University of Illinois at Urbana-Champaign His research focuses on computer architecture , including microprocessors, multiprocessors, memory subsystems, embedded systems, and processing-in-memory (PIM) innovations. He co-leads the DARPA/SRC ACE Center for Evolvable Computing and NSF's CROPPS Center, and previously co-founded Cornell's Institute for Digital Agriculture (CIDA). His work bridges hardware-software co-design and sustainable computing. Key Contributions: Advancing energy-efficient multiprocessor architectures Developing PIM frameworks like PUMICE and Membrane Market-based resource allocation systems (e.g., XChange, ReBudget) His advising spans over 15 graduate students and has guided Merrill Presidential Scholars. Current research explores evolvable computing paradigms and AI hardware acceleration, reflecting his dual focus on foundational architecture and applied systems innovation.
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.
