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X-WR-CALDESC:Events for Ashoka University
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DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251124T010000
DTEND;TZID=Asia/Kolkata:20260125T140000
DTSTAMP:20251124T033054Z
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LAST-MODIFIED:20251124T033054Z
UID:86670-1763946000-1769349600@www.ashoka.edu.in
SUMMARY:CWC Winter School 2026
DESCRIPTION:The 2026 edition of the Centre for Writing and Communication Winter School offers a diverse range of workshops and sessions on research writing and communication within the liberal arts academy.  \n1. The Winter School on Research Writing in the Humanities and Social Sciences which will be held on campus\, and is open to research students at Ashoka University (UG Fourth year\, Masters and PhD). Link to the application form: https://forms.gle/nQz4UFD1YsdsdPfq6 \n2. Online introductory workshops on niche areas of research writing (The Diary in Research: Stylistic Notes on Academic Writing\, Feminist Research: Meaning-making in Personal Interviews\, and Living Voices and Silent Files: Ethnography and the Archives). Open to all.  \n3. “Translation in the Classroom” offered in collaboration with Ashoka Centre for Translation is open to Ashoka University Faculty\, Staff and Students. Link to the application form: https://forms.gle/j1UJ5sQw8vPukGmf9 \nThe full catalogue of workshops can be viewed here. Please write to cwc@ashoka.edu.in incase of any queries. 
URL:https://www.ashoka.edu.in/event/cwc-winter-school-2026/
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251217T093000
DTEND;TZID=Asia/Kolkata:20251219T140000
DTSTAMP:20251211T044754Z
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UID:87226-1765963800-1766152800@www.ashoka.edu.in
SUMMARY:ResComm 2025_Happening from 17-19 December at Ashoka University
DESCRIPTION:Event Name: ResComm 2025 – Research Communication Conclave \nWhen: 17–19 December 2025 \nWhere: Ashoka University campus \nJoin us for ResComm 2025 – A Research Communication Conclave organised by Ashoka University\, happening from 17–19 December at the Ashoka University campus. The Conclave aims to bring research into action! Register and get access to:\n– Keynote Lectures\n– Plenary Talks\n– Hands-on workshops\n– Poster Presentations\n– Knowledge Exhibition\n– Networking Sessions
URL:https://www.ashoka.edu.in/event/rescomm-2025happening-from-1719-december-at-ashoka-university/
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DTSTART;TZID=Asia/Kolkata:20251218T110000
DTEND;TZID=Asia/Kolkata:20251218T130000
DTSTAMP:20251217T024817Z
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SUMMARY:Algorithm and Incentive Design for Sustainable Resource Allocation: Beyond Classical Fisher Markets
DESCRIPTION:Technical seminar announcement\nAlgorithm and Incentive Design for Sustainable Resource Allocation: Beyond Classical Fisher Markets\nby Devansh Jalota \n \nPost-doc Fellow\, Data Science Institute\, Columbia University \n\nSpeaker Bio\nDevansh Jalota is a PhD candidate in Computational and Mathematical Engineering at Stanford University\, where he is a Stanford Interdisciplinary Graduate Fellow. His research develops data-driven learning algorithms and incentive schemes to advance the science and practice of market design for sustainable resource allocation\, with a particular focus on applications in future mobility systems and electricity markets. Prior to joining Stanford\, he received his bachelor’s in applied mathematics and civil engineering at UC Berkeley.  \nAbstract\nTechnological advances have opened new avenues for designing market mechanisms for resource allocation\, from enhancing resource allocation efficiency with widespread data availability to enabling real-time algorithm implementation. While these technological advancements hold significant promise\, they also introduce new societal challenges pertaining to equity\, privacy\, data uncertainty\, and security that existing market mechanisms often fail to address. My research develops data-driven and online learning algorithms and incentive schemes to address these challenges of traditional market mechanisms\, thereby advancing the science and practice of market design for sustainable and society-aware resource allocation.  In this talk\, I focus on addressing data uncertainty and privacy issues in the context of Fisher markets\, a classical framework for fair resource allocation where the problem of computing equilibrium prices relies on complete information of user attributes\, which are typically unavailable in practice. Motivated by this practical limitation\, we study a modified online incomplete information variant of Fisher markets\, where users with privately known utility and budget parameters\, drawn i.i.d. from a distribution\, arrive sequentially. In this novel market\, we establish the limitations of static pricing and design dynamic posted-price algorithms with improved guarantees. Our main result is a posted-price algorithm that solely relies on revealed preference (RP) feedback\, i.e.\, observations of user consumption\, achieving the best-known guarantees for first-order algorithms in the RP setting while providing a regret analysis of a fairness-promoting logarithmic objective\, unlike typical non- negative and bounded eDiciency-promoting objectives in online learning.\nDate: Thursday\, December 18\, 2025 Time: 11:30 AM – 12:30 PM\nVenue: AC-03-LR-005\nFor details: scdlds@ashoka.edu.in
URL:https://www.ashoka.edu.in/event/algorithm-and-incentive-design-for-sustainable-resource-allocation-beyond-classical-fisher-markets/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20251218T113000
DTEND;TZID=Asia/Kolkata:20251218T123000
DTSTAMP:20251212T132208Z
CREATED:20251211T125230Z
LAST-MODIFIED:20251212T132208Z
UID:87514-1766057400-1766061000@www.ashoka.edu.in
SUMMARY:Algorithm and Incentive Design for Sustainable Resource Allocation: Beyond Classical Fisher Markets | Devansh Jalota\, Columbia University
DESCRIPTION:Technical seminar announcement\nAlgorithm and Incentive Design for Sustainable Resource Allocation: Beyond Classical Fisher Markets\nby Devansh Jalota \n \nPost-doc Fellow\, Data Science Institute\, Columbia University \n\nSpeaker Bio\nDevansh Jalota is a PhD candidate in Computational and Mathematical Engineering at Stanford University\, where he is a Stanford Interdisciplinary Graduate Fellow. His research develops data-driven learning algorithms and incentive schemes to advance the science and practice of market design for sustainable resource allocation\, with a particular focus on applications in future mobility systems and electricity markets. Prior to joining Stanford\, he received his bachelor’s in applied mathematics and civil engineering at UC Berkeley.\n \nAbstract\nTechnological advances have opened new avenues for designing market mechanisms for resource allocation\, from enhancing resource allocation efficiency with widespread data availability to enabling real-time algorithm implementation. While these technological advancements hold significant promise\, they also introduce new societal challenges pertaining to equity\, privacy\, data uncertainty\, and security that existing market mechanisms often fail to address. My research develops data-driven and online learning algorithms and incentive schemes to address these challenges of traditional market mechanisms\, thereby advancing the science and practice of market design for sustainable and society-aware resource allocation.  \nIn this talk\, I focus on addressing data uncertainty and privacy issues in the context of Fisher markets\, a classical framework for fair resource allocation where the problem of computing equilibrium prices relies on complete information of user attributes\, which are typically unavailable in practice. Motivated by this practical limitation\, we study a modified online incomplete information variant of Fisher markets\, where users with privately known utility and budget parameters\, drawn i.i.d. from a distribution\, arrive sequentially. In this novel market\, we establish the limitations of static pricing and design dynamic posted-price algorithms with improved guarantees. Our main result is a posted-price algorithm that solely relies on revealed preference (RP) feedback\, i.e.\, observations of user consumption\, achieving the best-known guarantees for first-order algorithms in the RP setting while providing a regret analysis of a fairness-promoting logarithmic objective\, unlike typical non- negative and bounded eDiciency-promoting objectives in online learning. \n\nDate: Thursday\, December 18\, 2025\nTime: 11:30 AM – 12:30 PM\nVenue: AC-03-LR-005\nFor details: scdlds@ashoka.edu.in
URL:https://www.ashoka.edu.in/event/scdlds-ts04/
LOCATION:AC-03-LR-005
CATEGORIES:Online Event,Seminar,Offline
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ORGANIZER;CN="SCDLDS":MAILTO:ashoka-cdlds@ashoka.edu.in
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